Iteration

Tuesday, 5/20

Today we will…

Project Proposal + Data
New Material¹
- Iteration aka Performing Repeated Tasks
- Vectorization
- Efficient Iteration: the map() family
Midterm Feedback
PA 8.1: The Twelve Days of Christmas

Project Proposal + Data

You must complete the objectives and write up the written components outlined under Section 1 on the Project Details page on Canvas.

Choose variables that you think would feasibly be related (you have a hypothesis)
You may want to check there isn’t a huge amount of missing data
Due on Canvas by 11:59pm on Friday, 5/23
“Group” Canvas assignment, so only one person needs to submit it in your group

Performing Repeated Tasks

Repetition

Type out the task over and over.

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

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

But loops tend to be slow!

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)
x

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

Loop:

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

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)
x

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

Loop:

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

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

Vectorized:

abs(x)

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

Vectorization

Not every function is vectorized.

E.g., a function using 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!")
  }
}

x <- seq(from = -4, to = 4)
pos_neg_zero(x)

Error in if (x > 0) {: the condition has length > 1

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

Vectorization

Not every function is vectorized.

E.g., a function using 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!")
  }
}

x <- seq(from = -4, to = 3)
pos_neg_zero(x)

Error in if (x > 0) {: the condition has length > 1

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!"

Vectorization

Not every function is vectorized.

Vectorized versions of if() statements?

if_else() and case_when()

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

x <- seq(from = -4, to = 3)
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!"

Some functions cannot be vectorized!

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

class(penguins[[1]])

[1] "factor"

class(penguins$species)

[1] "factor"

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

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 1:ncol(penguins)){
  data_type[i] <- class(penguins[[i]])
}

# format table nicely
data.frame(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()

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
factor	factor	numeric	numeric	integer	integer	factor	integer

Ugh. Internally, across() uses a for() loop!

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

What can we do instead?

…

Functional Programming with `purrr`

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

Brief Review: Lists

A list is a 1-dimensional, heterogeneous data structure.

There are no restrictions on what data type or structure it can contain – values, vectors, other lists, dataframes, etc.
Lists are indexed with [] or [[]].

[[1]]
[1]  TRUE FALSE  TRUE  TRUE

[[2]]
     [,1] [,2]
[1,] 6.70  4.4
[2,] 5.58  6.0

[[3]]
[1] "A"

my_list[1]

[[1]]
[1]  TRUE FALSE  TRUE  TRUE

my_list[[2]]

     [,1] [,2]
[1,] 6.70  4.4
[2,] 5.58  6.0

my_list[[2]][1,2]

[1] 4.4

Brief Review: Lists

A dataframe / tibble is a specially formatted list of columns!

small_penguins <- penguins[1:8,]
small_penguins[3]

# A tibble: 8 × 1
  bill_length_mm
           <dbl>
1           39.1
2           39.5
3           40.3
4           NA  
5           36.7
6           39.3
7           38.9
8           39.2

small_penguins[[3]]

[1] 39.1 39.5 40.3   NA 36.7 39.3 38.9 39.2

The purrr package works for lists, so it works for dataframes.

`map()`

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

Note: the first argument in map() is the list, so if we pipe into it, we only specify the function!

`map()` + Dataframes

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

penguins |> 
  select(bill_length_mm:body_mass_g) |>
  map(~ 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

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 or vector and a function as arguments.

Calculate the mean of each column.

penguins |> 
  select(bill_length_mm:body_mass_g) |> 
  map_dbl(~ 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(~ 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 if there are any NAs in each column.

penguins |> 
  map_lgl(~ sum(is.na(.x)) > 0)

          species            island    bill_length_mm     bill_depth_mm 
            FALSE             FALSE              TRUE              TRUE 
flipper_length_mm       body_mass_g               sex              year 
             TRUE              TRUE              TRUE             FALSE

Output is a vector of booleans.

Calculate the number of NAs in each column.

penguins |> 
  map_lgl(~ 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!

`map_if()`

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

across()
map_if()
map_if() to data (tibble)

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
[137,] -1.52427919
[138,] -0.68172411
[139,] -1.26784938
[140,] -0.77330618
[141,] -0.68172411
[142,] -0.60845845
[143,] -2.16535371
[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
[219,]  0.41726078
[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
[286,]  1.35139794
[287,]  0.49052644
[288,]  1.42466360
[289,]  0.56379210
[290,]  1.47961284
[291,]  0.36231154
[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
 [30,]  0.88557650
 [31,] -0.22846423
 [32,]  0.48047078
 [33,]  0.32855614
 [34,]  0.88557650
 [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
 [46,]  0.83493828
 [47,]  0.93621471
 [48,]  0.88557650
 [49,]  0.37919435
 [50,]  2.05025544
 [51,]  0.27791792
 [52,]  0.88557650
 [53,]  0.37919435
 [54,]  1.18940579
 [55,]  0.48047078
 [56,]  0.73366185
 [57,]  0.17664149
 [58,]  0.83493828
 [59,] -0.27910244
 [60,]  0.98685293
 [61,] -0.12718780
 [62,]  1.99961722
 [63,] -0.07654958
 [64,]  0.53110900
 [65,] -0.02591137
 [66,]  0.42983257
 [67,] -0.48165530
 [68,]  0.98685293
 [69,] -0.27910244
 [70,]  1.13876757
 [71,]  0.93621471
 [72,]  0.63238542
 [73,]  0.02472685
 [74,]  0.88557650
 [75,]  0.17664149
 [76,]  0.68302364
 [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
 [87,]  1.18940579
 [88,]  0.73366185
 [89,]  1.03749114
 [90,]  0.83493828
 [91,]  0.42983257
 [92,]  0.48047078
 [93,] -0.02591137
 [94,]  0.48047078
 [95,]  0.07536506
 [96,]  0.88557650
 [97,]  0.73366185
 [98,]  0.68302364
 [99,] -0.53229351
[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
[179,] -1.44378138
[180,] -1.08931388
[181,] -1.44378138
[182,] -0.93739923
[183,] -0.93739923
[184,] -1.49441960
[185,] -1.34250495
[186,] -0.07654958
[187,] -1.19059031
[188,] -0.43101709
[189,] -1.74761067
[190,]  0.07536506
[191,] -1.79824888
[192,] -0.73484637
[193,] -1.74761067
[194,] -0.58293173
[195,] -1.74761067
[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
 [14,] -0.705120649
 [15,] -0.207315036
 [16,] -1.131811175
 [17,] -0.420660299
 [18,] -0.278430124
 [19,] -1.202926262
 [20,] -0.491775386
 [21,] -1.914077138
 [22,] -1.487386613
 [23,] -0.847350824
 [24,] -1.131811175
 [25,] -1.487386613
 [26,] -0.989581000
 [27,] -1.274041350
 [28,] -0.989581000
 [29,] -2.056307313
 [30,] -1.487386613
 [31,] -1.629616788
 [32,] -1.629616788
 [33,] -0.918465912
 [34,] -1.202926262
 [35,] -0.420660299
 [36,] -0.349545211
 [37,] -0.776235737
 [38,] -1.487386613
 [39,] -1.416271525
 [40,] -1.202926262
 [41,] -1.345156438
 [42,] -0.420660299
 [43,] -1.060696087
 [44,] -0.349545211
 [45,] -1.131811175
 [46,] -0.776235737
 [47,] -1.345156438
 [48,] -1.558501700
 [49,] -0.776235737
 [50,] -0.705120649
 [51,] -1.060696087
 [52,] -0.918465912
 [53,] -0.776235737
 [54,] -0.065084861
 [55,] -0.989581000
 [56,] -0.705120649
 [57,] -1.060696087
 [58,] -0.562890474
 [59,] -1.416271525
 [60,] -0.491775386
 [61,] -1.131811175
 [62,] -0.420660299
 [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
 [75,] -0.776235737
 [76,] -0.420660299
 [77,] -0.705120649
 [78,] -1.202926262
 [79,] -0.989581000
 [80,] -0.420660299
 [81,] -0.847350824
 [82,] -0.349545211
 [83,] -0.989581000
 [84,] -0.562890474
 [85,] -0.705120649
 [86,] -0.491775386
 [87,] -0.776235737
 [88,] -0.847350824
 [89,] -0.847350824
 [90,] -0.776235737
 [91,]  0.077145314
 [92,]  0.290490577
 [93,] -1.131811175
 [94,] -1.060696087
 [95,] -0.989581000
 [96,]  0.503835840
 [97,] -0.776235737
 [98,] -0.349545211
 [99,] -1.629616788
[100,] -0.634005562
[101,] -0.634005562
[102,]  0.148260402
[103,] -1.274041350
[104,] -0.776235737
[105,] -0.562890474
[106,] -1.202926262
[107,] -0.136199948
[108,] -0.776235737
[109,] -1.416271525
[110,] -0.278430124
[111,] -0.207315036
[112,] -0.705120649
[113,] -0.562890474
[114,] -0.278430124
[115,] -0.705120649
[116,] -0.349545211
[117,] -0.918465912
[118,] -0.136199948
[119,] -0.847350824
[120,] -0.847350824
[121,] -0.989581000
[122,] -0.207315036
[123,] -1.771846963
[124,]  0.077145314
[125,] -1.060696087
[126,] -0.136199948
[127,] -0.705120649
[128,] -0.420660299
[129,] -0.705120649
[130,]  0.646066015
[131,] -0.776235737
[132,] -0.278430124
[133,] -0.562890474
[134,] -0.136199948
[135,] -0.989581000
[136,] -0.776235737
[137,] -0.705120649
[138,] -0.065084861
[139,] -1.131811175
[140,] -0.562890474
[141,] -0.562890474
[142,] -0.989581000
[143,] -0.918465912
[144,] -0.776235737
[145,] -0.634005562
[146,] -1.131811175
[147,] -0.776235737
[148,] -1.202926262
[149,] -0.420660299
[150,] -0.562890474
[151,] -0.989581000
[152,]  0.006030227
[153,]  0.717181103
[154,]  2.068367767
[155,]  0.646066015
[156,]  1.214986716
[157,]  1.001641453
[158,]  0.646066015
[159,]  0.717181103
[160,]  1.286101803
[161,]  0.574950927
[162,]  1.001641453
[163,]  0.930526365
[164,]  1.072756541
[165,]  0.930526365
[166,]  0.859411278
[167,]  0.646066015
[168,]  1.143871628
[169,]  0.646066015
[170,]  1.428331979
[171,]  0.574950927
[172,]  1.499447066
[173,]  1.214986716
[174,]  1.001641453
[175,]  0.859411278
[176,]  1.001641453
[177,]  1.001641453
[178,]  1.001641453
[179,]  1.072756541
[180,]  1.001641453
[181,]  0.646066015
[182,]  1.357216891
[183,]  1.499447066
[184,]  0.574950927
[185,]  0.432720752
[186,]  2.068367767
[187,]  1.357216891
[188,]  1.357216891
[189,]  0.859411278
[190,]  1.286101803
[191,]  0.503835840
[192,]  0.503835840
[193,]  0.503835840
[194,]  1.712792329
[195,]  0.646066015
[196,]  1.072756541
[197,]  1.499447066
[198,]  1.143871628
[199,]  0.646066015
[200,]  1.712792329
[201,]  0.859411278
[202,]  1.001641453
[203,]  0.646066015
[204,]  1.357216891
[205,]  0.646066015
[206,]  1.712792329
[207,]  1.143871628
[208,]  1.357216891
[209,]  0.503835840
[210,]  1.357216891
[211,]  0.503835840
[212,]  1.641677241
[213,]  0.503835840
[214,]  1.428331979
[215,]  0.930526365
[216,]  2.139482854
[217,]  1.286101803
[218,]  2.068367767
[219,]  0.930526365
[220,]  1.997252679
[221,]  1.357216891
[222,]  1.570562154
[223,]  1.072756541
[224,]  1.428331979
[225,]  1.428331979
[226,]  1.143871628
[227,]  1.072756541
[228,]  2.068367767
[229,]  0.574950927
[230,]  1.357216891
[231,]  1.001641453
[232,]  1.570562154
[233,]  0.788296190
[234,]  1.428331979
[235,]  0.788296190
[236,]  1.641677241
[237,]  0.788296190
[238,]  1.926137592
[239,]  1.214986716
[240,]  1.214986716
[241,]  0.788296190
[242,]  2.068367767
[243,]  1.214986716
[244,]  1.926137592
[245,]  0.788296190
[246,]  1.641677241
[247,]  0.930526365
[248,]  1.783907417
[249,]  1.072756541
[250,]  1.499447066
[251,]  0.148260402
[252,]  1.712792329
[253,]  1.286101803
[254,]  1.926137592
[255,]  1.001641453
[256,]  1.926137592
[257,]  1.072756541
[258,]  1.001641453
[259,]  0.646066015
[260,]  1.286101803
[261,]  0.503835840
[262,]  0.574950927
[263,]  1.072756541
[264,]  1.997252679
[265,]  0.859411278
[266,]  2.068367767
[267,]  1.143871628
[268,]  2.068367767
[269,]  1.143871628
[270,]  1.499447066
[271,]  0.930526365
[272,]           NA
[273,]  1.001641453
[274,]  1.499447066
[275,]  0.788296190
[276,]  0.859411278
[277,] -0.634005562
[278,] -0.349545211
[279,] -0.562890474
[280,] -0.918465912
[281,] -0.278430124
[282,] -0.207315036
[283,] -1.629616788
[284,] -0.278430124
[285,] -0.420660299
[286,] -0.207315036
[287,] -0.562890474
[288,] -0.491775386
[289,] -1.131811175
[290,]  0.006030227
[291,] -0.776235737
[292,]  0.006030227
[293,] -0.278430124
[294,] -1.416271525
[295,] -0.776235737
[296,] -0.420660299
[297,] -1.416271525
[298,] -0.705120649
[299,] -0.989581000
[300,] -0.562890474
[301,] -0.420660299
[302,] -0.278430124
[303,] -0.065084861
[304,] -0.065084861
[305,] -0.705120649
[306,]  0.290490577
[307,] -0.989581000
[308,]  0.006030227
[309,] -0.989581000
[310,]  0.148260402
[311,] -0.420660299
[312,] -0.136199948
[313,] -0.420660299
[314,]  0.646066015
[315,] -0.634005562
[316,]  0.290490577
[317,]  0.646066015
[318,] -0.989581000
[319,] -0.349545211
[320,] -0.349545211
[321,] -0.349545211
[322,]  0.006030227
[323,] -0.776235737
[324,]  0.788296190
[325,] -0.989581000
[326,] -0.207315036
[327,] -0.136199948
[328,]  0.006030227
[329,] -0.562890474
[330,]  0.148260402
[331,] -0.989581000
[332,] -0.278430124
[333,] -0.705120649
[334,]  0.148260402
[335,]  0.077145314
[336,] -0.491775386
[337,]  0.361605665
[338,] -0.847350824
[339,] -0.420660299
[340,]  0.432720752
[341,]  0.077145314
[342,] -0.562890474
[343,]  0.646066015
[344,] -0.207315036
attr(,"scaled:center")
[1] 200.9152
attr(,"scaled:scale")
[1] 14.06171

$body_mass_g
               [,1]
  [1,] -0.563316704
  [2,] -0.500969030
  [3,] -1.186793445
  [4,]           NA
  [5,] -0.937402749
  [6,] -0.688012052
  [7,] -0.719185889
  [8,]  0.590115266
  [9,] -0.906228912
 [10,]  0.060160036
 [11,] -1.124445771
 [12,] -0.625664378
 [13,] -1.249141119
 [14,] -0.500969030
 [15,]  0.247203059
 [16,] -0.625664378
 [17,] -0.937402749
 [18,]  0.371898407
 [19,] -1.093271934
 [20,] -0.002187638
 [21,] -0.999750423
 [22,] -0.750359726
 [23,] -0.500969030
 [24,] -0.313926008
 [25,] -0.500969030
 [26,] -0.500969030
 [27,] -0.812707400
 [28,] -1.249141119
 [29,] -1.311488793
 [30,] -0.313926008
 [31,] -1.186793445
 [32,] -0.376273682
 [33,] -1.124445771
 [34,] -0.376273682
 [35,] -1.093271934
 [36,] -0.064535312
 [37,] -0.313926008
 [38,] -0.812707400
 [39,] -1.124445771
 [40,]  0.558941429
 [41,] -1.311488793
 [42,] -0.376273682
 [43,] -1.373836467
 [44,]  0.247203059
 [45,] -1.498531815
 [46,]  0.496593755
 [47,] -0.968576586
 [48,] -1.529705652
 [49,] -0.937402749
 [50,] -0.064535312
 [51,] -0.875055074
 [52,]  0.122507710
 [53,] -0.937402749
 [54,] -0.189230660
 [55,] -1.623227163
 [56,] -0.625664378
 [57,] -0.812707400
 [58,] -0.500969030
 [59,] -1.685574837
 [60,] -0.563316704
 [61,] -1.311488793
 [62,]  0.247203059
 [63,] -0.750359726
 [64,] -0.189230660
 [65,] -1.685574837
 [66,] -0.313926008
 [67,] -1.062098097
 [68,] -0.126882986
 [69,] -1.436184141
 [70,]  0.309550733
 [71,] -0.750359726
 [72,] -0.376273682
 [73,] -0.812707400
 [74,] -0.064535312
 [75,] -0.625664378
 [76,]  0.060160036
 [77,] -0.625664378
 [78,] -0.376273682
 [79,] -0.812707400
 [80,] -0.251578334
 [81,] -1.249141119
 [82,]  0.621289103
 [83,] -0.500969030
 [84,] -0.002187638
 [85,] -1.062098097
 [86,] -0.812707400
 [87,] -0.500969030
 [88,] -0.875055074
 [89,] -0.313926008
 [90,] -0.750359726
 [91,] -0.812707400
 [92,]  0.122507710
 [93,] -0.999750423
 [94,]  0.309550733
 [95,] -1.124445771
 [96,]  0.122507710
 [97,] -0.625664378
 [98,]  0.184855384
 [99,] -1.623227163
[100,] -0.126882986
[101,] -0.594490541
[102,]  0.652462940
[103,] -1.405010304
[104,]  0.060160036
[105,] -1.592053326
[106,] -0.812707400
[107,] -0.563316704
[108,] -0.376273682
[109,] -1.280314956
[110,]  0.714810614
[111,] -0.469795193
[112,]  0.496593755
[113,] -1.249141119
[114,]  0.091333873
[115,] -0.376273682
[116,] -0.158056823
[117,] -1.623227163
[118,] -0.532142867
[119,] -1.062098097
[120,] -1.093271934
[121,] -1.311488793
[122,] -0.875055074
[123,] -0.937402749
[124,] -0.407447519
[125,] -1.436184141
[126,] -0.251578334
[127,] -1.155619608
[128,]  0.122507710
[129,] -1.436184141
[130,] -0.251578334
[131,] -1.093271934
[132,] -0.875055074
[133,] -0.875055074
[134,]  0.340724570
[135,] -0.968576586
[136,] -0.376273682
[137,] -1.280314956
[138,] -0.282752171
[139,] -0.999750423
[140,]  0.060160036
[141,] -0.999750423
[142,] -0.906228912
[143,] -1.436184141
[144,] -0.594490541
[145,] -1.498531815
[146,] -0.688012052
[147,]  0.060160036
[148,] -0.906228912
[149,] -0.937402749
[150,] -0.563316704
[151,] -0.625664378
[152,] -0.251578334
[153,]  0.371898407
[154,]  1.868242584
[155,]  0.309550733
[156,]  1.868242584
[157,]  1.494156540
[158,]  0.434246081
[159,]  0.745984451
[160,]  1.244765843
[161,]  0.247203059
[162,]  1.182418169
[163,]  0.558941429
[164,]  1.681199562
[165,]  0.558941429
[166,]  2.055285606
[167,] -0.002187638
[168,]  2.055285606
[169,] -0.064535312
[170,]  2.616414673
[171,]  0.745984451
[172,]  1.431808866
[173,]  1.868242584
[174,]  0.995375147
[175,]  0.247203059
[176,]  1.057722821
[177,]  0.995375147
[178,]  1.120070495
[179,] -0.126882986
[180,]  1.805894910
[181,]  0.496593755
[182,]  1.681199562
[183,]  1.307113518
[184,]  0.621289103
[185,]  1.057722821
[186,]  2.304676302
[187,]  1.182418169
[188,]  1.494156540
[189,]  0.933027473
[190,]  1.307113518
[191,]  0.184855384
[192,]  1.431808866
[193,] -0.313926008
[194,]  1.868242584
[195,]  0.122507710
[196,]  0.683636777
[197,]  1.681199562
[198,]  0.870679799
[199,] -0.002187638
[200,]  1.494156540
[201,]  1.120070495
[202,]  1.369461192
[203,]  0.808332125
[204,]  1.369461192
[205,]  0.247203059
[206,]  0.995375147
[207,]  0.870679799
[208,]  1.057722821
[209,]  0.122507710
[210,]  0.995375147
[211,]  0.309550733
[212,]  1.681199562
[213,] -0.002187638
[214,]  1.369461192
[215,]  0.247203059
[216,]  1.805894910
[217,]  0.621289103
[218,]  1.868242584
[219,]  0.558941429
[220,]  1.992937932
[221,]  0.621289103
[222,]  1.681199562
[223,]  0.683636777
[224,]  0.995375147
[225,]  1.120070495
[226,]  1.244765843
[227,]  0.621289103
[228,]  1.992937932
[229,]  0.496593755
[230,]  2.242328628
[231,]  0.683636777
[232,]  2.179980954
[233,]  0.527767592
[234,]  1.556504214
[235,]  0.652462940
[236,]  1.431808866
[237,]  0.683636777
[238,]  1.743547236
[239,]  0.496593755
[240,]  1.369461192
[241,]  0.839505962
[242,]  1.681199562
[243,]  0.933027473
[244,]  1.494156540
[245,]  0.683636777
[246,]  1.805894910
[247,]  0.808332125
[248,]  1.244765843
[249,]  0.901853636
[250,]  0.839505962
[251,]  0.527767592
[252,]  1.307113518
[253,]  0.808332125
[254,]  1.743547236
[255,]  0.964201310
[256,]  1.618851888
[257,]  0.652462940
[258,]  1.618851888
[259,]  0.621289103
[260,]  1.618851888
[261,]  0.465419918
[262,]  1.618851888
[263,]  0.995375147
[264,]  2.179980954
[265,]  0.558941429
[266,]  1.618851888
[267,]  0.216029222
[268,]  2.055285606
[269,]  0.839505962
[270,]  2.242328628
[271,]  0.901853636
[272,]           NA
[273,]  0.808332125
[274,]  1.930590258
[275,]  1.244765843
[276,]  1.494156540
[277,] -0.875055074
[278,] -0.376273682
[279,] -0.688012052
[280,] -0.843881237
[281,] -0.594490541
[282,] -0.313926008
[283,] -1.186793445
[284,] -0.563316704
[285,] -0.064535312
[286,] -0.625664378
[287,] -0.500969030
[288,] -0.532142867
[289,] -0.625664378
[290,] -0.189230660
[291,] -0.781533563
[292,] -0.189230660
[293,] -1.124445771
[294,] -0.625664378
[295,] -0.937402749
[296,]  0.247203059
[297,] -0.750359726
[298,] -0.999750423
[299,] -1.623227163
[300,] -0.500969030
[301,] -1.124445771
[302,] -0.064535312
[303,] -0.999750423
[304,] -0.500969030
[305,] -0.625664378
[306,]  0.434246081
[307,] -1.249141119
[308,]  0.122507710
[309,] -1.062098097
[310,] -0.126882986
[311,] -0.750359726
[312,] -0.376273682
[313,] -0.438621356
[314,]  0.745984451
[315,] -1.872617859
[316,]  0.371898407
[317,] -0.313926008
[318,] -0.688012052
[319,] -0.812707400
[320,] -0.875055074
[321,] -0.656838215
[322,]  0.309550733
[323,] -0.999750423
[324,]  0.122507710
[325,] -1.186793445
[326,] -0.656838215
[327,] -1.093271934
[328,] -0.313926008
[329,] -0.750359726
[330,] -0.189230660
[331,] -1.062098097
[332,] -0.937402749
[333,] -1.186793445
[334,] -0.189230660
[335,] -0.500969030
[336,] -0.843881237
[337,] -0.313926008
[338,] -0.688012052
[339,] -0.688012052
[340,] -0.251578334
[341,] -0.999750423
[342,] -0.532142867
[343,] -0.126882986
[344,] -0.532142867
attr(,"scaled:center")
[1] 4201.754
attr(,"scaled:scale")
[1] 801.9545

$sex
  [1] male   female female <NA>   female male   female male   <NA>   <NA>  
 [11] <NA>   <NA>   female male   male   female female male   female male  
 [21] female male   female male   male   female male   female female male  
 [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]
  [1,] -1.25748435
  [2,] -1.25748435
  [3,] -1.25748435
  [4,] -1.25748435
  [5,] -1.25748435
  [6,] -1.25748435
  [7,] -1.25748435
  [8,] -1.25748435
  [9,] -1.25748435
 [10,] -1.25748435
 [11,] -1.25748435
 [12,] -1.25748435
 [13,] -1.25748435
 [14,] -1.25748435
 [15,] -1.25748435
 [16,] -1.25748435
 [17,] -1.25748435
 [18,] -1.25748435
 [19,] -1.25748435
 [20,] -1.25748435
 [21,] -1.25748435
 [22,] -1.25748435
 [23,] -1.25748435
 [24,] -1.25748435
 [25,] -1.25748435
 [26,] -1.25748435
 [27,] -1.25748435
 [28,] -1.25748435
 [29,] -1.25748435
 [30,] -1.25748435
 [31,] -1.25748435
 [32,] -1.25748435
 [33,] -1.25748435
 [34,] -1.25748435
 [35,] -1.25748435
 [36,] -1.25748435
 [37,] -1.25748435
 [38,] -1.25748435
 [39,] -1.25748435
 [40,] -1.25748435
 [41,] -1.25748435
 [42,] -1.25748435
 [43,] -1.25748435
 [44,] -1.25748435
 [45,] -1.25748435
 [46,] -1.25748435
 [47,] -1.25748435
 [48,] -1.25748435
 [49,] -1.25748435
 [50,] -1.25748435
 [51,] -0.03552216
 [52,] -0.03552216
 [53,] -0.03552216
 [54,] -0.03552216
 [55,] -0.03552216
 [56,] -0.03552216
 [57,] -0.03552216
 [58,] -0.03552216
 [59,] -0.03552216
 [60,] -0.03552216
 [61,] -0.03552216
 [62,] -0.03552216
 [63,] -0.03552216
 [64,] -0.03552216
 [65,] -0.03552216
 [66,] -0.03552216
 [67,] -0.03552216
 [68,] -0.03552216
 [69,] -0.03552216
 [70,] -0.03552216
 [71,] -0.03552216
 [72,] -0.03552216
 [73,] -0.03552216
 [74,] -0.03552216
 [75,] -0.03552216
 [76,] -0.03552216
 [77,] -0.03552216
 [78,] -0.03552216
 [79,] -0.03552216
 [80,] -0.03552216
 [81,] -0.03552216
 [82,] -0.03552216
 [83,] -0.03552216
 [84,] -0.03552216
 [85,] -0.03552216
 [86,] -0.03552216
 [87,] -0.03552216
 [88,] -0.03552216
 [89,] -0.03552216
 [90,] -0.03552216
 [91,] -0.03552216
 [92,] -0.03552216
 [93,] -0.03552216
 [94,] -0.03552216
 [95,] -0.03552216
 [96,] -0.03552216
 [97,] -0.03552216
 [98,] -0.03552216
 [99,] -0.03552216
[100,] -0.03552216
[101,]  1.18644003
[102,]  1.18644003
[103,]  1.18644003
[104,]  1.18644003
[105,]  1.18644003
[106,]  1.18644003
[107,]  1.18644003
[108,]  1.18644003
[109,]  1.18644003
[110,]  1.18644003
[111,]  1.18644003
[112,]  1.18644003
[113,]  1.18644003
[114,]  1.18644003
[115,]  1.18644003
[116,]  1.18644003
[117,]  1.18644003
[118,]  1.18644003
[119,]  1.18644003
[120,]  1.18644003
[121,]  1.18644003
[122,]  1.18644003
[123,]  1.18644003
[124,]  1.18644003
[125,]  1.18644003
[126,]  1.18644003
[127,]  1.18644003
[128,]  1.18644003
[129,]  1.18644003
[130,]  1.18644003
[131,]  1.18644003
[132,]  1.18644003
[133,]  1.18644003
[134,]  1.18644003
[135,]  1.18644003
[136,]  1.18644003
[137,]  1.18644003
[138,]  1.18644003
[139,]  1.18644003
[140,]  1.18644003
[141,]  1.18644003
[142,]  1.18644003
[143,]  1.18644003
[144,]  1.18644003
[145,]  1.18644003
[146,]  1.18644003
[147,]  1.18644003
[148,]  1.18644003
[149,]  1.18644003
[150,]  1.18644003
[151,]  1.18644003
[152,]  1.18644003
[153,] -1.25748435
[154,] -1.25748435
[155,] -1.25748435
[156,] -1.25748435
[157,] -1.25748435
[158,] -1.25748435
[159,] -1.25748435
[160,] -1.25748435
[161,] -1.25748435
[162,] -1.25748435
[163,] -1.25748435
[164,] -1.25748435
[165,] -1.25748435
[166,] -1.25748435
[167,] -1.25748435
[168,] -1.25748435
[169,] -1.25748435
[170,] -1.25748435
[171,] -1.25748435
[172,] -1.25748435
[173,] -1.25748435
[174,] -1.25748435
[175,] -1.25748435
[176,] -1.25748435
[177,] -1.25748435
[178,] -1.25748435
[179,] -1.25748435
[180,] -1.25748435
[181,] -1.25748435
[182,] -1.25748435
[183,] -1.25748435
[184,] -1.25748435
[185,] -1.25748435
[186,] -1.25748435
[187,] -0.03552216
[188,] -0.03552216
[189,] -0.03552216
[190,] -0.03552216
[191,] -0.03552216
[192,] -0.03552216
[193,] -0.03552216
[194,] -0.03552216
[195,] -0.03552216
[196,] -0.03552216
[197,] -0.03552216
[198,] -0.03552216
[199,] -0.03552216
[200,] -0.03552216
[201,] -0.03552216
[202,] -0.03552216
[203,] -0.03552216
[204,] -0.03552216
[205,] -0.03552216
[206,] -0.03552216
[207,] -0.03552216
[208,] -0.03552216
[209,] -0.03552216
[210,] -0.03552216
[211,] -0.03552216
[212,] -0.03552216
[213,] -0.03552216
[214,] -0.03552216
[215,] -0.03552216
[216,] -0.03552216
[217,] -0.03552216
[218,] -0.03552216
[219,] -0.03552216
[220,] -0.03552216
[221,] -0.03552216
[222,] -0.03552216
[223,] -0.03552216
[224,] -0.03552216
[225,] -0.03552216
[226,] -0.03552216
[227,] -0.03552216
[228,] -0.03552216
[229,] -0.03552216
[230,] -0.03552216
[231,] -0.03552216
[232,] -0.03552216
[233,]  1.18644003
[234,]  1.18644003
[235,]  1.18644003
[236,]  1.18644003
[237,]  1.18644003
[238,]  1.18644003
[239,]  1.18644003
[240,]  1.18644003
[241,]  1.18644003
[242,]  1.18644003
[243,]  1.18644003
[244,]  1.18644003
[245,]  1.18644003
[246,]  1.18644003
[247,]  1.18644003
[248,]  1.18644003
[249,]  1.18644003
[250,]  1.18644003
[251,]  1.18644003
[252,]  1.18644003
[253,]  1.18644003
[254,]  1.18644003
[255,]  1.18644003
[256,]  1.18644003
[257,]  1.18644003
[258,]  1.18644003
[259,]  1.18644003
[260,]  1.18644003
[261,]  1.18644003
[262,]  1.18644003
[263,]  1.18644003
[264,]  1.18644003
[265,]  1.18644003
[266,]  1.18644003
[267,]  1.18644003
[268,]  1.18644003
[269,]  1.18644003
[270,]  1.18644003
[271,]  1.18644003
[272,]  1.18644003
[273,]  1.18644003
[274,]  1.18644003
[275,]  1.18644003
[276,]  1.18644003
[277,] -1.25748435
[278,] -1.25748435
[279,] -1.25748435
[280,] -1.25748435
[281,] -1.25748435
[282,] -1.25748435
[283,] -1.25748435
[284,] -1.25748435
[285,] -1.25748435
[286,] -1.25748435
[287,] -1.25748435
[288,] -1.25748435
[289,] -1.25748435
[290,] -1.25748435
[291,] -1.25748435
[292,] -1.25748435
[293,] -1.25748435
[294,] -1.25748435
[295,] -1.25748435
[296,] -1.25748435
[297,] -1.25748435
[298,] -1.25748435
[299,] -1.25748435
[300,] -1.25748435
[301,] -1.25748435
[302,] -1.25748435
[303,] -0.03552216
[304,] -0.03552216
[305,] -0.03552216
[306,] -0.03552216
[307,] -0.03552216
[308,] -0.03552216
[309,] -0.03552216
[310,] -0.03552216
[311,] -0.03552216
[312,] -0.03552216
[313,] -0.03552216
[314,] -0.03552216
[315,] -0.03552216
[316,] -0.03552216
[317,] -0.03552216
[318,] -0.03552216
[319,] -0.03552216
[320,] -0.03552216
[321,]  1.18644003
[322,]  1.18644003
[323,]  1.18644003
[324,]  1.18644003
[325,]  1.18644003
[326,]  1.18644003
[327,]  1.18644003
[328,]  1.18644003
[329,]  1.18644003
[330,]  1.18644003
[331,]  1.18644003
[332,]  1.18644003
[333,]  1.18644003
[334,]  1.18644003
[335,]  1.18644003
[336,]  1.18644003
[337,]  1.18644003
[338,]  1.18644003
[339,]  1.18644003
[340,]  1.18644003
[341,]  1.18644003
[342,]  1.18644003
[343,]  1.18644003
[344,]  1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559

penguins |> 
  map_if(is.numeric, scale) |> 
  bind_cols()

species	island	bill_length_mm	bill_depth_mm	sex
Adelie	Torgersen	-0.8832047	0.7843001	male
Adelie	Torgersen	-0.8099390	0.1260033	female
Adelie	Torgersen	-0.6634077	0.4298326	female
Adelie	Torgersen	NA	NA	NA
Adelie	Torgersen	-1.3227986	1.0881294	female
Adelie	Torgersen	-0.8465718	1.7464261	male
Adelie	Torgersen	-0.9198375	0.3285561	female
Adelie	Torgersen	-0.8648883	1.2400440	male

BUT DR. C we just figured out `across()` 😭😭😭!!!

I promise there are good reasons to learn purrr!

Functional programming is computationally faster than across()
You can complete a larger variety of data manipulations with map() functions
across() is just for datasets, while functional programming can be used for many different tasks

Tip

This doesn’t mean that across() is bad practice at all, just that there are times when using functional programming will be much better!

Comparing Speed

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

for()
across()
map()
Run
Compare

loop_func <- function(d){
  typ <- rep(NA, ncol(d))
  for(i in 1:ncol(d)){
    typ[i] <- class(d[,i])
  }
  return(typ)
}

across_func <- function(d){
  typ <- d |> 
    summarize(across(.cols = everything(),
                     .fns = class))
  return(typ)
}

map_func <- function(d){
  typ <- map_chr(d, class)
  return(typ)
}

df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 7))

loop_func(df)

[1] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"

across_func(df)

       V1      V2      V3      V4      V5      V6      V7
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.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 100000))

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

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 `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"

The `map()` and `pmap()` Family

There are so many functions – check out the purrr cheatsheet!

Use functional programming!

Midterm Exam Feedback

Midterm Exam - What went well!

Overall, code formatting looks very nice!
Pivoting data
Data joins and cleaning on take-home
Working with strings (stringr)
Lot’s of interesting and well-thought out open-ended analyses

Midterm Exam - What we still are working on

only saving needed intermediate objects
working with logical variables
quickly looking at variable values
citing sources
statistical language

Review: Environment Junk

Only save variables / data if you will use it again later

NO
YAY!
Saving makes sense here

q1 <- penguins |> 
  group_by(species) |> 
  slice_max(bill_length_mm)
  
kable(q1)

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Adelie	Torgersen	46.0	21.5	194	4200	male	2007
Chinstrap	Dream	58.0	17.8	181	3700	female	2007
Gentoo	Biscoe	59.6	17.0	230	6050	male	2007

penguins |> 
  group_by(species) |> 
  slice_max(bill_length_mm) |> 
  kable()

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Adelie	Torgersen	46.0	21.5	194	4200	male	2007
Chinstrap	Dream	58.0	17.8	181	3700	female	2007
Gentoo	Biscoe	59.6	17.0	230	6050	male	2007

penguins_long <- penguins |> 
  pivot_longer(cols = bill_length_mm:body_mass_g,
               names_to = "measure",
               values_to = "value")

penguins_long |> 
  slice_head(n = 3) |> 
  kable()

species	island	sex	year	measure	value
Adelie	Torgersen	male	2007	bill_length_mm	39.1
Adelie	Torgersen	male	2007	bill_depth_mm	18.7
Adelie	Torgersen	male	2007	flipper_length_mm	181.0

Review: Logical Variables

Logical / Boolean variables take the special values TRUE and FALSE
- can be treated like a numeric vector where
TRUE == 1 and FALSE == 0

What proportion of penguins in each species have a bill length less than 45 mm?

penguins |> 
  group_by(species) |> 
  summarize(prop_shortbeak = mean(bill_length_mm < 45, na.rm = TRUE),
            n_penguins = sum(!is.na(bill_length_mm))) |> 
  kable(digits = 3)

species	prop_shortbeak	n_penguins
Adelie	0.980	151
Chinstrap	0.088	68
Gentoo	0.179	123

Review: Logical Variables

Using this logic is very useful for function / output checks!

Lab 5 Q0.2: Design and implement a check that you created the address_number and address_street_name columns correctly.

person |> 
  mutate(address_check = str_c(address_number,
                               address_street_name,
                               sep = " "),
         correct = address_check == address) |> 
  pull(correct) |> 
  mean()

[1] 1

A Note: Logical Variables

While it works in some cases to use strings to reference the values TRUE and FALSE, it is very bad practice

TRUE == "TRUE"

[1] TRUE

sum(c("TRUE", "FALSE"))

Error in sum(c("TRUE", "FALSE")): invalid 'type' (character) of argument

sum(c(TRUE, FALSE))

[1] 1

Review: Quickly Exploring Data

If you want to look at all of the possible values/levels of a character/factor variable, you can use

table() (base) or

table(penguins$island)


   Biscoe     Dream Torgersen 
      168       124        52

table(penguins$island, penguins$species)

           
            Adelie Chinstrap Gentoo
  Biscoe        44         0    124
  Dream         56        68      0
  Torgersen     52         0      0

count() (dplyr):

penguins |> 
  count(island)

# A tibble: 3 × 2
  island        n
  <fct>     <int>
1 Biscoe      168
2 Dream       124
3 Torgersen    52

Reminder: Citing Coding Sources

Part of responsible coding is giving credit to other’s work if we use it
“Assumed knowledge:” course textbook, course slides, and course assignments / solutions.
You need to let me know if you use any other resources
See syllabus

Review: Statistical Langauge

Specific statistical vocabulary (use carefully and correctly):

Correlation: a statistic calculated between two quantitative variables
Significant: referring to results of statistical inference

Instead use general vocabulary:

association
relationship
meaningful / large
unsubstantial / small
“appears to be”

PA 8.1: The Twelve Days of Christmas

https://studioplayhouse.org/the-12-days-of-christmas/

`glue()`

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

library(glue)

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

My name is Dr. C.

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

An Example

99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around, 98 bottles of beer on the wall…

Lyrics
Song
Alternate Ending
Final Song

bottles_lyrics <- function(n){
  lyrics <- glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  return(lyrics)
}

bottles_lyrics(3)

3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall

bottles_song <- function(n){
  song <- map_chr(n:0, bottles_lyrics)
  return(glue("{song}"))
}

bottles_song(3)

3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
0 bottles of beer on the wall, 0 bottles of beer 
Take one down, pass it around, -1 bottles of beer on the wall

No more bottles of beer on the wall, no more bottles of beer. Go to the store, buy some more, 99 bottles of beer on the wall…

bottles_lyrics <- function(n){
  if(n == 0){
    lyrics <- glue("No more bottles of beer on the wall, no more bottles of beer. \nGo to the store, buy some more, 99 bottles of beer on the wall...")
  } else{
    lyrics <- glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  }
  return(lyrics)
}

bottles_song(4)

4 bottles of beer on the wall, 4 bottles of beer 
Take one down, pass it around, 3 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer. 
Go to the store, buy some more, 99 bottles of beer on the wall...

To do…

PA 8.1: The Twelve Days of Christmas
- Due Thursday 5/22 before class.
Project Proposal + Data
- Due Friday 5/23 at 11:59pm.
Lab 8: Searching for Efficiency
- Due Tuesday 5/27 at 11:59pm.

Iteration

Tuesday, 5/20

Project Proposal + Data

Performing Repeated Tasks

Repetition

Iteration

Iteration

Vectorization

Vectorization

Vectorization

Vectorization

Vectorization

Some functions cannot be vectorized!

What can we do instead?

What can we do instead?

What can we do instead?

Functional Programming with purrr

Brief Review: Lists

Brief Review: Lists

map()

map() + Dataframes

The map() Family

map() + penguins

map_if()

BUT DR. C we just figured out across() 😭😭😭!!!

Comparing Speed

The pmap() Family

The pmap() Family

The map() and pmap() Family

Use functional programming!

Midterm Exam Feedback

Midterm Exam - What went well!

Midterm Exam - What we still are working on

Review: Environment Junk

Review: Logical Variables

Review: Logical Variables

A Note: Logical Variables

Review: Quickly Exploring Data

Reminder: Citing Coding Sources

Review: Statistical Langauge

PA 8.1: The Twelve Days of Christmas

glue()

An Example

To do…

Functional Programming with `purrr`

`map()`

`map()` + Dataframes

The `map()` Family

`map()` + penguins

`map_if()`

BUT DR. C we just figured out `across()` 😭😭😭!!!

The `pmap()` Family

The `pmap()` Family

The `map()` and `pmap()` Family

`glue()`