R - Cheatsheet

Modern R Cheatsheet

This cheatsheet provides a concise overview of modern R programming, focusing on the tidyverse ecosystem for efficient and readable data analysis. It covers key concepts from basic syntax to data visualization and modeling.

1. The R Environment

Core commands for interacting with your R session.

# Get help on a function
?mean
help(mean)

# Install a package from CRAN
install.packages("dplyr")

# Load a package into the session
library(dplyr)

# List objects in the current workspace
ls()

# Remove an object
rm(my_variable)

2. Basic Syntax & Operators

The building blocks of R code.

# This is a comment

# Variable assignment (<- is the preferred convention)
my_variable <- "Hello R!"
my_number <- 100

# The Pipe Operator (from magrittr, loaded with tidyverse)
# Passes the result of one function to the next
# x %>% f(y) is equivalent to f(x, y)
# Since R 4.1, a native pipe |> is also available

# --- Arithmetic Operators ---
# +, -, *, /, ^ (exponent), %% (modulus), %/% (integer division)
result <- (5 + 3) * 2 # 16

# --- Relational & Logical Operators ---
# >, <, >=, <=, == (equal to), != (not equal to)
# & (element-wise AND), | (element-wise OR), ! (NOT)
# && (short-circuit AND), || (short-circuit OR)
TRUE & FALSE # FALSE
x > 5 & x < 10

3. Core Data Structures

R’s primary objects for storing data.

Data Structure	Description	Example
Vector	A 1D sequence of elements of the same type.	c(1, 2, 3) (numeric), c("a", "b", "c") (character)
List	A 1D collection of elements of any type.	list(name = "John", age = 30, scores = c(88, 95))
Matrix	A 2D array of elements of the same type.	matrix(1:6, nrow = 2, ncol = 3)
Data Frame	A 2D, table-like structure where columns can have different types.	data.frame(id = 1:3, name = c("A", "B", "C"))
Tibble	A modern, enhanced data frame (from tidyverse). Better printing and subsetting.	tibble(id = 1:3, name = c("A", "B", "C"))
Factor	A vector for categorical data with defined levels.	factor(c("low", "high", "low"), levels = c("low", "medium", "high"))

4. Data Import & Export

Use the readr and readxl packages for fast and reliable data import.

library(readr)
library(readxl)
library(writexl)

# Read a CSV file (returns a tibble)
my_data_csv <- read_csv("path/to/my_file.csv")

# Read an Excel file (specify sheet by name or number)
my_data_excel <- read_excel("path/to/my_file.xlsx", sheet = "Sheet1")

# Write data to a CSV file
write_csv(my_data_csv, "output/data.csv")

# Write data to an Excel file
write_xlsx(my_data_excel, "output/data.xlsx")

5. Data Transformation with dplyr

The core verbs of dplyr for data manipulation, often used with the pipe %>%.

library(dplyr)

# Example dataset
data <- starwars

# Filter rows
droids <- data %>% filter(species == "Droid")

# Select columns
name_and_height <- data %>% select(name, height, mass)

# Create or modify columns
heavy_hitters <- data %>% mutate(bmi = mass / ((height/100)^2))

# Arrange (sort) rows
sorted_by_height <- data %>% arrange(desc(height))

# Summarize data, often with group_by
species_summary <- data %>%
  filter(!is.na(mass)) %>%
  group_by(species) %>%
  summarize(
    count = n(),
    avg_mass = mean(mass)
  ) %>%
  arrange(desc(count))

6. Data Visualization with ggplot2

ggplot2 uses a layered grammar of graphics. You build plots by adding layers.

library(ggplot2)

# The structure of a ggplot:
# ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) + <GEOM_FUNCTION>()

# Scatter plot
ggplot(data = starwars, aes(x = mass, y = height)) +
  geom_point(aes(color = gender)) + # Add color mapping
  labs(
    title = "Height vs. Mass of Star Wars Characters",
    x = "Mass (kg)",
    y = "Height (cm)",
    caption = "Data from the dplyr package"
  ) +
  theme_minimal()

# Bar plot of species counts
starwars %>%
  count(species, sort = TRUE) %>%
  filter(n > 1) %>%
  ggplot(aes(x = reorder(species, n), y = n)) +
  geom_col() +
  coord_flip() + # Flip coordinates for readability
  labs(title = "Most Common Species", x = "Species", y = "Count")

7. Control Flow & Functions

# If-else statement
if (x > 10) {
  print("x is large")
} else {
  print("x is small")
}

# For loop
for (i in 1:5) {
  print(i)
}

# Writing a custom function
calculate_bmi <- function(mass_kg, height_m) {
  if (!is.numeric(mass_kg) || !is.numeric(height_m)) {
    stop("Inputs must be numeric.")
  }
  bmi <- mass_kg / (height_m ^ 2)
  return(bmi)
}

# Using the function
calculate_bmi(70, 1.75)

8. Basic Modeling

Fitting and inspecting a simple linear model.

# Fit a linear model: predict mpg from weight
model <- lm(mpg ~ wt, data = mtcars)

# View model summary
summary(model)

# Use 'broom' to get tidy model output
library(broom)
tidy(model) # Coefficients as a tibble
glance(model) # Model summary statistics as a tibble

9. Reproducible Analysis

Tool	Purpose
R Markdown (.Rmd)	A file format for making dynamic documents with R. Combine code, its output (plots, tables), and narrative text into high-quality reports (HTML, PDF, Word).
renv package	A dependency management tool. It creates a project-specific library of packages, ensuring your code runs the same way for you and others, now and in the future.