Iteration

Wednesday, 5/13

Today we will…

Project Checkpoint 2
New Material¹
- Iteration aka Performing Repeated Tasks
- Efficient Iteration: the map() family
- Nice Tables
Lab 7: Searching for Efficiency

Project Proposal + Group Contract

Together your group must:

Write a group contract for how you will work together
Write a short project proposal, based on the project type that you chose

See your project page for detailed instructions on the proposal
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 (or vector), 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(.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

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(.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 if there are any NAs in each column.

penguins |> 
  map_lgl(.f = ~ 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(.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!

`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(.p = is.numeric, .f = 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
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[150,] -1.12131806
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[153,]  0.39894437
[154,]  1.11328455
[155,]  0.87517115
[156,]  1.11328455
[157,]  0.67369059
[158,]  0.47221003
[159,]  0.27072946
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[164,]  0.93012040
[165,]  0.28904588
[166,]  0.82022191
[167,]  0.34399512
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[169,] -0.35202864
[170,]  0.96675323
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[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
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[189,] -0.24213015
[190,]  0.08756532
[191,]  0.01429966
[192,]  0.87517115
[193,] -0.22381374
[194,]  1.04001889
[195,]  0.25241305
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[201,]  0.17914739
[202,]  0.23409663
[203,]  0.49052644
[204,]  0.83853832
[205,]  0.21578022
[206,]  1.13160096
[207,]  0.47221003
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[209,] -0.02233317
[210,]  0.28904588
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[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
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[224,]  0.45389361
[225,]  0.78358908
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[228,]  0.85685474
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[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
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[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
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[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
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 [31,] -0.22846423
 [32,]  0.48047078
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 [35,] -0.07654958
 [36,]  1.99961722
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 [38,]  0.68302364
 [39,]  1.08812936
 [40,]  0.98685293
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 [44,]  1.29068222
 [45,] -0.12718780
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 [49,]  0.37919435
 [50,]  2.05025544
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 [80,]  0.98685293
 [81,]  0.02472685
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[101,]  0.37919435
[102,]  1.44259686
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[105,]  0.73366185
[106,]  0.88557650
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[108,]  1.44259686
[109,] -0.07654958
[110,]  0.93621471
[111,] -0.32974066
[112,]  1.59451151
[113,]  0.27791792
[114,]  1.18940579
[115,]  1.79706436
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[117,] -0.07654958
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[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
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[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
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[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
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[176,] -0.68420816
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[180,] -1.08931388
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[185,] -1.34250495
[186,] -0.07654958
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[196,] -1.08931388
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[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
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[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
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[314,]  1.79706436
[315,] -0.27910244
[316,]  1.39195865
[317,]  1.18940579
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[319,]  0.98685293
[320,] -0.07654958
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[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
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[338,] -0.32974066
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[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

$flipper_length_mm
               [,1]
  [1,] -1.416271525
  [2,] -1.060696087
  [3,] -0.420660299
  [4,]           NA
  [5,] -0.562890474
  [6,] -0.776235737
  [7,] -1.416271525
  [8,] -0.420660299
  [9,] -0.562890474
 [10,] -0.776235737
 [11,] -1.060696087
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 [24,] -1.131811175
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 [48,] -1.558501700
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 [51,] -1.060696087
 [52,] -0.918465912
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 [70,] -0.207315036
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 [72,] -0.776235737
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 [76,] -0.420660299
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 [78,] -1.202926262
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 [90,] -0.776235737
 [91,]  0.077145314
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 [93,] -1.131811175
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$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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 [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
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 [24,] -1.25748435
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 [42,] -1.25748435
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 [79,] -0.03552216
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 [86,] -0.03552216
 [87,] -0.03552216
 [88,] -0.03552216
 [89,] -0.03552216
 [90,] -0.03552216
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 [92,] -0.03552216
 [93,] -0.03552216
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 [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(.p = is.numeric, .f = 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!

map() is computationally faster than across()
You can complete a larger variety of data manipulations with map() functions
across() is just for datasets, while map() 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 purrr will be much better!

Use functional programming!

Nice Tables

Report Ready Tables in `R`

We have just shown data tables directly, midly formatting for html using kable()

We can make report-ready tables using kableExtra or gt!

Yay reproducibility!

Formatting tables in code makes them completely reproducible
No need to update results manually in a table
No room for copy-paste error
Can integrate directly into a report / paper

Yay reproducibility!

A table for one of my papers, produced directly in R

Nice tables with `kable()` and `kableExtra` functions

Great for tables that don’t need to be super fancy but you want to clean up a bit
Default options look nice in html
Nice options for changing rows / columns individually
Get started with these resources (1) (2)

Nice tables with the `gt` package

Fancy, report tables
Lots of formatting options for common variable types
Syntax less error-prone
Create labels directly with markdown!
Get started
Full index of functions

Table Design Example

“Raw” Table:

Code

tab_dat <- fish |> 
  group_by(species) |> 
  summarize(avg_weight = mean(weight, na.rm = T),
            sd_weight = sd(weight, na.rm = T),
            n = n()) 

tab_dat |> 
  kable()

species	avg_weight	sd_weight	n
Brown	425.9385	381.7946	3171
Bull	598.4199	635.4400	553
RBT	183.2303	182.3361	12341
WCT	266.3916	179.5302	2287

Table Design Example - `kableExtra`

Code

tab_dat |> 
  arrange(desc(avg_weight)) |> 
  kable(digits = c(0, 1, 1, 0),
        col.names = c("Species", "Mean", "SD", "N. Samples"),
        caption = "Summaries of fish weights by species across all sampling years (between 1989 - 2006) trips and sites.") |>
  kable_classic(full_width = F,
                bootstrap_options = "striped") |> 
  add_header_above(c(" " = 1, "Weight (g)" = 2," " = 1),
                   bold = TRUE) |> 
  row_spec(row = 0, bold = T, align = "c")

Summaries of fish weights by species across all sampling years (between 1989 - 2006) trips and sites.
	Weight (g)
Species	Mean	SD	N. Samples
Bull	598.4	635.4	553
Brown	425.9	381.8	3171
WCT	266.4	179.5	2287
RBT	183.2	182.3	12341

Table Design Example - `gt`

Code

tab_dat |> 
  arrange(desc(avg_weight)) |> 
  gt() |> 
  tab_options(table.font.size = 32) |> 
  tab_header(
    title = "Summary of Fish Weights by Species",
    subtitle = "all sampling years, trips, and sites"
  ) |> 
  tab_spanner(label = md("**Weight (g)**"), 
              columns = c(avg_weight, sd_weight)) |> 
  tab_style(style = cell_text(align = "center"),
    locations = cells_column_labels()) |> 
  cols_align(align = "left",
             columns = species) |> 
  fmt_number(columns = c(avg_weight, sd_weight),
             decimals = 1) |> 
  fmt_number(columns = n,
             decimals = 0) |> 
  cols_label(
    "avg_weight" = md("**Mean**"),
    "sd_weight" = md("**SD**"),
    "n" = md("**N. Samples**"),
    "species" = md("**Species**")
  )

Species	Weight (g)		N. Samples
Summary of Fish Weights by Species
all sampling years, trips, and sites
Species	Mean	SD	N. Samples
Bull	598.4	635.4	553
Brown	425.9	381.8	3,171
WCT	266.4	179.5	2,287
RBT	183.2	182.3	12,341

Table Design

What do you want to communicate / emphasize?
What should the rows and columns be?
- What are clear labels?
- Order of rows and columns?
Is there any grouping of rows and/or columns that would be helpful?

To do…

Project Proposal + Group Contract
- Due Friday 5/15 at 11:59pm.
Lab 7: Searching for Efficiency
- Due Sunday 5/17 at 11:59pm.
Required Reading
- Review all material from this week for the group quiz on Monday!

Iteration

Wednesday, 5/13

Project Proposal + Group Contract

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() 😭😭😭!!!

Use functional programming!

Nice Tables

Report Ready Tables in R

Yay reproducibility!

Yay reproducibility!

Nice tables with kable() and kableExtra functions

Nice tables with the gt package

Table Design Example

Table Design Example - kableExtra

Table Design Example - gt

Table Design

To do…

Functional Programming with `purrr`

`map()`

`map()` + Dataframes

The `map()` Family

`map()` + penguins

`map_if()`

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

Report Ready Tables in `R`

Nice tables with `kable()` and `kableExtra` functions

Nice tables with the `gt` package

Table Design Example - `kableExtra`

Table Design Example - `gt`