Repeating Actions in R

Learning Goals

At the end of this lesson, you should:

  • know how to use apply() to iterate across a data frame
  • know to use lapply() to interate across a list
  • know the structure of purrr functions for iterating across different object types
  • know when and how to construct a for loop

Repeating operations across a vector

  • Reminder: a vector is an object with a length attribute composed of items each of the same class. It can have a name attribute (not required)

  • ifelse(test, action-if-yes, action-if-no)

  • The ‘test’ should be an R function that will return a TRUE or FALSE, e.g., is.numeric()

  • Vector example

x <- 1:10
y <- ifelse(x < 5, NA, x)
  • Vector example inside a data.frame
data(storms, package = "dplyr")
storms$category_simple <- ifelse(storms$wind <= 50, "small", "big")

Repeating operations across a data frame

  • apply() a simple handy function to repeat things across a data.frame (or tibble, or matrix)

  • This operation is vectorised, meaning all processes proceed simultaneously

  • Across rows

storm_num <- select_if(storms, is.numeric)
apply(storm_num, 1, median, na.rm = TRUE)
  • Across columns
storm_num <- select_if(storms, is.numeric)
apply(storm_num, 2, median, na.rm = TRUE)

Special R functions for rows and columns

  • These functions are very, very fast
  • They are not forgiving of non-numeric data
rowSums(); colSums()
rowMeans(); colMeans()
library(dplyr); data("storms")

Attaching package: 'dplyr'
The following object is masked _by_ '.GlobalEnv':

The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
storms %>% select_if(is.numeric) %>% colMeans(na.rm=TRUE)
                        year                        month 
                 2002.754364                     8.705635 
                         day                         hour 
                   15.732303                     9.100937 
                         lat                         long 
                   27.005763                   -61.560086 
                    category                         wind 
                    1.895690                    50.049393 
                    pressure tropicalstorm_force_diameter 
                  993.484465                   147.869327 

::: {.callout-note} # Comments on NA values

  • I often get the question “how can I replace”NA” across my entire data object?”
  • NA is a reserved word in the R language referring to missing data. Often the best way to handle how missing values from your data are handled is to specify that when the file is read in. Check the documentation for your import function, e.g. read.csv(..., na.string = "...").
  • To do a global replacement of NA with another value, use tidyr::replace_na()
  • To do a global replacement of another value with NA, this can be handled in the import, or use dplyr::na_if()

Repeating operations across a list

😍😍😍😍😍 lapply() 😍😍😍😍😍

  • Vectorised over lists
  • If you can express one aspect of your operation as a list, this can probably work for you!
  • Downside: everything comes back as a list (it takes an effort to convert this into a more exportable form)
  • It uses very simple notation:
lapply(list, some_function)
  • Example
integers <- sample(1:100, 200, replace = TRUE)
thrice_int <- lapply(integers, function(x) x*(c(1,2,3)))

I’m very fond of this for doing complex repeat operations. Perhaps I have a group of experiments, all with identical experimental design, that each need to be analyzed the same. Using a list can accomplish this efficiently.

lapply() flotsam & jetsam

  • Dealing with lists can be challenging: they follow different rules; they typically require lots and lots of indexing to extract content. And you usually can’t write a list straight to file like a data.frame.
  • sapply() is just like lapply(), except it tries to simplify to common R data objects - a matrix or array. This works if the return data is one row.
  • dplyr::bind_rows() can concatenate data.frames better than rbind().
  • Getting things out of a list and into the desired format can be one of the most challenging aspects of working with lapply() (bonus: it makes you understand R data types really well!).
  • purrr to the rescue!

purrr for repeat operations

purrr does all of the hard work of iteration plus conversion of output to the object type you want! It works on data structures of all types: vectors, data frames and lists.


mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  (Intercept)        wt
1    39.57120 -5.647025
2    28.40884 -2.780106
3    23.86803 -2.192438

purrr is complicated! Make use of the cheatsheet].

Base/Tidyverse equivalents

base functions tidyverse equivalent
lapply() sapply() vapply() purrr package
mapply() purrr::pmap()
tapply() dplyr::group_by() %>% dplyr::summarise()
replicate() purrr:rerun()
ifelse() dplyr::case_when()

The oft-abused for

A for loop:

for (i in thingy) {

I see things like this frequently:

x <- LETTERS[1:10]
for (i in x) print(i)
# same as sapply(x, print)

Or worse:

for (i in item1) {
  for(j in item2) {
  } }

Optimal use of for

  • Better usage of for is when you require the previous value(s) to proceed through the loop
  • pre-allocation of your vector/data.frame/list/etc will result in faster code
# vector pre-allocation
f <- c(0, 1, rep(NA, 98)) 
 [1]  0  1 NA NA NA NA NA NA NA NA
# Fibonacci sequence
for (i in 3:100){
  f[i] = f[i-1] + f[i-2]

 [1]  0  1  1  2  3  5  8 13 21 34

Traditional control flow

  • These are standard control variables that exist across many languages to repeat operations.
  • These are not vectorized; they only work with a single input at time.
  • Note that these are reserved words in the R language; you cannot use these words for any other purpose in the R language than what they are designed to do (no function masking is possible).

Teaching how to use these traditional control flow variables is beyond an introductory course in R. However, you can learn more about it here and Introduction to R manual.

Putting it all together

Understanding how to do repeat operations in R often requires a strong understanding of the underlying data structures we are trying to perform those operations on. This aspect of R, sometimes called “data conditioning” can be one of the most challenging aspects of using R. When writing repeat operations, check back on Lessons 3 and 4 that address basics of data types and data structures if you are having trouble.