Iteration

Today we will…

New Material
- Performing Repeated Tasks + Vectorization
- Functional Programming: the map() family
Lab 8 Recap
Lab 9 Preview

It’s Week 8 (or 9)…

How are you taking care of yourself?

Performing Repeated Tasks

Repetition

Type out the task over and over.

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

Do not do this.

Iteration

Repeatedly execute the same operation over and over.

Loops (e.g., for() and while()) allow us to iterate.

for(i in 1:6){
  print(i^2)
}

[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36

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

Vectorization

Many operations in R are vectorized.

These functions operate on vectors of values rather than a single value.
We can iterate without writing a loop.

x <- seq(from = -4, to = 6)

abs(x)

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

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

x

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

Vectorization

Not every function is vectorized.

For example, if() statements cannot operate on vectors.

pos_neg_zero <- function(x){
  if(x > 0){
    return("Greater than 0!")
  } 
  else if (x < 0){
    return("Less than 0!")
  } 
  else {
    return("Equal to 0!")
  }
}

pos_neg_zero(x)

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

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

What do we do instead?

Option 1 – Looping

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

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

result

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

What do we do instead?

Option 2 – Write a Vectorized Function

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

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

pos_neg_zero(x)

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

Some functions cannot be vectorized!

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

class(penguins[["species"]])

[1] "factor"

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

class(penguins)

[1] "tbl_df"     "tbl"        "data.frame"

What can we do instead?

Write a for() loop…

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

for(i in seq_along(penguins)){
  data_type[i] <- class(penguins[[i]])
}

Code

tibble(column = names(penguins), 
       type = data_type) |> 
  pivot_wider(names_from = column, 
              values_from = type) |>  
  knitr::kable() |>
  kableExtra::kable_styling(font_size = 30)

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

…but loops make it difficult to understand what the code is doing

Which is easier to understand what is being made?

A long recipe

For the génoise sponge

4 large eggs
100g caster sugar
Pinch of salt
1 tsp vanilla extract
80g plain flour
35g cornflour
30g unsalted butter, melted

For the soaking syrup

50g caster sugar
3 tbsp water
2 tbsp raspberry liqueur

For the pistachio paste

100g pistachios
2 tsp pistachio oil
15 drops pistachio extract

For the crème mousseline

500ml whole milk
1 vanilla pod, halved & seeds scraped
4 egg yolks
100g caster sugar
40g cornflour
200g unsalted butter, softened

For the filling

450g fresh large raspberries

For the marzipan

100g ground almonds
100g icing sugar, plus extra for dusting
20g–25g pasteurised egg white
Pistachio green food colouring

For the sugar dome

50g water
100g caster sugar
50g glucose

A picture

An image of Paul Hollywood's Framboisier from Season 13 of Bake Off. The cake is made of two layers of génoise sponge with a layer of crème mousseline on the inside. A layer of raspberries separates the two layers of cake. On the top of the cake there sits a clear sugar dome with a pink (candy) flower on the inside.

Code that is easier to understand

penguins |> 
  summarise(
    across(.cols = everything(),
           .fns = ~ class(.x)
           )
            ) |>  
  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

`across()` is easier to understand…

…but across() uses a for() loop which is computationally intensive!

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

Enter `purrr`

The functions in the purrr package iterate over the elements of .x, but don’t use a for() loop at the wrapper level.

Instead, the looping happens inside an internal helper function (map_()), which is written in C++.

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

So, the loops run much faster than loops written in R.

The use of C++ code is not unique to purrr!

The dplyr, tidyr, data.table, and rstan packages also use C code.

`map()`

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

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

Using the |>

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

`map()` + Dataframes

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

penguins |> 
  select(where(is.numeric)) |>
  map(.f = ~ mean(.x, na.rm = TRUE))

$bill_length_mm
[1] 43.92193

$bill_depth_mm
[1] 17.15117

$flipper_length_mm
[1] 200.9152

$body_mass_g
[1] 4201.754

$year
[1] 2008.029

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

The `map()` Family

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

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

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

Output is a vector of doubles

Calculate the mean of each penguin measurement column.

penguins |> 
  select(starts_with(c("bill", "body", "flipper"))) |> 
  map_dbl(.f = ~ mean(.x, na.rm = TRUE))

   bill_length_mm     bill_depth_mm       body_mass_g flipper_length_mm 
         43.92193          17.15117        4201.75439         200.91520

Output is a vector of integers.

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 logicals

Calculate the number of NAs in each column.

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

Error in `map_lgl()`:
ℹ In index: 3.
ℹ With name: bill_length_mm.
Caused by error:
! Can't coerce from an integer to a logical.

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

Comparing Speed

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

for()
map()
Run
Compare

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

map_func <- function(df){
  map_chr(df, .f = ~ class(.x))
}

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

loop_func(df)

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

map_func(df)

       V1        V2        V3        V4        V5        V6        V7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"

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

`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 frame

penguins |> 
  mutate(across(.cols = where(is.numeric), 
                .fns = ~ scale(.x, center = TRUE, scale = TRUE)
                )
         )

# 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(.x, center = TRUE, scale = TRUE))

$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
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[133,] -1.30448221
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[149,] -1.45101353
[150,] -1.12131806
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[153,]  0.39894437
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[156,]  1.11328455
[157,]  0.67369059
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[159,]  0.27072946
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[163,] -0.55350920
[164,]  0.93012040
[165,]  0.28904588
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[167,]  0.34399512
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[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
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[201,]  0.17914739
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[203,]  0.49052644
[204,]  0.83853832
[205,]  0.21578022
[206,]  1.13160096
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[209,] -0.02233317
[210,]  0.28904588
[211,] -0.13223166
[212,]  1.18655021
[213,]  0.25241305
[214,]  0.41726078
[215,]  0.32567871
[216,]  1.90089038
[217,]  0.34399512
[218,]  1.07665172
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[220,]  1.02170247
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[223,]  0.69200701
[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
[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
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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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 [42,]  0.63238542
 [43,]  0.68302364
 [44,]  1.29068222
 [45,] -0.12718780
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 [47,]  0.93621471
 [48,]  0.88557650
 [49,]  0.37919435
 [50,]  2.05025544
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 [54,]  1.18940579
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 [78,]  1.13876757
 [79,] -0.53229351
 [80,]  0.98685293
 [81,]  0.02472685
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 [83,]  0.83493828
 [84,]  1.13876757
 [85,]  0.32855614
 [86,]  1.59451151
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 [90,]  0.83493828
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[100,]  0.68302364
[101,]  0.37919435
[102,]  1.44259686
[103,] -0.58293173
[104,]  1.44259686
[105,]  0.73366185
[106,]  0.88557650
[107,]  0.02472685
[108,]  1.44259686
[109,] -0.07654958
[110,]  0.93621471
[111,] -0.32974066
[112,]  1.59451151
[113,]  0.27791792
[114,]  1.18940579
[115,]  1.79706436
[116,]  0.58174721
[117,] -0.07654958
[118,]  1.69578793
[119,] -0.07654958
[120,]  0.73366185
[121,]  0.02472685
[122,]  1.34132043
[123,] -0.07654958
[124,]  0.68302364
[125,] -0.63356994
[126,]  0.93621471
[127,]  0.22727971
[128,]  0.58174721
[129,] -0.02591137
[130,]  0.42983257
[131,]  0.37919435
[132,]  1.03749114
[133,]  0.68302364
[134,]  0.68302364
[135,]  0.22727971
[136,]  0.17664149
[137,]  0.17664149
[138,]  1.49323508
[139,] -0.32974066
[140,]  0.37919435
[141,] -0.02591137
[142,]  0.02472685
[143,] -0.83612280
[144,] -0.07654958
[145,] -0.17782601
[146,]  0.78430007
[147,]  0.73366185
[148,]  0.63238542
[149,]  0.32855614
[150,]  0.48047078
[151,] -0.02591137
[152,]  0.68302364
[153,] -2.00080174
[154,] -0.43101709
[155,] -1.54505781
[156,] -0.98803745
[157,] -1.34250495
[158,] -1.84888710
[159,] -1.29186674
[160,] -0.93739923
[161,] -1.89952531
[162,] -0.88676102
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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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[175,] -1.34250495
[176,] -0.68420816
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[180,] -1.08931388
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[184,] -1.49441960
[185,] -1.34250495
[186,] -0.07654958
[187,] -1.19059031
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[189,] -1.74761067
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[192,] -0.73484637
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[194,] -0.58293173
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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
[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
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[314,]  1.79706436
[315,] -0.27910244
[316,]  1.39195865
[317,]  1.18940579
[318,]  0.17664149
[319,]  0.98685293
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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
[337,]  1.18940579
[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

$flipper_length_mm
               [,1]
  [1,] -1.416271525
  [2,] -1.060696087
  [3,] -0.420660299
  [4,]           NA
  [5,] -0.562890474
  [6,] -0.776235737
  [7,] -1.416271525
  [8,] -0.420660299
  [9,] -0.562890474
 [10,] -0.776235737
 [11,] -1.060696087
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 [15,] -0.207315036
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 [20,] -0.491775386
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 [24,] -1.131811175
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 [43,] -1.060696087
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 [48,] -1.558501700
 [49,] -0.776235737
 [50,] -0.705120649
 [51,] -1.060696087
 [52,] -0.918465912
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 [54,] -0.065084861
 [55,] -0.989581000
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 [63,] -1.131811175
 [64,] -0.634005562
 [65,] -1.202926262
 [66,] -0.634005562
 [67,] -0.420660299
 [68,] -0.918465912
 [69,] -0.776235737
 [70,] -0.207315036
 [71,] -0.776235737
 [72,] -0.776235737
 [73,] -0.349545211
 [74,] -0.278430124
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 [76,] -0.420660299
 [77,] -0.705120649
 [78,] -1.202926262
 [79,] -0.989581000
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attr(,"scaled:center")
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attr(,"scaled:scale")
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$body_mass_g
               [,1]
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attr(,"scaled:center")
[1] 4201.754
attr(,"scaled:scale")
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$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]
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[100,] -0.03552216
[101,]  1.18644003
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[148,]  1.18644003
[149,]  1.18644003
[150,]  1.18644003
[151,]  1.18644003
[152,]  1.18644003
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[170,] -1.25748435
[171,] -1.25748435
[172,] -1.25748435
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[174,] -1.25748435
[175,] -1.25748435
[176,] -1.25748435
[177,] -1.25748435
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[180,] -1.25748435
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[182,] -1.25748435
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[186,] -1.25748435
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[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
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[248,]  1.18644003
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[250,]  1.18644003
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[255,]  1.18644003
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[258,]  1.18644003
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[344,]  1.18644003
attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559

penguins |> 
  map_if(.p = is.numeric, .f = ~ scale(.x, center = TRUE, scale = TRUE)) |> 
  bind_cols()

# A tibble: 344 × 4
   species island    bill_length_mm[,1] bill_depth_mm[,1]
   <fct>   <fct>                  <dbl>             <dbl>
 1 Adelie  Torgersen             -0.883             0.784
 2 Adelie  Torgersen             -0.810             0.126
 3 Adelie  Torgersen             -0.663             0.430
 4 Adelie  Torgersen             NA                NA    
 5 Adelie  Torgersen             -1.32              1.09 
 6 Adelie  Torgersen             -0.847             1.75 
 7 Adelie  Torgersen             -0.920             0.329
 8 Adelie  Torgersen             -0.865             1.24 
 9 Adelie  Torgersen             -1.80              0.480
10 Adelie  Torgersen             -0.352             1.54 
# ℹ 334 more rows

`map_int()` to a data frame

The enframe() function converts a named vector to a data frame.

penguins |> 
  map_int(.f = ~ sum(is.na(.x))) |> 
  enframe()

# A tibble: 8 × 2
  name              value
  <chr>             <int>
1 species               0
2 island                0
3 bill_length_mm        2
4 bill_depth_mm         2
5 flipper_length_mm     2
6 body_mass_g           2
7 sex                  11
8 year                  0

`map_int()` to a data frame

You can set the names of the columns!

penguins |> 
  map_int(.f = ~ sum(is.na(.x))) |> 
  enframe(name = "Variable", 
          value = "Number Missing")

# A tibble: 8 × 2
  Variable          `Number Missing`
  <chr>                        <int>
1 species                          0
2 island                           0
3 bill_length_mm                   2
4 bill_depth_mm                    2
5 flipper_length_mm                2
6 body_mass_g                      2
7 sex                             11
8 year                             0

Use functional programming!

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

Lab 8: Searching for Efficiency

Revisit previous lab problems through the lens of efficiency

Use functions from map() instead of across()

Reduce separate pipelines into a single pipeline

Lab 8: Making Great Tables

Simple Tables

the kable() function from the knitr package
the gt() function from the gt package

More Sophisticated Tables

The kableExtra package
Add-on functions from the gt package (e.g., cols_label(), tab_header(), fmt_percent())

Interactive Tables

The datatable() function from the DT package!

library(DT)
datatable(penguins)

Adding Color to cells

Adding Color to Cells

Lab 7 & Challenge 7 Revisions

Input Checks

What types of inputs do your functions expect?

numbers?

stopifnot("Input vector must be numeric" = is.numeric(vec))

values greater than 0?

stopifnot("Input vector must be numeric" = is.numeric(vec), 
          "Input vector must have values larger than 0" = any(vec > 0))

vectors of the same length?

stopifnot("Input vector must be numeric" = is.numeric(vec), 
          "Input vector must have values larger than 0" = any(vec > 0), 
          "Max must be a scaler value" = length(max) == 1, 
          "Min must be a scaler value" = length(min) == 1)

Clear & Creative Plots

Missing Values

Code

fish |>
  mutate(has_missing = if_any(weight, is.na),
         trip = fct_recode(factor(trip),
                      "First Survey (Trip 1)" = "1",
                      "Second Survey (Trip 2)" = "2")) |>
  group_by(year, section, trip) |>
  summarize(
    n_missing = sum(has_missing),
    n_total = n(),
    pct_missing = (n_missing / n_total) * 100,
    .groups = "drop"
  ) |>
  ggplot(aes(x = factor(year), 
             y = n_missing, 
             fill = section)) +
  geom_col(position = "dodge", 
           color = "white", 
           linewidth = 0.3) +
  geom_text(aes(label = n_missing), 
            position = position_dodge(width = 0.9),
            vjust = -0.5, 
            size = 3) +
  facet_wrap(~trip) +
  labs(
    x = "",
    y = "",
    fill = "River Section",
    title = "Missing Values in Fish Measurements by Year, Section, and Trip",
    subtitle = "Rainbow and brown trout measurements across the Blackfoot River"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 13),
    plot.subtitle = element_text(size = 10, color = "gray40"),
    strip.text = element_text(face = "bold", size = 11),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "Set1")

Condition Indices

Code

mycolors <- RColorBrewer::brewer.pal(8, "BrBG")[c(3, 2, 1, 8)] # color palette
  
fish |> 
  mutate(condition = condition_index(length, weight)) |> 
  group_by(year, species) |> 
  summarize(mean_condition = mean(condition, na.rm = TRUE),
            .groups = "drop") |> 
  mutate(species = fct_reorder2(.f = species,
                               .x = year,
                               .y = mean_condition)) |> 
  ggplot(mapping = aes(x = year, y = mean_condition, color = species)) +
  geom_rug(sides = "b", color = "gray85", lineend = "round", length = unit(100, "npc")) +
  geom_smooth(se = FALSE) +
  geom_point() +
  theme_linedraw() +
  scale_color_manual(values = mycolors,
                     labels = c("Rainbow Trout",
                                "Brown Trout",
                                "Westslope Cutthroat Trout",
                                "Bull Trout")) +
  scale_x_continuous(breaks = seq(1988, 2007, by = 1)) +
  theme(axis.text.x = element_text(size = 7),
        panel.grid.minor.x = element_blank(),
        panel.grid.major.x = element_blank(),
        plot.title = element_text(face = "bold"),
        plot.caption.position = "plot") +
  labs(title = "Bull Trout in Worse Condition than Other Trout Species",
       subtitle = "Mean Condition Index",
       x = NULL,
       y = NULL,
       color = "Trout Species",
       caption = "Fish captured on the Blackfoot River outside Helena, Montana")

Removing Your Legend

Code

fish |>
  group_by(year, section, trip) |>
  summarize(missing_count = sum(is.na(weight)), 
            .groups = "drop") |>
  mutate(trip = if_else(trip == 1,
                        "Trip 1",
                        "Trip 2")
         ) |>
  ggplot(aes(x = year, y = missing_count, color = section)) +
  geom_line(size = 1) +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = str_c("Missing Fish Weights for the ",
      "<span style = 'color:#66C2A5'>**Johnsrud**</span>",
      " and <span style = 'color:#FC8D62'>**ScottyBrown**</span>",
      " Sections of the Blackfoot River"),
    x = "",
    y = "",
    color = "Section") +
  facet_wrap(~ trip) +
  theme_minimal() + 
  theme(plot.title = ggtext::element_markdown(size = 16), 
        legend.position = "none",
        axis.text.x = element_text(size = 12), 
        axis.text.y = element_text(size = 12), 
        axis.title = element_text(size = 12),
        strip.text.x = element_text(size = 12)
        )

Using Annotations Instead

Code

plot <- fish |>
  group_by(year, section, trip) |>
  summarize(missing_count = sum(is.na(weight)), .groups = "drop") |>
  mutate(trip = case_when(
    trip == 1 ~ "Trip 1",
    trip == 2 ~ "Trip 2")) |>
  ggplot(aes(x = year, y = missing_count, color = section)) +
  geom_line(linewidth = 1) +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "Missing Fish Weights for Trout Species on the Blackfoot River in Montana",
    x = "",
    y = "",
    color = "Section") +
  facet_wrap(~ trip) +
  theme_minimal() +
  theme(legend.position = "none", 
        plot.title = element_text(size = 18), 
        axis.text.x = element_text(size = 12), 
        axis.text.y = element_text(size = 12)
        )


annotate_text <- data.frame(year = c(1994, 1993), 
                            missing_count = c(150, 40),
                            trip = factor(c("Trip 2", "Trip 2"), 
                                          levels = c("Trip 1","Trip 2"), 
                                          ), 
                            section = factor(c("Johnsrud", "ScottyBrown"), 
                                             levels = c("Johnsrud", "ScottyBrown"))
                            )
plot + 
  geom_text(data = annotate_text, label = c("Johnsrud", "ScottyBrown"))

Iteration

Today we will…

It’s Week 8 (or 9)…

Performing Repeated Tasks

Repetition

Iteration

Vectorization

Vectorization

What do we do instead?

What do we do instead?

Some functions cannot be vectorized!

What can we do instead?

…but loops make it difficult to understand what the code is doing

Which is easier to understand what is being made?

A long recipe

A picture

Code that is easier to understand

across() is easier to understand…

Enter purrr

map()

map() + Dataframes

The map() Family

map() + penguins

Comparing Speed

map_if()

map_int() to a data frame

map_int() to a data frame

Use functional programming!

Lab 8: Searching for Efficiency

Lab 8: Making Great Tables

Simple Tables

More Sophisticated Tables

Interactive Tables

Adding Color to cells

Adding Color to Cells

Lab 7 & Challenge 7 Revisions

Input Checks

Clear & Creative Plots

Missing Values

Condition Indices

Removing Your Legend

Using Annotations Instead

`across()` is easier to understand…

Enter `purrr`

`map()`

`map()` + Dataframes

The `map()` Family

`map()` + penguins

`map_if()`

`map_int()` to a data frame

`map_int()` to a data frame