R for Data Science and Tidyverse Cheat Sheet

Updated 2026-05-15

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R for Data Science combines the R programming language with the tidyverse, a collection of packages designed around consistent grammar and workflow principles for data manipulation, visualization, and analysis. The tidyverse provides tidy data as a unifying structure (observational units as rows, variables as columns) and emphasizes readable code through pipes and verb-based functions. At its core sits dplyr for data transformation, tidyr for reshaping, purrr for functional programming, ggplot2 for visualization, readr for fast I/O, stringr for text, lubridate for dates, forcats for factors, and broom for model output—all integrated with R Markdown and Quarto for reproducible reporting. Keep in mind that the native pipe |> (R ≥ 4.1) behaves slightly differently from magrittr's %>%—the native pipe doesn't auto-expose . and requires explicit function calls.

What This Cheat Sheet Covers

This topic spans 31 focused tables and 242 indexed concepts, 177 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: dplyr Core Verbs for Row and Column OperationsTable 2: dplyr Grouping and AggregationTable 3: dplyr Joins for Combining Data FramesTable 4: dplyr Advanced Column and Row OperationsTable 5: dplyr Select Helpers for Column SelectionTable 6: tidyr Reshaping Data with PivotsTable 7: tidyr Nested and List-Column DataTable 8: tidyr Missing Data HandlingTable 9: purrr Iteration and MappingTable 10: purrr List Manipulation and SelectionTable 11: purrr Functional Programming UtilitiesTable 12: lubridate Parsing Date-TimesTable 13: lubridate Extracting Date-Time ComponentsTable 14: lubridate Date-Time Arithmetic and ManipulationTable 15: lubridate Time SpansTable 16: stringr Detection and ExtractionTable 17: stringr Modification and ReplacementTable 18: stringr String Manipulation and AssemblyTable 19: stringr Advanced String OperationsTable 20: forcats Factor ReorderingTable 21: forcats Factor ModificationTable 22: forcats Factor UtilitiesTable 23: readr Data Import FunctionsTable 24: readr Column Type SpecificationsTable 25: readr Data Export FunctionsTable 26: tibble Creation and ConversionTable 27: tibble Manipulation and InspectionTable 28: broom for Tidy Model OutputTable 29: Base R Statistical Functions with Formula InterfaceTable 30: R Markdown and Quarto for Reproducible ReportingTable 31: Pipe Operators - Native vs Magrittr

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Table 1: dplyr Core Verbs for Row and Column Operations

These are the workhorse verbs you reach for in almost every analysis—each one does a single, predictable thing to a data frame, and chaining them with the pipe is the heart of the dplyr grammar. Filter rows, select and reshape columns, sort, deduplicate, and collapse to summaries; learn these and most everyday wrangling falls into place.

Verb	Example	Description
filter	`df %>% filter(age > 30, city == "NYC")`	• Keeps rows that satisfy logical conditions • multiple conditions combine with AND by default
select	`df %>% select(name, age, starts_with("val"))`	• Picks columns by name or helper • can rename inline (e.g., `new = old`).
mutate	`df %>% mutate(total = price * quantity)`	• Creates new columns or modifies existing ones • transformations applied row-wise
summarise / summarize	`df %>% summarise(avg = mean(value), n = n())`	• Collapses rows into summary statistics • often combined with `group_by()`.
arrange	`df %>% arrange(desc(date), name)`	• Sorts rows by one or more columns • use `desc()` for descending order
slice	`df %>% slice(1:10)`	• Selects rows by position • variants: `slice_head()`, `slice_tail()`, `slice_sample()`, `slice_max()`, `slice_min()`.
distinct	`df %>% distinct(category, .keep_all = TRUE)`	• Removes duplicate rows based on selected columns • `.keep_all = TRUE` retains all columns
pull	`df %>% pull(name)`	• Extracts a single column as a vector • useful at the end of a pipeline when a vector is needed
rename	`df %>% rename(new_name = old_name)`	• Changes column names with `new = old` syntax • doesn't alter position
relocate	`df %>% relocate(id, .before = name)`	• Moves columns to new positions • use `.before`, `.after`, or tidyselect helpers

Table 2: dplyr Grouping and Aggregation

Grouping turns a flat table into per-category calculations—count customers by region, average sales by product—without writing a single loop. Note the split here between persistent grouping (group_by() followed by ungroup()) and the newer per-operation .by argument, plus the family of counting shortcuts that save you from spelling out summarise(n = n()) every time.

Function	Example	Description
group_by	`df %>% group_by(category, region)`	• Groups rows by one or more variables • subsequent verbs operate within each group.

The rest of this section is part of the full cheat sheet

Table 3: dplyr Joins for Combining Data Frames

Joins stitch two tables together on a shared key, and which one you pick decides which rows survive. The mutating joins (inner, left, right, full) bring columns across, while the filtering joins (semi, anti) only keep or drop rows based on whether a match exists—anti_join in particular is the quickest way to find records present in one table but missing from another.

Join	Example	Description
inner_join	`df1 %>% inner_join(df2, by = "id")`	• Returns rows with matches in both tables • unmatched rows are dropped

The rest of this section is part of the full cheat sheet

Table 4: dplyr Advanced Column and Row Operations

When the basic verbs aren't enough, these handle the messier cases: applying a transformation across many columns at once with across(), branching logic with if_else() and case_when(), filling or flagging missing values, and stacking data frames together. They're the tools that keep a pipeline tidy instead of forcing you to drop back into base R.

Function	Example	Description
across	`df %>% mutate(across(starts_with("val"), log))`	• Applies function to multiple columns at once • works in `mutate()`, `summarise()`, `filter()`.

The rest of this section is part of the full cheat sheet

Table 1: dplyr Core Verbs for Row and Column Operations

Verb	Example	Description
filter	`df %>% filter(age > 30, city == "NYC")`	• Keeps rows that satisfy logical conditions • multiple conditions combine with AND by default
select	`df %>% select(name, age, starts_with("val"))`	• Picks columns by name or helper • can rename inline (e.g., `new = old`).
mutate	`df %>% mutate(total = price * quantity)`	• Creates new columns or modifies existing ones • transformations applied row-wise
summarise / summarize	`df %>% summarise(avg = mean(value), n = n())`	• Collapses rows into summary statistics • often combined with `group_by()`.
arrange	`df %>% arrange(desc(date), name)`	• Sorts rows by one or more columns • use `desc()` for descending order
slice	`df %>% slice(1:10)`	• Selects rows by position • variants: `slice_head()`, `slice_tail()`, `slice_sample()`, `slice_max()`, `slice_min()`.
distinct	`df %>% distinct(category, .keep_all = TRUE)`	• Removes duplicate rows based on selected columns • `.keep_all = TRUE` retains all columns
pull	`df %>% pull(name)`	• Extracts a single column as a vector • useful at the end of a pipeline when a vector is needed
rename	`df %>% rename(new_name = old_name)`	• Changes column names with `new = old` syntax • doesn't alter position
relocate	`df %>% relocate(id, .before = name)`	• Moves columns to new positions • use `.before`, `.after`, or tidyselect helpers

Table 2: dplyr Grouping and Aggregation

Function	Example	Description
group_by	`df %>% group_by(category, region)`	• Groups rows by one or more variables • subsequent verbs operate within each group.

Table 3: dplyr Joins for Combining Data Frames

Join	Example	Description
inner_join	`df1 %>% inner_join(df2, by = "id")`	• Returns rows with matches in both tables • unmatched rows are dropped

Table 4: dplyr Advanced Column and Row Operations

Function	Example	Description
across	`df %>% mutate(across(starts_with("val"), log))`	• Applies function to multiple columns at once • works in `mutate()`, `summarise()`, `filter()`.