Iteration

Jeff Stevens

2025-03-26

Introduction

The problems

  • Run the same model on multiple data sets
  • Create the same plot multiple data sets
  • Read in data files from multiple subjects

Set-up

Iteration

For loops

For loops

for (counter in min:max) {
  # What you want repeated.
  # Index counter-specific vector with [counter]
}

Building objects with for loops

for (i in 1:nrow(penguins)) {
  bill_size[i] <- penguins$bill_length_mm[i] * penguins$bill_depth_mm[i]
}
Error: object 'bill_size' not found

You must initialize variables that you build in for loops.

bill_size <- NA
for (i in 1:nrow(penguins)) {
  bill_size[i] <- penguins$bill_length_mm[i] * penguins$bill_depth_mm[i]
}
head(bill_size)
[1] 731.17 687.30 725.40     NA 708.31 809.58

For loops with vectors

month.name
 [1] "January"   "February"  "March"     "April"     "May"       "June"     
 [7] "July"      "August"    "September" "October"   "November"  "December" 
for (i in month.name) {
  print(paste0(i, " has ", str_length(i), " letters."))
}
[1] "January has 7 letters."
[1] "February has 8 letters."
[1] "March has 5 letters."
[1] "April has 5 letters."
[1] "May has 3 letters."
[1] "June has 4 letters."
[1] "July has 4 letters."
[1] "August has 6 letters."
[1] "September has 9 letters."
[1] "October has 7 letters."
[1] "November has 8 letters."
[1] "December has 8 letters."

R is vectorized

Most R functions apply to vectors, so no need for for loops

bill_size <- penguins$bill_length_mm * penguins$bill_depth_mm
head(bill_size)
[1] 731.17 687.30 725.40     NA 708.31 809.58
print(paste0(month.name, " has ", str_length(month.name), " letters."))
 [1] "January has 7 letters."   "February has 8 letters." 
 [3] "March has 5 letters."     "April has 5 letters."    
 [5] "May has 3 letters."       "June has 4 letters."     
 [7] "July has 4 letters."      "August has 6 letters."   
 [9] "September has 9 letters." "October has 7 letters."  
[11] "November has 8 letters."  "December has 8 letters."

Lagged and leading values

To compute based on previous or subsequent values, use dplyr::lag() and dplyr::lead()

data.frame(trial = 1:8, cond = rep(1:2, each = 4), resp = c(1, 3, 4, 6, 1, 4, 8, 10)) |> 
  mutate(behind = lag(trial),
         ahead = lead(trial),
         diff = resp - lag(resp))
  trial cond resp behind ahead diff
1     1    1    1     NA     2   NA
2     2    1    3      1     3    2
3     3    1    4      2     4    1
4     4    1    6      3     5    2
5     5    2    1      4     6   -5
6     6    2    4      5     7    3
7     7    2    8      6     8    4
8     8    2   10      7    NA    2

Lagged and leading values

To compute based on previous or subsequent values, use dplyr::lag() and dplyr::lead()

data.frame(trial = 1:8, cond = rep(1:2, each = 4), resp = c(1, 3, 4, 6, 1, 4, 8, 10)) |> 
  mutate(behind = lag(trial),
         ahead = lead(trial),
         diff = resp - lag(resp),
         diff = ifelse(cond != lag(cond), NA, diff))
  trial cond resp behind ahead diff
1     1    1    1     NA     2   NA
2     2    1    3      1     3    2
3     3    1    4      2     4    1
4     4    1    6      3     5    2
5     5    2    1      4     6   NA
6     6    2    4      5     7    3
7     7    2    8      6     8    4
8     8    2   10      7    NA    2

Numbering groups

Renumber row numbers based on groups

data.frame(trial = 1:8, cond = rep(1:2, each = 4)) |> 
  mutate(.by = cond,
         cond_trial = row_number())
  trial cond cond_trial
1     1    1          1
2     2    1          2
3     3    1          3
4     4    1          4
5     5    2          1
6     6    2          2
7     7    2          3
8     8    2          4

Mapping over multiple arguments

Mapping with {purrr}

Mapping functions

purrr::map() applies functions repeatedly across data

map(select(penguins, ends_with("_mm")), 
    ~ mean(.x, na.rm = TRUE))
$bill_length_mm
[1] 43.92193

$bill_depth_mm
[1] 17.15117

$flipper_length_mm
[1] 200.9152

What kind of data type does map() return?

Mapping function

Need different data types as output?

map_dbl(select(penguins, ends_with("_mm")), 
        ~ mean(.x, na.rm = TRUE))
   bill_length_mm     bill_depth_mm flipper_length_mm 
         43.92193          17.15117         200.91520 
map_chr(select(penguins, ends_with("_mm")), 
        ~ mean(.x, na.rm = TRUE))
   bill_length_mm     bill_depth_mm flipper_length_mm 
      "43.921930"       "17.151170"      "200.915205"

Mapping function

Use split() like dplyr::group_by()

penguins |>
  split(penguins$species) |> # split() inputs vector (not column)
  map(~ lm(bill_length_mm ~ bill_depth_mm, data = .x))
$Adelie

Call:
lm(formula = bill_length_mm ~ bill_depth_mm, data = .x)

Coefficients:
  (Intercept)  bill_depth_mm  
       23.068          0.857  


$Chinstrap

Call:
lm(formula = bill_length_mm ~ bill_depth_mm, data = .x)

Coefficients:
  (Intercept)  bill_depth_mm  
       13.428          1.922  


$Gentoo

Call:
lm(formula = bill_length_mm ~ bill_depth_mm, data = .x)

Coefficients:
  (Intercept)  bill_depth_mm  
       17.230          2.021

Mapping function

Create multiple plots

penguins |>
  split(penguins$species) |>
  map(~ ggplot(.x, aes(bill_length_mm, bill_depth_mm)) + geom_point())
$Adelie


$Chinstrap


$Gentoo

Importing multiple data files

First, we’ll create multiple data files

species_list <- penguins |> 
  split(penguins$species)
map2(species_list, names(species_list), 
     ~ write_csv(.x, here(paste0("data/", tolower(.y), "_penguin_data.csv"))))
$Adelie
# A tibble: 152 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 142 more rows
# ℹ 2 more variables: sex <fct>, year <int>

$Chinstrap
# A tibble: 68 × 8
   species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>     <fct>           <dbl>         <dbl>             <int>       <int>
 1 Chinstrap Dream            46.5          17.9               192        3500
 2 Chinstrap Dream            50            19.5               196        3900
 3 Chinstrap Dream            51.3          19.2               193        3650
 4 Chinstrap Dream            45.4          18.7               188        3525
 5 Chinstrap Dream            52.7          19.8               197        3725
 6 Chinstrap Dream            45.2          17.8               198        3950
 7 Chinstrap Dream            46.1          18.2               178        3250
 8 Chinstrap Dream            51.3          18.2               197        3750
 9 Chinstrap Dream            46            18.9               195        4150
10 Chinstrap Dream            51.3          19.9               198        3700
# ℹ 58 more rows
# ℹ 2 more variables: sex <fct>, year <int>

$Gentoo
# A tibble: 124 × 8
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>           <dbl>         <dbl>             <int>       <int>
 1 Gentoo  Biscoe           46.1          13.2               211        4500
 2 Gentoo  Biscoe           50            16.3               230        5700
 3 Gentoo  Biscoe           48.7          14.1               210        4450
 4 Gentoo  Biscoe           50            15.2               218        5700
 5 Gentoo  Biscoe           47.6          14.5               215        5400
 6 Gentoo  Biscoe           46.5          13.5               210        4550
 7 Gentoo  Biscoe           45.4          14.6               211        4800
 8 Gentoo  Biscoe           46.7          15.3               219        5200
 9 Gentoo  Biscoe           43.3          13.4               209        4400
10 Gentoo  Biscoe           46.8          15.4               215        5150
# ℹ 114 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Importing multiple data files

Get file names and paths with dir()

(penguin_files <- dir(path = "../data", 
                      pattern = "penguin_data.csv", 
                      full.names = TRUE))
[1] "../data/adelie_penguin_data.csv"    "../data/chinstrap_penguin_data.csv"
[3] "../data/gentoo_penguin_data.csv"

Importing multiple data files

Map readr::read_csv() to each element of penguin_files

(penguin_data1 <- map(penguin_files, read_csv))
[[1]]
# A tibble: 152 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 142 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

[[2]]
# A tibble: 68 × 8
   species   island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>     <chr>           <dbl>         <dbl>             <dbl>       <dbl>
 1 Chinstrap Dream            46.5          17.9               192        3500
 2 Chinstrap Dream            50            19.5               196        3900
 3 Chinstrap Dream            51.3          19.2               193        3650
 4 Chinstrap Dream            45.4          18.7               188        3525
 5 Chinstrap Dream            52.7          19.8               197        3725
 6 Chinstrap Dream            45.2          17.8               198        3950
 7 Chinstrap Dream            46.1          18.2               178        3250
 8 Chinstrap Dream            51.3          18.2               197        3750
 9 Chinstrap Dream            46            18.9               195        4150
10 Chinstrap Dream            51.3          19.9               198        3700
# ℹ 58 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

[[3]]
# A tibble: 124 × 8
   species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>           <dbl>         <dbl>             <dbl>       <dbl>
 1 Gentoo  Biscoe           46.1          13.2               211        4500
 2 Gentoo  Biscoe           50            16.3               230        5700
 3 Gentoo  Biscoe           48.7          14.1               210        4450
 4 Gentoo  Biscoe           50            15.2               218        5700
 5 Gentoo  Biscoe           47.6          14.5               215        5400
 6 Gentoo  Biscoe           46.5          13.5               210        4550
 7 Gentoo  Biscoe           45.4          14.6               211        4800
 8 Gentoo  Biscoe           46.7          15.3               219        5200
 9 Gentoo  Biscoe           43.3          13.4               209        4400
10 Gentoo  Biscoe           46.8          15.4               215        5150
# ℹ 114 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

Importing multiple data files

Use purrr::list_rbind() to return output as data frame

(penguin_data2 <- map(penguin_files, read_csv) |> 
   list_rbind())
# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

Solving the problems

  • Run the same model on multiple data sets
  • Create the same plot for multiple data sets
  • Read in data files from multiple subjects

Let’s code!

Iteration