Code Handout - Introduction to R

Last updated on 2023-01-05 | Edit this page

This document contains all of the functions that were covered in the Introduction to R workshop. Each function is presented alongside an example of how it can be used.

Creating Objects

  • <- – “assignment arrow”, assigns a value (vector, dataframe, single value) to the name of a variable

R 

x <- 3
y <- c(1, 2, 3)
z <- x + y
  • c() – the “concatenate” function combines inputs to form a vector, the values have to be the same data type.

R 

animals <- c("bird", "cat", "dog")
numbers <- c(1, 14, 57, 89)
logicals <- c(TRUE, FALSE, TRUE, TRUE)

Inspecting Objects

  • str() – compact display of the structure of an R object

R 

str(animals)
  • class() – returns the type of element of any R object

R 

class(logicals)
  • typeof() – returns the data type or storage mode of any R object

R 

typeof(numbers)

Functions in R

  • args() – returns the arguments of a function

R 

args(round)
  • named arguments – the name of the argument the function expects
    • You can choose to not name your arguments, if you know the exact order they should be in!
    • However, we generally discourage this.

R 

## Either of these work, since the digits argument is named explicitly.
round(3.14159, digits = 2)
round(digits = 2, 3.14159)

## This does not work, since the arguments are not named and in the incorrect order. 
round(2, 3.14159)

Functions to Summarize Data

  • sqrt() – returns the square root of a numeric variable

R 

sqrt(numbers)
  • mean() – returns the mean of a numeric variable
    • You can add the na.rm argument, to remove NA values before calculating the mean.

R 

sqrt(numbers)
  • max() – returns the maximum of a numeric variable
    • You can add the na.rm argument, to remove NA values before calculating the max.

R 

sqrt(numbers)
  • sum() – returns the sum of a numeric variable
    • You can add the na.rm argument, to remove NA values before calculating the sum.

R 

sqrt(numbers)
  • length() – returns the length of a vector (of any datatype)

R 

length(animals)

Subsetting Data

  • [] – used to subset elements from a vector

R 

animals[3]
## selects the third element

animals[2:3]
## selects the second and third element

animals[c(1, 3)]
## selects the first and third element
  • relational operators – return logical values indicating where a relation is satisfied. The most commonly used logical operators for data analysis are as follows:
    • == means “equal to”
    • != means “not equal to”
    • > or < means “greater than” or “less than”
    • >= or <= means “greater than or equal to” or “less than or equal to”

R 

animals == "dog"

animals != "cat"

numbers > 4

numbers <= 12
  • logical operators – join subset criteria together
    • & means “and” – where two criteria must both be satisfied
    • | means “or” – where at least one criteria must be satisfied

R 

numbers > 4 & numbers < 20

animals == "dog" | animals == "cat"
  • %in% – the “inclusion operator”, allows you to test if any of the elements of a search vector (on the left hand side) are found in the target vector (on the right hand side).
    • The levels of the target vector must be included in a vector (c()).

R 

possessions <- c("car", "bicycle", "radio", "television", "mobile_phone")

possessions %in% c("car", "bicycle", "motorcycle")

Missing Data

  • is.na() – returns a vector of logical values indicating which elements of a vector have NA values
    • Often combined with !, where the ! negates the previous statement (e.g. !TRUE is equal to FALSE).

R 

missing <- c(1, 3, NA, 7, 12, NA)

is.na(missing)

!is.na(missing)
  • na.omit() – removes the observations with NA values

R 

na.omit(missing)
  • complete.cases() – returns a vector of logical values indicating which elements of a vector are not missing (NA) values

R 

complete.cases(missing)