Iteration

Same Seating Chart!

Today we will…

  • New Material
    • Performing Repeated Tasks + Vectorization
    • Functional Programming: the map() family
  • PA 8: The Twelve Days of Christmas Starter Functions

It’s Week 8…

How are you taking care of yourself?

Performing Repeated Tasks

Repetition

Type out the task over and over.

The image shows a chalkboard scene styled after The Simpsons with Bart Simpson standing beside a chalkboard, where he has written repeatedly in large, white text: 'I WILL NOT USE LOOPS IN R.' This is a humorous reference to programming practices in R, suggesting a preference for avoiding loops in favor of other approaches. Bart is holding a piece of chalk and looking over his shoulder at the viewer with a mischievous expression.

Do not do this.

Iteration

Repeatedly execute the same operation over and over.

  • Loops (e.g., for() and while()) allow us to iterate.
for(i in 1:6){
  print(i^2)
}
[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36

The image shows a chalkboard with Bart Simpson standing beside it, holding a piece of chalk. The chalkboard displays R code that humorously contradicts the message by using a loop to discourage using loops in R. The code reads: `FOR (I IN 1:100) { PRINT(I WILL NOT USE LOOPS IN R) }`. This satirical twist emphasizes a preference in R programming for avoiding traditional loops in favor of functional programming methods. Bart appears to be looking at the code on the chalkboard.

Vectorization

Many operations in R are vectorized.

  • These functions operate on vectors of values rather than a single value.
  • We can iterate without writing a loop.
x <- seq(from = -4, to = 6)


abs(x)
 [1] 4 3 2 1 0 1 2 3 4 5 6
for(i in 1:length(x)){
  x[i] <- abs(x[i])
}

x
 [1] 4 3 2 1 0 1 2 3 4 5 6

Vectorization

Not every function is vectorized.

  • For example, if() statements cannot operate on vectors.
pos_neg_zero <- function(x){
  if(x > 0){
    return("Greater than 0!")
  } 
  else if (x < 0){
    return("Less than 0!")
  } 
  else {
    return("Equal to 0!")
  }
}
pos_neg_zero(x)
Error in if (x > 0) {: the condition has length > 1

This means that if(x > 0) can only be checked for something of length 1 (a single number, not a vector).

What do we do instead?

Option 1 – Looping

result <- rep(NA, length(x))

for(i in 1:length(x)){
  result[i] <- pos_neg_zero(x[i])
}

result
 [1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
 [5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
 [9] "Greater than 0!" "Greater than 0!" "Greater than 0!"

What do we do instead?

Option 2 – Write a Vectorized Function

if_else() and case_when() are the vectorized versions of if() and else if()

pos_neg_zero <- function(x){
  state <- case_when(x > 0 ~ "Greater than 0!", 
                     x < 0 ~ "Less than 0!", 
                     .default = "Equal to 0!")
  return(state)
}
pos_neg_zero(x)
 [1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
 [5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"
 [9] "Greater than 0!" "Greater than 0!" "Greater than 0!"

Some functions cannot be vectorized!

Applying class() to a single variable in a data frame returns the data type of that column:

class(penguins[[1]])
[1] "factor"


Trying to apply class() to every variable in a data frame returns the data type of the data frame:

class(penguins)
[1] "tbl_df"     "tbl"        "data.frame"

What can we do instead?

Write a for() loop…

data_type <- rep(NA, length = ncol(penguins))

for(i in seq_along(penguins)){
  data_type[i] <- class(penguins[[i]])
}
Code 
tibble(column = names(penguins), 
       type = data_type) |> 
  pivot_wider(names_from = column, 
              values_from = type) |>  
  knitr::kable() |>
  kableExtra::kable_styling(font_size = 30)
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
factor factor numeric numeric integer integer factor integer

…but loops are computationally intensive

What can we do instead?

What about across()?

  • Easily perform the same operation on multiple columns.
penguins |> 
  summarise(across(.cols = everything(), 
                   .fns = class)
            ) |>  
  knitr::kable() |>
  kableExtra::kable_styling(font_size = 30)
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
factor factor numeric numeric integer integer factor integer

But across() uses a for() loop!



for (j in seq_fns) {
  fn <- fns[[j]]
  out[[k]] <- fn(col, ...)
  k <- k + 1L

Functional Programming

To understand computations in R, two slogans are helpful:

  • Everything that exists is an object.

  • Everything that happens is a function call.

John Chambers (creator of the pre-cursor to R)

Functional Programming – Big Picture

  • We will use functions as the arguments to other functions!


Note

There are a slew of apply() functions you will likely come across.

We will instead focus on the purrr package and the map() family of functions.

purrr

The purrr package breaks common list manipulations into small, independent pieces.


This strategy involves two steps:

The image is a hexagonal logo for the R package purrr. It features a simple, hand-drawn outline of a content, sleeping cat with closed eyes, representing the package's name, which sounds like a cat's purr. The logo's design conveys a sense of calm and ease, suggesting that the purrr package makes functional programming in R more intuitive and manageable.

  1. Break a complex problem into smaller sub-problems.
  1. Solve each sub-problem for a single element of a list.
    • Once done, purrr will generalize the solution to every element in the list.

map()

The map() function iterates through each item in a list and applies a function, then returns the new list.

The image illustrates the concept of a mapping function in programming, specifically in the context of applying a function to each element of a list or vector. On the left, there is a vertical stack of colored blocks in varying shades of orange, representing individual elements. The text `map( , f)` suggests the application of a function f to each element in the sequence. An arrow points to the right, leading to a new vertical stack where each block is enclosed in a box labeled `f()`, indicating that the function `f` has been applied to each element individually. This visual representation demonstrates how mapping functions work by transforming each element in a sequence.

Using the |>

The first argument in map() is the list, so if we pipe into it, we only specify the function we want to iterate!

map() + Dataframes

A data frame is just a list of columns – map() will apply a function to every column.

penguins |> 
  select(where(is.numeric)) |>
  map(.f = ~ mean(.x, na.rm = TRUE))
$bill_length_mm
[1] 43.92193

$bill_depth_mm
[1] 17.15117

$flipper_length_mm
[1] 200.9152

$body_mass_g
[1] 4201.754

$year
[1] 2008.029

Use a lambda function (with ~ and .x), just like in across()!

The map() Family

The map_xxx() variants allow you to specify the type of output you want.

  • map() creates a list.
  • map_chr() creates a character vector.
  • map_lgl() creates an logical vector.
  • map_int() creates a integer vector.
  • map_dbl() creates a numeric vector.

All take in a list (.x) and a function (.f) as arguments.

map() + penguins

Calculate the mean of each column.

penguins |> 
  select(bill_length_mm:body_mass_g) |> 
  map_dbl(.f = ~ mean(.x, na.rm = TRUE))
   bill_length_mm     bill_depth_mm flipper_length_mm       body_mass_g 
         43.92193          17.15117         200.91520        4201.75439


Output is a vector of doubles.

Calculate the number of NAs in each column.

penguins |> 
  map_int(.f = ~ sum(is.na(.x)))
          species            island    bill_length_mm     bill_depth_mm 
                0                 0                 2                 2 
flipper_length_mm       body_mass_g               sex              year 
                2                 2                11                 0

Output is a vector of integers.

Calculate the number of NAs in each column.

penguins |> 
  map_lgl(.f = ~ sum(is.na(.x)))
Error in `map_lgl()`:
ℹ In index: 3.
ℹ With name: bill_length_mm.
Caused by error:
! Can't coerce from an integer to a logical.

R returns an error if the output is of the wrong type!

Comparing Speed

Using functional programming can be much faster than using for loops.

loop_func <- function(df){
  typ <- rep(NA, ncol(df))
  for(i in 1:ncol(df)){
    typ[i] <- class(df[[i]])
  }
  return(typ)
}
map_func <- function(df){
  typ <- map_chr(df, class)
  return(typ)
}
df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 7))
loop_func(df)
[1] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
map_func(df)
       V1        V2        V3        V4        V5        V6        V7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
df <- as_tibble(
  matrix(1,
         nrow = 5,
         ncol = 100000)
  )

microbenchmark::microbenchmark(loop_func(df),
                               map_func(df),
                               times = 20)

The image shows a benchmarking result table comparing the execution times of two functions, loop_func(df) and map_func(df), in milliseconds. The table has columns labeled expr, min, lq, mean, median, uq, max, and neval. For loop_func(df), the mean time is approximately 7182.34 milliseconds, with a minimum of 6996.32 milliseconds and a maximum of 8215.57 milliseconds. For map_func(df), the mean time is about 61.49 milliseconds, with a minimum of 54.02 milliseconds and a maximum of 135.86 milliseconds. The results show that map_func(df) performs significantly faster than loop_func(df). Both functions were evaluated 20 times, as indicated in the neval column.

map_if()

The map_if() function allows us to conditionally apply a function to each item in a list.

penguins |> 
  mutate(across(.cols = where(is.numeric), 
                .fns = scale))
# A tibble: 8 × 5
  species island    bill_length_mm[,1] bill_depth_mm[,1] sex   
  <fct>   <fct>                  <dbl>             <dbl> <fct> 
1 Adelie  Torgersen             -0.883             0.784 male  
2 Adelie  Torgersen             -0.810             0.126 female
3 Adelie  Torgersen             -0.663             0.430 female
4 Adelie  Torgersen             NA                NA     <NA>  
5 Adelie  Torgersen             -1.32              1.09  female
6 Adelie  Torgersen             -0.847             1.75  male  
7 Adelie  Torgersen             -0.920             0.329 female
8 Adelie  Torgersen             -0.865             1.24  male  
penguins |> 
  map_if(is.numeric, scale)
$species
  [1] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
  [8] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [15] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [22] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [29] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [36] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [43] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [50] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [57] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [64] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [71] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [78] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [85] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [92] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [99] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[106] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[113] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[120] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[127] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[134] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[141] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[148] Adelie    Adelie    Adelie    Adelie    Adelie    Gentoo    Gentoo   
[155] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[162] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[169] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[176] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[183] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[190] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[197] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[204] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[211] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[218] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[225] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[232] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[239] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[246] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[253] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[260] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[267] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[274] Gentoo    Gentoo    Gentoo    Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo

$island
  [1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
  [8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe   
 [22] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [29] Biscoe    Biscoe    Dream     Dream     Dream     Dream     Dream    
 [36] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [43] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [50] Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [57] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [64] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen
 [71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [85] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [92] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [99] Dream     Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[106] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[113] Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen Torgersen
[120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream    
[134] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[141] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[148] Dream     Dream     Dream     Dream     Dream     Biscoe    Biscoe   
[155] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[162] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[169] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[176] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[183] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[190] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[197] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[204] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[211] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[218] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[225] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[232] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[239] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[246] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[253] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[260] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[267] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[274] Biscoe    Biscoe    Biscoe    Dream     Dream     Dream     Dream    
[281] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[288] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[295] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[302] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[309] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[316] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[323] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[330] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[337] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[344] Dream    
Levels: Biscoe Dream Torgersen

$bill_length_mm
              [,1]
  [1,] -0.88320467
  [2,] -0.80993901
  [3,] -0.66340769
  [4,]          NA
  [5,] -1.32279862
  [6,] -0.84657184
  [7,] -0.91983750
  [8,] -0.86488825
  [9,] -1.79902541
 [10,] -0.35202864
 [11,] -1.12131806
 [12,] -1.12131806
 [13,] -0.51687637
 [14,] -0.97478674
 [15,] -1.70744334
 [16,] -1.34111504
 [17,] -0.95647033
 [18,] -0.26044656
 [19,] -1.74407616
 [20,]  0.38062795
 [21,] -1.12131806
 [22,] -1.13963448
 [23,] -1.46932994
 [24,] -1.04805240
 [25,] -0.93815391
 [26,] -1.57922843
 [27,] -0.60845845
 [28,] -0.62677486
 [29,] -1.10300165
 [30,] -0.62677486
 [31,] -0.80993901
 [32,] -1.23121655
 [33,] -0.80993901
 [34,] -0.55350920
 [35,] -1.37774787
 [36,] -0.86488825
 [37,] -0.93815391
 [38,] -0.31539581
 [39,] -1.15795089
 [40,] -0.75498976
 [41,] -1.35943145
 [42,] -0.57182562
 [43,] -1.45101353
 [44,]  0.03261607
 [45,] -1.26784938
 [46,] -0.79162259
 [47,] -0.51687637
 [48,] -1.17626731
 [49,] -1.45101353
 [50,] -0.29707939
 [51,] -0.79162259
 [52,] -0.70004052
 [53,] -1.63417768
 [54,] -0.35202864
 [55,] -1.72575975
 [56,] -0.46192713
 [57,] -0.90152108
 [58,] -0.60845845
 [59,] -1.35943145
 [60,] -1.15795089
 [61,] -1.50596277
 [62,] -0.48024354
 [63,] -1.15795089
 [64,] -0.51687637
 [65,] -1.37774787
 [66,] -0.42529430
 [67,] -1.54259560
 [68,] -0.51687637
 [69,] -1.46932994
 [70,] -0.38866147
 [71,] -1.90892390
 [72,] -0.77330618
 [73,] -0.79162259
 [74,]  0.34399512
 [75,] -1.54259560
 [76,] -0.20549732
 [77,] -0.55350920
 [78,] -1.23121655
 [79,] -1.41438070
 [80,] -0.33371222
 [81,] -1.70744334
 [82,] -0.18718091
 [83,] -1.32279862
 [84,] -1.61586126
 [85,] -1.21290014
 [86,] -0.48024354
 [87,] -1.39606428
 [88,] -1.28616579
 [89,] -1.02973599
 [90,] -0.91983750
 [91,] -1.50596277
 [92,] -0.51687637
 [93,] -1.81734182
 [94,] -0.79162259
 [95,] -1.41438070
 [96,] -0.57182562
 [97,] -1.06636882
 [98,] -0.66340769
 [99,] -1.98218956
[100,] -0.13223166
[101,] -1.63417768
[102,] -0.53519279
[103,] -1.13963448
[104,] -1.12131806
[105,] -1.10300165
[106,] -0.77330618
[107,] -0.97478674
[108,] -1.04805240
[109,] -1.06636882
[110,] -0.13223166
[111,] -1.06636882
[112,]  0.30736229
[113,] -0.77330618
[114,] -0.31539581
[115,] -0.79162259
[116,] -0.22381374
[117,] -0.97478674
[118,] -1.21290014
[119,] -1.50596277
[120,] -0.51687637
[121,] -1.41438070
[122,] -1.13963448
[123,] -0.68172411
[124,] -0.46192713
[125,] -1.59754485
[126,] -0.60845845
[127,] -0.93815391
[128,] -0.44361071
[129,] -0.90152108
[130,]  0.03261607
[131,] -0.99310316
[132,] -0.15054808
[133,] -1.30448221
[134,] -1.17626731
[135,] -1.06636882
[136,] -0.51687637
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[144,] -0.59014203
[145,] -1.21290014
[146,] -0.90152108
[147,] -0.86488825
[148,] -1.34111504
[149,] -1.45101353
[150,] -1.12131806
[151,] -1.45101353
[152,] -0.44361071
[153,]  0.39894437
[154,]  1.11328455
[155,]  0.87517115
[156,]  1.11328455
[157,]  0.67369059
[158,]  0.47221003
[159,]  0.27072946
[160,]  0.50884286
[161,] -0.11391525
[162,]  0.52715927
[163,] -0.55350920
[164,]  0.93012040
[165,]  0.28904588
[166,]  0.82022191
[167,]  0.34399512
[168,]  0.98506964
[169,] -0.35202864
[170,]  0.96675323
[171,]  0.41726078
[172,]  0.87517115
[173,]  1.14991738
[174,]  0.21578022
[175,]  0.47221003
[176,]  0.43557720
[177,] -0.18718091
[178,]  0.39894437
[179,]  0.10588173
[180,]  0.71032342
[181,]  0.78358908
[182,]  1.11328455
[183,]  0.61874135
[184,] -0.20549732
[185,]  0.21578022
[186,]  2.87166037
[187,]  0.94843681
[188,]  0.82022191
[189,] -0.24213015
[190,]  0.08756532
[191,]  0.01429966
[192,]  0.87517115
[193,] -0.22381374
[194,]  1.04001889
[195,]  0.25241305
[196,]  1.04001889
[197,]  1.20486662
[198,] -0.05896600
[199,]  0.28904588
[200,]  1.20486662
[201,]  0.17914739
[202,]  0.23409663
[203,]  0.49052644
[204,]  0.83853832
[205,]  0.21578022
[206,]  1.13160096
[207,]  0.47221003
[208,]  0.19746381
[209,] -0.02233317
[210,]  0.28904588
[211,] -0.13223166
[212,]  1.18655021
[213,]  0.25241305
[214,]  0.41726078
[215,]  0.32567871
[216,]  1.90089038
[217,]  0.34399512
[218,]  1.07665172
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[220,]  1.02170247
[221,] -0.07728242
[222,]  1.24149945
[223,]  0.69200701
[224,]  0.45389361
[225,]  0.78358908
[226,]  0.47221003
[227,]  0.45389361
[228,]  0.85685474
[229,]  0.65537418
[230,]  1.31476511
[231,]  0.23409663
[232,]  0.23409663
[233,]  0.94843681
[234,]  1.57119492
[235,]  0.63705776
[236,]  1.11328455
[237,]  0.17914739
[238,]  1.25981586
[239,] -0.09559883
[240,]  1.35139794
[241,]  0.65537418
[242,]  1.49792926
[243,]  0.65537418
[244,]  1.51624567
[245,]  0.28904588
[246,]  1.02170247
[247,]  0.10588173
[248,]  1.25981586
[249,]  1.00338606
[250,]  0.54547569
[251,]  0.82022191
[252,]  1.31476511
[253,]  0.83853832
[254,]  2.19395302
[255,]  0.60042493
[256,]  0.94843681
[257,]  0.61874135
[258,]  0.52715927
[259,] -0.40697788
[260,]  1.73604265
[261,] -0.11391525
[262,]  0.76527266
[263,]  1.20486662
[264,]  1.07665172
[265,] -0.07728242
[266,]  1.38803077
[267,]  0.41726078
[268,]  2.04742170
[269,]  0.10588173
[270,]  0.89348757
[271,]  0.60042493
[272,]          NA
[273,]  0.52715927
[274,]  1.18655021
[275,]  0.23409663
[276,]  1.09496813
[277,]  0.47221003
[278,]  1.11328455
[279,]  1.35139794
[280,]  0.27072946
[281,]  1.60782775
[282,]  0.23409663
[283,]  0.39894437
[284,]  1.35139794
[285,]  0.38062795
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[288,]  1.42466360
[289,]  0.56379210
[290,]  1.47961284
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[292,]  1.20486662
[293,]  1.16823379
[294,]  2.57859773
[295,]  0.45389361
[296,]  0.96675323
[297,] -0.27876298
[298,]  0.83853832
[299,] -0.13223166
[300,]  1.22318303
[301,]  0.50884286
[302,]  1.47961284
[303,]  1.20486662
[304,]  1.02170247
[305,]  0.45389361
[306,]  1.62614416
[307,] -0.55350920
[308,]  1.88257397
[309,] -0.26044656
[310,]  1.29644869
[311,]  1.05833530
[312,]  0.65537418
[313,]  0.67369059
[314,]  1.47961284
[315,]  0.54547569
[316,]  1.75435906
[317,]  0.93012040
[318,]  0.41726078
[319,]  1.27813228
[320,]  0.28904588
[321,]  1.27813228
[322,]  1.25981586
[323,]  1.13160096
[324,]  0.93012040
[325,]  1.38803077
[326,]  1.07665172
[327,]  0.76527266
[328,]  1.36971435
[329,]  0.32567871
[330,]  1.24149945
[331,] -0.26044656
[332,]  1.51624567
[333,]  0.23409663
[334,]  0.98506964
[335,]  1.14991738
[336,]  0.30736229
[337,]  1.46129643
[338,]  0.52715927
[339,]  0.32567871
[340,]  2.17563660
[341,] -0.07728242
[342,]  1.04001889
[343,]  1.25981586
[344,]  1.14991738
attr(,"scaled:center")
[1] 43.92193
attr(,"scaled:scale")
[1] 5.459584

$bill_depth_mm
              [,1]
  [1,]  0.78430007
  [2,]  0.12600328
  [3,]  0.42983257
  [4,]          NA
  [5,]  1.08812936
  [6,]  1.74642615
  [7,]  0.32855614
  [8,]  1.24004400
  [9,]  0.48047078
 [10,]  1.54387329
 [11,] -0.02591137
 [12,]  0.07536506
 [13,]  0.22727971
 [14,]  2.05025544
 [15,]  1.99961722
 [16,]  0.32855614
 [17,]  0.93621471
 [18,]  1.79706436
 [19,]  0.63238542
 [20,]  2.20217008
 [21,]  0.58174721
 [22,]  0.78430007
 [23,]  1.03749114
 [24,]  0.48047078
 [25,]  0.02472685
 [26,]  0.88557650
 [27,]  0.73366185
 [28,]  0.37919435
 [29,]  0.73366185
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 [31,] -0.22846423
 [32,]  0.48047078
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 [35,] -0.07654958
 [36,]  1.99961722
 [37,]  1.44259686
 [38,]  0.68302364
 [39,]  1.08812936
 [40,]  0.98685293
 [41,]  0.42983257
 [42,]  0.63238542
 [43,]  0.68302364
 [44,]  1.29068222
 [45,] -0.12718780
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 [47,]  0.93621471
 [48,]  0.88557650
 [49,]  0.37919435
 [50,]  2.05025544
 [51,]  0.27791792
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 [54,]  1.18940579
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 [58,]  0.83493828
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 [77,] -0.17782601
 [78,]  1.13876757
 [79,] -0.53229351
 [80,]  0.98685293
 [81,]  0.02472685
 [82,]  0.22727971
 [83,]  0.83493828
 [84,]  1.13876757
 [85,]  0.32855614
 [86,]  1.59451151
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[100,]  0.68302364
[101,]  0.37919435
[102,]  1.44259686
[103,] -0.58293173
[104,]  1.44259686
[105,]  0.73366185
[106,]  0.88557650
[107,]  0.02472685
[108,]  1.44259686
[109,] -0.07654958
[110,]  0.93621471
[111,] -0.32974066
[112,]  1.59451151
[113,]  0.27791792
[114,]  1.18940579
[115,]  1.79706436
[116,]  0.58174721
[117,] -0.07654958
[118,]  1.69578793
[119,] -0.07654958
[120,]  0.73366185
[121,]  0.02472685
[122,]  1.34132043
[123,] -0.07654958
[124,]  0.68302364
[125,] -0.63356994
[126,]  0.93621471
[127,]  0.22727971
[128,]  0.58174721
[129,] -0.02591137
[130,]  0.42983257
[131,]  0.37919435
[132,]  1.03749114
[133,]  0.68302364
[134,]  0.68302364
[135,]  0.22727971
[136,]  0.17664149
[137,]  0.17664149
[138,]  1.49323508
[139,] -0.32974066
[140,]  0.37919435
[141,] -0.02591137
[142,]  0.02472685
[143,] -0.83612280
[144,] -0.07654958
[145,] -0.17782601
[146,]  0.78430007
[147,]  0.73366185
[148,]  0.63238542
[149,]  0.32855614
[150,]  0.48047078
[151,] -0.02591137
[152,]  0.68302364
[153,] -2.00080174
[154,] -0.43101709
[155,] -1.54505781
[156,] -0.98803745
[157,] -1.34250495
[158,] -1.84888710
[159,] -1.29186674
[160,] -0.93739923
[161,] -1.89952531
[162,] -0.88676102
[163,] -1.74761067
[164,] -0.53229351
[165,] -1.74761067
[166,] -1.29186674
[167,] -1.29186674
[168,] -0.73484637
[169,] -1.84888710
[170,] -0.98803745
[171,] -1.34250495
[172,] -1.03867566
[173,] -1.44378138
[174,] -1.34250495
[175,] -1.34250495
[176,] -0.68420816
[177,] -2.05143996
[178,] -1.03867566
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[180,] -1.08931388
[181,] -1.44378138
[182,] -0.93739923
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[184,] -1.49441960
[185,] -1.34250495
[186,] -0.07654958
[187,] -1.19059031
[188,] -0.43101709
[189,] -1.74761067
[190,]  0.07536506
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[192,] -0.73484637
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[194,] -0.58293173
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[196,] -1.08931388
[197,] -0.63356994
[198,] -1.64633424
[199,] -1.64633424
[200,] -0.63356994
[201,] -1.95016353
[202,] -0.68420816
[203,] -1.49441960
[204,] -1.54505781
[205,] -1.39314317
[206,] -1.08931388
[207,] -1.39314317
[208,] -0.88676102
[209,] -1.64633424
[210,] -1.08931388
[211,] -1.34250495
[212,] -0.93739923
[213,] -1.69697245
[214,] -1.13995209
[215,] -1.64633424
[216,] -0.73484637
[217,] -1.49441960
[218,] -0.17782601
[219,] -1.39314317
[220,] -0.48165530
[221,] -1.49441960
[222,] -1.08931388
[223,] -1.08931388
[224,] -0.78548459
[225,] -0.78548459
[226,] -1.19059031
[227,] -1.08931388
[228,] -0.58293173
[229,] -1.49441960
[230,] -0.43101709
[231,] -1.69697245
[232,] -0.38037887
[233,] -1.34250495
[234,] -0.78548459
[235,] -1.29186674
[236,] -0.63356994
[237,] -1.69697245
[238,]  0.07536506
[239,] -1.39314317
[240,] -1.49441960
[241,] -1.59569603
[242,] -0.07654958
[243,] -1.08931388
[244,] -0.02591137
[245,] -1.34250495
[246,] -0.53229351
[247,] -1.24122852
[248,] -0.73484637
[249,] -0.68420816
[250,] -1.29186674
[251,] -1.39314317
[252,] -0.32974066
[253,] -1.08931388
[254,] -0.07654958
[255,] -0.83612280
[256,] -1.08931388
[257,] -1.69697245
[258,] -0.53229351
[259,] -1.24122852
[260,] -0.68420816
[261,] -1.59569603
[262,] -1.03867566
[263,] -0.98803745
[264,] -0.63356994
[265,] -0.98803745
[266,] -0.43101709
[267,] -1.54505781
[268,] -0.58293173
[269,] -0.73484637
[270,] -0.48165530
[271,] -1.74761067
[272,]          NA
[273,] -1.44378138
[274,] -0.73484637
[275,] -1.19059031
[276,] -0.53229351
[277,]  0.37919435
[278,]  1.18940579
[279,]  1.03749114
[280,]  0.78430007
[281,]  1.34132043
[282,]  0.32855614
[283,]  0.53110900
[284,]  0.53110900
[285,]  0.88557650
[286,]  1.39195865
[287,]  0.32855614
[288,]  1.59451151
[289,]  0.07536506
[290,]  0.48047078
[291,] -0.02591137
[292,]  1.24004400
[293,]  1.44259686
[294,]  0.32855614
[295,]  0.73366185
[296,]  0.53110900
[297,]  0.07536506
[298,]  0.17664149
[299,] -0.27910244
[300,]  1.13876757
[301,]  0.37919435
[302,]  0.93621471
[303,]  0.63238542
[304,]  0.93621471
[305,]  0.32855614
[306,]  1.44259686
[307,] -0.27910244
[308,]  1.84770258
[309,] -0.22846423
[310,]  0.83493828
[311,]  0.73366185
[312,] -0.17782601
[313,]  0.58174721
[314,]  1.79706436
[315,] -0.27910244
[316,]  1.39195865
[317,]  1.18940579
[318,]  0.17664149
[319,]  0.98685293
[320,] -0.07654958
[321,]  0.37919435
[322,]  0.68302364
[323,]  0.37919435
[324,]  1.24004400
[325,]  0.78430007
[326,]  0.07536506
[327,] -0.38037887
[328,]  0.93621471
[329,]  0.07536506
[330,]  1.29068222
[331,]  0.07536506
[332,]  0.83493828
[333,] -0.27910244
[334,]  1.39195865
[335,]  0.83493828
[336,]  1.13876757
[337,]  1.18940579
[338,] -0.32974066
[339,] -0.07654958
[340,]  1.34132043
[341,]  0.48047078
[342,]  0.53110900
[343,]  0.93621471
[344,]  0.78430007
attr(,"scaled:center")
[1] 17.15117
attr(,"scaled:scale")
[1] 1.974793

$flipper_length_mm
               [,1]
  [1,] -1.416271525
  [2,] -1.060696087
  [3,] -0.420660299
  [4,]           NA
  [5,] -0.562890474
  [6,] -0.776235737
  [7,] -1.416271525
  [8,] -0.420660299
  [9,] -0.562890474
 [10,] -0.776235737
 [11,] -1.060696087
 [12,] -1.487386613
 [13,] -1.345156438
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 [15,] -0.207315036
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 [18,] -0.278430124
 [19,] -1.202926262
 [20,] -0.491775386
 [21,] -1.914077138
 [22,] -1.487386613
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 [24,] -1.131811175
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 [30,] -1.487386613
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 [42,] -0.420660299
 [43,] -1.060696087
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 [46,] -0.776235737
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 [48,] -1.558501700
 [49,] -0.776235737
 [50,] -0.705120649
 [51,] -1.060696087
 [52,] -0.918465912
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 [54,] -0.065084861
 [55,] -0.989581000
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 [63,] -1.131811175
 [64,] -0.634005562
 [65,] -1.202926262
 [66,] -0.634005562
 [67,] -0.420660299
 [68,] -0.918465912
 [69,] -0.776235737
 [70,] -0.207315036
 [71,] -0.776235737
 [72,] -0.776235737
 [73,] -0.349545211
 [74,] -0.278430124
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 [76,] -0.420660299
 [77,] -0.705120649
 [78,] -1.202926262
 [79,] -0.989581000
 [80,] -0.420660299
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 [82,] -0.349545211
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 [84,] -0.562890474
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 [89,] -0.847350824
 [90,] -0.776235737
 [91,]  0.077145314
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$body_mass_g
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$sex
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 [11] <NA>   <NA>   female male   male   female female male   female male  
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 [31] female male   female male   female male   male   female female male  
 [41] female male   female male   female male   male   <NA>   female male  
 [51] female male   female male   female male   female male   female male  
 [61] female male   female male   female male   female male   female male  
 [71] female male   female male   female male   female male   female male  
 [81] female male   female male   female male   male   female male   female
 [91] female male   female male   female male   female male   female male  
[101] female male   female male   female male   female male   female male  
[111] female male   female male   female male   female male   female male  
[121] female male   female male   female male   female male   female male  
[131] female male   female male   female male   female male   female male  
[141] female male   female male   female male   male   female female male  
[151] female male   female male   female male   male   female female male  
[161] female male   female male   female male   female male   female male  
[171] female male   male   female female male   female male   <NA>   male  
[181] female male   male   female female male   female male   female male  
[191] female male   female male   female male   male   female female male  
[201] female male   female male   female male   female male   female male  
[211] female male   female male   female male   female male   <NA>   male  
[221] female male   female male   male   female female male   female male  
[231] female male   female male   female male   female male   female male  
[241] female male   female male   female male   female male   male   female
[251] female male   female male   female male   <NA>   male   female male  
[261] female male   female male   female male   female male   <NA>   male  
[271] female <NA>   female male   female male   female male   male   female
[281] male   female female male   female male   female male   female male  
[291] female male   male   female female male   female male   female male  
[301] female male   female male   female male   female male   female male  
[311] male   female female male   female male   male   female male   female
[321] female male   female male   male   female female male   female male  
[331] female male   female male   male   female male   female female male  
[341] female male   male   female
Levels: female male

$year
              [,1]
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[344,]  1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559
penguins |> 
  map_if(is.numeric, scale) |> 
  bind_cols()
# A tibble: 8 × 5
  species island    bill_length_mm[,1] bill_depth_mm[,1] sex   
  <fct>   <fct>                  <dbl>             <dbl> <fct> 
1 Adelie  Torgersen             -0.883             0.784 male  
2 Adelie  Torgersen             -0.810             0.126 female
3 Adelie  Torgersen             -0.663             0.430 female
4 Adelie  Torgersen             NA                NA     <NA>  
5 Adelie  Torgersen             -1.32              1.09  female
6 Adelie  Torgersen             -0.847             1.75  male  
7 Adelie  Torgersen             -0.920             0.329 female
8 Adelie  Torgersen             -0.865             1.24  male

The pmap() Family

These functions take in a list of vectors and a function.

  • The function must accept a number of arguments equal to the length of the list,

The image illustrates the concept of a parallel mapping function, specifically pmap, which applies a function to multiple lists or vectors element-wise. On the left, there are three vertical stacks of colored blocks in different shades of orange, blue, and yellow, each representing a separate list or vector. The text `pmap( , f)` indicates that a function f will be applied to elements from each of these lists in parallel. An arrow points to the right, leading to a new layout where each row contains a function application `f( , , )` with one element from each list placed within the function. This visual demonstrates how pmap applies a function to corresponding elements from multiple inputs simultaneously.

The pmap() Family

The vectors need to have the same names as the arguments of the function you are applying.

fruit <- data.frame(string = c("apple", "banana", "cherry"),
                    pattern = c("p", "n", "h"),
                    replacement = c("P", "N", "H"))
fruit
  string pattern replacement
1  apple       p           P
2 banana       n           N
3 cherry       h           H


fruit |> 
  pmap_chr(str_replace_all)
[1] "aPPle"  "baNaNa" "cHerry"

Use functional programming!

The image shows a chalkboard with Bart Simpson standing beside it, holding a piece of chalk. The chalkboard contains R code that encourages the use of mapping functions instead of loops. The code reads: `S <- I WILL REPLACE LOOPS BY MAPPING FUNCTIONS`, `V <- REP(S, 100)`, and `PURRR::MAP_CHR(V, PRINT)`. This is a humorous take on functional programming in R, advocating for the use of functions from the purrr package as an alternative to traditional loops. Bart looks toward the chalkboard with a satisfied expression.

PA 8: The Twelve Days of Christmas

The image is an illustrated representation of the Christmas song 'The 12 Days of Christmas,' with each square depicting the gifts given on each day. From left to right and top to bottom, the gifts are a partridge in a pear tree, two turtle doves, three French hens, four calling birds, five golden rings, six geese a-laying, seven swans a-swimming, eight maids a-milking, nine ladies dancing, ten lords a-leaping, eleven pipers piping, and twelve drummers drumming. The title 'The 12 Days of Christmas' is displayed in the center in festive green and red lettering, surrounded by small seasonal decorations. Each illustration captures the traditional imagery associated with this beloved Christmas carol.

glue()

The glue package embeds R expressions in curly brackets that are then evaluated and inserted into the argument string.

The image is a hexagonal logo for the R package 'glue.' It features an illustration of a glue bottle with an orange label that reads 'glue' in white, playful font. The bottle has an orange nozzle and a gray cap, and the background of the hexagon is textured with a subtle pattern. This design conveys the package's purpose, which is to glue together strings in R, making it easier to combine text and data dynamically.

library(glue)

name <- "Dr. Theobold"
glue('My name is {name}.')
My name is Dr. Theobold.


This will be a handy function (and package) for putting our song together!

PA 8

You will write two functions, one helper function and one body function, to assist you in singing phrases from the 12 Days of Christmas.

Many of the functions have been started for you, but none of them are complete as is.

This activity will require knowledge of:

  • detecting patterns in strings
  • replacing patterns in strings
  • conditional operations (if, else if, else)
  • function syntax
  • function arguments



None of us have all these abilities. Each of us has some of these abilities.

Task Card

Every group should have a task card!

On the front:

  • Reminders about string functions from stringr
  • Reminders about regular expressions

On the back:

  • if(), else if(), and else() statements
  • if_else() and case_when() functions

Getting Started

The partner who has the most vowels in their name starts as the Coder (dictating their proposed solution to the Developer (typing and listening).

If you are tied, play rock-paper-scissors. :)

  • The Coder does not type.
    • The collaborative editing feature should allow you to track what is being typed.
  • The Developer only types what they are told to type.

Submission

Your full_phrase column is the answer to this week’s Practice Activity.

  • Each person will copy and paste your full_phrase column to show me the phrases your group made!

  • The person who last occupied the role of Developer will download and submit the PA-8-iteration.html file for the group.

    • Only one submission per group!