R cheat sheet

A scannable R reference: 14 short snippets across 8 topics, each linking back to the lesson it came from.

At a glance

Quick snippets

Installing R and RStudio: the environment

Install and orient yourself

# macOS with Homebrew
brew install --cask r rstudio

# Debian and Ubuntu
sudo apt install r-base r-base-dev

# then, in R
R.version.string
.libPaths()                # where packages are found, in order

Projects and the working directory

getwd()            # the working directory: the source of half of all confusion
setwd("C:/Users/me/analysis")   # avoid this in scripts

# a project instead: File > New Project, then paths are relative to the project root
library(here)
here::here()                    # always the project root, no matter where you run from
read.csv(here("data", "raw", "sales.csv"))

Packages and session facts

install.packages("tidyverse")     # once per library
library(tidyverse)                # once per session

sessionInfo()                     # R version, platform, every loaded package
packageVersion("dplyr")
search()                          # attached packages and objects
ls("package:stats")               # what a package exports

?lm                               # help for a function
??regression                      # full-text search across help pages

Full lesson: Installing R and RStudio: the environment →

Reading and writing data

Databases

library(DBI)
con <- dbConnect(RPostgres::Postgres(),
                 dbname = "app", host = "localhost",
                 user = Sys.getenv("DB_USER"), password = Sys.getenv("DB_PASSWORD"))

df <- dbGetQuery(con, "SELECT id, region, amount FROM orders WHERE amount > $1",
                 params = list(100))

# use parameters, never paste strings into SQL
dbDisconnect(con)

Full lesson: Reading and writing data →

Tidyverse workflow: dplyr and tidyr

Joins

# check the cardinality before you join
customers |> count(customer_id) |> filter(n > 1)
orders |> count(customer_id) |> filter(n > 1)

joined <- orders |>
  left_join(customers, by = "customer_id", relationship = "many-to-one") |>
  anti_join(blacklist, by = "customer_id")

# which rows failed to match?
unmatched <- orders |> anti_join(customers, by = "customer_id")

Full lesson: Tidyverse workflow: dplyr and tidyr →

Strings and dates with stringr and lubridate

Time zones and arithmetic

x <- ymd_hms("2026-03-29 00:30:00", tz = "Europe/London")
x + hours(2)                     # crosses the DST jump: the wall clock skips an hour
x + dhours(2)                    # exactly 7200 seconds later
force_tz(x, "UTC")               # reinterpret in another zone
with_tz(x, "America/New_York")   # convert to another zone

Full lesson: Strings and dates with stringr and lubridate →

Statistical modelling: lm, glm and the formula interface

The formula interface

# y depends on x, with an intercept by default
lm(mpg ~ wt, data = mtcars)

# more terms
lm(mpg ~ wt + hp + factor(cyl), data = mtcars)
lm(mpg ~ wt * hp, data = mtcars)          # main effects AND interaction
lm(mpg ~ wt + I(wt^2), data = mtcars)     # protect arithmetic with I()
lm(mpg ~ ., data = mtcars)                # everything else in the frame
lm(mpg ~ wt - 1, data = mtcars)           # no intercept

Diagnostics

par(mfrow = c(2, 2))
plot(fit)                            # residuals, scale-location, leverage, influence
par(mfrow = c(1, 1))

car::vif(fit)                        # variance inflation: above about 5 is worth a look
shapiro.test(residuals(fit))         # normality of residuals, for small samples
cor(model.matrix(fit)[, -1])         # collinearity between predictors

Full lesson: Statistical modelling: lm, glm and the formula interface →

R Markdown and reproducible reports

Structure of a document

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE,
                      fig.width = 7, fig.height = 4, dpi = 150)

library(readr)
df <- read_csv("data/sales.csv")

library(ggplot2)
ggplot(df, aes(month, revenue)) + geom_col()

Rendering and parameters

Rscript -e 'rmarkdown::render("report.Rmd", output_file = "build/aug.html")'

# parameterised rendering, one report per month
Rscript -e 'rmarkdown::render("report.Rmd", params = list(month = "2026-08"), output_dir = "build")'

Rendering and parameters

# a parameterised render driven from R
library(rmarkdown)
for (m in c("2026-06", "2026-07", "2026-08")) {
  render("report.Rmd",
         params = list(month = m),
         output_file = paste0("report-", m, ".html"),
         envir = new.env())          # a clean environment per run
}

Full lesson: R Markdown and reproducible reports →

Package management with CRAN, renv and Bioconductor

Project libraries with renv

renv::init()                    # project-local library plus renv.lock
renv::install("[email protected]")    # installs into the project, not the user library
renv::snapshot()                # write current versions into the lockfile
renv::restore()                 # rebuild the library from the lockfile
renv::status()
renv::upgrade()                 # check for newer versions without installing

renv::status()                  # confirm the library matches the lockfile
renv::diagnostics()             # when the two disagree and you need detail

Licences and provenance

# licences of everything installed
pkgs <- installed.packages()[, c("Package", "Version", "License")]
head(pkgs[order(pkgs$Package), ])

# the licence of one package, plus its dependencies
pak::pkg_deps("dplyr")[, c("package", "version", "needscompilation")]

Full lesson: Package management with CRAN, renv and Bioconductor →

Debugging R: warnings, factors, NA and performance traps

NA, NaN, NULL and empty strings

x <- c(1, NA, 3, NaN)

mean(x)                    # NA: any missing value poisons the result
mean(x, na.rm = TRUE)      # 2, once NaN and NA are excluded

x == NA                    # all NA: comparison with a missing value is unknown
is.na(x)                   # the only correct test

# filtering with a condition containing NA silently drops rows
df[df$amount > 100, ]      # NA rows vanish; is that what you intended?
subset(df, amount > 100)

Full lesson: Debugging R: warnings, factors, NA and performance traps →

FAQ

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Last refreshed 2026-09-27.