Week 3: Intro to {purrr}

& Lab 2

Author

Affiliation

Joe Nese

University of Oregon
Spring 2026

{purrr} & Lab 2

Week 3

Agenda

• Review Lab 1
• Thinking more about functional programming
  • A small example
• Introduce you to {purrr} and contrast it with the base functions we learned last week

Learning objectives

• Understand how purrr::map() relates to lapply() and for loops
• Understand the four basic variants of purrr::map(), when they should be used, and when they will fail
• Understand what functional programming is, and how {purrr} can help facilitate the process

Review Lab 1

Functional Programming

What is Functional Programming?

decomposing a big problem into smaller pieces, then solving each piece with a function or combination of functions
-- Adv-R

Example

Calculate top mpg manufactures

(let’s just subset the data to 4 cylinders for this example)

library(tidyverse)
four_cyl <- filter(mpg, cyl == 4)

Example

Now we’ll filter for cases where the city miles per gallon is in the top 10% (i.e., greater than or equal to the \(90^{th}\) percentile)

ninety <- four_cyl |>
    filter(cty >= quantile(cty, probs = 0.9))
ninety

# A tibble: 10 × 11
   manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
   <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
 1 honda        civic        1.6  1999     4 manu… f        28    33 r     subc…
 2 honda        civic        1.6  1999     4 manu… f        25    32 r     subc…
 3 honda        civic        1.8  2008     4 manu… f        26    34 r     subc…
 4 honda        civic        1.8  2008     4 auto… f        25    36 r     subc…
 5 toyota       corolla      1.8  1999     4 manu… f        26    35 r     comp…
 6 toyota       corolla      1.8  2008     4 manu… f        28    37 r     comp…
 7 toyota       corolla      1.8  2008     4 auto… f        26    35 r     comp…
 8 volkswagen   jetta        1.9  1999     4 manu… f        33    44 d     comp…
 9 volkswagen   new beetle   1.9  1999     4 manu… f        35    44 d     subc…
10 volkswagen   new beetle   1.9  1999     4 auto… f        29    41 d     subc…

Example

Now let’s count the unique occurrences for each manufacturer, manufacturer/model, and class

(count_mfr <- count(ninety, manufacturer))

# A tibble: 3 × 2
  manufacturer     n
  <chr>        <int>
1 honda            4
2 toyota           3
3 volkswagen       3

(count_model <- count(ninety, manufacturer, model))

# A tibble: 4 × 3
  manufacturer model          n
  <chr>        <chr>      <int>
1 honda        civic          4
2 toyota       corolla        3
3 volkswagen   jetta          1
4 volkswagen   new beetle     2

(count_class <- count(ninety, class))

# A tibble: 2 × 2
  class          n
  <chr>      <int>
1 compact        4
2 subcompact     6

Example

Produce a plot for each

plot_mfr <- count_mfr |> 
    ggplot(aes(fct_reorder(manufacturer, -n), n)) +
        geom_col(aes(fill = manufacturer)) +
        scale_fill_brewer(palette = "Set3") +
        labs(title = "Manufacturers",
             x = "",
             y = "") +
        guides(fill = "none")
plot_mfr

plot_car <- count_model |>
    unite(car, manufacturer, model, sep = " ") |>
    ggplot(aes(fct_reorder(car, -n), n)) +
        geom_col(aes(fill = car)) +
        scale_fill_brewer(palette = "Set3") +
        labs(title = "Top 10% of city mpg",
             subtitle = "Car frequency",
             x = "",
             y = "") +
        guides(fill = "none")
plot_car

plot_class <- count_class |> 
  ggplot(aes(fct_reorder(class, -n), n)) +
        geom_col(aes(fill = class)) +
        scale_fill_brewer(palette = "Set3") +
        labs(title = "Car Class",
             x = "", 
             y = "") +
        guides(fill = "none")
plot_class

Assemble the plots

library(patchwork)
plot_car / (plot_mfr + plot_class)

Functional Programming Version

At least in spirit

Let’s Expand from 4 cyl

We did a trial run with one piece (4 cyl), now let’s do some functional programming and apply it to all cyl groups

by_cyl <- split(mpg, mpg$cyl)

. . .

And filter all

top_10 <- lapply(by_cyl, function(x) {
    filter(x, cty >= quantile(cty, probs = 0.9))
})
str(top_10)

List of 4
 $ 4: tibble [10 × 11] (S3: tbl_df/tbl/data.frame)
  ..$ manufacturer: chr [1:10] "honda" "honda" "honda" "honda" ...
  ..$ model       : chr [1:10] "civic" "civic" "civic" "civic" ...
  ..$ displ       : num [1:10] 1.6 1.6 1.8 1.8 1.8 1.8 1.8 1.9 1.9 1.9
  ..$ year        : int [1:10] 1999 1999 2008 2008 1999 2008 2008 1999 1999 1999
  ..$ cyl         : int [1:10] 4 4 4 4 4 4 4 4 4 4
  ..$ trans       : chr [1:10] "manual(m5)" "manual(m5)" "manual(m5)" "auto(l5)" ...
  ..$ drv         : chr [1:10] "f" "f" "f" "f" ...
  ..$ cty         : int [1:10] 28 25 26 25 26 28 26 33 35 29
  ..$ hwy         : int [1:10] 33 32 34 36 35 37 35 44 44 41
  ..$ fl          : chr [1:10] "r" "r" "r" "r" ...
  ..$ class       : chr [1:10] "subcompact" "subcompact" "subcompact" "subcompact" ...
 $ 5: tibble [2 × 11] (S3: tbl_df/tbl/data.frame)
  ..$ manufacturer: chr [1:2] "volkswagen" "volkswagen"
  ..$ model       : chr [1:2] "jetta" "jetta"
  ..$ displ       : num [1:2] 2.5 2.5
  ..$ year        : int [1:2] 2008 2008
  ..$ cyl         : int [1:2] 5 5
  ..$ trans       : chr [1:2] "auto(s6)" "manual(m5)"
  ..$ drv         : chr [1:2] "f" "f"
  ..$ cty         : int [1:2] 21 21
  ..$ hwy         : int [1:2] 29 29
  ..$ fl          : chr [1:2] "r" "r"
  ..$ class       : chr [1:2] "compact" "compact"
 $ 6: tibble [23 × 11] (S3: tbl_df/tbl/data.frame)
  ..$ manufacturer: chr [1:23] "audi" "audi" "chevrolet" "chevrolet" ...
  ..$ model       : chr [1:23] "a4" "a4" "malibu" "malibu" ...
  ..$ displ       : num [1:23] 2.8 3.1 3.1 3.5 3.8 3.8 2.5 2.5 3.3 3.5 ...
  ..$ year        : int [1:23] 1999 2008 1999 2008 1999 1999 1999 1999 2008 2008 ...
  ..$ cyl         : int [1:23] 6 6 6 6 6 6 6 6 6 6 ...
  ..$ trans       : chr [1:23] "manual(m5)" "auto(av)" "auto(l4)" "auto(l4)" ...
  ..$ drv         : chr [1:23] "f" "f" "f" "f" ...
  ..$ cty         : int [1:23] 18 18 18 18 18 18 18 18 19 19 ...
  ..$ hwy         : int [1:23] 26 27 26 29 26 25 26 26 28 27 ...
  ..$ fl          : chr [1:23] "p" "p" "r" "r" ...
  ..$ class       : chr [1:23] "compact" "compact" "midsize" "midsize" ...
 $ 8: tibble [11 × 11] (S3: tbl_df/tbl/data.frame)
  ..$ manufacturer: chr [1:11] "audi" "chevrolet" "chevrolet" "chevrolet" ...
  ..$ model       : chr [1:11] "a6 quattro" "corvette" "corvette" "corvette" ...
  ..$ displ       : num [1:11] 4.2 5.7 5.7 6.2 6.2 7 4.6 4.6 4.6 4.6 ...
  ..$ year        : int [1:11] 2008 1999 1999 2008 2008 2008 1999 1999 2008 2008 ...
  ..$ cyl         : int [1:11] 8 8 8 8 8 8 8 8 8 8 ...
  ..$ trans       : chr [1:11] "auto(s6)" "manual(m6)" "auto(l4)" "manual(m6)" ...
  ..$ drv         : chr [1:11] "4" "r" "r" "r" ...
  ..$ cty         : int [1:11] 16 16 15 16 15 15 15 15 15 15 ...
  ..$ hwy         : int [1:11] 23 26 23 26 25 24 21 22 23 22 ...
  ..$ fl          : chr [1:11] "p" "p" "p" "p" ...
  ..$ class       : chr [1:11] "midsize" "2seater" "2seater" "2seater" ...

Counts for all

More complex anonymous function here

counts <- lapply(top_10, function(x) {
    
  count_manufacturer <- count(x, manufacturer)
    
  count_class <- count(x, class)
  
    count_model <- count(x, manufacturer, model) |>
        unite(car, manufacturer, model, sep = " ")
    
  
    return(list(mfr = count_manufacturer, 
                car = count_model, 
                class = count_class))
})
counts

$`4`
$`4`$mfr
# A tibble: 3 × 2
  manufacturer     n
  <chr>        <int>
1 honda            4
2 toyota           3
3 volkswagen       3

$`4`$car
# A tibble: 4 × 2
  car                       n
  <chr>                 <int>
1 honda civic               4
2 toyota corolla            3
3 volkswagen jetta          1
4 volkswagen new beetle     2

$`4`$class
# A tibble: 2 × 2
  class          n
  <chr>      <int>
1 compact        4
2 subcompact     6


$`5`
$`5`$mfr
# A tibble: 1 × 2
  manufacturer     n
  <chr>        <int>
1 volkswagen       2

$`5`$car
# A tibble: 1 × 2
  car                  n
  <chr>            <int>
1 volkswagen jetta     2

$`5`$class
# A tibble: 1 × 2
  class       n
  <chr>   <int>
1 compact     2


$`6`
$`6`$mfr
# A tibble: 8 × 2
  manufacturer     n
  <chr>        <int>
1 audi             2
2 chevrolet        2
3 ford             2
4 hyundai          3
5 nissan           5
6 pontiac          2
7 toyota           6
8 volkswagen       1

$`6`$car
# A tibble: 10 × 2
   car                     n
   <chr>               <int>
 1 audi a4                 2
 2 chevrolet malibu        2
 3 ford mustang            2
 4 hyundai sonata          3
 5 nissan altima           2
 6 nissan maxima           3
 7 pontiac grand prix      2
 8 toyota camry            3
 9 toyota camry solara     3
10 volkswagen passat       1

$`6`$class
# A tibble: 3 × 2
  class          n
  <chr>      <int>
1 compact        5
2 midsize       16
3 subcompact     2


$`8`
$`8`$mfr
# A tibble: 4 × 2
  manufacturer     n
  <chr>        <int>
1 audi             1
2 chevrolet        5
3 ford             4
4 pontiac          1

$`8`$car
# A tibble: 4 × 2
  car                    n
  <chr>              <int>
1 audi a6 quattro        1
2 chevrolet corvette     5
3 ford mustang           4
4 pontiac grand prix     1

$`8`$class
# A tibble: 3 × 2
  class          n
  <chr>      <int>
1 2seater        5
2 midsize        2
3 subcompact     4

Plots for all

Let’s write a couple functions

(we’ll mostly ignore how for now)

counts_plot <- function(counts_df) {
  
  var <- names(counts_df)[1]
    
  p <- ggplot(counts_df, aes(fct_reorder(!!sym(var), -n), n)) +
        geom_col(aes(fill = !!sym(var))) +
        scale_fill_brewer(palette = "Set3") +
        labs(title = stringr::str_to_title(var),
             x = "",
             y = "") +
        guides(fill = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

    if(var == "car") {
        p <- p + labs(title = "Top 10% of city mpg",
                      subtitle = var)
    }
 p
}

Test it “4”

counts_plot(counts[["4"]]$mfr)

Test it “6”

counts_plot(counts[["6"]]$class)    

Test it “8”

counts_plot(counts[["8"]]$car)

Compile plots function

full_plot <- function(l) {
    counts_plot(l[["car"]]) / (
      counts_plot(l[["mfr"]]) +
      counts_plot(l[["class"]])
    )
}

Test it

full_plot(counts[[1]])

Finish up

plots <- lapply(counts, full_plot)

4 cyl (1)

plots[[1]]

5 cyl (2)

plots[[2]]

6 cyl (3)

plots[[3]]

8 cyl (4)

plots[[4]]

{purrr}

Functionals

functional

“a function that takes a function as input and returns a vector as output”

. . .

You remember from last week and lapply() et al.

purrr::map()

library(purrr) # loaded automatically with library(tidyverse)
map(1:3, rnorm)

[[1]]
[1] -0.7343803

[[2]]
[1]  0.5394204 -0.2436382

[[3]]
[1] -0.425156 -1.689734  1.036313

Graphically

Comparison to base::lapply()

lapply()

lapply(1:3, rnorm)

[[1]]
[1] 1.523724

[[2]]
[1] -0.4173061 -1.6324232

[[3]]
[1]  0.5623075 -0.7353186  0.7508274

map()

map(1:3, rnorm)

[[1]]
[1] 0.3064929

[[2]]
[1] -0.3705654  0.1049110

[[3]]
[1]  1.6357612  1.1316940 -0.2074827

side note: What exactly is going on here?

map() & lapply()

The base equivalent to map() is lapply(). The only difference is that lapply() does not support the helpers that you’ll learn about (next), so if you’re only using map() from {purrr}, you can skip the additional dependency and use lapply() directly.

- Adv R

Equivalents

The following are equivalent

map(mtcars, function(x) length(unique(x)))

lapply(mtcars, function(x) length(unique(x)))

EXPLAIN .X

So why

But {purrr} also allows you to specify anonymous functions more succinctly using the formula interface

. . .

From this:

map(mtcars, function(x) length(unique(x)))

To this:

map(mtcars, ~length(unique(.x)))

$mpg
[1] 25

$cyl
[1] 3

$disp
[1] 27

$hp
[1] 22

$drat
[1] 22

$wt
[1] 29

$qsec
[1] 30

$vs
[1] 2

$am
[1] 2

$gear
[1] 3

$carb
[1] 6

. . .

If you forget the ~ (I do sometimes!)

map(mtcars, length(unique(.x)))

Error:
! object '.x' not found

Anonymous function?

• An anonymous function is one that is defined without being given a name
• Since it has no name assigned with <-, it’s anonymous
• It’s usually created for short, one-time use—often inside another function like lapply(), sapply(), purrr::map(), etc.

function(arguments) {
  # code
}

~ is used in place of function(x)

More examples

Vary the n

map(1:3, ~rnorm(n = .x))

[[1]]
[1] 0.9557764

[[2]]
[1] -0.3952842 -0.4518243

[[3]]
[1]  0.3697117  0.4974838 -0.6173932

Vary the mean

map(1:3, ~rnorm(n = 1, mean = .x))

[[1]]
[1] 2.706037

[[2]]
[1] 3.207659

[[3]]
[1] 5.709939

Vary the sd

map(1:3, ~rnorm(n = 1, sd = .x))

[[1]]
[1] -0.3307834

[[2]]
[1] 0.9844692

[[3]]
[1] -0.0367064

Remember lapply()

This doesn’t work

lapply(1:3, rnorm(n = x))

Error:
! object 'x' not found

or this

lapply(1:3, ~rnorm(n = x))

Error in `match.fun()`:
! '~rnorm(n = x)' is not a function, character or symbol

It has to be this

lapply(1:3, function(x) rnorm(n = x))

[[1]]
[1] 0.2696294

[[2]]
[1] 0.1187362 0.4896607

[[3]]
[1]  0.9476157 -0.7585117 -0.5467824

Extracting elements

Let’s make a complicated list

(l <- list(
  list(-1, x = 1, y = 2, z = "a"),
  list(-2, x = 4, y = c(5, 6), z = "b"),
  list(-3, x = 8, y = c(9, 10, 11))
))

[[1]]
[[1]][[1]]
[1] -1

[[1]]$x
[1] 1

[[1]]$y
[1] 2

[[1]]$z
[1] "a"


[[2]]
[[2]][[1]]
[1] -2

[[2]]$x
[1] 4

[[2]]$y
[1] 5 6

[[2]]$z
[1] "b"


[[3]]
[[3]][[1]]
[1] -3

[[3]]$x
[1] 8

[[3]]$y
[1]  9 10 11

Extract second element from each

map() 😎

map(l, 2)

[[1]]
[1] 1

[[2]]
[1] 4

[[3]]
[1] 8

lapply 👎

lapply(l, 2)

Error in `match.fun()`:
! '2' is not a function, character or symbol

Doesn’t work with lapply()

Instead, you have to apply an anonymous function

lapply(l, function(x) x[[2]])

[[1]]
[1] 1

[[2]]
[1] 4

[[3]]
[1] 8

. . .

Alternatively the following is also the same (and my brain hates it)

lapply(l, `[[`, 2)

Extract by name

map(l, "y")

[[1]]
[1] 2

[[2]]
[1] 5 6

[[3]]
[1]  9 10 11

Multiple arguments

Extract first element 1 from y

map(l, list("y", 1))

[[1]]
[1] 2

[[2]]
[1] 5

[[3]]
[1] 9

{purrr} variants

Return a vector

And specify the type

• map_dbl()
• map_int()
• map_chr()
• map_lgl()

l

str(l)

List of 3
 $ :List of 4
  ..$  : num -1
  ..$ x: num 1
  ..$ y: num 2
  ..$ z: chr "a"
 $ :List of 4
  ..$  : num -2
  ..$ x: num 4
  ..$ y: num [1:2] 5 6
  ..$ z: chr "b"
 $ :List of 3
  ..$  : num -3
  ..$ x: num 8
  ..$ y: num [1:3] 9 10 11

map_*

map(l, "x")

[[1]]
[1] 1

[[2]]
[1] 4

[[3]]
[1] 8

map_dbl(l, "x")

[1] 1 4 8

map_dbl(l, 1)

[1] -1 -2 -3

Type Match

You’ll get an error for coercion

map_chr(l, "x")

Error in `map_chr()`:
ℹ In index: 1.
Caused by error:
! Can't coerce from a number to a string.

. . .

You have to explicitly coerce

map_chr(l, ~as.character(.x$x))

[1] "1" "4" "8"

. . .

You’ll get an error if element doesn’t exist

map_chr(l, "z")

Error in `map_chr()`:
ℹ In index: 3.
Caused by error:
! Result must be length 1, not 0.

. . .

Unless you set a default value

map_chr(l, "z", .default = NA_character_)

[1] "a" "b" NA 

Quick note: missing values

• In the prior case, specifying NA would work, instead of NA_character_
• Generally, I think it’s better to specify the type
• General programming rule: The more strict the better
• Because (base) R likes to be inconsistent, here are the NA types

. . .

Type	NA value
character	NA_character_
integer	NA_integer_
double	NA_real_
logical	NA (see here)

. . .

typeof(NA)

[1] "logical"

More quick examples

Please copy the code below so you have it locally

df_list <- list(
  data.frame(var1 = 1:5),
  data.frame(var1 = 1:3),
  data.frame(var1 = 1)
)

Compute mean(var1)

For each data frame. You try first!

Answer

map(df_list, ~mean(.x$var1))

[[1]]
[1] 3

[[2]]
[1] 2

[[3]]
[1] 1

. . .

Return a vector

map_dbl(df_list, ~mean(.x$var1))

[1] 3 2 1

Some more examples

Please follow along

econ <- economics |>
    mutate(year = lubridate::year(date))
econ

# A tibble: 574 × 7
   date         pce    pop psavert uempmed unemploy  year
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>
 1 1967-07-01  507. 198712    12.6     4.5     2944  1967
 2 1967-08-01  510. 198911    12.6     4.7     2945  1967
 3 1967-09-01  516. 199113    11.9     4.6     2958  1967
 4 1967-10-01  512. 199311    12.9     4.9     3143  1967
 5 1967-11-01  517. 199498    12.8     4.7     3066  1967
 6 1967-12-01  525. 199657    11.8     4.8     3018  1967
 7 1968-01-01  531. 199808    11.7     5.1     2878  1968
 8 1968-02-01  534. 199920    12.3     4.5     3001  1968
 9 1968-03-01  544. 200056    11.7     4.1     2877  1968
10 1968-04-01  544  200208    12.3     4.6     2709  1968
# ℹ 564 more rows

Let’s fit a simple model to each year

Caveats

• We’ll discuss a more elegant way to do this later
• This is not (statistically) the best way to approach this problem
• It’s a good illustration, and in my experience there are lots of times where this approach works well, even if this particular example is a bit artificial

split by year

by_year <- split(econ, econ$year)
str(by_year)

List of 49
 $ 1967: tibble [6 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:6], format: "1967-07-01" "1967-08-01" ...
  ..$ pce     : num [1:6] 507 510 516 512 517 ...
  ..$ pop     : num [1:6] 198712 198911 199113 199311 199498 ...
  ..$ psavert : num [1:6] 12.6 12.6 11.9 12.9 12.8 11.8
  ..$ uempmed : num [1:6] 4.5 4.7 4.6 4.9 4.7 4.8
  ..$ unemploy: num [1:6] 2944 2945 2958 3143 3066 ...
  ..$ year    : num [1:6] 1967 1967 1967 1967 1967 ...
 $ 1968: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1968-01-01" "1968-02-01" ...
  ..$ pce     : num [1:12] 531 534 544 544 550 ...
  ..$ pop     : num [1:12] 2e+05 2e+05 2e+05 2e+05 2e+05 ...
  ..$ psavert : num [1:12] 11.7 12.3 11.7 12.3 12 11.7 10.7 10.5 10.6 10.8 ...
  ..$ uempmed : num [1:12] 5.1 4.5 4.1 4.6 4.4 4.4 4.5 4.2 4.6 4.8 ...
  ..$ unemploy: num [1:12] 2878 3001 2877 2709 2740 ...
  ..$ year    : num [1:12] 1968 1968 1968 1968 1968 ...
 $ 1969: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1969-01-01" "1969-02-01" ...
  ..$ pce     : num [1:12] 584 589 589 594 600 ...
  ..$ pop     : num [1:12] 201760 201881 202023 202161 202331 ...
  ..$ psavert : num [1:12] 10.3 9.7 10.2 9.7 10.1 11.1 11.8 11.5 11.6 11.4 ...
  ..$ uempmed : num [1:12] 4.4 4.9 4 4 4.2 4.4 4.4 4.4 4.7 4.5 ...
  ..$ unemploy: num [1:12] 2718 2692 2712 2758 2713 ...
  ..$ year    : num [1:12] 1969 1969 1969 1969 1969 ...
 $ 1970: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1970-01-01" "1970-02-01" ...
  ..$ pce     : num [1:12] 629 634 632 636 642 ...
  ..$ pop     : num [1:12] 203849 204008 204156 204401 204607 ...
  ..$ psavert : num [1:12] 11.8 11.7 12.4 13.3 12.4 12.3 13.5 13.4 12.9 13.1 ...
  ..$ uempmed : num [1:12] 4.6 4.5 4.6 4.1 4.7 4.9 5.1 5.4 5.2 5.2 ...
  ..$ unemploy: num [1:12] 3201 3453 3635 3797 3919 ...
  ..$ year    : num [1:12] 1970 1970 1970 1970 1970 1970 1970 1970 1970 1970 ...
 $ 1971: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1971-01-01" "1971-02-01" ...
  ..$ pce     : num [1:12] 676 679 682 689 691 ...
  ..$ pop     : num [1:12] 206466 206668 206855 207065 207260 ...
  ..$ psavert : num [1:12] 13.3 13.3 13.5 13.2 13.6 14.7 13.8 13.6 13.3 13.3 ...
  ..$ uempmed : num [1:12] 6.2 6.3 6.4 6.5 6.7 5.7 6.2 6.4 5.8 6.5 ...
  ..$ unemploy: num [1:12] 4986 4903 4987 4959 4996 ...
  ..$ year    : num [1:12] 1971 1971 1971 1971 1971 ...
 $ 1972: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1972-01-01" "1972-02-01" ...
  ..$ pce     : num [1:12] 732 736 749 752 758 ...
  ..$ pop     : num [1:12] 208917 209061 209212 209386 209545 ...
  ..$ psavert : num [1:12] 12.5 12.8 11.8 11.5 11.7 11.7 11.7 12 12.2 13 ...
  ..$ uempmed : num [1:12] 6.2 6.6 6.6 6.7 6.6 5.4 6.1 6 5.6 5.7 ...
  ..$ unemploy: num [1:12] 5019 4928 5038 4959 4922 ...
  ..$ year    : num [1:12] 1972 1972 1972 1972 1972 ...
 $ 1973: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1973-01-01" "1973-02-01" ...
  ..$ pce     : num [1:12] 816 826 833 836 842 ...
  ..$ pop     : num [1:12] 210985 211120 211254 211420 211577 ...
  ..$ psavert : num [1:12] 12.4 12.5 12.7 13.2 13.2 13.6 13.2 13.9 13.1 14.4 ...
  ..$ uempmed : num [1:12] 5.7 5.2 5.5 5 4.9 5 5.2 4.9 5.4 5.5 ...
  ..$ unemploy: num [1:12] 4326 4452 4394 4459 4329 ...
  ..$ year    : num [1:12] 1973 1973 1973 1973 1973 ...
 $ 1974: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1974-01-01" "1974-02-01" ...
  ..$ pce     : num [1:12] 884 890 901 911 922 ...
  ..$ pop     : num [1:12] 212932 213074 213211 213361 213513 ...
  ..$ psavert : num [1:12] 14.3 14.2 13.4 13.1 12.8 12.8 12.8 12.1 12.9 13.4 ...
  ..$ uempmed : num [1:12] 5 5.1 4.8 5 4.6 5.3 5.7 5 5.3 5.5 ...
  ..$ unemploy: num [1:12] 4644 4731 4634 4618 4705 ...
  ..$ year    : num [1:12] 1974 1974 1974 1974 1974 ...
 $ 1975: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1975-01-01" "1975-02-01" ...
  ..$ pce     : num [1:12] 976 989 991 995 1019 ...
  ..$ pop     : num [1:12] 214931 215065 215198 215353 215523 ...
  ..$ psavert : num [1:12] 13.2 12.5 12.7 14.2 17.3 14.3 12.6 13 13 13.4 ...
  ..$ uempmed : num [1:12] 6.3 7.1 7.2 8.7 9.4 8.8 8.6 9.2 9.2 8.6 ...
  ..$ unemploy: num [1:12] 7501 7520 7978 8210 8433 ...
  ..$ year    : num [1:12] 1975 1975 1975 1975 1975 ...
 $ 1976: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1976-01-01" "1976-02-01" ...
  ..$ pce     : num [1:12] 1107 1108 1115 1125 1123 ...
  ..$ pop     : num [1:12] 217095 217249 217381 217528 217685 ...
  ..$ psavert : num [1:12] 11.7 12.3 12.2 11.7 12.3 11.4 11.7 11.7 11.4 11.1 ...
  ..$ uempmed : num [1:12] 9 8.2 8.7 8.2 8.3 7.8 7.7 7.9 7.8 7.7 ...
  ..$ unemploy: num [1:12] 7534 7326 7230 7330 7053 ...
  ..$ year    : num [1:12] 1976 1976 1976 1976 1976 ...
 $ 1977: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1977-01-01" "1977-02-01" ...
  ..$ pce     : num [1:12] 1215 1231 1238 1247 1257 ...
  ..$ pop     : num [1:12] 219179 219344 219504 219684 219859 ...
  ..$ psavert : num [1:12] 10.6 9.3 10.5 10.5 10.3 10.6 10.5 10.9 11.1 11 ...
  ..$ uempmed : num [1:12] 7.5 7.2 7.2 7.3 7.9 6.2 7.1 7 6.7 6.9 ...
  ..$ unemploy: num [1:12] 7280 7443 7307 7059 6911 ...
  ..$ year    : num [1:12] 1977 1977 1977 1977 1977 ...
 $ 1978: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1978-01-01" "1978-02-01" ...
  ..$ pce     : num [1:12] 1330 1355 1378 1396 1412 ...
  ..$ pop     : num [1:12] 221477 221629 221792 221991 222176 ...
  ..$ psavert : num [1:12] 11.9 11.1 11 10.8 10.3 10 10.9 10.5 10.6 10.7 ...
  ..$ uempmed : num [1:12] 6.5 6.7 6.2 6.1 5.7 6 5.8 5.8 5.6 5.9 ...
  ..$ unemploy: num [1:12] 6489 6318 6337 6180 6127 ...
  ..$ year    : num [1:12] 1978 1978 1978 1978 1978 ...
 $ 1979: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1979-01-01" "1979-02-01" ...
  ..$ pce     : num [1:12] 1502 1518 1531 1538 1559 ...
  ..$ pop     : num [1:12] 223865 224053 224235 224438 224632 ...
  ..$ psavert : num [1:12] 11.1 11.1 11.2 11 10.3 9.9 10.6 9.7 9.4 9.7 ...
  ..$ uempmed : num [1:12] 5.9 5.9 5.9 5.4 5.6 5.6 5.9 4.8 5.5 5.5 ...
  ..$ unemploy: num [1:12] 6109 6173 6109 6069 5840 ...
  ..$ year    : num [1:12] 1979 1979 1979 1979 1979 ...
 $ 1980: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1980-01-01" "1980-02-01" ...
  ..$ pce     : num [1:12] 1697 1701 1708 1695 1700 ...
  ..$ pop     : num [1:12] 226451 226656 226849 227061 227251 ...
  ..$ psavert : num [1:12] 9.9 10.1 10.2 11.3 11.4 11.2 11.3 11.3 11.7 11.3 ...
  ..$ uempmed : num [1:12] 5.3 5.8 6 5.8 5.7 6.4 7 7.5 7.7 7.5 ...
  ..$ unemploy: num [1:12] 6683 6702 6729 7358 7984 ...
  ..$ year    : num [1:12] 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
 $ 1981: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1981-01-01" "1981-02-01" ...
  ..$ pce     : num [1:12] 1870 1884 1903 1904 1914 ...
  ..$ pop     : num [1:12] 228937 229071 229224 229403 229575 ...
  ..$ psavert : num [1:12] 10.9 10.8 10.8 10.9 11 10.8 12.3 12 12.4 13 ...
  ..$ uempmed : num [1:12] 7.4 7.1 7.1 7.4 6.9 6.6 7.1 7.2 6.8 6.8 ...
  ..$ unemploy: num [1:12] 8071 8051 7982 7869 8174 ...
  ..$ year    : num [1:12] 1981 1981 1981 1981 1981 ...
 $ 1982: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1982-01-01" "1982-02-01" ...
  ..$ pce     : num [1:12] 1997 2021 2024 2026 2044 ...
  ..$ pop     : num [1:12] 231157 231313 231470 231645 231809 ...
  ..$ psavert : num [1:12] 12.7 12.1 12.2 12.9 12.3 12.3 12.5 12.6 11.8 11.3 ...
  ..$ uempmed : num [1:12] 7.1 7.5 7.7 8.1 8.5 9.5 8.5 8.7 9.5 9.7 ...
  ..$ unemploy: num [1:12] 9397 9705 9895 10244 10335 ...
  ..$ year    : num [1:12] 1982 1982 1982 1982 1982 ...
 $ 1983: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1983-01-01" "1983-02-01" ...
  ..$ pce     : num [1:12] 2174 2177 2203 2226 2246 ...
  ..$ pop     : num [1:12] 233322 233473 233613 233781 233922 ...
  ..$ psavert : num [1:12] 11.1 11.1 10.6 10.3 9.9 9.1 9.6 9.2 9.6 9.7 ...
  ..$ uempmed : num [1:12] 11.1 9.8 10.4 10.9 12.3 11.3 10.1 9.3 9.3 9.4 ...
  ..$ unemploy: num [1:12] 11534 11545 11408 11268 11154 ...
  ..$ year    : num [1:12] 1983 1983 1983 1983 1983 ...
 $ 1984: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1984-01-01" "1984-02-01" ...
  ..$ pce     : num [1:12] 2419 2404 2432 2458 2474 ...
  ..$ pop     : num [1:12] 235385 235527 235675 235839 235993 ...
  ..$ psavert : num [1:12] 10 11.7 11.5 11.5 11.1 11.1 11.6 11.8 11.8 11.7 ...
  ..$ uempmed : num [1:12] 9.1 8.3 8.3 8.2 9.1 7.5 7.5 7.3 7.6 7.2 ...
  ..$ unemploy: num [1:12] 9008 8791 8746 8762 8456 ...
  ..$ year    : num [1:12] 1984 1984 1984 1984 1984 ...
 $ 1985: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1985-01-01" "1985-02-01" ...
  ..$ pce     : num [1:12] 2619 2641 2648 2660 2696 ...
  ..$ pop     : num [1:12] 237468 237602 237732 237900 238074 ...
  ..$ psavert : num [1:12] 10.3 9.1 8.7 9.9 11.1 9.6 9.1 8.2 7.3 9.1 ...
  ..$ uempmed : num [1:12] 6.8 7.1 7.1 6.9 6.9 6.6 6.9 7.1 6.9 7.1 ...
  ..$ unemploy: num [1:12] 8423 8321 8339 8395 8302 ...
  ..$ year    : num [1:12] 1985 1985 1985 1985 1985 ...
 $ 1986: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1986-01-01" "1986-02-01" ...
  ..$ pce     : num [1:12] 2827 2820 2824 2835 2858 ...
  ..$ pop     : num [1:12] 239638 239788 239928 240094 240271 ...
  ..$ psavert : num [1:12] 8.6 9.3 9.9 9.7 9.3 9.4 9.3 9 7.2 8.4 ...
  ..$ uempmed : num [1:12] 6.7 6.9 6.8 6.7 6.8 7 6.9 7.1 7.4 7 ...
  ..$ unemploy: num [1:12] 7795 8402 8383 8364 8439 ...
  ..$ year    : num [1:12] 1986 1986 1986 1986 1986 ...
 $ 1987: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1987-01-01" "1987-02-01" ...
  ..$ pce     : num [1:12] 2936 3002 3013 3039 3048 ...
  ..$ pop     : num [1:12] 241784 241930 242079 242252 242423 ...
  ..$ psavert : num [1:12] 9.7 8.5 8.5 4.5 8.2 7.7 7.5 7.2 7.6 8.3 ...
  ..$ uempmed : num [1:12] 6.9 6.6 6.6 7.1 6.6 6.5 6.5 6.4 6 6.3 ...
  ..$ unemploy: num [1:12] 7892 7865 7862 7542 7574 ...
  ..$ year    : num [1:12] 1987 1987 1987 1987 1987 ...
 $ 1988: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1988-01-01" "1988-02-01" ...
  ..$ pce     : num [1:12] 3214 3221 3260 3263 3294 ...
  ..$ pop     : num [1:12] 243981 244131 244279 244445 244610 ...
  ..$ psavert : num [1:12] 8.1 8.6 8.2 8.8 8.4 8.4 8.6 8.4 8.9 8.6 ...
  ..$ uempmed : num [1:12] 6.2 6.3 6.4 5.9 5.9 5.8 6.1 5.9 5.7 5.6 ...
  ..$ unemploy: num [1:12] 6953 6929 6876 6601 6779 ...
  ..$ year    : num [1:12] 1988 1988 1988 1988 1988 ...
 $ 1989: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1989-01-01" "1989-02-01" ...
  ..$ pce     : num [1:12] 3484 3488 3499 3543 3552 ...
  ..$ pop     : num [1:12] 246224 246378 246530 246721 246906 ...
  ..$ psavert : num [1:12] 8.5 9 9.5 8.4 8.1 8.2 8.2 7.6 8.1 8.5 ...
  ..$ uempmed : num [1:12] 5.6 5.4 5.4 5.4 5.3 5.4 5.6 5 4.9 4.9 ...
  ..$ unemploy: num [1:12] 6682 6359 6205 6468 6375 ...
  ..$ year    : num [1:12] 1989 1989 1989 1989 1989 ...
 $ 1990: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1990-01-01" "1990-02-01" ...
  ..$ pce     : num [1:12] 3731 3728 3755 3770 3776 ...
  ..$ pop     : num [1:12] 248659 248827 249012 249306 249565 ...
  ..$ psavert : num [1:12] 8 8.6 8.3 8.8 8.7 8.6 8.7 8.1 8.1 7.8 ...
  ..$ uempmed : num [1:12] 5.1 5.3 5.1 4.8 5.2 5.2 5.4 5.4 5.6 5.8 ...
  ..$ unemploy: num [1:12] 6752 6651 6598 6797 6742 ...
  ..$ year    : num [1:12] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
 $ 1991: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1991-01-01" "1991-02-01" ...
  ..$ pce     : num [1:12] 3841 3867 3913 3907 3933 ...
  ..$ pop     : num [1:12] 251889 252135 252372 252643 252913 ...
  ..$ psavert : num [1:12] 9.3 8.8 8 8.6 8.4 8.9 8.2 8.6 8.8 9.3 ...
  ..$ uempmed : num [1:12] 6 6.2 6.7 6.6 6.4 6.9 7 7.3 6.8 7.2 ...
  ..$ unemploy: num [1:12] 8015 8265 8586 8439 8736 ...
  ..$ year    : num [1:12] 1991 1991 1991 1991 1991 ...
 $ 1992: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1992-01-01" "1992-02-01" ...
  ..$ pce     : num [1:12] 4085 4100 4117 4132 4158 ...
  ..$ pop     : num [1:12] 255214 255448 255703 255992 256285 ...
  ..$ psavert : num [1:12] 9.4 9.8 9.7 9.9 9.9 10.1 9.6 9.7 8.7 8 ...
  ..$ uempmed : num [1:12] 8.1 8.2 8.3 8.5 8.8 8.7 8.6 8.8 8.6 9 ...
  ..$ unemploy: num [1:12] 9283 9454 9460 9415 9744 ...
  ..$ year    : num [1:12] 1992 1992 1992 1992 1992 ...
 $ 1993: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1993-01-01" "1993-02-01" ...
  ..$ pce     : num [1:12] 4341 4355 4352 4393 4422 ...
  ..$ pop     : num [1:12] 258679 258919 259152 259414 259680 ...
  ..$ psavert : num [1:12] 8.6 8.9 8.9 8.7 8.3 7.8 7.6 7.7 6.9 6.3 ...
  ..$ uempmed : num [1:12] 8.6 8.5 8.5 8.4 8.1 8.3 8.2 8.2 8.3 8 ...
  ..$ unemploy: num [1:12] 9325 9183 9056 9110 9149 ...
  ..$ year    : num [1:12] 1993 1993 1993 1993 1993 ...
 $ 1994: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1994-01-01" "1994-02-01" ...
  ..$ pce     : num [1:12] 4585 4633 4646 4671 4670 ...
  ..$ pop     : num [1:12] 261919 262123 262352 262631 262877 ...
  ..$ psavert : num [1:12] 7.1 6.5 6.8 6.4 7.6 6.9 7 6.5 6.8 7.1 ...
  ..$ uempmed : num [1:12] 8.6 9.2 9.3 9.1 9.2 9.3 9 8.9 9.2 10 ...
  ..$ unemploy: num [1:12] 8630 8583 8470 8331 7915 ...
  ..$ year    : num [1:12] 1994 1994 1994 1994 1994 ...
 $ 1995: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1995-01-01" "1995-02-01" ...
  ..$ pce     : num [1:12] 4851 4851 4885 4890 4933 ...
  ..$ pop     : num [1:12] 265044 265270 265495 265755 265998 ...
  ..$ psavert : num [1:12] 7.5 7.8 7.5 6.9 7.1 6.7 7.1 6.7 6.8 7.1 ...
  ..$ uempmed : num [1:12] 8 8.1 8.3 8.3 9.1 7.9 8.5 8.3 7.9 8.2 ...
  ..$ unemploy: num [1:12] 7375 7187 7153 7645 7430 ...
  ..$ year    : num [1:12] 1995 1995 1995 1995 1995 ...
 $ 1996: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1996-01-01" "1996-02-01" ...
  ..$ pce     : num [1:12] 5086 5133 5173 5208 5224 ...
  ..$ pop     : num [1:12] 268151 268364 268595 268853 269108 ...
  ..$ psavert : num [1:12] 6.7 6.7 6.6 5.7 6.7 7.1 6.7 6.6 6.7 6.4 ...
  ..$ uempmed : num [1:12] 8.3 7.8 8.3 8.6 8.6 8.3 8.3 8.4 8.5 8.3 ...
  ..$ unemploy: num [1:12] 7491 7313 7318 7415 7423 ...
  ..$ year    : num [1:12] 1996 1996 1996 1996 1996 ...
 $ 1997: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1997-01-01" "1997-02-01" ...
  ..$ pce     : num [1:12] 5411 5434 5454 5459 5460 ...
  ..$ pop     : num [1:12] 271360 271585 271821 272083 272342 ...
  ..$ psavert : num [1:12] 6.2 6.2 6.4 6.5 6.8 6.6 6.1 6 6.2 6.2 ...
  ..$ uempmed : num [1:12] 7.8 8.1 7.9 8.3 8 8 8.3 7.8 8.2 7.7 ...
  ..$ unemploy: num [1:12] 7158 7102 7000 6873 6655 ...
  ..$ year    : num [1:12] 1997 1997 1997 1997 1997 ...
 $ 1998: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1998-01-01" "1998-02-01" ...
  ..$ pce     : num [1:12] 5690 5724 5750 5788 5838 ...
  ..$ pop     : num [1:12] 274626 274838 275047 275304 275564 ...
  ..$ psavert : num [1:12] 7.4 7.4 7.5 7.2 6.9 6.8 6.9 6.8 6.4 6.2 ...
  ..$ uempmed : num [1:12] 7.4 7 6.8 6.7 6 6.9 6.7 6.8 6.7 5.8 ...
  ..$ unemploy: num [1:12] 6368 6306 6422 5941 6047 ...
  ..$ year    : num [1:12] 1998 1998 1998 1998 1998 ...
 $ 1999: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1999-01-01" "1999-02-01" ...
  ..$ pce     : num [1:12] 6073 6102 6133 6196 6226 ...
  ..$ pop     : num [1:12] 277790 277992 278198 278451 278717 ...
  ..$ psavert : num [1:12] 6.4 6.2 5.9 5.2 4.9 4.8 4.8 4.7 4.2 4.6 ...
  ..$ uempmed : num [1:12] 6.9 6.8 6.8 6.2 6.5 6.3 5.8 6.5 6 6.1 ...
  ..$ unemploy: num [1:12] 5976 6111 5783 6004 5796 ...
  ..$ year    : num [1:12] 1999 1999 1999 1999 1999 ...
 $ 2000: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2000-01-01" "2000-02-01" ...
  ..$ pce     : num [1:12] 6535 6620 6686 6671 6708 ...
  ..$ pop     : num [1:12] 280976 281190 281409 281653 281877 ...
  ..$ psavert : num [1:12] 5.4 4.8 4.5 5 4.9 4.9 5.2 5.2 4.5 4.6 ...
  ..$ uempmed : num [1:12] 5.8 6.1 6 6.1 5.8 5.7 6 6.3 5.2 6.1 ...
  ..$ unemploy: num [1:12] 5708 5858 5733 5481 5758 ...
  ..$ year    : num [1:12] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
 $ 2001: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2001-01-01" "2001-02-01" ...
  ..$ pce     : num [1:12] 6977 6996 6988 7001 7047 ...
  ..$ pop     : num [1:12] 283920 284137 284350 284581 284810 ...
  ..$ psavert : num [1:12] 4.8 4.9 5.3 5 4.5 4.5 5.6 6.8 7 3.4 ...
  ..$ uempmed : num [1:12] 5.8 6.1 6.6 5.9 6.3 6 6.8 6.9 7.2 7.3 ...
  ..$ unemploy: num [1:12] 6023 6089 6141 6271 6226 ...
  ..$ year    : num [1:12] 2001 2001 2001 2001 2001 ...
 $ 2002: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2002-01-01" "2002-02-01" ...
  ..$ pce     : num [1:12] 7174 7218 7237 7305 7283 ...
  ..$ pop     : num [1:12] 286788 286994 287190 287397 287623 ...
  ..$ psavert : num [1:12] 6.1 5.8 5.9 5.8 6.5 6.4 5.5 5.4 5.7 5.7 ...
  ..$ uempmed : num [1:12] 8.4 8.3 8.4 8.9 9.5 11 8.9 9 9.5 9.6 ...
  ..$ unemploy: num [1:12] 8182 8215 8304 8599 8399 ...
  ..$ year    : num [1:12] 2002 2002 2002 2002 2002 ...
 $ 2003: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2003-01-01" "2003-02-01" ...
  ..$ pce     : num [1:12] 7533 7536 7598 7621 7628 ...
  ..$ pop     : num [1:12] 289518 289714 289911 290125 290346 ...
  ..$ psavert : num [1:12] 5.5 5.6 5.3 5.3 5.8 5.6 6.3 6 5.2 5.3 ...
  ..$ uempmed : num [1:12] 9.6 9.5 9.7 10.2 9.9 11.5 10.3 10.1 10.2 10.4 ...
  ..$ unemploy: num [1:12] 8520 8618 8588 8842 8957 ...
  ..$ year    : num [1:12] 2003 2003 2003 2003 2003 ...
 $ 2004: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2004-01-01" "2004-02-01" ...
  ..$ pce     : num [1:12] 7987 8020 8076 8089 8163 ...
  ..$ pop     : num [1:12] 292192 292368 292561 292779 292997 ...
  ..$ psavert : num [1:12] 5 5 4.9 5.3 5.3 5.8 5.3 5.2 4.6 4.5 ...
  ..$ uempmed : num [1:12] 10.6 10.2 10.2 9.5 9.9 11 8.9 9.2 9.6 9.5 ...
  ..$ unemploy: num [1:12] 8370 8167 8491 8170 8212 ...
  ..$ year    : num [1:12] 2004 2004 2004 2004 2004 ...
 $ 2005: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2005-01-01" "2005-02-01" ...
  ..$ pce     : num [1:12] 8470 8529 8570 8646 8644 ...
  ..$ pop     : num [1:12] 294914 295105 295287 295490 295704 ...
  ..$ psavert : num [1:12] 3.7 3.4 3.6 3.1 3.5 2.9 2.2 2.7 2.7 3.1 ...
  ..$ uempmed : num [1:12] 9.4 9.2 9.3 9 9.1 9 8.8 9.2 8.4 8.6 ...
  ..$ unemploy: num [1:12] 7784 7980 7737 7672 7651 ...
  ..$ year    : num [1:12] 2005 2005 2005 2005 2005 ...
 $ 2006: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2006-01-01" "2006-02-01" ...
  ..$ pce     : num [1:12] 9060 9090 9122 9175 9215 ...
  ..$ pop     : num [1:12] 297647 297854 298060 298281 298496 ...
  ..$ psavert : num [1:12] 4.2 4.2 4.2 4 3.8 4 3.4 3.6 3.6 3.6 ...
  ..$ uempmed : num [1:12] 8.6 9.1 8.7 8.4 8.5 7.3 8 8.4 8 7.9 ...
  ..$ unemploy: num [1:12] 7064 7184 7072 7120 6980 ...
  ..$ year    : num [1:12] 2006 2006 2006 2006 2006 ...
 $ 2007: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2007-01-01" "2007-02-01" ...
  ..$ pce     : num [1:12] 9516 9547 9585 9616 9651 ...
  ..$ pop     : num [1:12] 300574 300802 301021 301254 301483 ...
  ..$ psavert : num [1:12] 3.7 4.1 4.4 4.2 4 3.8 3.7 3.4 3.5 3.4 ...
  ..$ uempmed : num [1:12] 8.3 8.5 9.1 8.6 8.2 7.7 8.7 8.8 8.7 8.4 ...
  ..$ unemploy: num [1:12] 7116 6927 6731 6850 6766 ...
  ..$ year    : num [1:12] 2007 2007 2007 2007 2007 ...
 $ 2008: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2008-01-01" "2008-02-01" ...
  ..$ pce     : num [1:12] 9930 9913 9959 9997 10054 ...
  ..$ pop     : num [1:12] 303506 303711 303907 304117 304323 ...
  ..$ psavert : num [1:12] 3.7 4.1 4 3.4 7.8 5.5 4.4 3.8 4.7 5.5 ...
  ..$ uempmed : num [1:12] 9 8.7 8.7 9.4 7.9 9 9.7 9.7 10.2 10.4 ...
  ..$ unemploy: num [1:12] 7685 7497 7822 7637 8395 ...
  ..$ year    : num [1:12] 2008 2008 2008 2008 2008 ...
 $ 2009: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2009-01-01" "2009-02-01" ...
  ..$ pce     : num [1:12] 9784 9766 9718 9725 9749 ...
  ..$ pop     : num [1:12] 306208 306402 306588 306787 306984 ...
  ..$ psavert : num [1:12] 6.2 5.5 5.9 6.8 8.2 6.7 6 4.9 5.9 5.4 ...
  ..$ uempmed : num [1:12] 10.7 11.7 12.3 13.1 14.2 17.2 16 16.3 17.8 18.9 ...
  ..$ unemploy: num [1:12] 12058 12898 13426 13853 14499 ...
  ..$ year    : num [1:12] 2009 2009 2009 2009 2009 ...
 $ 2010: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2010-01-01" "2010-02-01" ...
  ..$ pce     : num [1:12] 10002 10031 10089 10113 10131 ...
  ..$ pop     : num [1:12] 308833 309027 309212 309191 309369 ...
  ..$ psavert : num [1:12] 6.1 5.8 5.7 6.4 7 6.9 6.8 6.9 6.7 6.6 ...
  ..$ uempmed : num [1:12] 20 19.9 20.4 22.1 22.3 25.2 22.3 21 20.3 21.2 ...
  ..$ unemploy: num [1:12] 15046 15113 15202 15325 14849 ...
  ..$ year    : num [1:12] 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
 $ 2011: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2011-01-01" "2011-02-01" ...
  ..$ pce     : num [1:12] 10436 10470 10550 10588 10612 ...
  ..$ pop     : num [1:12] 310961 311113 311265 311436 311607 ...
  ..$ psavert : num [1:12] 7.4 7.6 7 6.9 6.9 7.2 7.3 7.2 6.8 6.8 ...
  ..$ uempmed : num [1:12] 21.5 21.1 21.5 20.9 21.6 22.4 22 22.4 22 20.6 ...
  ..$ unemploy: num [1:12] 14013 13820 13737 13957 13855 ...
  ..$ year    : num [1:12] 2011 2011 2011 2011 2011 ...
 $ 2012: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2012-01-01" "2012-02-01" ...
  ..$ pce     : num [1:12] 10862 10954 10952 10980 10969 ...
  ..$ pop     : num [1:12] 313183 313339 313499 313667 313831 ...
  ..$ psavert : num [1:12] 8 8 8.5 8.7 8.8 9.1 8.2 8 8.2 8.8 ...
  ..$ uempmed : num [1:12] 20.8 19.7 19.2 19.1 19.9 20.4 17.5 18.4 18.8 19.9 ...
  ..$ unemploy: num [1:12] 12797 12813 12713 12646 12660 ...
  ..$ year    : num [1:12] 2012 2012 2012 2012 2012 ...
 $ 2013: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2013-01-01" "2013-02-01" ...
  ..$ pce     : num [1:12] 11203 11240 11227 11205 11245 ...
  ..$ pop     : num [1:12] 315390 315520 315662 315818 315984 ...
  ..$ psavert : num [1:12] 6.3 5.8 5.9 6.4 6.7 6.8 6.6 6.7 6.8 6.3 ...
  ..$ uempmed : num [1:12] 15.8 17.2 17.6 17.1 17.1 17 16.2 16.5 16.5 16.3 ...
  ..$ unemploy: num [1:12] 12471 11950 11689 11760 11654 ...
  ..$ year    : num [1:12] 2013 2013 2013 2013 2013 ...
 $ 2014: tibble [12 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2014-01-01" "2014-02-01" ...
  ..$ pce     : num [1:12] 11512 11566 11643 11703 11748 ...
  ..$ pop     : num [1:12] 317594 317754 317917 318089 318270 ...
  ..$ psavert : num [1:12] 7.1 7.3 7.4 7.4 7.4 7.4 7.5 7.2 7.4 7.2 ...
  ..$ uempmed : num [1:12] 15.4 15.9 15.8 15.7 14.6 13.8 13.1 12.9 13.4 13.6 ...
  ..$ unemploy: num [1:12] 10202 10349 10380 9702 9859 ...
  ..$ year    : num [1:12] 2014 2014 2014 2014 2014 ...
 $ 2015: tibble [4 × 7] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:4], format: "2015-01-01" "2015-02-01" ...
  ..$ pce     : num [1:4] 12046 12082 12158 12194
  ..$ pop     : num [1:4] 319929 320075 320231 320402
  ..$ psavert : num [1:4] 7.7 7.9 7.4 7.6
  ..$ uempmed : num [1:4] 13.2 12.9 12 11.5
  ..$ unemploy: num [1:4] 8903 8610 8504 8526
  ..$ year    : num [1:4] 2015 2015 2015 2015

Model

What is the relation between personal consumption expenditures (pce) and the unemployment percentage over time?

. . .

Problem: We don’t have the percentage. Let’s compute!

You try first!

. . .

by_year <- map(by_year, 
               ~mutate(.x, percent = unemploy / pop))
str(by_year)

List of 49
 $ 1967: tibble [6 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:6], format: "1967-07-01" "1967-08-01" ...
  ..$ pce     : num [1:6] 507 510 516 512 517 ...
  ..$ pop     : num [1:6] 198712 198911 199113 199311 199498 ...
  ..$ psavert : num [1:6] 12.6 12.6 11.9 12.9 12.8 11.8
  ..$ uempmed : num [1:6] 4.5 4.7 4.6 4.9 4.7 4.8
  ..$ unemploy: num [1:6] 2944 2945 2958 3143 3066 ...
  ..$ year    : num [1:6] 1967 1967 1967 1967 1967 ...
  ..$ percent : num [1:6] 0.0148 0.0148 0.0149 0.0158 0.0154 ...
 $ 1968: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1968-01-01" "1968-02-01" ...
  ..$ pce     : num [1:12] 531 534 544 544 550 ...
  ..$ pop     : num [1:12] 2e+05 2e+05 2e+05 2e+05 2e+05 ...
  ..$ psavert : num [1:12] 11.7 12.3 11.7 12.3 12 11.7 10.7 10.5 10.6 10.8 ...
  ..$ uempmed : num [1:12] 5.1 4.5 4.1 4.6 4.4 4.4 4.5 4.2 4.6 4.8 ...
  ..$ unemploy: num [1:12] 2878 3001 2877 2709 2740 ...
  ..$ year    : num [1:12] 1968 1968 1968 1968 1968 ...
  ..$ percent : num [1:12] 0.0144 0.015 0.0144 0.0135 0.0137 ...
 $ 1969: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1969-01-01" "1969-02-01" ...
  ..$ pce     : num [1:12] 584 589 589 594 600 ...
  ..$ pop     : num [1:12] 201760 201881 202023 202161 202331 ...
  ..$ psavert : num [1:12] 10.3 9.7 10.2 9.7 10.1 11.1 11.8 11.5 11.6 11.4 ...
  ..$ uempmed : num [1:12] 4.4 4.9 4 4 4.2 4.4 4.4 4.4 4.7 4.5 ...
  ..$ unemploy: num [1:12] 2718 2692 2712 2758 2713 ...
  ..$ year    : num [1:12] 1969 1969 1969 1969 1969 ...
  ..$ percent : num [1:12] 0.0135 0.0133 0.0134 0.0136 0.0134 ...
 $ 1970: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1970-01-01" "1970-02-01" ...
  ..$ pce     : num [1:12] 629 634 632 636 642 ...
  ..$ pop     : num [1:12] 203849 204008 204156 204401 204607 ...
  ..$ psavert : num [1:12] 11.8 11.7 12.4 13.3 12.4 12.3 13.5 13.4 12.9 13.1 ...
  ..$ uempmed : num [1:12] 4.6 4.5 4.6 4.1 4.7 4.9 5.1 5.4 5.2 5.2 ...
  ..$ unemploy: num [1:12] 3201 3453 3635 3797 3919 ...
  ..$ year    : num [1:12] 1970 1970 1970 1970 1970 1970 1970 1970 1970 1970 ...
  ..$ percent : num [1:12] 0.0157 0.0169 0.0178 0.0186 0.0192 ...
 $ 1971: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1971-01-01" "1971-02-01" ...
  ..$ pce     : num [1:12] 676 679 682 689 691 ...
  ..$ pop     : num [1:12] 206466 206668 206855 207065 207260 ...
  ..$ psavert : num [1:12] 13.3 13.3 13.5 13.2 13.6 14.7 13.8 13.6 13.3 13.3 ...
  ..$ uempmed : num [1:12] 6.2 6.3 6.4 6.5 6.7 5.7 6.2 6.4 5.8 6.5 ...
  ..$ unemploy: num [1:12] 4986 4903 4987 4959 4996 ...
  ..$ year    : num [1:12] 1971 1971 1971 1971 1971 ...
  ..$ percent : num [1:12] 0.0241 0.0237 0.0241 0.0239 0.0241 ...
 $ 1972: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1972-01-01" "1972-02-01" ...
  ..$ pce     : num [1:12] 732 736 749 752 758 ...
  ..$ pop     : num [1:12] 208917 209061 209212 209386 209545 ...
  ..$ psavert : num [1:12] 12.5 12.8 11.8 11.5 11.7 11.7 11.7 12 12.2 13 ...
  ..$ uempmed : num [1:12] 6.2 6.6 6.6 6.7 6.6 5.4 6.1 6 5.6 5.7 ...
  ..$ unemploy: num [1:12] 5019 4928 5038 4959 4922 ...
  ..$ year    : num [1:12] 1972 1972 1972 1972 1972 ...
  ..$ percent : num [1:12] 0.024 0.0236 0.0241 0.0237 0.0235 ...
 $ 1973: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1973-01-01" "1973-02-01" ...
  ..$ pce     : num [1:12] 816 826 833 836 842 ...
  ..$ pop     : num [1:12] 210985 211120 211254 211420 211577 ...
  ..$ psavert : num [1:12] 12.4 12.5 12.7 13.2 13.2 13.6 13.2 13.9 13.1 14.4 ...
  ..$ uempmed : num [1:12] 5.7 5.2 5.5 5 4.9 5 5.2 4.9 5.4 5.5 ...
  ..$ unemploy: num [1:12] 4326 4452 4394 4459 4329 ...
  ..$ year    : num [1:12] 1973 1973 1973 1973 1973 ...
  ..$ percent : num [1:12] 0.0205 0.0211 0.0208 0.0211 0.0205 ...
 $ 1974: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1974-01-01" "1974-02-01" ...
  ..$ pce     : num [1:12] 884 890 901 911 922 ...
  ..$ pop     : num [1:12] 212932 213074 213211 213361 213513 ...
  ..$ psavert : num [1:12] 14.3 14.2 13.4 13.1 12.8 12.8 12.8 12.1 12.9 13.4 ...
  ..$ uempmed : num [1:12] 5 5.1 4.8 5 4.6 5.3 5.7 5 5.3 5.5 ...
  ..$ unemploy: num [1:12] 4644 4731 4634 4618 4705 ...
  ..$ year    : num [1:12] 1974 1974 1974 1974 1974 ...
  ..$ percent : num [1:12] 0.0218 0.0222 0.0217 0.0216 0.022 ...
 $ 1975: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1975-01-01" "1975-02-01" ...
  ..$ pce     : num [1:12] 976 989 991 995 1019 ...
  ..$ pop     : num [1:12] 214931 215065 215198 215353 215523 ...
  ..$ psavert : num [1:12] 13.2 12.5 12.7 14.2 17.3 14.3 12.6 13 13 13.4 ...
  ..$ uempmed : num [1:12] 6.3 7.1 7.2 8.7 9.4 8.8 8.6 9.2 9.2 8.6 ...
  ..$ unemploy: num [1:12] 7501 7520 7978 8210 8433 ...
  ..$ year    : num [1:12] 1975 1975 1975 1975 1975 ...
  ..$ percent : num [1:12] 0.0349 0.035 0.0371 0.0381 0.0391 ...
 $ 1976: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1976-01-01" "1976-02-01" ...
  ..$ pce     : num [1:12] 1107 1108 1115 1125 1123 ...
  ..$ pop     : num [1:12] 217095 217249 217381 217528 217685 ...
  ..$ psavert : num [1:12] 11.7 12.3 12.2 11.7 12.3 11.4 11.7 11.7 11.4 11.1 ...
  ..$ uempmed : num [1:12] 9 8.2 8.7 8.2 8.3 7.8 7.7 7.9 7.8 7.7 ...
  ..$ unemploy: num [1:12] 7534 7326 7230 7330 7053 ...
  ..$ year    : num [1:12] 1976 1976 1976 1976 1976 ...
  ..$ percent : num [1:12] 0.0347 0.0337 0.0333 0.0337 0.0324 ...
 $ 1977: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1977-01-01" "1977-02-01" ...
  ..$ pce     : num [1:12] 1215 1231 1238 1247 1257 ...
  ..$ pop     : num [1:12] 219179 219344 219504 219684 219859 ...
  ..$ psavert : num [1:12] 10.6 9.3 10.5 10.5 10.3 10.6 10.5 10.9 11.1 11 ...
  ..$ uempmed : num [1:12] 7.5 7.2 7.2 7.3 7.9 6.2 7.1 7 6.7 6.9 ...
  ..$ unemploy: num [1:12] 7280 7443 7307 7059 6911 ...
  ..$ year    : num [1:12] 1977 1977 1977 1977 1977 ...
  ..$ percent : num [1:12] 0.0332 0.0339 0.0333 0.0321 0.0314 ...
 $ 1978: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1978-01-01" "1978-02-01" ...
  ..$ pce     : num [1:12] 1330 1355 1378 1396 1412 ...
  ..$ pop     : num [1:12] 221477 221629 221792 221991 222176 ...
  ..$ psavert : num [1:12] 11.9 11.1 11 10.8 10.3 10 10.9 10.5 10.6 10.7 ...
  ..$ uempmed : num [1:12] 6.5 6.7 6.2 6.1 5.7 6 5.8 5.8 5.6 5.9 ...
  ..$ unemploy: num [1:12] 6489 6318 6337 6180 6127 ...
  ..$ year    : num [1:12] 1978 1978 1978 1978 1978 ...
  ..$ percent : num [1:12] 0.0293 0.0285 0.0286 0.0278 0.0276 ...
 $ 1979: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1979-01-01" "1979-02-01" ...
  ..$ pce     : num [1:12] 1502 1518 1531 1538 1559 ...
  ..$ pop     : num [1:12] 223865 224053 224235 224438 224632 ...
  ..$ psavert : num [1:12] 11.1 11.1 11.2 11 10.3 9.9 10.6 9.7 9.4 9.7 ...
  ..$ uempmed : num [1:12] 5.9 5.9 5.9 5.4 5.6 5.6 5.9 4.8 5.5 5.5 ...
  ..$ unemploy: num [1:12] 6109 6173 6109 6069 5840 ...
  ..$ year    : num [1:12] 1979 1979 1979 1979 1979 ...
  ..$ percent : num [1:12] 0.0273 0.0276 0.0272 0.027 0.026 ...
 $ 1980: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1980-01-01" "1980-02-01" ...
  ..$ pce     : num [1:12] 1697 1701 1708 1695 1700 ...
  ..$ pop     : num [1:12] 226451 226656 226849 227061 227251 ...
  ..$ psavert : num [1:12] 9.9 10.1 10.2 11.3 11.4 11.2 11.3 11.3 11.7 11.3 ...
  ..$ uempmed : num [1:12] 5.3 5.8 6 5.8 5.7 6.4 7 7.5 7.7 7.5 ...
  ..$ unemploy: num [1:12] 6683 6702 6729 7358 7984 ...
  ..$ year    : num [1:12] 1980 1980 1980 1980 1980 1980 1980 1980 1980 1980 ...
  ..$ percent : num [1:12] 0.0295 0.0296 0.0297 0.0324 0.0351 ...
 $ 1981: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1981-01-01" "1981-02-01" ...
  ..$ pce     : num [1:12] 1870 1884 1903 1904 1914 ...
  ..$ pop     : num [1:12] 228937 229071 229224 229403 229575 ...
  ..$ psavert : num [1:12] 10.9 10.8 10.8 10.9 11 10.8 12.3 12 12.4 13 ...
  ..$ uempmed : num [1:12] 7.4 7.1 7.1 7.4 6.9 6.6 7.1 7.2 6.8 6.8 ...
  ..$ unemploy: num [1:12] 8071 8051 7982 7869 8174 ...
  ..$ year    : num [1:12] 1981 1981 1981 1981 1981 ...
  ..$ percent : num [1:12] 0.0353 0.0351 0.0348 0.0343 0.0356 ...
 $ 1982: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1982-01-01" "1982-02-01" ...
  ..$ pce     : num [1:12] 1997 2021 2024 2026 2044 ...
  ..$ pop     : num [1:12] 231157 231313 231470 231645 231809 ...
  ..$ psavert : num [1:12] 12.7 12.1 12.2 12.9 12.3 12.3 12.5 12.6 11.8 11.3 ...
  ..$ uempmed : num [1:12] 7.1 7.5 7.7 8.1 8.5 9.5 8.5 8.7 9.5 9.7 ...
  ..$ unemploy: num [1:12] 9397 9705 9895 10244 10335 ...
  ..$ year    : num [1:12] 1982 1982 1982 1982 1982 ...
  ..$ percent : num [1:12] 0.0407 0.042 0.0427 0.0442 0.0446 ...
 $ 1983: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1983-01-01" "1983-02-01" ...
  ..$ pce     : num [1:12] 2174 2177 2203 2226 2246 ...
  ..$ pop     : num [1:12] 233322 233473 233613 233781 233922 ...
  ..$ psavert : num [1:12] 11.1 11.1 10.6 10.3 9.9 9.1 9.6 9.2 9.6 9.7 ...
  ..$ uempmed : num [1:12] 11.1 9.8 10.4 10.9 12.3 11.3 10.1 9.3 9.3 9.4 ...
  ..$ unemploy: num [1:12] 11534 11545 11408 11268 11154 ...
  ..$ year    : num [1:12] 1983 1983 1983 1983 1983 ...
  ..$ percent : num [1:12] 0.0494 0.0494 0.0488 0.0482 0.0477 ...
 $ 1984: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1984-01-01" "1984-02-01" ...
  ..$ pce     : num [1:12] 2419 2404 2432 2458 2474 ...
  ..$ pop     : num [1:12] 235385 235527 235675 235839 235993 ...
  ..$ psavert : num [1:12] 10 11.7 11.5 11.5 11.1 11.1 11.6 11.8 11.8 11.7 ...
  ..$ uempmed : num [1:12] 9.1 8.3 8.3 8.2 9.1 7.5 7.5 7.3 7.6 7.2 ...
  ..$ unemploy: num [1:12] 9008 8791 8746 8762 8456 ...
  ..$ year    : num [1:12] 1984 1984 1984 1984 1984 ...
  ..$ percent : num [1:12] 0.0383 0.0373 0.0371 0.0372 0.0358 ...
 $ 1985: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1985-01-01" "1985-02-01" ...
  ..$ pce     : num [1:12] 2619 2641 2648 2660 2696 ...
  ..$ pop     : num [1:12] 237468 237602 237732 237900 238074 ...
  ..$ psavert : num [1:12] 10.3 9.1 8.7 9.9 11.1 9.6 9.1 8.2 7.3 9.1 ...
  ..$ uempmed : num [1:12] 6.8 7.1 7.1 6.9 6.9 6.6 6.9 7.1 6.9 7.1 ...
  ..$ unemploy: num [1:12] 8423 8321 8339 8395 8302 ...
  ..$ year    : num [1:12] 1985 1985 1985 1985 1985 ...
  ..$ percent : num [1:12] 0.0355 0.035 0.0351 0.0353 0.0349 ...
 $ 1986: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1986-01-01" "1986-02-01" ...
  ..$ pce     : num [1:12] 2827 2820 2824 2835 2858 ...
  ..$ pop     : num [1:12] 239638 239788 239928 240094 240271 ...
  ..$ psavert : num [1:12] 8.6 9.3 9.9 9.7 9.3 9.4 9.3 9 7.2 8.4 ...
  ..$ uempmed : num [1:12] 6.7 6.9 6.8 6.7 6.8 7 6.9 7.1 7.4 7 ...
  ..$ unemploy: num [1:12] 7795 8402 8383 8364 8439 ...
  ..$ year    : num [1:12] 1986 1986 1986 1986 1986 ...
  ..$ percent : num [1:12] 0.0325 0.035 0.0349 0.0348 0.0351 ...
 $ 1987: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1987-01-01" "1987-02-01" ...
  ..$ pce     : num [1:12] 2936 3002 3013 3039 3048 ...
  ..$ pop     : num [1:12] 241784 241930 242079 242252 242423 ...
  ..$ psavert : num [1:12] 9.7 8.5 8.5 4.5 8.2 7.7 7.5 7.2 7.6 8.3 ...
  ..$ uempmed : num [1:12] 6.9 6.6 6.6 7.1 6.6 6.5 6.5 6.4 6 6.3 ...
  ..$ unemploy: num [1:12] 7892 7865 7862 7542 7574 ...
  ..$ year    : num [1:12] 1987 1987 1987 1987 1987 ...
  ..$ percent : num [1:12] 0.0326 0.0325 0.0325 0.0311 0.0312 ...
 $ 1988: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1988-01-01" "1988-02-01" ...
  ..$ pce     : num [1:12] 3214 3221 3260 3263 3294 ...
  ..$ pop     : num [1:12] 243981 244131 244279 244445 244610 ...
  ..$ psavert : num [1:12] 8.1 8.6 8.2 8.8 8.4 8.4 8.6 8.4 8.9 8.6 ...
  ..$ uempmed : num [1:12] 6.2 6.3 6.4 5.9 5.9 5.8 6.1 5.9 5.7 5.6 ...
  ..$ unemploy: num [1:12] 6953 6929 6876 6601 6779 ...
  ..$ year    : num [1:12] 1988 1988 1988 1988 1988 ...
  ..$ percent : num [1:12] 0.0285 0.0284 0.0281 0.027 0.0277 ...
 $ 1989: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1989-01-01" "1989-02-01" ...
  ..$ pce     : num [1:12] 3484 3488 3499 3543 3552 ...
  ..$ pop     : num [1:12] 246224 246378 246530 246721 246906 ...
  ..$ psavert : num [1:12] 8.5 9 9.5 8.4 8.1 8.2 8.2 7.6 8.1 8.5 ...
  ..$ uempmed : num [1:12] 5.6 5.4 5.4 5.4 5.3 5.4 5.6 5 4.9 4.9 ...
  ..$ unemploy: num [1:12] 6682 6359 6205 6468 6375 ...
  ..$ year    : num [1:12] 1989 1989 1989 1989 1989 ...
  ..$ percent : num [1:12] 0.0271 0.0258 0.0252 0.0262 0.0258 ...
 $ 1990: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1990-01-01" "1990-02-01" ...
  ..$ pce     : num [1:12] 3731 3728 3755 3770 3776 ...
  ..$ pop     : num [1:12] 248659 248827 249012 249306 249565 ...
  ..$ psavert : num [1:12] 8 8.6 8.3 8.8 8.7 8.6 8.7 8.1 8.1 7.8 ...
  ..$ uempmed : num [1:12] 5.1 5.3 5.1 4.8 5.2 5.2 5.4 5.4 5.6 5.8 ...
  ..$ unemploy: num [1:12] 6752 6651 6598 6797 6742 ...
  ..$ year    : num [1:12] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
  ..$ percent : num [1:12] 0.0272 0.0267 0.0265 0.0273 0.027 ...
 $ 1991: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1991-01-01" "1991-02-01" ...
  ..$ pce     : num [1:12] 3841 3867 3913 3907 3933 ...
  ..$ pop     : num [1:12] 251889 252135 252372 252643 252913 ...
  ..$ psavert : num [1:12] 9.3 8.8 8 8.6 8.4 8.9 8.2 8.6 8.8 9.3 ...
  ..$ uempmed : num [1:12] 6 6.2 6.7 6.6 6.4 6.9 7 7.3 6.8 7.2 ...
  ..$ unemploy: num [1:12] 8015 8265 8586 8439 8736 ...
  ..$ year    : num [1:12] 1991 1991 1991 1991 1991 ...
  ..$ percent : num [1:12] 0.0318 0.0328 0.034 0.0334 0.0345 ...
 $ 1992: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1992-01-01" "1992-02-01" ...
  ..$ pce     : num [1:12] 4085 4100 4117 4132 4158 ...
  ..$ pop     : num [1:12] 255214 255448 255703 255992 256285 ...
  ..$ psavert : num [1:12] 9.4 9.8 9.7 9.9 9.9 10.1 9.6 9.7 8.7 8 ...
  ..$ uempmed : num [1:12] 8.1 8.2 8.3 8.5 8.8 8.7 8.6 8.8 8.6 9 ...
  ..$ unemploy: num [1:12] 9283 9454 9460 9415 9744 ...
  ..$ year    : num [1:12] 1992 1992 1992 1992 1992 ...
  ..$ percent : num [1:12] 0.0364 0.037 0.037 0.0368 0.038 ...
 $ 1993: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1993-01-01" "1993-02-01" ...
  ..$ pce     : num [1:12] 4341 4355 4352 4393 4422 ...
  ..$ pop     : num [1:12] 258679 258919 259152 259414 259680 ...
  ..$ psavert : num [1:12] 8.6 8.9 8.9 8.7 8.3 7.8 7.6 7.7 6.9 6.3 ...
  ..$ uempmed : num [1:12] 8.6 8.5 8.5 8.4 8.1 8.3 8.2 8.2 8.3 8 ...
  ..$ unemploy: num [1:12] 9325 9183 9056 9110 9149 ...
  ..$ year    : num [1:12] 1993 1993 1993 1993 1993 ...
  ..$ percent : num [1:12] 0.036 0.0355 0.0349 0.0351 0.0352 ...
 $ 1994: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1994-01-01" "1994-02-01" ...
  ..$ pce     : num [1:12] 4585 4633 4646 4671 4670 ...
  ..$ pop     : num [1:12] 261919 262123 262352 262631 262877 ...
  ..$ psavert : num [1:12] 7.1 6.5 6.8 6.4 7.6 6.9 7 6.5 6.8 7.1 ...
  ..$ uempmed : num [1:12] 8.6 9.2 9.3 9.1 9.2 9.3 9 8.9 9.2 10 ...
  ..$ unemploy: num [1:12] 8630 8583 8470 8331 7915 ...
  ..$ year    : num [1:12] 1994 1994 1994 1994 1994 ...
  ..$ percent : num [1:12] 0.0329 0.0327 0.0323 0.0317 0.0301 ...
 $ 1995: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1995-01-01" "1995-02-01" ...
  ..$ pce     : num [1:12] 4851 4851 4885 4890 4933 ...
  ..$ pop     : num [1:12] 265044 265270 265495 265755 265998 ...
  ..$ psavert : num [1:12] 7.5 7.8 7.5 6.9 7.1 6.7 7.1 6.7 6.8 7.1 ...
  ..$ uempmed : num [1:12] 8 8.1 8.3 8.3 9.1 7.9 8.5 8.3 7.9 8.2 ...
  ..$ unemploy: num [1:12] 7375 7187 7153 7645 7430 ...
  ..$ year    : num [1:12] 1995 1995 1995 1995 1995 ...
  ..$ percent : num [1:12] 0.0278 0.0271 0.0269 0.0288 0.0279 ...
 $ 1996: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1996-01-01" "1996-02-01" ...
  ..$ pce     : num [1:12] 5086 5133 5173 5208 5224 ...
  ..$ pop     : num [1:12] 268151 268364 268595 268853 269108 ...
  ..$ psavert : num [1:12] 6.7 6.7 6.6 5.7 6.7 7.1 6.7 6.6 6.7 6.4 ...
  ..$ uempmed : num [1:12] 8.3 7.8 8.3 8.6 8.6 8.3 8.3 8.4 8.5 8.3 ...
  ..$ unemploy: num [1:12] 7491 7313 7318 7415 7423 ...
  ..$ year    : num [1:12] 1996 1996 1996 1996 1996 ...
  ..$ percent : num [1:12] 0.0279 0.0273 0.0272 0.0276 0.0276 ...
 $ 1997: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1997-01-01" "1997-02-01" ...
  ..$ pce     : num [1:12] 5411 5434 5454 5459 5460 ...
  ..$ pop     : num [1:12] 271360 271585 271821 272083 272342 ...
  ..$ psavert : num [1:12] 6.2 6.2 6.4 6.5 6.8 6.6 6.1 6 6.2 6.2 ...
  ..$ uempmed : num [1:12] 7.8 8.1 7.9 8.3 8 8 8.3 7.8 8.2 7.7 ...
  ..$ unemploy: num [1:12] 7158 7102 7000 6873 6655 ...
  ..$ year    : num [1:12] 1997 1997 1997 1997 1997 ...
  ..$ percent : num [1:12] 0.0264 0.0262 0.0258 0.0253 0.0244 ...
 $ 1998: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1998-01-01" "1998-02-01" ...
  ..$ pce     : num [1:12] 5690 5724 5750 5788 5838 ...
  ..$ pop     : num [1:12] 274626 274838 275047 275304 275564 ...
  ..$ psavert : num [1:12] 7.4 7.4 7.5 7.2 6.9 6.8 6.9 6.8 6.4 6.2 ...
  ..$ uempmed : num [1:12] 7.4 7 6.8 6.7 6 6.9 6.7 6.8 6.7 5.8 ...
  ..$ unemploy: num [1:12] 6368 6306 6422 5941 6047 ...
  ..$ year    : num [1:12] 1998 1998 1998 1998 1998 ...
  ..$ percent : num [1:12] 0.0232 0.0229 0.0233 0.0216 0.0219 ...
 $ 1999: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "1999-01-01" "1999-02-01" ...
  ..$ pce     : num [1:12] 6073 6102 6133 6196 6226 ...
  ..$ pop     : num [1:12] 277790 277992 278198 278451 278717 ...
  ..$ psavert : num [1:12] 6.4 6.2 5.9 5.2 4.9 4.8 4.8 4.7 4.2 4.6 ...
  ..$ uempmed : num [1:12] 6.9 6.8 6.8 6.2 6.5 6.3 5.8 6.5 6 6.1 ...
  ..$ unemploy: num [1:12] 5976 6111 5783 6004 5796 ...
  ..$ year    : num [1:12] 1999 1999 1999 1999 1999 ...
  ..$ percent : num [1:12] 0.0215 0.022 0.0208 0.0216 0.0208 ...
 $ 2000: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2000-01-01" "2000-02-01" ...
  ..$ pce     : num [1:12] 6535 6620 6686 6671 6708 ...
  ..$ pop     : num [1:12] 280976 281190 281409 281653 281877 ...
  ..$ psavert : num [1:12] 5.4 4.8 4.5 5 4.9 4.9 5.2 5.2 4.5 4.6 ...
  ..$ uempmed : num [1:12] 5.8 6.1 6 6.1 5.8 5.7 6 6.3 5.2 6.1 ...
  ..$ unemploy: num [1:12] 5708 5858 5733 5481 5758 ...
  ..$ year    : num [1:12] 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
  ..$ percent : num [1:12] 0.0203 0.0208 0.0204 0.0195 0.0204 ...
 $ 2001: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2001-01-01" "2001-02-01" ...
  ..$ pce     : num [1:12] 6977 6996 6988 7001 7047 ...
  ..$ pop     : num [1:12] 283920 284137 284350 284581 284810 ...
  ..$ psavert : num [1:12] 4.8 4.9 5.3 5 4.5 4.5 5.6 6.8 7 3.4 ...
  ..$ uempmed : num [1:12] 5.8 6.1 6.6 5.9 6.3 6 6.8 6.9 7.2 7.3 ...
  ..$ unemploy: num [1:12] 6023 6089 6141 6271 6226 ...
  ..$ year    : num [1:12] 2001 2001 2001 2001 2001 ...
  ..$ percent : num [1:12] 0.0212 0.0214 0.0216 0.022 0.0219 ...
 $ 2002: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2002-01-01" "2002-02-01" ...
  ..$ pce     : num [1:12] 7174 7218 7237 7305 7283 ...
  ..$ pop     : num [1:12] 286788 286994 287190 287397 287623 ...
  ..$ psavert : num [1:12] 6.1 5.8 5.9 5.8 6.5 6.4 5.5 5.4 5.7 5.7 ...
  ..$ uempmed : num [1:12] 8.4 8.3 8.4 8.9 9.5 11 8.9 9 9.5 9.6 ...
  ..$ unemploy: num [1:12] 8182 8215 8304 8599 8399 ...
  ..$ year    : num [1:12] 2002 2002 2002 2002 2002 ...
  ..$ percent : num [1:12] 0.0285 0.0286 0.0289 0.0299 0.0292 ...
 $ 2003: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2003-01-01" "2003-02-01" ...
  ..$ pce     : num [1:12] 7533 7536 7598 7621 7628 ...
  ..$ pop     : num [1:12] 289518 289714 289911 290125 290346 ...
  ..$ psavert : num [1:12] 5.5 5.6 5.3 5.3 5.8 5.6 6.3 6 5.2 5.3 ...
  ..$ uempmed : num [1:12] 9.6 9.5 9.7 10.2 9.9 11.5 10.3 10.1 10.2 10.4 ...
  ..$ unemploy: num [1:12] 8520 8618 8588 8842 8957 ...
  ..$ year    : num [1:12] 2003 2003 2003 2003 2003 ...
  ..$ percent : num [1:12] 0.0294 0.0297 0.0296 0.0305 0.0308 ...
 $ 2004: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2004-01-01" "2004-02-01" ...
  ..$ pce     : num [1:12] 7987 8020 8076 8089 8163 ...
  ..$ pop     : num [1:12] 292192 292368 292561 292779 292997 ...
  ..$ psavert : num [1:12] 5 5 4.9 5.3 5.3 5.8 5.3 5.2 4.6 4.5 ...
  ..$ uempmed : num [1:12] 10.6 10.2 10.2 9.5 9.9 11 8.9 9.2 9.6 9.5 ...
  ..$ unemploy: num [1:12] 8370 8167 8491 8170 8212 ...
  ..$ year    : num [1:12] 2004 2004 2004 2004 2004 ...
  ..$ percent : num [1:12] 0.0286 0.0279 0.029 0.0279 0.028 ...
 $ 2005: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2005-01-01" "2005-02-01" ...
  ..$ pce     : num [1:12] 8470 8529 8570 8646 8644 ...
  ..$ pop     : num [1:12] 294914 295105 295287 295490 295704 ...
  ..$ psavert : num [1:12] 3.7 3.4 3.6 3.1 3.5 2.9 2.2 2.7 2.7 3.1 ...
  ..$ uempmed : num [1:12] 9.4 9.2 9.3 9 9.1 9 8.8 9.2 8.4 8.6 ...
  ..$ unemploy: num [1:12] 7784 7980 7737 7672 7651 ...
  ..$ year    : num [1:12] 2005 2005 2005 2005 2005 ...
  ..$ percent : num [1:12] 0.0264 0.027 0.0262 0.026 0.0259 ...
 $ 2006: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2006-01-01" "2006-02-01" ...
  ..$ pce     : num [1:12] 9060 9090 9122 9175 9215 ...
  ..$ pop     : num [1:12] 297647 297854 298060 298281 298496 ...
  ..$ psavert : num [1:12] 4.2 4.2 4.2 4 3.8 4 3.4 3.6 3.6 3.6 ...
  ..$ uempmed : num [1:12] 8.6 9.1 8.7 8.4 8.5 7.3 8 8.4 8 7.9 ...
  ..$ unemploy: num [1:12] 7064 7184 7072 7120 6980 ...
  ..$ year    : num [1:12] 2006 2006 2006 2006 2006 ...
  ..$ percent : num [1:12] 0.0237 0.0241 0.0237 0.0239 0.0234 ...
 $ 2007: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2007-01-01" "2007-02-01" ...
  ..$ pce     : num [1:12] 9516 9547 9585 9616 9651 ...
  ..$ pop     : num [1:12] 300574 300802 301021 301254 301483 ...
  ..$ psavert : num [1:12] 3.7 4.1 4.4 4.2 4 3.8 3.7 3.4 3.5 3.4 ...
  ..$ uempmed : num [1:12] 8.3 8.5 9.1 8.6 8.2 7.7 8.7 8.8 8.7 8.4 ...
  ..$ unemploy: num [1:12] 7116 6927 6731 6850 6766 ...
  ..$ year    : num [1:12] 2007 2007 2007 2007 2007 ...
  ..$ percent : num [1:12] 0.0237 0.023 0.0224 0.0227 0.0224 ...
 $ 2008: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2008-01-01" "2008-02-01" ...
  ..$ pce     : num [1:12] 9930 9913 9959 9997 10054 ...
  ..$ pop     : num [1:12] 303506 303711 303907 304117 304323 ...
  ..$ psavert : num [1:12] 3.7 4.1 4 3.4 7.8 5.5 4.4 3.8 4.7 5.5 ...
  ..$ uempmed : num [1:12] 9 8.7 8.7 9.4 7.9 9 9.7 9.7 10.2 10.4 ...
  ..$ unemploy: num [1:12] 7685 7497 7822 7637 8395 ...
  ..$ year    : num [1:12] 2008 2008 2008 2008 2008 ...
  ..$ percent : num [1:12] 0.0253 0.0247 0.0257 0.0251 0.0276 ...
 $ 2009: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2009-01-01" "2009-02-01" ...
  ..$ pce     : num [1:12] 9784 9766 9718 9725 9749 ...
  ..$ pop     : num [1:12] 306208 306402 306588 306787 306984 ...
  ..$ psavert : num [1:12] 6.2 5.5 5.9 6.8 8.2 6.7 6 4.9 5.9 5.4 ...
  ..$ uempmed : num [1:12] 10.7 11.7 12.3 13.1 14.2 17.2 16 16.3 17.8 18.9 ...
  ..$ unemploy: num [1:12] 12058 12898 13426 13853 14499 ...
  ..$ year    : num [1:12] 2009 2009 2009 2009 2009 ...
  ..$ percent : num [1:12] 0.0394 0.0421 0.0438 0.0452 0.0472 ...
 $ 2010: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2010-01-01" "2010-02-01" ...
  ..$ pce     : num [1:12] 10002 10031 10089 10113 10131 ...
  ..$ pop     : num [1:12] 308833 309027 309212 309191 309369 ...
  ..$ psavert : num [1:12] 6.1 5.8 5.7 6.4 7 6.9 6.8 6.9 6.7 6.6 ...
  ..$ uempmed : num [1:12] 20 19.9 20.4 22.1 22.3 25.2 22.3 21 20.3 21.2 ...
  ..$ unemploy: num [1:12] 15046 15113 15202 15325 14849 ...
  ..$ year    : num [1:12] 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
  ..$ percent : num [1:12] 0.0487 0.0489 0.0492 0.0496 0.048 ...
 $ 2011: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2011-01-01" "2011-02-01" ...
  ..$ pce     : num [1:12] 10436 10470 10550 10588 10612 ...
  ..$ pop     : num [1:12] 310961 311113 311265 311436 311607 ...
  ..$ psavert : num [1:12] 7.4 7.6 7 6.9 6.9 7.2 7.3 7.2 6.8 6.8 ...
  ..$ uempmed : num [1:12] 21.5 21.1 21.5 20.9 21.6 22.4 22 22.4 22 20.6 ...
  ..$ unemploy: num [1:12] 14013 13820 13737 13957 13855 ...
  ..$ year    : num [1:12] 2011 2011 2011 2011 2011 ...
  ..$ percent : num [1:12] 0.0451 0.0444 0.0441 0.0448 0.0445 ...
 $ 2012: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2012-01-01" "2012-02-01" ...
  ..$ pce     : num [1:12] 10862 10954 10952 10980 10969 ...
  ..$ pop     : num [1:12] 313183 313339 313499 313667 313831 ...
  ..$ psavert : num [1:12] 8 8 8.5 8.7 8.8 9.1 8.2 8 8.2 8.8 ...
  ..$ uempmed : num [1:12] 20.8 19.7 19.2 19.1 19.9 20.4 17.5 18.4 18.8 19.9 ...
  ..$ unemploy: num [1:12] 12797 12813 12713 12646 12660 ...
  ..$ year    : num [1:12] 2012 2012 2012 2012 2012 ...
  ..$ percent : num [1:12] 0.0409 0.0409 0.0406 0.0403 0.0403 ...
 $ 2013: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2013-01-01" "2013-02-01" ...
  ..$ pce     : num [1:12] 11203 11240 11227 11205 11245 ...
  ..$ pop     : num [1:12] 315390 315520 315662 315818 315984 ...
  ..$ psavert : num [1:12] 6.3 5.8 5.9 6.4 6.7 6.8 6.6 6.7 6.8 6.3 ...
  ..$ uempmed : num [1:12] 15.8 17.2 17.6 17.1 17.1 17 16.2 16.5 16.5 16.3 ...
  ..$ unemploy: num [1:12] 12471 11950 11689 11760 11654 ...
  ..$ year    : num [1:12] 2013 2013 2013 2013 2013 ...
  ..$ percent : num [1:12] 0.0395 0.0379 0.037 0.0372 0.0369 ...
 $ 2014: tibble [12 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:12], format: "2014-01-01" "2014-02-01" ...
  ..$ pce     : num [1:12] 11512 11566 11643 11703 11748 ...
  ..$ pop     : num [1:12] 317594 317754 317917 318089 318270 ...
  ..$ psavert : num [1:12] 7.1 7.3 7.4 7.4 7.4 7.4 7.5 7.2 7.4 7.2 ...
  ..$ uempmed : num [1:12] 15.4 15.9 15.8 15.7 14.6 13.8 13.1 12.9 13.4 13.6 ...
  ..$ unemploy: num [1:12] 10202 10349 10380 9702 9859 ...
  ..$ year    : num [1:12] 2014 2014 2014 2014 2014 ...
  ..$ percent : num [1:12] 0.0321 0.0326 0.0327 0.0305 0.031 ...
 $ 2015: tibble [4 × 8] (S3: tbl_df/tbl/data.frame)
  ..$ date    : Date[1:4], format: "2015-01-01" "2015-02-01" ...
  ..$ pce     : num [1:4] 12046 12082 12158 12194
  ..$ pop     : num [1:4] 319929 320075 320231 320402
  ..$ psavert : num [1:4] 7.7 7.9 7.4 7.6
  ..$ uempmed : num [1:4] 13.2 12.9 12 11.5
  ..$ unemploy: num [1:4] 8903 8610 8504 8526
  ..$ year    : num [1:4] 2015 2015 2015 2015
  ..$ percent : num [1:4] 0.0278 0.0269 0.0266 0.0266

. . .

Could also do

by_year <- map(by_year,
               ~.x |> 
                mutate(percent = unemploy / pop))

Fit the models

Fit a model of the form lm(percent ~ pce) to each year

You try first!

Fit the models

. . .

mods <- map(by_year, 
            ~lm(percent ~ pce, data = .x)
            )
str(mods)

List of 49
 $ 1967:List of 12
  ..$ coefficients : Named num [1:2] 8.40e-03 1.31e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:6] -0.000205 -0.000255 -0.000281 0.000677 0.000208 ...
  .. ..- attr(*, "names")= chr [1:6] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:6] -0.037041 0.00019 -0.000261 0.000737 0.000208 ...
  .. ..- attr(*, "names")= chr [1:6] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:6] 0.015 0.0151 0.0151 0.0151 0.0152 ...
  .. ..- attr(*, "names")= chr [1:6] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:6, 1:2] -2.449 0.408 0.408 0.408 0.408 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:6] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.41 1.17
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 4
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347b816c10> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   6 obs. of  2 variables:
  .. ..$ percent: num [1:6] 0.0148 0.0148 0.0149 0.0158 0.0154 ...
  .. ..$ pce    : num [1:6] 507 510 516 512 517 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347b816c10> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1968:List of 12
  ..$ coefficients : Named num [1:2] 0.027846 -0.000025
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000186 0.000489 0.000126 -0.000732 -0.000443 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.048297 -0.001335 0.000117 -0.000743 -0.00041 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0146 0.0145 0.0143 0.0143 0.0141 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.33
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347b922af8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0144 0.015 0.0144 0.0135 0.0137 ...
  .. ..$ pce    : num [1:12] 531 534 544 544 550 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347b922af8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1969:List of 12
  ..$ coefficients : Named num [1:2] -0.005363 0.000032
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 1.52e-04 -1.51e-04 -6.82e-05 -9.87e-06 -4.49e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -4.84e-02 1.42e-03 -6.75e-05 -2.62e-05 -4.87e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0133 0.0135 0.0135 0.0137 0.0139 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.24
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347bf7f628> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0135 0.0133 0.0134 0.0136 0.0134 ...
  .. ..$ pce    : num [1:12] 584 589 589 594 600 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347bf7f628> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1970:List of 12
  ..$ coefficients : Named num [1:2] -0.118058 0.000214
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -5.71e-04 -4.81e-04 7.62e-04 7.42e-04 -4.76e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -6.97e-02 8.58e-03 9.65e-04 9.19e-04 8.25e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0163 0.0174 0.017 0.0178 0.0192 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.22
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c060000> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0157 0.0169 0.0178 0.0186 0.0192 ...
  .. ..$ pce    : num [1:12] 629 634 632 636 642 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c060000> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1971:List of 12
  ..$ coefficients : Named num [1:2] 1.59e-02 1.18e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 2.40e-04 -2.24e-04 1.30e-04 -1.10e-04 1.92e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -8.38e-02 6.67e-04 1.25e-04 -1.41e-04 -2.11e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0239 0.0239 0.024 0.0241 0.0241 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c14fcb8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0241 0.0237 0.0241 0.0239 0.0241 ...
  .. ..$ pce    : num [1:12] 676 679 682 689 691 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c14fcb8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1972:List of 12
  ..$ coefficients : Named num [1:2] 4.40e-02 -2.71e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -2.07e-04 -5.31e-04 3.30e-04 2.16e-05 -2.40e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -8.05e-02 -2.19e-03 4.26e-04 1.02e-04 3.13e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0242 0.0241 0.0238 0.0237 0.0235 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.29
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c2c0cb8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.024 0.0236 0.0241 0.0237 0.0235 ...
  .. ..$ pce    : num [1:12] 732 736 749 752 758 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c2c0cb8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1973:List of 12
  ..$ coefficients : Named num [1:2] 2.57e-02 -6.05e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000273 0.000367 0.000121 0.00043 -0.000165 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.071281 -0.000404 0.000134 0.000458 -0.000106 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0208 0.0207 0.0207 0.0207 0.0206 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.24
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c363858> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0205 0.0211 0.0208 0.0211 0.0205 ...
  .. ..$ pce    : num [1:12] 816 826 833 836 842 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c363858> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1974:List of 12
  ..$ coefficients : Named num [1:2] -5.07e-02 8.05e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.001297 0.001272 -0.000139 -0.000986 -0.001528 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.08379 0.007552 -0.000576 -0.001349 -0.001799 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0205 0.0209 0.0219 0.0226 0.0236 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.32
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c40b150> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0218 0.0222 0.0217 0.0216 0.022 ...
  .. ..$ pce    : num [1:12] 884 890 901 911 922 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c40b150> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1975:List of 12
  ..$ coefficients : Named num [1:2] 3.85e-02 -1.64e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.001967 -0.001878 0.000231 0.001289 0.002333 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.127397 -0.000204 0.000931 0.001948 0.002767 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0369 0.0368 0.0368 0.0368 0.0368 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.23
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c5536d0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0349 0.035 0.0371 0.0381 0.0391 ...
  .. ..$ pce    : num [1:12] 976 989 991 995 1019 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c5536d0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1976:List of 12
  ..$ coefficients : Named num [1:2] 2.31e-02 9.41e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 1.15e-03 1.62e-04 -3.68e-04 -2.95e-05 -1.30e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.117558 0.00106 -0.00061 -0.000278 -0.001548 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0336 0.0336 0.0336 0.0337 0.0337 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c66ece8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0347 0.0337 0.0333 0.0337 0.0324 ...
  .. ..$ pce    : num [1:12] 1107 1108 1115 1125 1123 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c66ece8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1977:List of 12
  ..$ coefficients : Named num [1:2] 7.45e-02 -3.37e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000415 0.000852 0.000444 -0.000409 -0.000778 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.109618 -0.004303 0.000402 -0.000398 -0.000707 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0336 0.0331 0.0328 0.0325 0.0322 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.23
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c72a0b0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0332 0.0339 0.0333 0.0321 0.0314 ...
  .. ..$ pce    : num [1:12] 1215 1231 1238 1247 1257 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c72a0b0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1978:List of 12
  ..$ coefficients : Named num [1:2] 4.42e-02 -1.15e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 4.25e-04 -7.19e-05 2.51e-04 -2.64e-04 -3.46e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.096321 -0.00194 0.000174 -0.000356 -0.00045 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0289 0.0286 0.0283 0.0281 0.0279 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.28
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c83c890> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0293 0.0285 0.0286 0.0278 0.0276 ...
  .. ..$ pce    : num [1:12] 1330 1355 1378 1396 1412 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c83c890> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1979:List of 12
  ..$ coefficients : Named num [1:2] 1.93e-02 5.01e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 4.38e-04 6.24e-04 2.49e-04 9.64e-06 -1.14e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -9.45e-02 9.45e-04 7.17e-05 -1.51e-04 -1.25e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0269 0.0269 0.027 0.027 0.0271 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.26
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c89d968> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0273 0.0276 0.0272 0.027 0.026 ...
  .. ..$ pce    : num [1:12] 1502 1518 1531 1538 1559 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c89d968> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1980:List of 12
  ..$ coefficients : Named num [1:2] -7.45e-03 2.35e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -2.93e-03 -2.97e-03 -3.03e-03 1.61e-05 2.63e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.11672 0.00441 -0.00206 0.00112 0.00368 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0324 0.0325 0.0327 0.0324 0.0325 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.2
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c90e078> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0295 0.0296 0.0297 0.0324 0.0351 ...
  .. ..$ pce    : num [1:12] 1697 1701 1708 1695 1700 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c90e078> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1981:List of 12
  ..$ coefficients : Named num [1:2] -2.49e-02 3.15e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.001276 0.00072 -0.000193 -0.00076 0.000246 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.124684 0.004009 -0.000524 -0.001087 -0.000055 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.034 0.0344 0.035 0.0351 0.0354 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.28
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c9a6120> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0353 0.0351 0.0348 0.0343 0.0356 ...
  .. ..$ pce    : num [1:12] 1870 1884 1903 1904 1914 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347c9a6120> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1982:List of 12
  ..$ coefficients : Named num [1:2] -8.88e-02 6.52e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000667 -0.000933 -0.00033 0.001001 0.000176 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -1.60e-01 1.16e-02 -6.76e-05 1.26e-03 3.63e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0413 0.0429 0.0431 0.0432 0.0444 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.19
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cb12ba0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0407 0.042 0.0427 0.0442 0.0446 ...
  .. ..$ pce    : num [1:12] 1997 2021 2024 2026 2044 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cb12ba0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1983:List of 12
  ..$ coefficients : Named num [1:2] 0.146101 -0.000044
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000964 -0.000816 -0.000297 0.000108 0.00045 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.158172 -0.011088 0.000018 0.00038 0.000687 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0504 0.0503 0.0491 0.0481 0.0472 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.32
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cba0c80> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0494 0.0494 0.0488 0.0482 0.0477 ...
  .. ..$ pce    : num [1:12] 2174 2177 2203 2226 2246 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cba0c80> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1984:List of 12
  ..$ coefficients : Named num [1:2] 7.89e-02 -1.72e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 9.20e-04 -2.97e-04 -2.89e-05 4.58e-04 -5.71e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.125039 -0.003282 -0.00015 0.000287 -0.000774 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0373 0.0376 0.0371 0.0367 0.0364 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.38
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347ccc33b8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0383 0.0373 0.0371 0.0372 0.0358 ...
  .. ..$ pce    : num [1:12] 2419 2404 2432 2458 2474 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347ccc33b8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1985:List of 12
  ..$ coefficients : Named num [1:2] 5.37e-02 -6.93e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -5.19e-05 -3.49e-04 -2.39e-04 4.79e-05 -1.13e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.120795 -0.00147 -0.000172 0.000101 -0.000107 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0355 0.0354 0.0353 0.0352 0.035 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.24
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cdc20e8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0355 0.035 0.0351 0.0353 0.0349 ...
  .. ..$ pce    : num [1:12] 2619 2641 2648 2660 2696 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cdc20e8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1986:List of 12
  ..$ coefficients : Named num [1:2] 5.38e-02 -6.78e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.002143 0.000321 0.000244 0.00022 0.000657 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.118716 -0.001349 0.000567 0.000579 0.001088 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0347 0.0347 0.0347 0.0346 0.0345 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cf23620> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0325 0.035 0.0349 0.0348 0.0351 ...
  .. ..$ pce    : num [1:12] 2827 2820 2824 2835 2858 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347cf23620> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1987:List of 12
  ..$ coefficients : Named num [1:2] 9.15e-02 -1.98e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.00069 0.000489 0.000687 -0.000152 0.000148 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -1.06e-01 -4.64e-03 7.68e-04 -1.16e-05 3.11e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0333 0.032 0.0318 0.0313 0.0311 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.18
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d07da88> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0326 0.0325 0.0325 0.0311 0.0312 ...
  .. ..$ pce    : num [1:12] 2936 3002 3013 3039 3048 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d07da88> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1988:List of 12
  ..$ coefficients : Named num [1:2] 5.12e-02 -7.17e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.000326 0.000265 0.000311 -0.000815 0.000114 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -9.47e-02 -1.95e-03 2.15e-04 -9.10e-04 3.56e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0282 0.0281 0.0278 0.0278 0.0276 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.3
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d1bf3f0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0285 0.0284 0.0281 0.027 0.0277 ...
  .. ..$ pce    : num [1:12] 3214 3221 3260 3263 3294 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d1bf3f0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1989:List of 12
  ..$ coefficients : Named num [1:2] 1.05e-02 4.44e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 1.17e-03 -1.76e-04 -8.65e-04 -1.46e-05 -4.50e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.091384 0.000969 -0.00104 -0.000257 -0.000706 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.026 0.026 0.026 0.0262 0.0263 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.31
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d260770> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0271 0.0258 0.0252 0.0262 0.0258 ...
  .. ..$ pce    : num [1:12] 3484 3488 3499 3543 3552 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d260770> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1990:List of 12
  ..$ coefficients : Named num [1:2] -7.12e-02 2.61e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 9.66e-04 6.07e-04 -3.23e-04 4.93e-05 -3.51e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.097801 0.004826 -0.000597 -0.000201 -0.000592 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0262 0.0261 0.0268 0.0272 0.0274 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.34
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d36eaf0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0272 0.0267 0.0265 0.0273 0.027 ...
  .. ..$ pce    : num [1:12] 3731 3728 3755 3770 3776 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d36eaf0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1991:List of 12
  ..$ coefficients : Named num [1:2] -3.86e-02 1.84e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -3.86e-04 1.01e-04 4.89e-04 -2.06e-05 6.37e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -1.18e-01 3.31e-03 5.69e-04 5.51e-05 7.34e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0322 0.0327 0.0335 0.0334 0.0339 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.3
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d47be78> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0318 0.0328 0.034 0.0334 0.0345 ...
  .. ..$ pce    : num [1:12] 3841 3867 3913 3907 3933 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d47be78> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1992:List of 12
  ..$ coefficients : Named num [1:2] 3.49e-02 6.09e-07
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000987 -0.00036 -0.000385 -0.000611 0.000614 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.12966 0.000169 -0.000141 -0.000373 0.000841 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0374 0.0374 0.0374 0.0374 0.0374 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.26
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d5778c8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0364 0.037 0.037 0.0368 0.038 ...
  .. ..$ pce    : num [1:12] 4085 4100 4117 4132 4158 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d5778c8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1993:List of 12
  ..$ coefficients : Named num [1:2] 9.54e-02 -1.37e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 2.04e-04 -1.77e-04 -7.38e-04 -3.91e-06 5.08e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -1.19e-01 -3.68e-03 -7.35e-04 -2.76e-05 4.65e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0358 0.0356 0.0357 0.0351 0.0347 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d676ea8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.036 0.0355 0.0349 0.0351 0.0352 ...
  .. ..$ pce    : num [1:12] 4341 4355 4352 4393 4422 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d676ea8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1994:List of 12
  ..$ coefficients : Named num [1:2] 1.3e-01 -2.1e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000201 0.000593 0.000415 0.000379 -0.001266 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.104941 -0.005787 0.000375 0.000387 -0.001262 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0332 0.0322 0.0319 0.0313 0.0314 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.21
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d69ec10> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0329 0.0327 0.0323 0.0317 0.0301 ...
  .. ..$ pce    : num [1:12] 4585 4633 4646 4671 4670 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d69ec10> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1995:List of 12
  ..$ coefficients : Named num [1:2] 2.40e-02 7.72e-07
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.000114 -0.000618 -0.000796 0.001025 0.000158 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.096293 0.000207 -0.000727 0.001086 0.00014 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0277 0.0277 0.0277 0.0277 0.0278 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.32
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d7c28c8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0278 0.0271 0.0269 0.0288 0.0279 ...
  .. ..$ pce    : num [1:12] 4851 4851 4885 4890 4933 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d7c28c8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1996:List of 12
  ..$ coefficients : Named num [1:2] 5.60e-02 -5.57e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 2.26e-04 -1.97e-04 2.33e-05 5.51e-04 6.43e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -9.29e-02 -1.62e-03 -3.18e-06 5.01e-04 5.82e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0277 0.0274 0.0272 0.027 0.0269 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.26
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d822190> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0279 0.0273 0.0272 0.0276 0.0276 ...
  .. ..$ pce    : num [1:12] 5086 5133 5173 5208 5224 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d822190> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1997:List of 12
  ..$ coefficients : Named num [1:2] 7.97e-02 -9.94e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.000463 0.000462 0.000265 -0.000176 -0.000991 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.085448 -0.003197 0.000112 -0.000324 -0.001139 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0259 0.0257 0.0255 0.0254 0.0254 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.23
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d88d498> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0264 0.0262 0.0258 0.0253 0.0244 ...
  .. ..$ pce    : num [1:12] 5411 5434 5454 5459 5460 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d88d498> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1998:List of 12
  ..$ coefficients : Named num [1:2] 3.59e-02 -2.28e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.000283 0.000117 0.000582 -0.0011 -0.000622 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.077862 -0.00093 0.00051 -0.001169 -0.000686 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0229 0.0228 0.0228 0.0227 0.0226 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d9501f8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0232 0.0229 0.0233 0.0216 0.0219 ...
  .. ..$ pce    : num [1:12] 5690 5724 5750 5788 5838 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d9501f8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 1999:List of 12
  ..$ coefficients : Named num [1:2] 3.92e-02 -2.89e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000142 0.000412 -0.000693 0.000265 -0.000417 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.072946 -0.001374 -0.000735 0.000269 -0.00039 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0217 0.0216 0.0215 0.0213 0.0212 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.28
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d98b7e0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0215 0.022 0.0208 0.0216 0.0208 ...
  .. ..$ pce    : num [1:12] 6073 6102 6133 6196 6226 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347d98b7e0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2000:List of 12
  ..$ coefficients : Named num [1:2] 3.1e-02 -1.6e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000188 0.000465 0.000111 -0.000825 0.000201 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.069765 -0.000669 0.000127 -0.000823 0.000239 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0205 0.0204 0.0203 0.0203 0.0202 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.22
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347da77580> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0203 0.0208 0.0204 0.0195 0.0204 ...
  .. ..$ pce    : num [1:12] 6535 6620 6686 6671 6708 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347da77580> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2001:List of 12
  ..$ coefficients : Named num [1:2] -0.18813 0.00003
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -6.43e-05 -4.13e-04 -8.60e-06 3.15e-05 -1.52e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -8.29e-02 7.69e-03 8.11e-05 1.04e-04 -1.51e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0213 0.0218 0.0216 0.022 0.0234 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.2
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347db7b238> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0212 0.0214 0.0216 0.022 0.0219 ...
  .. ..$ pce    : num [1:12] 6977 6996 6988 7001 7047 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347db7b238> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2002:List of 12
  ..$ coefficients : Named num [1:2] 1.43e-02 2.01e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -2.10e-04 -2.04e-04 4.85e-05 9.17e-04 2.44e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.100731 0.000692 0.00012 0.000964 0.000299 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0287 0.0288 0.0289 0.029 0.029 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.25
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dc28430> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0285 0.0286 0.0289 0.0299 0.0292 ...
  .. ..$ pce    : num [1:12] 7174 7218 7237 7305 7283 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dc28430> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2003:List of 12
  ..$ coefficients : Named num [1:2] 4.03e-02 -1.31e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000987 -0.000665 -0.000707 0.000176 0.000559 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.104501 -0.000616 -0.000427 0.00044 0.000817 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0304 0.0304 0.0303 0.0303 0.0303 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.31
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dcb30e0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0294 0.0297 0.0296 0.0305 0.0308 ...
  .. ..$ pce    : num [1:12] 7533 7536 7598 7621 7628 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dcb30e0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2004:List of 12
  ..$ coefficients : Named num [1:2] 5.76e-02 -3.64e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 8.04e-05 -5.13e-04 7.80e-04 -2.92e-04 1.02e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -9.61e-02 -1.94e-03 8.30e-04 -2.52e-04 8.35e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0286 0.0284 0.0282 0.0282 0.0279 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347ddb0b68> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0286 0.0279 0.029 0.0279 0.028 ...
  .. ..$ pce    : num [1:12] 7987 8020 8076 8089 8163 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347ddb0b68> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2005:List of 12
  ..$ coefficients : Named num [1:2] 5.78e-02 -3.68e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -2.22e-04 6.42e-04 -4.89e-05 -6.86e-06 -1.03e-04 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -8.87e-02 -2.11e-03 -1.09e-04 5.32e-06 -9.24e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0266 0.0264 0.0263 0.026 0.026 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347deade00> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0264 0.027 0.0262 0.026 0.0259 ...
  .. ..$ pce    : num [1:12] 8470 8529 8570 8646 8644 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347deade00> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2006:List of 12
  ..$ coefficients : Named num [1:2] 5.46e-02 -3.37e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000332 0.000157 -0.000128 0.000193 -0.000157 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -8.10e-02 -1.45e-03 -9.23e-05 2.50e-04 -8.33e-05 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0241 0.024 0.0239 0.0237 0.0235 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.29
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dfb8740> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0237 0.0241 0.0237 0.0239 0.0234 ...
  .. ..$ pce    : num [1:12] 9060 9090 9122 9175 9215 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347dfb8740> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2007:List of 12
  ..$ coefficients : Named num [1:2] -1.72e-02 4.19e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.001044 0.00027 -0.000558 -0.000309 -0.000754 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.081153 0.001834 -0.000797 -0.000546 -0.000988 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0226 0.0228 0.0229 0.023 0.0232 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.27
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e07d900> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0237 0.023 0.0224 0.0227 0.0224 ...
  .. ..$ pce    : num [1:12] 9516 9547 9585 9616 9651 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e07d900> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2008:List of 12
  ..$ coefficients : Named num [1:2] 1.78e-01 -1.49e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.004727 -0.005612 -0.003871 -0.003938 -0.000612 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.101691 -0.005787 -0.002744 -0.003198 -0.000463 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.03 0.0303 0.0296 0.029 0.0282 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.14
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e199b60> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0253 0.0247 0.0257 0.0251 0.0276 ...
  .. ..$ pce    : num [1:12] 9930 9913 9959 9997 10054 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e199b60> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2009:List of 12
  ..$ coefficients : Named num [1:2] -1.73e-01 2.23e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -5.82e-03 -2.70e-03 5.17e-05 1.27e-03 2.81e-03 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.16107 0.00727 0.00175 0.00295 0.0044 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0452 0.0448 0.0437 0.0439 0.0444 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.19
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e3211c0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0394 0.0421 0.0438 0.0452 0.0472 ...
  .. ..$ pce    : num [1:12] 9784 9766 9718 9725 9749 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e3211c0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2010:List of 12
  ..$ coefficients : Named num [1:2] 1.07e-01 -5.84e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000166 0.000189 0.000789 0.001329 -0.000132 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.165615 -0.002394 0.000802 0.001353 -0.000101 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0489 0.0487 0.0484 0.0482 0.0481 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.28
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e3b9c08> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0487 0.0489 0.0492 0.0496 0.048 ...
  .. ..$ pce    : num [1:12] 10002 10031 10089 10113 10131 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e3b9c08> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2011:List of 12
  ..$ coefficients : Named num [1:2] 1.04e-01 -5.65e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000141 -0.000588 -0.000423 0.000469 0.000255 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.152571 -0.002124 -0.000341 0.000509 0.000268 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0452 0.045 0.0446 0.0443 0.0442 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.33
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e527888> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0451 0.0444 0.0441 0.0448 0.0445 ...
  .. ..$ pce    : num [1:12] 10436 10470 10550 10588 10612 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e527888> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2012:List of 12
  ..$ coefficients : Named num [1:2] 1.58e-01 -1.07e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000475 0.000534 0.000176 0.000239 0.000144 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.137828 -0.003027 0.000234 0.000357 0.000238 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0413 0.0404 0.0404 0.0401 0.0402 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.07
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e6083c0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0409 0.0409 0.0406 0.0403 0.0403 ...
  .. ..$ pce    : num [1:12] 10862 10954 10952 10980 10969 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e6083c0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2013:List of 12
  ..$ coefficients : Named num [1:2] 2.10e-01 -1.54e-05
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] 0.001561 0.000459 -0.000577 -0.000704 -0.000456 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.125478 -0.005544 -0.000944 -0.001076 -0.000819 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.038 0.0374 0.0376 0.0379 0.0373 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.14
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e694970> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0395 0.0379 0.037 0.0372 0.0369 ...
  .. ..$ pce    : num [1:12] 11203 11240 11227 11205 11245 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e694970> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2014:List of 12
  ..$ coefficients : Named num [1:2] 1.32e-01 -8.59e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:12] -0.000692 0.000216 0.000957 -0.00068 0.000189 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:12] -0.104402 -0.005422 0.001061 -0.000549 0.000341 ...
  .. ..- attr(*, "names")= chr [1:12] "(Intercept)" "pce" "" "" ...
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:12] 0.0328 0.0324 0.0317 0.0312 0.0308 ...
  .. ..- attr(*, "names")= chr [1:12] "1" "2" "3" "4" ...
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:12, 1:2] -3.464 0.289 0.289 0.289 0.289 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:12] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.29 1.3
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e74a7e0> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   12 obs. of  2 variables:
  .. ..$ percent: num [1:12] 0.0321 0.0326 0.0327 0.0305 0.031 ...
  .. ..$ pce    : num [1:12] 11512 11566 11643 11703 11748 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e74a7e0> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"
 $ 2015:List of 12
  ..$ coefficients : Named num [1:2] 1.18e-01 -7.48e-06
  .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "pce"
  ..$ residuals    : Named num [1:4] 0.0003 -0.000356 -0.000132 0.000188
  .. ..- attr(*, "names")= chr [1:4] "1" "2" "3" "4"
  ..$ effects      : Named num [1:4] -0.053947 -0.000879 -0.000452 -0.000256
  .. ..- attr(*, "names")= chr [1:4] "(Intercept)" "pce" "" ""
  ..$ rank         : int 2
  ..$ fitted.values: Named num [1:4] 0.0275 0.0273 0.0267 0.0264
  .. ..- attr(*, "names")= chr [1:4] "1" "2" "3" "4"
  ..$ assign       : int [1:2] 0 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:4, 1:2] -2 0.5 0.5 0.5 -24240.2 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:4] "1" "2" "3" "4"
  .. .. .. ..$ : chr [1:2] "(Intercept)" "pce"
  .. .. ..- attr(*, "assign")= int [1:2] 0 1
  .. ..$ qraux: num [1:2] 1.5 1.11
  .. ..$ pivot: int [1:2] 1 2
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 2
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 2
  ..$ xlevels      : Named list()
  ..$ call         : language lm(formula = percent ~ pce, data = .x)
  ..$ terms        :Classes 'terms', 'formula'  language percent ~ pce
  .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. ..$ : chr "pce"
  .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e82e7e8> 
  .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..$ model        :'data.frame':   4 obs. of  2 variables:
  .. ..$ percent: num [1:4] 0.0278 0.0269 0.0266 0.0266
  .. ..$ pce    : num [1:4] 12046 12082 12158 12194
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language percent ~ pce
  .. .. .. ..- attr(*, "variables")= language list(percent, pce)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "percent" "pce"
  .. .. .. .. .. ..$ : chr "pce"
  .. .. .. ..- attr(*, "term.labels")= chr "pce"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: 0x000002347e82e7e8> 
  .. .. .. ..- attr(*, "predvars")= language list(percent, pce)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "percent" "pce"
  ..- attr(*, "class")= chr "lm"

Extract coefficients

You try first

Hint: use coef(). For example, try coef(mods[[1]])

Solution

. . .

coefs <- map(mods, coef)
coefs[c(1:2, length(coefs))]

$`1967`
 (Intercept)          pce 
8.397192e-03 1.307101e-05 

$`1968`
  (Intercept)           pce 
 2.784626e-02 -2.496983e-05 

$`2015`
  (Intercept)           pce 
 1.176573e-01 -7.482080e-06 

Extract slopes

AKA - the coefficient that is not the intercept

You try first

Extract slopes

. . .

slopes <- map_dbl(coefs, 2)
slopes

         1967          1968          1969          1970          1971 
 1.307101e-05 -2.496983e-05  3.201794e-05  2.136662e-04  1.177357e-05 
         1972          1973          1974          1975          1976 
-2.708060e-05 -6.047148e-06  8.050869e-05 -1.643992e-06  9.413733e-06 
         1977          1978          1979          1980          1981 
-3.366894e-05 -1.151756e-05  5.011539e-06  2.350410e-05  3.150845e-05 
         1982          1983          1984          1985          1986 
 6.516215e-05 -4.402208e-05 -1.718497e-05 -6.926535e-06 -6.782516e-06 
         1987          1988          1989          1990          1991 
-1.980421e-05 -7.174276e-06  4.436762e-06  2.611286e-05  1.842612e-05 
         1992          1993          1994          1995          1996 
 6.091330e-07 -1.371548e-05 -2.102375e-05  7.718004e-07 -5.567328e-06 
         1997          1998          1999          2000          2001 
-9.936855e-06 -2.283149e-06 -2.893646e-06 -1.601844e-06  3.001365e-05 
         2002          2003          2004          2005          2006 
 2.014789e-06 -1.306818e-06 -3.640025e-06 -3.679151e-06 -3.368536e-06 
         2007          2008          2009          2010          2011 
 4.187576e-06 -1.494605e-05  2.230078e-05 -5.844266e-06 -5.647401e-06 
         2012          2013          2014          2015 
-1.070319e-05 -1.536441e-05 -8.590683e-06 -7.482080e-06 

Plot

relation <- tibble(year = names(slopes),
                   slope = slopes)

ggplot(relation, aes(slope)) +
    geom_histogram(fill = "dodgerblue", color = "white")

Piping

Piping

We could also have done the previous in a pipeline

by_year %>%
  map(~mutate(.x, percent = unemploy / pop))

$`1967`
# A tibble: 6 × 8
  date         pce    pop psavert uempmed unemploy  year percent
  <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
1 1967-07-01  507. 198712    12.6     4.5     2944  1967  0.0148
2 1967-08-01  510. 198911    12.6     4.7     2945  1967  0.0148
3 1967-09-01  516. 199113    11.9     4.6     2958  1967  0.0149
4 1967-10-01  512. 199311    12.9     4.9     3143  1967  0.0158
5 1967-11-01  517. 199498    12.8     4.7     3066  1967  0.0154
6 1967-12-01  525. 199657    11.8     4.8     3018  1967  0.0151

$`1968`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1968-01-01  531. 199808    11.7     5.1     2878  1968  0.0144
 2 1968-02-01  534. 199920    12.3     4.5     3001  1968  0.0150
 3 1968-03-01  544. 200056    11.7     4.1     2877  1968  0.0144
 4 1968-04-01  544  200208    12.3     4.6     2709  1968  0.0135
 5 1968-05-01  550. 200361    12       4.4     2740  1968  0.0137
 6 1968-06-01  556. 200536    11.7     4.4     2938  1968  0.0147
 7 1968-07-01  563. 200706    10.7     4.5     2883  1968  0.0144
 8 1968-08-01  567  200898    10.5     4.2     2768  1968  0.0138
 9 1968-09-01  568. 201095    10.6     4.6     2686  1968  0.0134
10 1968-10-01  572. 201290    10.8     4.8     2689  1968  0.0134
11 1968-11-01  577. 201466    10.6     4.4     2715  1968  0.0135
12 1968-12-01  576. 201621    11.1     4.4     2685  1968  0.0133

$`1969`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1969-01-01  584. 201760    10.3     4.4     2718  1969  0.0135
 2 1969-02-01  589. 201881     9.7     4.9     2692  1969  0.0133
 3 1969-03-01  589. 202023    10.2     4       2712  1969  0.0134
 4 1969-04-01  594. 202161     9.7     4       2758  1969  0.0136
 5 1969-05-01  600. 202331    10.1     4.2     2713  1969  0.0134
 6 1969-06-01  601. 202507    11.1     4.4     2816  1969  0.0139
 7 1969-07-01  603. 202677    11.8     4.4     2868  1969  0.0142
 8 1969-08-01  610. 202877    11.5     4.4     2856  1969  0.0141
 9 1969-09-01  613. 203090    11.6     4.7     3040  1969  0.0150
10 1969-10-01  618. 203302    11.4     4.5     3049  1969  0.0150
11 1969-11-01  620. 203500    11.6     4.8     2856  1969  0.0140
12 1969-12-01  623. 203675    11.8     4.6     2884  1969  0.0142

$`1970`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1970-01-01  629. 203849    11.8     4.6     3201  1970  0.0157
 2 1970-02-01  634  204008    11.7     4.5     3453  1970  0.0169
 3 1970-03-01  632. 204156    12.4     4.6     3635  1970  0.0178
 4 1970-04-01  636  204401    13.3     4.1     3797  1970  0.0186
 5 1970-05-01  642. 204607    12.4     4.7     3919  1970  0.0192
 6 1970-06-01  646. 204830    12.3     4.9     4071  1970  0.0199
 7 1970-07-01  648. 205052    13.5     5.1     4175  1970  0.0204
 8 1970-08-01  653. 205295    13.4     5.4     4256  1970  0.0207
 9 1970-09-01  659. 205540    12.9     5.2     4456  1970  0.0217
10 1970-10-01  658. 205788    13.1     5.2     4591  1970  0.0223
11 1970-11-01  657. 206024    13.6     5.6     4898  1970  0.0238
12 1970-12-01  666. 206238    13.2     5.9     5076  1970  0.0246

$`1971`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1971-01-01  676. 206466    13.3     6.2     4986  1971  0.0241
 2 1971-02-01  679. 206668    13.3     6.3     4903  1971  0.0237
 3 1971-03-01  682  206855    13.5     6.4     4987  1971  0.0241
 4 1971-04-01  689. 207065    13.2     6.5     4959  1971  0.0239
 5 1971-05-01  691. 207260    13.6     6.7     4996  1971  0.0241
 6 1971-06-01  700. 207462    14.7     5.7     4949  1971  0.0239
 7 1971-07-01  699. 207661    13.8     6.2     5035  1971  0.0242
 8 1971-08-01  705. 207881    13.6     6.4     5134  1971  0.0247
 9 1971-09-01  713  208114    13.3     5.8     5042  1971  0.0242
10 1971-10-01  716. 208345    13.3     6.5     4954  1971  0.0238
11 1971-11-01  721. 208555    13.1     6.4     5161  1971  0.0247
12 1971-12-01  728. 208740    13       6.2     5154  1971  0.0247

$`1972`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1972-01-01  732. 208917    12.5     6.2     5019  1972  0.0240
 2 1972-02-01  736. 209061    12.8     6.6     4928  1972  0.0236
 3 1972-03-01  749. 209212    11.8     6.6     5038  1972  0.0241
 4 1972-04-01  752. 209386    11.5     6.7     4959  1972  0.0237
 5 1972-05-01  758  209545    11.7     6.6     4922  1972  0.0235
 6 1972-06-01  762. 209725    11.7     5.4     4923  1972  0.0235
 7 1972-07-01  770. 209896    11.7     6.1     4913  1972  0.0234
 8 1972-08-01  776. 210075    12       6       4939  1972  0.0235
 9 1972-09-01  781. 210278    12.2     5.6     4849  1972  0.0231
10 1972-10-01  795. 210479    13       5.7     4875  1972  0.0232
11 1972-11-01  800. 210656    13.6     5.7     4602  1972  0.0218
12 1972-12-01  806. 210821    13.7     6.1     4543  1972  0.0215

$`1973`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1973-01-01  816. 210985    12.4     5.7     4326  1973  0.0205
 2 1973-02-01  826. 211120    12.5     5.2     4452  1973  0.0211
 3 1973-03-01  833. 211254    12.7     5.5     4394  1973  0.0208
 4 1973-04-01  836. 211420    13.2     5       4459  1973  0.0211
 5 1973-05-01  842. 211577    13.2     4.9     4329  1973  0.0205
 6 1973-06-01  844. 211746    13.6     5       4363  1973  0.0206
 7 1973-07-01  854. 211909    13.2     5.2     4305  1973  0.0203
 8 1973-08-01  853. 212092    13.9     4.9     4305  1973  0.0203
 9 1973-09-01  869. 212289    13.1     5.4     4350  1973  0.0205
10 1973-10-01  868. 212475    14.4     5.5     4144  1973  0.0195
11 1973-11-01  877. 212634    14.4     5.1     4396  1973  0.0207
12 1973-12-01  877. 212785    14.8     4.7     4489  1973  0.0211

$`1974`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1974-01-01  884. 212932    14.3     5       4644  1974  0.0218
 2 1974-02-01  890. 213074    14.2     5.1     4731  1974  0.0222
 3 1974-03-01  901. 213211    13.4     4.8     4634  1974  0.0217
 4 1974-04-01  911. 213361    13.1     5       4618  1974  0.0216
 5 1974-05-01  922. 213513    12.8     4.6     4705  1974  0.0220
 6 1974-06-01  928  213686    12.8     5.3     4927  1974  0.0231
 7 1974-07-01  938. 213854    12.8     5.7     5063  1974  0.0237
 8 1974-08-01  955. 214042    12.1     5       5022  1974  0.0235
 9 1974-09-01  955. 214246    12.9     5.3     5437  1974  0.0254
10 1974-10-01  959. 214451    13.4     5.5     5523  1974  0.0258
11 1974-11-01  956. 214625    13.8     5.2     6140  1974  0.0286
12 1974-12-01  962. 214782    14       5.7     6636  1974  0.0309

$`1975`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1975-01-01  976. 214931    13.2     6.3     7501  1975  0.0349
 2 1975-02-01  989. 215065    12.5     7.1     7520  1975  0.0350
 3 1975-03-01  991. 215198    12.7     7.2     7978  1975  0.0371
 4 1975-04-01  995  215353    14.2     8.7     8210  1975  0.0381
 5 1975-05-01 1019. 215523    17.3     9.4     8433  1975  0.0391
 6 1975-06-01 1027. 215768    14.3     8.8     8220  1975  0.0381
 7 1975-07-01 1040. 215973    12.6     8.6     8127  1975  0.0376
 8 1975-08-01 1047  216195    13       9.2     7928  1975  0.0367
 9 1975-09-01 1055. 216393    13       9.2     7923  1975  0.0366
10 1975-10-01 1061. 216587    13.4     8.6     7897  1975  0.0365
11 1975-11-01 1076. 216771    12.7     9.5     7794  1975  0.0360
12 1975-12-01 1092. 216931    12       9       7744  1975  0.0357

$`1976`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1976-01-01 1107. 217095    11.7     9       7534  1976  0.0347
 2 1976-02-01 1108. 217249    12.3     8.2     7326  1976  0.0337
 3 1976-03-01 1115. 217381    12.2     8.7     7230  1976  0.0333
 4 1976-04-01 1125. 217528    11.7     8.2     7330  1976  0.0337
 5 1976-05-01 1123. 217685    12.3     8.3     7053  1976  0.0324
 6 1976-06-01 1140. 217861    11.4     7.8     7322  1976  0.0336
 7 1976-07-01 1150. 218035    11.7     7.7     7490  1976  0.0344
 8 1976-08-01 1158  218233    11.7     7.9     7518  1976  0.0344
 9 1976-09-01 1169. 218440    11.4     7.8     7380  1976  0.0338
10 1976-10-01 1177. 218644    11.1     7.7     7430  1976  0.0340
11 1976-11-01 1189  218834    11.4     8.4     7620  1976  0.0348
12 1976-12-01 1212. 219006    10.6     8       7545  1976  0.0345

$`1977`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1977-01-01 1215  219179    10.6     7.5     7280  1977  0.0332
 2 1977-02-01 1231. 219344     9.3     7.2     7443  1977  0.0339
 3 1977-03-01 1238. 219504    10.5     7.2     7307  1977  0.0333
 4 1977-04-01 1247. 219684    10.5     7.3     7059  1977  0.0321
 5 1977-05-01 1257. 219859    10.3     7.9     6911  1977  0.0314
 6 1977-06-01 1264. 220046    10.6     6.2     7134  1977  0.0324
 7 1977-07-01 1280. 220239    10.5     7.1     6829  1977  0.0310
 8 1977-08-01 1286. 220458    10.9     7       6925  1977  0.0314
 9 1977-09-01 1294. 220688    11.1     6.7     6751  1977  0.0306
10 1977-10-01 1311. 220904    11       6.9     6763  1977  0.0306
11 1977-11-01 1327  221109    11.2     7       6815  1977  0.0308
12 1977-12-01 1336  221303    11.4     6.8     6386  1977  0.0289

$`1978`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1978-01-01 1330. 221477    11.9     6.5     6489  1978  0.0293
 2 1978-02-01 1355. 221629    11.1     6.7     6318  1978  0.0285
 3 1978-03-01 1378. 221792    11       6.2     6337  1978  0.0286
 4 1978-04-01 1396. 221991    10.8     6.1     6180  1978  0.0278
 5 1978-05-01 1412  222176    10.3     5.7     6127  1978  0.0276
 6 1978-06-01 1426. 222379    10       6       6028  1978  0.0271
 7 1978-07-01 1427. 222585    10.9     5.8     6309  1978  0.0283
 8 1978-08-01 1447  222805    10.5     5.8     6080  1978  0.0273
 9 1978-09-01 1453. 223053    10.6     5.6     6125  1978  0.0275
10 1978-10-01 1467. 223271    10.7     5.9     5947  1978  0.0266
11 1978-11-01 1481. 223477    10.5     5.5     6077  1978  0.0272
12 1978-12-01 1496. 223670    10.4     5.6     6228  1978  0.0278

$`1979`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1979-01-01 1502. 223865    11.1     5.9     6109  1979  0.0273
 2 1979-02-01 1518. 224053    11.1     5.9     6173  1979  0.0276
 3 1979-03-01 1531. 224235    11.2     5.9     6109  1979  0.0272
 4 1979-04-01 1538. 224438    11       5.4     6069  1979  0.0270
 5 1979-05-01 1559. 224632    10.3     5.6     5840  1979  0.0260
 6 1979-06-01 1576. 224843     9.9     5.6     5959  1979  0.0265
 7 1979-07-01 1586. 225055    10.6     5.9     5996  1979  0.0266
 8 1979-08-01 1616. 225295     9.7     4.8     6320  1979  0.0281
 9 1979-09-01 1634. 225547     9.4     5.5     6190  1979  0.0274
10 1979-10-01 1642. 225801     9.7     5.5     6296  1979  0.0279
11 1979-11-01 1657. 226027     9.7     5.3     6238  1979  0.0276
12 1979-12-01 1666. 226243    10.1     5.7     6325  1979  0.0280

$`1980`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1980-01-01 1697. 226451     9.9     5.3     6683  1980  0.0295
 2 1980-02-01 1701. 226656    10.1     5.8     6702  1980  0.0296
 3 1980-03-01 1708. 226849    10.2     6       6729  1980  0.0297
 4 1980-04-01 1695. 227061    11.3     5.8     7358  1980  0.0324
 5 1980-05-01 1700. 227251    11.4     5.7     7984  1980  0.0351
 6 1980-06-01 1719. 227522    11.2     6.4     8098  1980  0.0356
 7 1980-07-01 1747. 227726    11.3     7       8363  1980  0.0367
 8 1980-08-01 1764. 227953    11.3     7.5     8281  1980  0.0363
 9 1980-09-01 1780. 228186    11.7     7.7     8021  1980  0.0352
10 1980-10-01 1817. 228417    11.3     7.5     8088  1980  0.0354
11 1980-11-01 1827. 228612    11.6     7.7     8023  1980  0.0351
12 1980-12-01 1852. 228779    11.4     7.5     7718  1980  0.0337

$`1981`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1981-01-01 1870  228937    10.9     7.4     8071  1981  0.0353
 2 1981-02-01 1884. 229071    10.8     7.1     8051  1981  0.0351
 3 1981-03-01 1903. 229224    10.8     7.1     7982  1981  0.0348
 4 1981-04-01 1904. 229403    10.9     7.4     7869  1981  0.0343
 5 1981-05-01 1914. 229575    11       6.9     8174  1981  0.0356
 6 1981-06-01 1934. 229761    10.8     6.6     8098  1981  0.0352
 7 1981-07-01 1942. 229966    12.3     7.1     7863  1981  0.0342
 8 1981-08-01 1967. 230187    12       7.2     8036  1981  0.0349
 9 1981-09-01 1966. 230412    12.4     6.8     8230  1981  0.0357
10 1981-10-01 1964. 230641    13       6.8     8646  1981  0.0375
11 1981-11-01 1971. 230822    13.2     6.9     9029  1981  0.0391
12 1981-12-01 1989. 230989    12.5     6.9     9267  1981  0.0401

$`1982`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1982-01-01 1997. 231157    12.7     7.1     9397  1982  0.0407
 2 1982-02-01 2021. 231313    12.1     7.5     9705  1982  0.0420
 3 1982-03-01 2024. 231470    12.2     7.7     9895  1982  0.0427
 4 1982-04-01 2026. 231645    12.9     8.1    10244  1982  0.0442
 5 1982-05-01 2044. 231809    12.3     8.5    10335  1982  0.0446
 6 1982-06-01 2048. 231992    12.3     9.5    10538  1982  0.0454
 7 1982-07-01 2072. 232188    12.5     8.5    10849  1982  0.0467
 8 1982-08-01 2080. 232392    12.6     8.7    10881  1982  0.0468
 9 1982-09-01 2105. 232599    11.8     9.5    11217  1982  0.0482
10 1982-10-01 2126. 232816    11.3     9.7    11529  1982  0.0495
11 1982-11-01 2149. 232993    10.9    10      11938  1982  0.0512
12 1982-12-01 2162. 233160    10.9    10.2    12051  1982  0.0517

$`1983`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1983-01-01 2174  233322    11.1    11.1    11534  1983  0.0494
 2 1983-02-01 2177  233473    11.1     9.8    11545  1983  0.0494
 3 1983-03-01 2203. 233613    10.6    10.4    11408  1983  0.0488
 4 1983-04-01 2226. 233781    10.3    10.9    11268  1983  0.0482
 5 1983-05-01 2246. 233922     9.9    12.3    11154  1983  0.0477
 6 1983-06-01 2276  234118     9.1    11.3    11246  1983  0.0480
 7 1983-07-01 2304. 234307     9.6    10.1    10548  1983  0.0450
 8 1983-08-01 2320. 234501     9.2     9.3    10623  1983  0.0453
 9 1983-09-01 2335. 234701     9.6     9.3    10282  1983  0.0438
10 1983-10-01 2358. 234907     9.7     9.4     9887  1983  0.0421
11 1983-11-01 2366. 235078    10.3     9.3     9499  1983  0.0404
12 1983-12-01 2394. 235235    10.1     8.7     9331  1983  0.0397

$`1984`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1984-01-01 2419. 235385    10       9.1     9008  1984  0.0383
 2 1984-02-01 2404. 235527    11.7     8.3     8791  1984  0.0373
 3 1984-03-01 2432. 235675    11.5     8.3     8746  1984  0.0371
 4 1984-04-01 2458. 235839    11.5     8.2     8762  1984  0.0372
 5 1984-05-01 2474. 235993    11.1     9.1     8456  1984  0.0358
 6 1984-06-01 2496. 236160    11.1     7.5     8226  1984  0.0348
 7 1984-07-01 2495. 236348    11.6     7.5     8537  1984  0.0361
 8 1984-08-01 2512. 236549    11.8     7.3     8519  1984  0.0360
 9 1984-09-01 2534. 236760    11.8     7.6     8367  1984  0.0353
10 1984-10-01 2531. 236976    11.7     7.2     8381  1984  0.0354
11 1984-11-01 2571. 237159    10.9     7.2     8198  1984  0.0346
12 1984-12-01 2583. 237316    11.2     7.3     8358  1984  0.0352

$`1985`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1985-01-01 2619. 237468    10.3     6.8     8423  1985  0.0355
 2 1985-02-01 2641. 237602     9.1     7.1     8321  1985  0.0350
 3 1985-03-01 2648. 237732     8.7     7.1     8339  1985  0.0351
 4 1985-04-01 2660. 237900     9.9     6.9     8395  1985  0.0353
 5 1985-05-01 2696. 238074    11.1     6.9     8302  1985  0.0349
 6 1985-06-01 2689. 238270     9.6     6.6     8460  1985  0.0355
 7 1985-07-01 2716. 238466     9.1     6.9     8513  1985  0.0357
 8 1985-08-01 2752. 238679     8.2     7.1     8196  1985  0.0343
 9 1985-09-01 2795. 238898     7.3     6.9     8248  1985  0.0345
10 1985-10-01 2756. 239113     9.1     7.1     8298  1985  0.0347
11 1985-11-01 2771. 239307     9       7       8128  1985  0.0340
12 1985-12-01 2811. 239477     8.6     6.8     8138  1985  0.0340

$`1986`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1986-01-01 2827. 239638     8.6     6.7     7795  1986  0.0325
 2 1986-02-01 2820. 239788     9.3     6.9     8402  1986  0.0350
 3 1986-03-01 2824. 239928     9.9     6.8     8383  1986  0.0349
 4 1986-04-01 2835. 240094     9.7     6.7     8364  1986  0.0348
 5 1986-05-01 2858. 240271     9.3     6.8     8439  1986  0.0351
 6 1986-06-01 2862. 240459     9.4     7       8508  1986  0.0354
 7 1986-07-01 2881. 240651     9.3     6.9     8319  1986  0.0346
 8 1986-08-01 2899. 240854     9       7.1     8135  1986  0.0338
 9 1986-09-01 2972. 241068     7.2     7.4     8310  1986  0.0345
10 1986-10-01 2933. 241274     8.4     7       8243  1986  0.0342
11 1986-11-01 2928. 241467     8.8     7.1     8159  1986  0.0338
12 1986-12-01 2997. 241620     7       7.1     7883  1986  0.0326

$`1987`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1987-01-01 2936. 241784     9.7     6.9     7892  1987  0.0326
 2 1987-02-01 3002. 241930     8.5     6.6     7865  1987  0.0325
 3 1987-03-01 3013. 242079     8.5     6.6     7862  1987  0.0325
 4 1987-04-01 3039. 242252     4.5     7.1     7542  1987  0.0311
 5 1987-05-01 3048. 242423     8.2     6.6     7574  1987  0.0312
 6 1987-06-01 3073. 242608     7.7     6.5     7398  1987  0.0305
 7 1987-07-01 3095. 242804     7.5     6.5     7268  1987  0.0299
 8 1987-08-01 3131. 243012     7.2     6.4     7261  1987  0.0299
 9 1987-09-01 3126. 243223     7.6     6       7102  1987  0.0292
10 1987-10-01 3134. 243446     8.3     6.3     7227  1987  0.0297
11 1987-11-01 3144. 243639     8.5     6.2     7035  1987  0.0289
12 1987-12-01 3174. 243809     8.7     6       6936  1987  0.0284

$`1988`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1988-01-01 3214. 243981     8.1     6.2     6953  1988  0.0285
 2 1988-02-01 3221. 244131     8.6     6.3     6929  1988  0.0284
 3 1988-03-01 3260. 244279     8.2     6.4     6876  1988  0.0281
 4 1988-04-01 3263  244445     8.8     5.9     6601  1988  0.0270
 5 1988-05-01 3294. 244610     8.4     5.9     6779  1988  0.0277
 6 1988-06-01 3318. 244806     8.4     5.8     6546  1988  0.0267
 7 1988-07-01 3343. 245021     8.6     6.1     6605  1988  0.0270
 8 1988-08-01 3368  245240     8.4     5.9     6843  1988  0.0279
 9 1988-09-01 3375  245464     8.9     5.7     6604  1988  0.0269
10 1988-10-01 3414. 245693     8.6     5.6     6568  1988  0.0267
11 1988-11-01 3430. 245884     8.4     5.7     6537  1988  0.0266
12 1988-12-01 3460. 246056     8.3     5.9     6518  1988  0.0265

$`1989`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1989-01-01 3484. 246224     8.5     5.6     6682  1989  0.0271
 2 1989-02-01 3488  246378     9       5.4     6359  1989  0.0258
 3 1989-03-01 3499. 246530     9.5     5.4     6205  1989  0.0252
 4 1989-04-01 3543  246721     8.4     5.4     6468  1989  0.0262
 5 1989-05-01 3552. 246906     8.1     5.3     6375  1989  0.0258
 6 1989-06-01 3567. 247114     8.2     5.4     6577  1989  0.0266
 7 1989-07-01 3586. 247342     8.2     5.6     6495  1989  0.0263
 8 1989-08-01 3621. 247573     7.6     5       6511  1989  0.0263
 9 1989-09-01 3622. 247816     8.1     4.9     6590  1989  0.0266
10 1989-10-01 3634. 248067     8.5     4.9     6630  1989  0.0267
11 1989-11-01 3643. 248281     8.6     4.8     6725  1989  0.0271
12 1989-12-01 3684. 248479     7.8     4.9     6667  1989  0.0268

$`1990`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1990-01-01 3731. 248659     8       5.1     6752  1990  0.0272
 2 1990-02-01 3728. 248827     8.6     5.3     6651  1990  0.0267
 3 1990-03-01 3755. 249012     8.3     5.1     6598  1990  0.0265
 4 1990-04-01 3770  249306     8.8     4.8     6797  1990  0.0273
 5 1990-05-01 3776. 249565     8.7     5.2     6742  1990  0.0270
 6 1990-06-01 3804. 249849     8.6     5.2     6590  1990  0.0264
 7 1990-07-01 3822. 250132     8.7     5.4     6922  1990  0.0277
 8 1990-08-01 3848. 250439     8.1     5.4     7188  1990  0.0287
 9 1990-09-01 3870. 250751     8.1     5.6     7368  1990  0.0294
10 1990-10-01 3871. 251057     7.8     5.8     7459  1990  0.0297
11 1990-11-01 3872. 251346     7.9     5.7     7764  1990  0.0309
12 1990-12-01 3861. 251626     8.8     5.9     7901  1990  0.0314

$`1991`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1991-01-01 3841  251889     9.3     6       8015  1991  0.0318
 2 1991-02-01 3867. 252135     8.8     6.2     8265  1991  0.0328
 3 1991-03-01 3913  252372     8       6.7     8586  1991  0.0340
 4 1991-04-01 3907. 252643     8.6     6.6     8439  1991  0.0334
 5 1991-05-01 3933. 252913     8.4     6.4     8736  1991  0.0345
 6 1991-06-01 3940. 253207     8.9     6.9     8692  1991  0.0343
 7 1991-07-01 3966  253493     8.2     7       8586  1991  0.0339
 8 1991-08-01 3969. 253807     8.6     7.3     8666  1991  0.0341
 9 1991-09-01 3985. 254126     8.8     6.8     8722  1991  0.0343
10 1991-10-01 3976  254435     9.3     7.2     8842  1991  0.0348
11 1991-11-01 4004. 254718     9       7.5     8931  1991  0.0351
12 1991-12-01 4020. 254964     9.7     7.8     9198  1991  0.0361

$`1992`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1992-01-01 4085. 255214     9.4     8.1     9283  1992  0.0364
 2 1992-02-01 4100. 255448     9.8     8.2     9454  1992  0.0370
 3 1992-03-01 4117  255703     9.7     8.3     9460  1992  0.0370
 4 1992-04-01 4132. 255992     9.9     8.5     9415  1992  0.0368
 5 1992-05-01 4158. 256285     9.9     8.8     9744  1992  0.0380
 6 1992-06-01 4177. 256589    10.1     8.7    10040  1992  0.0391
 7 1992-07-01 4205. 256894     9.6     8.6     9850  1992  0.0383
 8 1992-08-01 4221. 257232     9.7     8.8     9787  1992  0.0380
 9 1992-09-01 4255. 257548     8.7     8.6     9781  1992  0.0380
10 1992-10-01 4285. 257861     8       9       9398  1992  0.0364
11 1992-11-01 4300. 258147     8       9       9565  1992  0.0371
12 1992-12-01 4336. 258413    10.6     9.3     9557  1992  0.0370

$`1993`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1993-01-01 4341. 258679     8.6     8.6     9325  1993  0.0360
 2 1993-02-01 4355. 258919     8.9     8.5     9183  1993  0.0355
 3 1993-03-01 4352. 259152     8.9     8.5     9056  1993  0.0349
 4 1993-04-01 4393. 259414     8.7     8.4     9110  1993  0.0351
 5 1993-05-01 4422. 259680     8.3     8.1     9149  1993  0.0352
 6 1993-06-01 4440  259963     7.8     8.3     9121  1993  0.0351
 7 1993-07-01 4469. 260255     7.6     8.2     8930  1993  0.0343
 8 1993-08-01 4481. 260566     7.7     8.2     8763  1993  0.0336
 9 1993-09-01 4512. 260867     6.9     8.3     8714  1993  0.0334
10 1993-10-01 4533. 261163     6.3     8       8750  1993  0.0335
11 1993-11-01 4554. 261425     6.3     8.3     8542  1993  0.0327
12 1993-12-01 4571. 261674     9.1     8.3     8477  1993  0.0324

$`1994`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1994-01-01 4585. 261919     7.1     8.6     8630  1994  0.0329
 2 1994-02-01 4633. 262123     6.5     9.2     8583  1994  0.0327
 3 1994-03-01 4646  262352     6.8     9.3     8470  1994  0.0323
 4 1994-04-01 4671. 262631     6.4     9.1     8331  1994  0.0317
 5 1994-05-01 4670. 262877     7.6     9.2     7915  1994  0.0301
 6 1994-06-01 4709. 263152     6.9     9.3     7927  1994  0.0301
 7 1994-07-01 4721. 263436     7       9       7946  1994  0.0302
 8 1994-08-01 4763. 263724     6.5     8.9     7933  1994  0.0301
 9 1994-09-01 4775  264017     6.8     9.2     7734  1994  0.0293
10 1994-10-01 4813. 264301     7.1    10       7632  1994  0.0289
11 1994-11-01 4826. 264559     7       9       7375  1994  0.0279
12 1994-12-01 4842. 264804     7.2     8.7     7230  1994  0.0273

$`1995`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1995-01-01 4851. 265044     7.5     8       7375  1995  0.0278
 2 1995-02-01 4851. 265270     7.8     8.1     7187  1995  0.0271
 3 1995-03-01 4885. 265495     7.5     8.3     7153  1995  0.0269
 4 1995-04-01 4890. 265755     6.9     8.3     7645  1995  0.0288
 5 1995-05-01 4933. 265998     7.1     9.1     7430  1995  0.0279
 6 1995-06-01 4978. 266270     6.7     7.9     7427  1995  0.0279
 7 1995-07-01 4970. 266557     7.1     8.5     7527  1995  0.0282
 8 1995-08-01 5005. 266843     6.7     8.3     7484  1995  0.0280
 9 1995-09-01 5020. 267152     6.8     7.9     7478  1995  0.0280
10 1995-10-01 5014. 267456     7.1     8.2     7328  1995  0.0274
11 1995-11-01 5056. 267715     6.6     8       7426  1995  0.0277
12 1995-12-01 5098. 267943     6.1     8.3     7423  1995  0.0277

$`1996`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1996-01-01 5086. 268151     6.7     8.3     7491  1996  0.0279
 2 1996-02-01 5133. 268364     6.7     7.8     7313  1996  0.0273
 3 1996-03-01 5173. 268595     6.6     8.3     7318  1996  0.0272
 4 1996-04-01 5208  268853     5.7     8.6     7415  1996  0.0276
 5 1996-05-01 5224. 269108     6.7     8.6     7423  1996  0.0276
 6 1996-06-01 5230. 269386     7.1     8.3     7095  1996  0.0263
 7 1996-07-01 5252. 269667     6.7     8.3     7337  1996  0.0272
 8 1996-08-01 5275  269976     6.6     8.4     6882  1996  0.0255
 9 1996-09-01 5297. 270284     6.7     8.5     6979  1996  0.0258
10 1996-10-01 5328. 270581     6.4     8.3     7031  1996  0.0260
11 1996-11-01 5351. 270878     6.4     7.7     7236  1996  0.0267
12 1996-12-01 5379. 271125     6.4     7.8     7253  1996  0.0268

$`1997`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1997-01-01 5411. 271360     6.2     7.8     7158  1997  0.0264
 2 1997-02-01 5434  271585     6.2     8.1     7102  1997  0.0262
 3 1997-03-01 5454. 271821     6.4     7.9     7000  1997  0.0258
 4 1997-04-01 5459. 272083     6.5     8.3     6873  1997  0.0253
 5 1997-05-01 5460. 272342     6.8     8       6655  1997  0.0244
 6 1997-06-01 5494. 272622     6.6     8       6799  1997  0.0249
 7 1997-07-01 5549. 272912     6.1     8.3     6655  1997  0.0244
 8 1997-08-01 5587  273237     6       7.8     6608  1997  0.0242
 9 1997-09-01 5602. 273553     6.2     8.2     6656  1997  0.0243
10 1997-10-01 5638. 273852     6.2     7.7     6454  1997  0.0236
11 1997-11-01 5661. 274126     6.4     7.6     6308  1997  0.0230
12 1997-12-01 5692. 274372     6.4     7.5     6476  1997  0.0236

$`1998`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1998-01-01 5690. 274626     7.4     7.4     6368  1998  0.0232
 2 1998-02-01 5724. 274838     7.4     7       6306  1998  0.0229
 3 1998-03-01 5750. 275047     7.5     6.8     6422  1998  0.0233
 4 1998-04-01 5788. 275304     7.2     6.7     5941  1998  0.0216
 5 1998-05-01 5838. 275564     6.9     6       6047  1998  0.0219
 6 1998-06-01 5872. 275836     6.8     6.9     6212  1998  0.0225
 7 1998-07-01 5890  276115     6.9     6.7     6259  1998  0.0227
 8 1998-08-01 5925  276418     6.8     6.8     6179  1998  0.0224
 9 1998-09-01 5966. 276714     6.4     6.7     6300  1998  0.0228
10 1998-10-01 5999. 277003     6.2     5.8     6280  1998  0.0227
11 1998-11-01 6015. 277277     6.3     6.6     6100  1998  0.0220
12 1998-12-01 6070. 277526     5.8     6.8     6032  1998  0.0217

$`1999`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 1999-01-01 6073. 277790     6.4     6.9     5976  1999  0.0215
 2 1999-02-01 6102. 277992     6.2     6.8     6111  1999  0.0220
 3 1999-03-01 6133. 278198     5.9     6.8     5783  1999  0.0208
 4 1999-04-01 6196. 278451     5.2     6.2     6004  1999  0.0216
 5 1999-05-01 6226. 278717     4.9     6.5     5796  1999  0.0208
 6 1999-06-01 6254  279001     4.8     6.3     5951  1999  0.0213
 7 1999-07-01 6282. 279295     4.8     5.8     6025  1999  0.0216
 8 1999-08-01 6326. 279602     4.7     6.5     5838  1999  0.0209
 9 1999-09-01 6379. 279903     4.2     6       5915  1999  0.0211
10 1999-10-01 6402. 280203     4.6     6.1     5778  1999  0.0206
11 1999-11-01 6438. 280471     4.8     6.2     5716  1999  0.0204
12 1999-12-01 6539. 280716     4.4     5.8     5653  1999  0.0201

$`2000`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2000-01-01 6535. 280976     5.4     5.8     5708  2000  0.0203
 2 2000-02-01 6620. 281190     4.8     6.1     5858  2000  0.0208
 3 2000-03-01 6686. 281409     4.5     6       5733  2000  0.0204
 4 2000-04-01 6671. 281653     5       6.1     5481  2000  0.0195
 5 2000-05-01 6708. 281877     4.9     5.8     5758  2000  0.0204
 6 2000-06-01 6744. 282126     4.9     5.7     5651  2000  0.0200
 7 2000-07-01 6764. 282385     5.2     6       5747  2000  0.0204
 8 2000-08-01 6799. 282653     5.2     6.3     5853  2000  0.0207
 9 2000-09-01 6883. 282932     4.5     5.2     5625  2000  0.0199
10 2000-10-01 6888. 283201     4.6     6.1     5534  2000  0.0195
11 2000-11-01 6902. 283453     4.5     6.1     5639  2000  0.0199
12 2000-12-01 6946. 283696     4.2     6       5634  2000  0.0199

$`2001`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2001-01-01 6977  283920     4.8     5.8     6023  2001  0.0212
 2 2001-02-01 6996. 284137     4.9     6.1     6089  2001  0.0214
 3 2001-03-01 6988. 284350     5.3     6.6     6141  2001  0.0216
 4 2001-04-01 7001. 284581     5       5.9     6271  2001  0.0220
 5 2001-05-01 7047. 284810     4.5     6.3     6226  2001  0.0219
 6 2001-06-01 7061. 285062     4.5     6       6484  2001  0.0227
 7 2001-07-01 7072. 285309     5.6     6.8     6583  2001  0.0231
 8 2001-08-01 7109. 285570     6.8     6.9     7042  2001  0.0247
 9 2001-09-01 7013. 285843     7       7.2     7142  2001  0.0250
10 2001-10-01 7208. 286098     3.4     7.3     7694  2001  0.0269
11 2001-11-01 7168. 286341     4.1     7.7     8003  2001  0.0279
12 2001-12-01 7148. 286570     4.5     8.2     8258  2001  0.0288

$`2002`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2002-01-01 7174. 286788     6.1     8.4     8182  2002  0.0285
 2 2002-02-01 7218. 286994     5.8     8.3     8215  2002  0.0286
 3 2002-03-01 7237. 287190     5.9     8.4     8304  2002  0.0289
 4 2002-04-01 7305. 287397     5.8     8.9     8599  2002  0.0299
 5 2002-05-01 7283. 287623     6.5     9.5     8399  2002  0.0292
 6 2002-06-01 7318. 287864     6.4    11       8393  2002  0.0292
 7 2002-07-01 7380. 288105     5.5     8.9     8390  2002  0.0291
 8 2002-08-01 7402. 288360     5.4     9       8304  2002  0.0288
 9 2002-09-01 7391  288618     5.7     9.5     8251  2002  0.0286
10 2002-10-01 7431. 288870     5.7     9.6     8307  2002  0.0288
11 2002-11-01 7460. 289106     5.7     9.3     8520  2002  0.0295
12 2002-12-01 7513. 289313     5.5     9.6     8640  2002  0.0299

$`2003`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2003-01-01 7533. 289518     5.5     9.6     8520  2003  0.0294
 2 2003-02-01 7536. 289714     5.6     9.5     8618  2003  0.0297
 3 2003-03-01 7598. 289911     5.3     9.7     8588  2003  0.0296
 4 2003-04-01 7621  290125     5.3    10.2     8842  2003  0.0305
 5 2003-05-01 7628. 290346     5.8     9.9     8957  2003  0.0308
 6 2003-06-01 7679. 290584     5.6    11.5     9266  2003  0.0319
 7 2003-07-01 7738. 290820     6.3    10.3     9011  2003  0.0310
 8 2003-08-01 7834. 291072     6      10.1     8896  2003  0.0306
 9 2003-09-01 7835  291321     5.2    10.2     8921  2003  0.0306
10 2003-10-01 7846. 291574     5.3    10.4     8732  2003  0.0299
11 2003-11-01 7900. 291807     5.4    10.3     8576  2003  0.0294
12 2003-12-01 7929. 292008     5.4    10.4     8317  2003  0.0285

$`2004`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2004-01-01 7987. 292192     5      10.6     8370  2004  0.0286
 2 2004-02-01 8020. 292368     5      10.2     8167  2004  0.0279
 3 2004-03-01 8076  292561     4.9    10.2     8491  2004  0.0290
 4 2004-04-01 8089. 292779     5.3     9.5     8170  2004  0.0279
 5 2004-05-01 8163. 292997     5.3     9.9     8212  2004  0.0280
 6 2004-06-01 8147. 293223     5.8    11       8286  2004  0.0283
 7 2004-07-01 8219. 293463     5.3     8.9     8136  2004  0.0277
 8 2004-08-01 8253. 293719     5.2     9.2     7990  2004  0.0272
 9 2004-09-01 8321. 293971     4.6     9.6     7927  2004  0.0270
10 2004-10-01 8375. 294230     4.5     9.5     8061  2004  0.0274
11 2004-11-01 8421. 294466     4.1     9.7     7932  2004  0.0269
12 2004-12-01 8482. 294694     6.9     9.5     7934  2004  0.0269

$`2005`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2005-01-01 8470. 294914     3.7     9.4     7784  2005  0.0264
 2 2005-02-01 8529. 295105     3.4     9.2     7980  2005  0.0270
 3 2005-03-01 8570. 295287     3.6     9.3     7737  2005  0.0262
 4 2005-04-01 8646. 295490     3.1     9       7672  2005  0.0260
 5 2005-05-01 8644. 295704     3.5     9.1     7651  2005  0.0259
 6 2005-06-01 8725. 295936     2.9     9       7524  2005  0.0254
 7 2005-07-01 8830. 296186     2.2     8.8     7406  2005  0.0250
 8 2005-08-01 8832. 296440     2.7     9.2     7345  2005  0.0248
 9 2005-09-01 8886. 296707     2.7     8.4     7553  2005  0.0255
10 2005-10-01 8927. 296972     3.1     8.6     7453  2005  0.0251
11 2005-11-01 8938. 297207     3.5     8.5     7566  2005  0.0255
12 2005-12-01 8970. 297431     3.7     8.7     7279  2005  0.0245

$`2006`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2006-01-01 9060. 297647     4.2     8.6     7064  2006  0.0237
 2 2006-02-01 9090. 297854     4.2     9.1     7184  2006  0.0241
 3 2006-03-01 9122. 298060     4.2     8.7     7072  2006  0.0237
 4 2006-04-01 9175. 298281     4       8.4     7120  2006  0.0239
 5 2006-05-01 9215. 298496     3.8     8.5     6980  2006  0.0234
 6 2006-06-01 9241. 298739     4       7.3     7001  2006  0.0234
 7 2006-07-01 9323. 298996     3.4     8       7175  2006  0.0240
 8 2006-08-01 9322. 299263     3.6     8.4     7091  2006  0.0237
 9 2006-09-01 9355. 299554     3.6     8       6847  2006  0.0229
10 2006-10-01 9373. 299835     3.6     7.9     6727  2006  0.0224
11 2006-11-01 9380. 300094     3.9     8.3     6872  2006  0.0229
12 2006-12-01 9469  300340     3.7     7.5     6762  2006  0.0225

$`2007`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2007-01-01 9516. 300574     3.7     8.3     7116  2007  0.0237
 2 2007-02-01 9547. 300802     4.1     8.5     6927  2007  0.0230
 3 2007-03-01 9585. 301021     4.4     9.1     6731  2007  0.0224
 4 2007-04-01 9616. 301254     4.2     8.6     6850  2007  0.0227
 5 2007-05-01 9651. 301483     4       8.2     6766  2007  0.0224
 6 2007-06-01 9667. 301739     3.8     7.7     6979  2007  0.0231
 7 2007-07-01 9710. 302004     3.7     8.7     7149  2007  0.0237
 8 2007-08-01 9754. 302267     3.4     8.8     7067  2007  0.0234
 9 2007-09-01 9798. 302546     3.5     8.7     7170  2007  0.0237
10 2007-10-01 9827  302807     3.4     8.4     7237  2007  0.0239
11 2007-11-01 9898. 303054     3.1     8.6     7240  2007  0.0239
12 2007-12-01 9908. 303287     3.6     8.4     7645  2007  0.0252

$`2008`
# A tibble: 12 × 8
   date          pce    pop psavert uempmed unemploy  year percent
   <date>      <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2008-01-01  9930  303506     3.7     9       7685  2008  0.0253
 2 2008-02-01  9913. 303711     4.1     8.7     7497  2008  0.0247
 3 2008-03-01  9959. 303907     4       8.7     7822  2008  0.0257
 4 2008-04-01  9997. 304117     3.4     9.4     7637  2008  0.0251
 5 2008-05-01 10054. 304323     7.8     7.9     8395  2008  0.0276
 6 2008-06-01 10108. 304556     5.5     9       8575  2008  0.0282
 7 2008-07-01 10105. 304798     4.4     9.7     8937  2008  0.0293
 8 2008-08-01 10095. 305045     3.8     9.7     9438  2008  0.0309
 9 2008-09-01 10044. 305309     4.7    10.2     9494  2008  0.0311
10 2008-10-01  9960. 305554     5.5    10.4    10074  2008  0.0330
11 2008-11-01  9821. 305786     6.4     9.8    10538  2008  0.0345
12 2008-12-01  9731. 306004     6.4    10.5    11286  2008  0.0369

$`2009`
# A tibble: 12 × 8
   date         pce    pop psavert uempmed unemploy  year percent
   <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2009-01-01 9784. 306208     6.2    10.7    12058  2009  0.0394
 2 2009-02-01 9766  306402     5.5    11.7    12898  2009  0.0421
 3 2009-03-01 9718. 306588     5.9    12.3    13426  2009  0.0438
 4 2009-04-01 9725. 306787     6.8    13.1    13853  2009  0.0452
 5 2009-05-01 9749. 306984     8.2    14.2    14499  2009  0.0472
 6 2009-06-01 9807. 307206     6.7    17.2    14707  2009  0.0479
 7 2009-07-01 9842. 307439     6      16      14601  2009  0.0475
 8 2009-08-01 9961  307685     4.9    16.3    14814  2009  0.0481
 9 2009-09-01 9883. 307946     5.9    17.8    15009  2009  0.0487
10 2009-10-01 9932. 308189     5.4    18.9    15352  2009  0.0498
11 2009-11-01 9940. 308418     5.9    19.8    15219  2009  0.0493
12 2009-12-01 9999. 308633     5.9    20.1    15098  2009  0.0489

$`2010`
# A tibble: 12 × 8
   date          pce     pop psavert uempmed unemploy  year percent
   <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2010-01-01 10002. 308833      6.1    20      15046  2010  0.0487
 2 2010-02-01 10031. 309027      5.8    19.9    15113  2010  0.0489
 3 2010-03-01 10089. 309212      5.7    20.4    15202  2010  0.0492
 4 2010-04-01 10113. 309191.     6.4    22.1    15325  2010  0.0496
 5 2010-05-01 10131  309369.     7      22.3    14849  2010  0.0480
 6 2010-06-01 10151. 309549.     6.9    25.2    14474  2010  0.0468
 7 2010-07-01 10185. 309746.     6.8    22.3    14512  2010  0.0469
 8 2010-08-01 10228. 309958.     6.9    21      14648  2010  0.0473
 9 2010-09-01 10249  310176.     6.7    20.3    14579  2010  0.0470
10 2010-10-01 10305. 310400.     6.6    21.2    14516  2010  0.0468
11 2010-11-01 10355. 310596.     6.6    21      15081  2010  0.0486
12 2010-12-01 10392. 310782.     7.1    21.9    14348  2010  0.0462

$`2011`
# A tibble: 12 × 8
   date          pce     pop psavert uempmed unemploy  year percent
   <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2011-01-01 10436. 310961.     7.4    21.5    14013  2011  0.0451
 2 2011-02-01 10470. 311113.     7.6    21.1    13820  2011  0.0444
 3 2011-03-01 10550. 311265.     7      21.5    13737  2011  0.0441
 4 2011-04-01 10588. 311436.     6.9    20.9    13957  2011  0.0448
 5 2011-05-01 10612  311607.     6.9    21.6    13855  2011  0.0445
 6 2011-06-01 10637. 311791.     7.2    22.4    13962  2011  0.0448
 7 2011-07-01 10678. 311997.     7.3    22      13763  2011  0.0441
 8 2011-08-01 10701. 312205.     7.2    22.4    13818  2011  0.0443
 9 2011-09-01 10738. 312429.     6.8    22      13948  2011  0.0446
10 2011-10-01 10753. 312644.     6.8    20.6    13594  2011  0.0435
11 2011-11-01 10760. 312830.     7      20.8    13302  2011  0.0425
12 2011-12-01 10772. 313010.     7.8    20.5    13093  2011  0.0418

$`2012`
# A tibble: 12 × 8
   date          pce     pop psavert uempmed unemploy  year percent
   <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2012-01-01 10862. 313183.     8      20.8    12797  2012  0.0409
 2 2012-02-01 10954. 313339.     8      19.7    12813  2012  0.0409
 3 2012-03-01 10952. 313499.     8.5    19.2    12713  2012  0.0406
 4 2012-04-01 10980. 313667.     8.7    19.1    12646  2012  0.0403
 5 2012-05-01 10969. 313831.     8.8    19.9    12660  2012  0.0403
 6 2012-06-01 10946. 314018.     9.1    20.4    12692  2012  0.0404
 7 2012-07-01 10977. 314211.     8.2    17.5    12656  2012  0.0403
 8 2012-08-01 11004. 314422.     8      18.4    12471  2012  0.0397
 9 2012-09-01 11062. 314647.     8.2    18.8    12115  2012  0.0385
10 2012-10-01 11100. 314854.     8.8    19.9    12124  2012  0.0385
11 2012-11-01 11137. 315054.     9.7    18.6    12005  2012  0.0381
12 2012-12-01 11140. 315233.    12      17.7    12298  2012  0.0390

$`2013`
# A tibble: 12 × 8
   date          pce     pop psavert uempmed unemploy  year percent
   <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2013-01-01 11203. 315390.     6.3    15.8    12471  2013  0.0395
 2 2013-02-01 11240. 315520.     5.8    17.2    11950  2013  0.0379
 3 2013-03-01 11227. 315662.     5.9    17.6    11689  2013  0.0370
 4 2013-04-01 11205. 315818.     6.4    17.1    11760  2013  0.0372
 5 2013-05-01 11245. 315984.     6.7    17.1    11654  2013  0.0369
 6 2013-06-01 11269. 316171.     6.8    17      11751  2013  0.0372
 7 2013-07-01 11297. 316359.     6.6    16.2    11335  2013  0.0358
 8 2013-08-01 11329. 316580.     6.7    16.5    11279  2013  0.0356
 9 2013-09-01 11367. 316806.     6.8    16.5    11270  2013  0.0356
10 2013-10-01 11420. 317022.     6.3    16.3    11136  2013  0.0351
11 2013-11-01 11488. 317228.     6.2    17.1    10787  2013  0.0340
12 2013-12-01 11518. 317412.     6.4    17.3    10404  2013  0.0328

$`2014`
# A tibble: 12 × 8
   date          pce     pop psavert uempmed unemploy  year percent
   <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
 1 2014-01-01 11512. 317594.     7.1    15.4    10202  2014  0.0321
 2 2014-02-01 11566. 317754.     7.3    15.9    10349  2014  0.0326
 3 2014-03-01 11643  317917.     7.4    15.8    10380  2014  0.0327
 4 2014-04-01 11703. 318089.     7.4    15.7     9702  2014  0.0305
 5 2014-05-01 11748. 318270.     7.4    14.6     9859  2014  0.0310
 6 2014-06-01 11817  318464.     7.4    13.8     9460  2014  0.0297
 7 2014-07-01 11860. 318662.     7.5    13.1     9608  2014  0.0302
 8 2014-08-01 11944. 318894.     7.2    12.9     9599  2014  0.0301
 9 2014-09-01 11957. 319125.     7.4    13.4     9262  2014  0.0290
10 2014-10-01 12023  319354.     7.2    13.6     8990  2014  0.0282
11 2014-11-01 12051. 319564.     7.3    13       9090  2014  0.0284
12 2014-12-01 12062  319746.     7.6    12.9     8717  2014  0.0273

$`2015`
# A tibble: 4 × 8
  date          pce     pop psavert uempmed unemploy  year percent
  <date>      <dbl>   <dbl>   <dbl>   <dbl>    <dbl> <dbl>   <dbl>
1 2015-01-01 12046  319929.     7.7    13.2     8903  2015  0.0278
2 2015-02-01 12082. 320075.     7.9    12.9     8610  2015  0.0269
3 2015-03-01 12158. 320231.     7.4    12       8504  2015  0.0266
4 2015-04-01 12194. 320402.     7.6    11.5     8526  2015  0.0266

Piping

We could also have done the previous in a pipeline

by_year %>%
  map(~mutate(.x, percent = unemploy / pop)) |> 
  map(~lm(percent ~ pce, data = .x))

$`1967`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  8.397e-03    1.307e-05  


$`1968`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  2.785e-02   -2.497e-05  


$`1969`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -5.363e-03    3.202e-05  


$`1970`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -0.1180578    0.0002137  


$`1971`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.595e-02    1.177e-05  


$`1972`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  4.404e-02   -2.708e-05  


$`1973`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  2.571e-02   -6.047e-06  


$`1974`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -5.070e-02    8.051e-05  


$`1975`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  3.847e-02   -1.644e-06  


$`1976`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  2.313e-02    9.414e-06  


$`1977`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  7.454e-02   -3.367e-05  


$`1978`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  4.419e-02   -1.152e-05  


$`1979`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.932e-02    5.012e-06  


$`1980`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -0.0074548    0.0000235  


$`1981`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -2.494e-02    3.151e-05  


$`1982`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -8.882e-02    6.516e-05  


$`1983`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.461e-01   -4.402e-05  


$`1984`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  7.893e-02   -1.718e-05  


$`1985`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.366e-02   -6.927e-06  


$`1986`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.385e-02   -6.783e-06  


$`1987`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  0.0914664   -0.0000198  


$`1988`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.123e-02   -7.174e-06  


$`1989`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.051e-02    4.437e-06  


$`1990`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -7.123e-02    2.611e-05  


$`1991`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -3.857e-02    1.843e-05  


$`1992`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  3.487e-02    6.091e-07  


$`1993`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  9.538e-02   -1.372e-05  


$`1994`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.295e-01   -2.102e-05  


$`1995`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  2.397e-02    7.718e-07  


$`1996`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.602e-02   -5.567e-06  


$`1997`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  7.968e-02   -9.937e-06  


$`1998`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  3.590e-02   -2.283e-06  


$`1999`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  3.923e-02   -2.894e-06  


$`2000`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  3.097e-02   -1.602e-06  


$`2001`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -1.881e-01    3.001e-05  


$`2002`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.428e-02    2.015e-06  


$`2003`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  4.026e-02   -1.307e-06  


$`2004`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.764e-02   -3.640e-06  


$`2005`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.778e-02   -3.679e-06  


$`2006`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  5.458e-02   -3.369e-06  


$`2007`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
 -1.722e-02    4.188e-06  


$`2008`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.785e-01   -1.495e-05  


$`2009`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  -1.73e-01     2.23e-05  


$`2010`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.073e-01   -5.844e-06  


$`2011`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.041e-01   -5.647e-06  


$`2012`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  0.1575956   -0.0000107  


$`2013`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  2.101e-01   -1.536e-05  


$`2014`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.317e-01   -8.591e-06  


$`2015`

Call:
lm(formula = percent ~ pce, data = .x)

Coefficients:
(Intercept)          pce  
  1.177e-01   -7.482e-06  

Piping

We could also have done the previous in a pipeline

by_year %>%
  map(~mutate(.x, percent = unemploy / pop)) |> 
  map(~lm(percent ~ pce, data = .x)) |> 
  map(coef)

$`1967`
 (Intercept)          pce 
8.397192e-03 1.307101e-05 

$`1968`
  (Intercept)           pce 
 2.784626e-02 -2.496983e-05 

$`1969`
  (Intercept)           pce 
-5.362991e-03  3.201794e-05 

$`1970`
  (Intercept)           pce 
-0.1180577869  0.0002136662 

$`1971`
 (Intercept)          pce 
1.594909e-02 1.177357e-05 

$`1972`
  (Intercept)           pce 
 0.0440400392 -0.0000270806 

$`1973`
  (Intercept)           pce 
 2.571465e-02 -6.047148e-06 

$`1974`
  (Intercept)           pce 
-5.069693e-02  8.050869e-05 

$`1975`
  (Intercept)           pce 
 3.847047e-02 -1.643992e-06 

$`1976`
 (Intercept)          pce 
2.313211e-02 9.413733e-06 

$`1977`
  (Intercept)           pce 
 7.453726e-02 -3.366894e-05 

$`1978`
  (Intercept)           pce 
 4.418640e-02 -1.151756e-05 

$`1979`
 (Intercept)          pce 
1.932149e-02 5.011539e-06 

$`1980`
  (Intercept)           pce 
-0.0074548418  0.0000235041 

$`1981`
  (Intercept)           pce 
-2.494231e-02  3.150845e-05 

$`1982`
  (Intercept)           pce 
-8.881640e-02  6.516215e-05 

$`1983`
  (Intercept)           pce 
 1.461015e-01 -4.402208e-05 

$`1984`
  (Intercept)           pce 
 7.892629e-02 -1.718497e-05 

$`1985`
  (Intercept)           pce 
 5.366118e-02 -6.926535e-06 

$`1986`
  (Intercept)           pce 
 5.384654e-02 -6.782516e-06 

$`1987`
  (Intercept)           pce 
 9.146640e-02 -1.980421e-05 

$`1988`
  (Intercept)           pce 
 5.122852e-02 -7.174276e-06 

$`1989`
 (Intercept)          pce 
1.051097e-02 4.436762e-06 

$`1990`
  (Intercept)           pce 
-7.123114e-02  2.611286e-05 

$`1991`
  (Intercept)           pce 
-3.856927e-02  1.842612e-05 

$`1992`
 (Intercept)          pce 
3.487276e-02 6.091330e-07 

$`1993`
  (Intercept)           pce 
 9.537911e-02 -1.371548e-05 

$`1994`
  (Intercept)           pce 
 1.295460e-01 -2.102375e-05 

$`1995`
 (Intercept)          pce 
2.396737e-02 7.718004e-07 

$`1996`
  (Intercept)           pce 
 5.602360e-02 -5.567328e-06 

$`1997`
  (Intercept)           pce 
 7.968484e-02 -9.936855e-06 

$`1998`
  (Intercept)           pce 
 3.589534e-02 -2.283149e-06 

$`1999`
  (Intercept)           pce 
 3.922715e-02 -2.893646e-06 

$`2000`
  (Intercept)           pce 
 3.097125e-02 -1.601844e-06 

$`2001`
  (Intercept)           pce 
-1.881272e-01  3.001365e-05 

$`2002`
 (Intercept)          pce 
1.428471e-02 2.014789e-06 

$`2003`
  (Intercept)           pce 
 4.025941e-02 -1.306818e-06 

$`2004`
  (Intercept)           pce 
 5.763951e-02 -3.640025e-06 

$`2005`
  (Intercept)           pce 
 5.777898e-02 -3.679151e-06 

$`2006`
  (Intercept)           pce 
 5.458272e-02 -3.368536e-06 

$`2007`
  (Intercept)           pce 
-1.721961e-02  4.187576e-06 

$`2008`
  (Intercept)           pce 
 1.784624e-01 -1.494605e-05 

$`2009`
  (Intercept)           pce 
-1.729902e-01  2.230078e-05 

$`2010`
  (Intercept)           pce 
 1.073378e-01 -5.844266e-06 

$`2011`
  (Intercept)           pce 
 1.041383e-01 -5.647401e-06 

$`2012`
  (Intercept)           pce 
 1.575956e-01 -1.070319e-05 

$`2013`
  (Intercept)           pce 
 2.101046e-01 -1.536441e-05 

$`2014`
  (Intercept)           pce 
 1.317146e-01 -8.590683e-06 

$`2015`
  (Intercept)           pce 
 1.176573e-01 -7.482080e-06 

Piping

We could also have done the previous in a pipeline

by_year %>%
  map(~mutate(.x, percent = unemploy / pop)) |> 
  map(~lm(percent ~ pce, data = .x)) |> 
  map(coef) |> 
  map_dbl(2)

         1967          1968          1969          1970          1971 
 1.307101e-05 -2.496983e-05  3.201794e-05  2.136662e-04  1.177357e-05 
         1972          1973          1974          1975          1976 
-2.708060e-05 -6.047148e-06  8.050869e-05 -1.643992e-06  9.413733e-06 
         1977          1978          1979          1980          1981 
-3.366894e-05 -1.151756e-05  5.011539e-06  2.350410e-05  3.150845e-05 
         1982          1983          1984          1985          1986 
 6.516215e-05 -4.402208e-05 -1.718497e-05 -6.926535e-06 -6.782516e-06 
         1987          1988          1989          1990          1991 
-1.980421e-05 -7.174276e-06  4.436762e-06  2.611286e-05  1.842612e-05 
         1992          1993          1994          1995          1996 
 6.091330e-07 -1.371548e-05 -2.102375e-05  7.718004e-07 -5.567328e-06 
         1997          1998          1999          2000          2001 
-9.936855e-06 -2.283149e-06 -2.893646e-06 -1.601844e-06  3.001365e-05 
         2002          2003          2004          2005          2006 
 2.014789e-06 -1.306818e-06 -3.640025e-06 -3.679151e-06 -3.368536e-06 
         2007          2008          2009          2010          2011 
 4.187576e-06 -1.494605e-05  2.230078e-05 -5.844266e-06 -5.647401e-06 
         2012          2013          2014          2015 
-1.070319e-05 -1.536441e-05 -8.590683e-06 -7.482080e-06 

List column

We could also have done the previous in a pipeline and a list column

mods_econ <- econ |> 
  group_by(year) |> 
  nest() |> # : split(econ, econ$year)
  mutate(data = map(data,
                    ~mutate(.x, percent = unemploy / pop)), # compute 'percent'
         mods = map(data, 
                   ~lm(percent ~ pce, data = .x)), # fit the model
         coefs = map(mods, coef), # extract coefficients
         slopes = map_dbl(coefs, 2) # extract slopes
         )
mods_econ  

# A tibble: 49 × 5
# Groups:   year [49]
    year data              mods   coefs          slopes
   <dbl> <list>            <list> <list>          <dbl>
 1  1967 <tibble [6 × 7]>  <lm>   <dbl [2]>  0.0000131 
 2  1968 <tibble [12 × 7]> <lm>   <dbl [2]> -0.0000250 
 3  1969 <tibble [12 × 7]> <lm>   <dbl [2]>  0.0000320 
 4  1970 <tibble [12 × 7]> <lm>   <dbl [2]>  0.000214  
 5  1971 <tibble [12 × 7]> <lm>   <dbl [2]>  0.0000118 
 6  1972 <tibble [12 × 7]> <lm>   <dbl [2]> -0.0000271 
 7  1973 <tibble [12 × 7]> <lm>   <dbl [2]> -0.00000605
 8  1974 <tibble [12 × 7]> <lm>   <dbl [2]>  0.0000805 
 9  1975 <tibble [12 × 7]> <lm>   <dbl [2]> -0.00000164
10  1976 <tibble [12 × 7]> <lm>   <dbl [2]>  0.00000941
# ℹ 39 more rows

What do our friends think about this…

What do our friends think about this…

Let’s plot it

mods_econ |> 
  ggplot(aes(slopes)) +
    geom_histogram(fill = "dodgerblue", color = "white")

More Practice

If we have time before the lab

Practice

• Compute the standard deviation of every mtcars column
• Copy and run the following code to obtain 50 bootstrap samples
• Fit the following model to each bootstrap sample:
  • mpg ~ disp
• Extract \(R^2\) and plot the distribution

bootstrap <- function(df) {
  df[sample(nrow(df), replace = TRUE), , drop = FALSE]
}

Next time

Before next class

• Readings
  • {fs}
  • AdvR(2e) 9.4-9.6
• Lab 2
• Clone course repo

Lab 2