11 Loops+Iter P1

##Libraries

library(tibble)
library(tidyverse)

🧩 Learning Goals

By the end of this lesson, you should be able to:

• Write a for loop in R to handle repeated tasks
  • Use seq_along() and seq_len() to iterate over vectors or rows of a data frame
  • Store output from a for loop in a list or other storage container
• Recognize that vectorized functions help us avoid for loops in many situations
• Use the across() function to wrangle multiple variables simultaneously

Iteration

When we want to reuse code, we can use:

• functions to save the steps of the task
• iteration to apply that function to new inputs

Let’s talk about iteration in R.

• Iterating using a for loop & how to do it appropriately in R (today)
• Iterating across data frame columns (today)
• Iterating using map and walk for iteration, instead of a for loop (next class)

Purposes of Iteration

Creating or saving a new object

• Performing an action with a return output
• Ex. creating new variables/datasets, fitting models, etc.
• We’ll need to consider storage (memory allocation).
• Next class, we’ll use map() for this…

Side effects

• Performing an action without a R return output
• Ex: saving data using write_csv() or saving a plot with png()
• We don’t need to worry about storage in R.
• We could use walk() for this…

for loops

In R, for loops have the following general structure:

for (i in some_vector) {
    # Code to do stuff based on i
}

some_vector can be any vector, including:

• An indexing integer vector: 1:3 [Most common!]
• A character vector: c("group1", "group2", "group3")
• A vector of any other class

groups <- c("group1", "group2", "group3") # a vector to iterate over
 
for (i in 1:3) { # i: index
    print(groups[i])
}

[1] "group1"
[1] "group2"
[1] "group3"

for (g in groups) { # iterating over the vector
    print(g)
}

[1] "group1"
[1] "group2"
[1] "group3"

To iterate over an indexing vector, it is useful to know about the following two functions:

• seq_along(v): generates an integer sequence from 1 to the length of the vector v supplied
• seq_len(n): generates an integer sequence from 1 to n

for (i in seq_along(groups)) {
    print(groups[i])
}

[1] "group1"
[1] "group2"
[1] "group3"

for (i in seq_len(length(groups))) {
    print(groups[i])
}

[1] "group1"
[1] "group2"
[1] "group3"

A nice feature of seq_along() is that it generates an empty iteration vector if the vector you’re iterating over itself has length 0.

no_groups <- c()
seq_along(no_groups)

integer(0)

for (i in seq_along(no_groups)) {
    print(no_groups[i])
}

seq_len() is useful for iterating over the rows of a data frame because seq_along() would iterate over columns:

small_data <- tibble(a = 1:2, b = 2:3, c = 4:5)

for (col in small_data) { # each element is the column 
    print(col)
}

[1] 1 2
[1] 2 3
[1] 4 5

for (r in seq_len(nrow(small_data))) {
    print(r)
}

[1] 1
[1] 2

Indexing is particularly important when we want to create/save new objects as we need to store output created during a for loop.

When using a for loop to create/save output, we should create storage containers ahead of time with the vector() function. This allocates memory for the output and is a more efficient use of memory.

char_storage <- vector("character", length = 3)
num_storage <- vector("numeric", length = 3)
list_storage <- vector("list", length = 3)

char_storage

[1] "" "" ""

num_storage

[1] 0 0 0

list_storage

[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

for (i in seq_len(3)) {
    char_storage[i] <- str_c("Number: ", i)
    num_storage[i] <- 2*i
    list_storage[[i]] <- i # Note the [[ for subsetting here
}

char_storage

[1] "Number: 1" "Number: 2" "Number: 3"

num_storage

[1] 2 4 6

list_storage

[[1]]
[1] 1

[[2]]
[1] 2

[[3]]
[1] 3

for loops and memory allocation

If you don’t create a storage container ahead of time, you will regret it. Let’s bench mark our code to see the difference!

# Example 1: No storage container; append new value to existing vector
x <- integer()
bench::mark(
  for (i in 1:1e5) {
    x <- c(x, i)
  }
)

# Example 2: Using a storage container!
#Notice median (time to run), mem_alloc (how much memory you are using), and gc (garbage collection)
x <- vector('integer', length = 1e5)
bench::mark(
  for (i in 1:1e5) {
    x[i] <- i
  }
)


# Example 3: Vectorized function
#Notice median (time to run), mem_alloc (how much memory you are using), and gc (garbage collection)
bench::mark(
  x <- seq(1, 1e5, by = 1)
)

Exercises

Pair programming exercises: Whoever has most recently eaten dessert (broadly interpreted) should be the driver first. Switch after Exercise 2.

Write for-loops that do each of the following:

1. Prints the even numbers from 2:20
   • Then, produce the same output with the seq() function

# for loop
for (i in seq_len(10)) {
  print(2*i)
}

[1] 2
[1] 4
[1] 6
[1] 8
[1] 10
[1] 12
[1] 14
[1] 16
[1] 18
[1] 20

# vectorized approach with `seq()`
seq(from = 2, to = 20, by = 2)

 [1]  2  4  6  8 10 12 14 16 18 20

2. Iterates over the month.name vector and stores a character vector of output containing strings like “Month 1: January”, “Month 2: February”.
   • Then, produce the same output with str_c() only.

month_string <- vector("character", length = length(month.name))

# for loop
for (i in seq_along(month.name)) {
  month_string[i] <- str_c("Month ", i, " : ", month.name[i])
}

month_string

 [1] "Month 1 : January"   "Month 2 : February"  "Month 3 : March"    
 [4] "Month 4 : April"     "Month 5 : May"       "Month 6 : June"     
 [7] "Month 7 : July"      "Month 8 : August"    "Month 9 : September"
[10] "Month 10 : October"  "Month 11 : November" "Month 12 : December"

# vectorized approach with `str_c()`
str_c("Month ", 1:12, ": ", month.name)

 [1] "Month 1: January"   "Month 2: February"  "Month 3: March"    
 [4] "Month 4: April"     "Month 5: May"       "Month 6: June"     
 [7] "Month 7: July"      "Month 8: August"    "Month 9: September"
[10] "Month 10: October"  "Month 11: November" "Month 12: December"

3. On the diamonds dataset, fit stratified models of price vs. carat, model separately estimated for each value of cut, and store the fitted models in a list storage container.

data(diamonds)

# for loop
cut_type <- diamonds |> pull(cut) |> levels()
cut_vector <- vector("list", length = length(cut_type))

for (i in seq_along(cut_type)) {
  specific_cut <- cut_type[i]
  sub_diamonds <- diamonds |> 
    filter(cut == specific_cut)
  cut_vector[[i]] <- lm(price ~ carat, data = sub_diamonds)
}

Iteration across data frame columns

Often we will have to perform the same data wrangling on many variables (e.g., rounding numbers)

diamonds |>
    mutate(
        carat = round(carat, 1),
        x = round(x, 1),
        y = round(y, 1),
        z = round(z, 1)
    )

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1   0.2 Ideal     E     SI2      61.5    55   326   4     4     2.4
 2   0.2 Premium   E     SI1      59.8    61   326   3.9   3.8   2.3
 3   0.2 Good      E     VS1      56.9    65   327   4     4.1   2.3
 4   0.3 Premium   I     VS2      62.4    58   334   4.2   4.2   2.6
 5   0.3 Good      J     SI2      63.3    58   335   4.3   4.3   2.8
 6   0.2 Very Good J     VVS2     62.8    57   336   3.9   4     2.5
 7   0.2 Very Good I     VVS1     62.3    57   336   4     4     2.5
 8   0.3 Very Good H     SI1      61.9    55   337   4.1   4.1   2.5
 9   0.2 Fair      E     VS2      65.1    61   337   3.9   3.8   2.5
10   0.2 Very Good H     VS1      59.4    61   338   4     4     2.4
# ℹ 53,930 more rows

dplyr provides the across() function for performing these repeated function calls:

# Option 1: Create our own named function
round_to_one <- function(x) {
    round(x, digits = 1)
}
diamonds |> 
    mutate(across(.cols = c(carat, x, y, z), .fns = round_to_one))

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1   0.2 Ideal     E     SI2      61.5    55   326   4     4     2.4
 2   0.2 Premium   E     SI1      59.8    61   326   3.9   3.8   2.3
 3   0.2 Good      E     VS1      56.9    65   327   4     4.1   2.3
 4   0.3 Premium   I     VS2      62.4    58   334   4.2   4.2   2.6
 5   0.3 Good      J     SI2      63.3    58   335   4.3   4.3   2.8
 6   0.2 Very Good J     VVS2     62.8    57   336   3.9   4     2.5
 7   0.2 Very Good I     VVS1     62.3    57   336   4     4     2.5
 8   0.3 Very Good H     SI1      61.9    55   337   4.1   4.1   2.5
 9   0.2 Fair      E     VS2      65.1    61   337   3.9   3.8   2.5
10   0.2 Very Good H     VS1      59.4    61   338   4     4     2.4
# ℹ 53,930 more rows

# Option 2: Use an "anonymous" or "lambda" function that isn't named/saved
diamonds |> 
    mutate(across(.cols = c(carat, x, y, z), .fns = function(x) {round(x, digits = 1)} ))

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1   0.2 Ideal     E     SI2      61.5    55   326   4     4     2.4
 2   0.2 Premium   E     SI1      59.8    61   326   3.9   3.8   2.3
 3   0.2 Good      E     VS1      56.9    65   327   4     4.1   2.3
 4   0.3 Premium   I     VS2      62.4    58   334   4.2   4.2   2.6
 5   0.3 Good      J     SI2      63.3    58   335   4.3   4.3   2.8
 6   0.2 Very Good J     VVS2     62.8    57   336   3.9   4     2.5
 7   0.2 Very Good I     VVS1     62.3    57   336   4     4     2.5
 8   0.3 Very Good H     SI1      61.9    55   337   4.1   4.1   2.5
 9   0.2 Fair      E     VS2      65.1    61   337   3.9   3.8   2.5
10   0.2 Very Good H     VS1      59.4    61   338   4     4     2.4
# ℹ 53,930 more rows

# Anonymous functions can be written in a more concise way
# function(x){ length(unique(x)) } OR ~ length(unique(.x))
# function(x,y){ length(unique(x + y)) } OR ~ length(unique(.x + .y))

When we look at the documentation for across(), we see that the .cols argument specifies which variables we want to transform, and it has a <tidy-select> tag. This means that the syntax we use for .cols follows certain rules.

Let’s click this link to explore the possibilities for selecting variables.

• Read through the “Overview of selection features” section to get an overall sense of the many ways to select variables.
• Navigate back to the across() documentation and read through the Examples section at the bottom. Click the “Run examples” link to view the output for all of the examples.

What if we wanted to perform multiple transformations on each of many variables?

Within the different values of diamond cut, let’s summarize the mean, median, and standard deviation of the numeric variables. When we look at the .fns argument in the across() documentation, we see that we can provide a list of functions:

diamonds |>
    group_by(cut) |> 
    summarize(across(.cols = where(is.numeric), .fns = list(mean = mean, med = median, sd = sd)))

# A tibble: 5 × 22
  cut     carat_mean carat_med carat_sd depth_mean depth_med depth_sd table_mean
  <ord>        <dbl>     <dbl>    <dbl>      <dbl>     <dbl>    <dbl>      <dbl>
1 Fair         1.05       1       0.516       64.0      65      3.64        59.1
2 Good         0.849      0.82    0.454       62.4      63.4    2.17        58.7
3 Very G…      0.806      0.71    0.459       61.8      62.1    1.38        58.0
4 Premium      0.892      0.86    0.515       61.3      61.4    1.16        58.7
5 Ideal        0.703      0.54    0.433       61.7      61.8    0.719       56.0
# ℹ 14 more variables: table_med <dbl>, table_sd <dbl>, price_mean <dbl>,
#   price_med <dbl>, price_sd <dbl>, x_mean <dbl>, x_med <dbl>, x_sd <dbl>,
#   y_mean <dbl>, y_med <dbl>, y_sd <dbl>, z_mean <dbl>, z_med <dbl>,
#   z_sd <dbl>

What does the list of functions look like? What is the structure of this list object?

list_of_fcts <- list(mean = mean, med = median, sd = sd)
list_of_fcts

$mean
function (x, ...) 
UseMethod("mean")
<bytecode: 0x106ce4a60>
<environment: namespace:base>

$med
function (x, na.rm = FALSE, ...) 
UseMethod("median")
<bytecode: 0x11fc23f50>
<environment: namespace:stats>

$sd
function (x, na.rm = FALSE) 
sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x), 
    na.rm = na.rm))
<bytecode: 0x11934adc8>
<environment: namespace:stats>

str(list_of_fcts)

List of 3
 $ mean:function (x, ...)  
 $ med :function (x, na.rm = FALSE, ...)  
 $ sd  :function (x, na.rm = FALSE)  

Exercises

Using the diamonds dataset:

1. Transform the x, y, and z columns so that the units of millimeters are displayed (e.g., “4.0 mm”).

diamonds |> 
  summarize(across(.cols = c(x, y, z), .fns = function(x) {str_c(x, " mn")}))

# A tibble: 53,940 × 3
   x       y       z      
   <chr>   <chr>   <chr>  
 1 3.95 mn 3.98 mn 2.43 mn
 2 3.89 mn 3.84 mn 2.31 mn
 3 4.05 mn 4.07 mn 2.31 mn
 4 4.2 mn  4.23 mn 2.63 mn
 5 4.34 mn 4.35 mn 2.75 mn
 6 3.94 mn 3.96 mn 2.48 mn
 7 3.95 mn 3.98 mn 2.47 mn
 8 4.07 mn 4.11 mn 2.53 mn
 9 3.87 mn 3.78 mn 2.49 mn
10 4 mn    4.05 mn 2.39 mn
# ℹ 53,930 more rows

2. Convert all numeric columns into character columns.
   • Hint: type is. and hit Tab in the Console. Scroll through the function options. Do the same with as.

diamonds |> 
  mutate(across(.cols = where(is.numeric), .fns = as.character))

# A tibble: 53,940 × 10
   carat cut       color clarity depth table price x     y     z    
   <chr> <ord>     <ord> <ord>   <chr> <chr> <chr> <chr> <chr> <chr>
 1 0.23  Ideal     E     SI2     61.5  55    326   3.95  3.98  2.43 
 2 0.21  Premium   E     SI1     59.8  61    326   3.89  3.84  2.31 
 3 0.23  Good      E     VS1     56.9  65    327   4.05  4.07  2.31 
 4 0.29  Premium   I     VS2     62.4  58    334   4.2   4.23  2.63 
 5 0.31  Good      J     SI2     63.3  58    335   4.34  4.35  2.75 
 6 0.24  Very Good J     VVS2    62.8  57    336   3.94  3.96  2.48 
 7 0.24  Very Good I     VVS1    62.3  57    336   3.95  3.98  2.47 
 8 0.26  Very Good H     SI1     61.9  55    337   4.07  4.11  2.53 
 9 0.22  Fair      E     VS2     65.1  61    337   3.87  3.78  2.49 
10 0.23  Very Good H     VS1     59.4  61    338   4     4.05  2.39 
# ℹ 53,930 more rows

Case study: performing many different versions of an analysis

One of my most common use cases for writing functions and iteration/looping is to perform some exploration or modeling repeatedly for several different “settings”.

For example, our broad goal might be to fit a linear regression model to our data. However, there are often multiple choices that we have to make in practice:

• Keep missing values or fill them in (imputation)?
• Fit the model only on a certain group of cases?
• Filter out outliers in one or more variables?
• Transform certain variables? (e.g., log transformation)

We can map any number of these choices to arguments in a custom model-fitting function:

• impute: TRUE or FALSE
• filter_to: This could be a string description like “All cases”, “Group 1”, or “Groups 1 and 2”

fit_model <- function(data, impute, filter_to) {
    if (impute) {
        data <- some_imputation_function(data)
    }
    
    if (filter_to=="Group 1") {
        data <- data |> 
            filter(group == 1)
    } else if (filter_to == "Groups 1 and 2") {
        data <- data |> 
            filter(group %in% c(1,2))
    }
    
    lm(y ~ x1 + x2, data = data)
}

The tidyr package has a useful function called crossing() that is useful for generating argument combinations.

For each argument, we specify all possible values for that argument. crossing() generates all combinations:

df_arg_combos <- crossing(
    impute = c(TRUE, FALSE),
    filter_to = c("All cases", "Group 1", "Groups 1 and 2")
)
df_arg_combos

# A tibble: 6 × 2
  impute filter_to     
  <lgl>  <chr>         
1 FALSE  All cases     
2 FALSE  Group 1       
3 FALSE  Groups 1 and 2
4 TRUE   All cases     
5 TRUE   Group 1       
6 TRUE   Groups 1 and 2

# Another example
crossing(
    option1 = 1:3,
    option2 = c(TRUE, FALSE),
    option3 = c("low", "medium", "high")
)

# A tibble: 18 × 3
   option1 option2 option3
     <int> <lgl>   <chr>  
 1       1 FALSE   high   
 2       1 FALSE   low    
 3       1 FALSE   medium 
 4       1 TRUE    high   
 5       1 TRUE    low    
 6       1 TRUE    medium 
 7       2 FALSE   high   
 8       2 FALSE   low    
 9       2 FALSE   medium 
10       2 TRUE    high   
11       2 TRUE    low    
12       2 TRUE    medium 
13       3 FALSE   high   
14       3 FALSE   low    
15       3 FALSE   medium 
16       3 TRUE    high   
17       3 TRUE    low    
18       3 TRUE    medium 

With iteration in our toolbox, we can iterate the fit_model() function over the combinations in df_arg_combos.

Exercises

Goal: In the diamonds dataset, we want to understand the relationship between price and size (carat). We want to explore variation along two choices:

• The variables included in the model. We’ll explore 3 sets of variables:
  • No further variables (just price and carat)
  • Adjusting for cut
  • Adjusting for cut and clarity
  • Adjusting for cut, clarity, and color
• Whether or not to remove outliers in the carat variable. We’ll define outliers as cases whose carat is over 3 SDs away from the mean.

Work with your partner on the following exercises. As you work, make note of what is challenging and any helpful thought processes/strategies that arise from the collaboration.

1. Use crossing() to create the data frame of argument combinations for our analyses. Call it df_arg_combos. Note that you can create a list of formula objects in R with c(y ~ x1, y ~ x1 + x2). (Something like this will be the right hand side of an argument to crossing().)

df_arg_combos <- crossing(
 mod_formula = c(price ~ carat, price ~ carat + cut, price ~ carat + cut + clarity, price ~ carat + cut + clarity + color), 
  remove_outliers = c(TRUE, FALSE)
)

2. Write a function called remove_outliers that removes outliers in a dataset. The user should be able to supply the dataset (data), the variable to remove outliers in (what_var), and a threshold on the number of SDs away from the mean used to define outliers (sd_thresh). Write your function so that it runs as follows: remove_outliers(diamonds, what_var = carat, sd_thresh = 3).

remove_outliers <- function(data, what_var, sd_thresh) {
  data |> 
    mutate(z_score = ({{what_var}} - mean({{what_var}}, na.rm = TRUE)) / sd({{what_var}}, na.rm = TRUE)) |> 
    filter(z_score <= sd_thresh)
}

remove_outliers(diamonds, what_var = carat, sd_thresh = 3)

# A tibble: 53,501 × 11
   carat cut       color clarity depth table price     x     y     z z_score
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>   <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43   -1.20
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31   -1.24
 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31   -1.20
 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63   -1.07
 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75   -1.03
 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48   -1.18
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47   -1.18
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53   -1.13
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49   -1.22
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39   -1.20
# ℹ 53,491 more rows

3. Write a function called fit_model that implements the different settings for our diamonds analysis.

fit_model <- function(data, mod_formula, remove_outliers) {
    # remove_outliers is a TRUE/FALSE flag of whether or not to remove outliers
  if(remove_outliers) {
    data_clean <- remove_outliers(data, what_var = carat, sd_thresh = 3)  # This function implements our specific use case: outliers are cases that are 3 SDs away from the mean for the carat variable
  } else {
    data_clean <- data
  }
   lm(mod_formula, data = data_clean)
    # Use mod_formula as the first argument of lm()
}

4. Write a for loop that stores the fitted linear models from all versions of the analysis.

Recall that you can pull out the contents of a single data frame column in many ways. For a data frame df with a variable named x:

• df$x
• df |> pull(x)
• df[["x"]]

Note that in df_arg_combos:

• mod_formula: this column is a list and you can extract the 1st element with [[1]]
• remove_outliers: this column is a vector and you can extract the 1st element with [1]

vec_for_mod <- vector(mode = "list", length = nrow(df_arg_combos))

for (i in seq_along(vec_for_mod)) {
  this_formula <- df_arg_combos$mod_formula[[i]] # Double [[ for the **list** of formulas
  this_remove_outliers <- df_arg_combos$remove_outliers[i] # Single [ for the **atomic vector** of logicals
    vec_for_mod[[i]] <- fit_model(
        data = diamonds,
        mod_formula = this_formula,
        remove_outliers = this_remove_outliers
    )
}

Done!

• Check the ICA Instructions for how to (a) push your code to GitHub and (b) update your portfolio website