R for Data Science Cheat Sheet

Introduces R's core syntax, vectors, data frames, and the tidyverse ecosystem for foundational data science workflows.

2 PagesBeginnerMar 2, 2026

Vectors, Types & Functions

The fundamental building blocks of R.

# Vectors and basic typesx <- c(1, 2, 3, 4, 5)names <- c("alice", "bob", "carol")is_active <- c(TRUE, FALSE, TRUE)# Vectorized arithmeticx * 2x[x > 2]              # logical indexing# Functionssquare <- function(n) {  return(n^2)}sapply(x, square)      # apply over a vector# Control flowif (mean(x) > 3) {  print("above average")} else {  print("below average")}

Data Frames

Create, inspect, and filter tabular data.

df <- data.frame(  name = c("Alice", "Bob", "Carol"),  age = c(25, 30, 35),  score = c(88.5, 92.1, 79.3))str(df)          # structure/typessummary(df)      # summary statisticshead(df, 2)df$age                    # access a columndf[df$score > 80, ]       # filter rowsdf[order(-df$score), ]    # sort descending

Base R Functions

Frequently used built-in functions.

c()- Combines values into a vector
str()- Displays the structure (types, dimensions) of an object
sapply()/lapply()- Apply a function over a vector/list; sapply simplifies to a vector, lapply returns a list
which()- Returns indices where a logical condition is TRUE
table()- Builds a contingency/frequency table of categorical values
NA handling- is.na() tests for missing values; na.rm=TRUE in functions like mean() ignores them

The Tidyverse Ecosystem

Packages that make up R's modern data science toolkit.

tidyverse- Collection of packages (dplyr, ggplot2, tidyr, readr, purrr) sharing a common design philosophy
readr::read_csv()- Faster, type-consistent CSV reader that returns a tibble
tibble- Modern re-implementation of data.frame with stricter, more predictable behavior
%>% / |>- Pipe operators that chain function calls left-to-right for readable transformations
RStudio- The standard IDE for R, with an integrated console, plots pane, and package management

Pro Tip

Use the native pipe |> (R 4.1+) or magrittr's %>% to chain transformations instead of nesting function calls - df |> filter(score > 80) |> arrange(desc(score)) reads top-to-bottom in the order operations actually happen.

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