R

Templates¶

A Minimal R Script Template

# ============================================================================ #
# Script: <script name>.R
# Description: <description of what this script does>
# ============================================================================ #

# ============================================================================ #
# Set up ----
# ============================================================================ #

## Source libraries ----
if (!require("pacman")) {
  install.packages("pacman")
  library(pacman)
} 
p_load(
  tidyverse
)

## Define paths ----
# input_path <- "../input"
# output_path <- "../output"
# lib_path <- "../../lib"
# temp_path <- "../temp"

## Source other scripts ----
# source()

## Define global data variables ----

# ============================================================================ #
# Define Functions ----
# Note: Factorize when the code stabilizes 
# ============================================================================ #

# main <- function() {
# 
# }
# 
# main()

# ============================================================================ #
# 1. Load data ----
# ============================================================================ #

# ============================================================================ #
# 2. Section 2 ----
# ============================================================================ #

# ============================================================================ #
# 3. Section 3 ----
# ============================================================================ #

Snippets¶

Loading packages:

Using `pacman`

if (!require("pacman")) {
  install.packages("pacman")
  library(pacman)
} 
p_load(
  tidyverse,
)

Manual loop

packages <- c("tidyverse")
for (pkg in packages) {
  if (!require(pkg, character.only = TRUE, quietly = TRUE)) {
    install.packages(pkg, dependencies = TRUE)
    library(pkg, character.only = TRUE)
  }
}

R Studio¶

Importing data¶

Import and Export SPSS, Stata and SAS Files • haven

Syntax¶

Pipe¶

Resources:

18 Pipes | R for Data Science

Pipes are most useful for rewriting a fairly short linear sequence of operations. I think you should reach for another tool when: Your pipes are longer than (say) ten steps... You have multiple inputs or outputs... You are starting to think about a directed graph with a complex dependency structure.

magrittr pipes:

base R pipe

base R pipe vs. magrittr pipe:

What are the differences between R's native pipe |> and the magrittr pipe %>%? - Stack Overflow

Many differences and limitations disappear when using |> in combination with an (anonymous) function.
Differences between the base R and magrittr pipes

R 4.2.0 added a _ placeholder to the base pipe, with one additional restriction: the argument has to be named. For example, x |> f(1, y = _) is equivalent to f(1, y = x).

%>% allows you to drop the parentheses when calling a function with no other arguments; |> always requires the parentheses.

Functions¶

3 Functions – Tidyverse style guide

Anonymous function¶

Inline documentation¶

In-Line Documentation for R • roxygen2

Example

#' The length of a string
#'
#' Technically this returns the number of "code points", in a string. One
#' code point usually corresponds to one character, but not always. For example,
#' an u with a umlaut might be represented as a single character or as the
#' combination a u and an umlaut.
#'
#' @inheritParams str_detect
#' @return A numeric vector giving number of characters (code points) in each
#'    element of the character vector. Missing string have missing length.
#' @seealso [stringi::stri_length()] which this function wraps.
#' @export
#' @examples
#' str_length(letters)
#' str_length(NA)
#' str_length(factor("abc"))
#' str_length(c("i", "like", "programming", NA))
str_length <- function(string) {
}

Documenting functions • roxygen2

Also see:

16 Function documentation – R Packages (2e)

Unit testing¶

Package index • testthat