Writing functions in R

Welcome!

• this session is an 🌶🌶 intermediate practical designed for those with some R experience
• we’ll get going properly at 14.35
• practical and interactive, so please chat away - and get whatever version of R you’re using ready
• if you can’t access the chat, you might need to join our Teams channel: tinyurl.com/kindnetwork

Future R training sessions

Session outline

• why functions?
• basic syntax
• adding arguments
• vectorised functions
• the mystery of the paired brackets
• …

Why functions?

• most beginners write repetitious code
• repetitious code is hard to maintain
• functions give you an easy way of repeating chunks of code

Basic syntax

• think of this as a way of repeating yourself
• in time-honoured fashion…

hi_wrld <- function(){
  "hello world"
}

hi_wrld()

[1] "hello world"

Adding arguments

• most of the time, you’ll want to add arguments to your function
  • add a variable name inside the round bracket of function
  • use that variable name in your function body

hi_wrld_n <- function(n){
  paste(rep("hello world", n))
}

hi_wrld_n(4)

[1] "hello world" "hello world" "hello world" "hello world"

Another argument

• you can add another argument
• either position or name can be used in the function call

hi_name_n <- function(name, n){
  rep(paste("hello", name) , n)
}

hi_name_n("sue", 4)

[1] "hello sue" "hello sue" "hello sue" "hello sue"

hi_name_n(n = 3, name = "tango") # evil but legal

[1] "hello tango" "hello tango" "hello tango"

Defaults

hi_name_n_def <- function(n, name = "janelle"){
  rep(paste("hello", name) , n)
}

hi_name_n_def(n = 4)

[1] "hello janelle" "hello janelle" "hello janelle" "hello janelle"

hi_name_n_def(n = 2, name = "bruce")

[1] "hello bruce" "hello bruce"

Vectorised functions

• most functions in R are vectorised

round(c(1.2, 3.2, 5.4, 2.7), 0)

[1] 1 3 5 3

Vectorised functions

• that means that mostly, our functions will end up vectorised without us doing any work at all

div_seven_n_round <- function(nums){
  round(nums / 7, 0)
}

numbers <- rnorm(4, 5, 50)

numbers

[1] -38.50315 -56.07585 -45.53310 -17.99504

div_seven_n_round(numbers)

[1] -6 -8 -7 -3

Vectorised functions

• but there are a few cases where that can fail: most famously, using if/else

is_even <- function(n){
  
  if(n %% 2){
  paste(n, "is odd")
} else {
  paste(n, "is even")
}
  
}
is_even(9)

[1] "9 is odd"

is_even(10)

[1] "10 is even"

try(is_even(9:10))

Error in if (n%%2) { : the condition has length > 1

vectorize with Vectorize

is_even_v <- Vectorize(is_even)
is_even_v(9:10)

[1] "9 is odd"   "10 is even"

apply

• apply with lapply / purrr::map with Vectorize

lapply(9:10, is_even)

[[1]]
[1] "9 is odd"

[[2]]
[1] "10 is even"

purrr::map(9:10, is_even)

[[1]]
[1] "9 is odd"

[[2]]
[1] "10 is even"

refactor

• refactor to avoid scalar functions

is_even_rf <- function(n){
  ifelse(n %% 2, paste(n, "is odd"), paste(n, "is even"))
}
is_even_rf(9:10)

[1] "9 is odd"   "10 is even"

what’s the problem with {{}}?

mtcars |>
  dplyr::summarise(average = round(mean(hp)))

  average
1     147

• so

carmo <- function(column){
  mtcars |>
    dplyr::summarise(average = round(mean(column)))
  }

try(carmo(hp))

Error in dplyr::summarise(mtcars, average = round(mean(column))) : 
  ℹ In argument: `average = round(mean(column))`.
Caused by error:
! object 'hp' not found

• we get used to R (and particularly tidyverse) helping us with some sugar when selecting column by their names
  • mtcars$hp / mtcars |> select(hp)
  • effectively, we’re just able to specify hp like an object, and R figures out the scope etc for us
• that misfires inside functions. R isn’t sure where to look for an object called hp

Enter {{}}

carmo_woo <- function(column){
  mtcars |>
    dplyr::summarise(average = round(mean({{column}})))
}

carmo_woo(hp)

  average
1     147

…

• pass arbitrary arguments into/through a function with …

dotty <- function(n, ...){
  rep(paste(..., collapse = ""), n)
}

dotty(4, letters[1:5])

[1] "abcde" "abcde" "abcde" "abcde"

Resources

• best = home made! Refactor something simple in your code today.
• hard to beat the treatment of functions in R4DS
• the Rlang page on data masking is surprisingly sane for such a complicated area

Feedback

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