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March 10, 2025 23:11
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dplyr Data Transformation Cheatsheet

A comprehensive cheat sheet for data manipulation using dplyr, covering essential functions for summarizing, grouping, manipulating, extracting, and combining data.

Summarizing and Grouping Data

Summarizing Cases

summarize(.data, ...): Computes a table of summary statistics.

Example:

mtcars |> summarize(avg_mpg = mean(mpg))

.data: The data frame to summarize.
...: Summary expressions that return a single value.

Common Summary Functions: mean(), median(), sum(), min(), max(), n()

Example:

mtcars |> summarize(avg_mpg = mean(mpg), total_cyl = sum(cyl))

Grouping Cases

group_by(.data, ...): Creates a grouped copy of the table, enabling group-wise operations.

Example:

mtcars |> group_by(cyl) |> summarize(avg_mpg = mean(mpg))

.data: The data frame to group.
...: Columns to group by.

ungroup(x): Returns an ungrouped copy of the table.

Example:

mtcars |> group_by(cyl, gear) |> summarize(avg_mpg = mean(mpg))

Counting Cases

count(.data, ..., wt = NULL, sort = FALSE, name = NULL): Counts the number of rows in each group.

Example:

mtcars |> count(cyl)

.data: The data frame to count.
...: Columns to group by.
wt: Optional weighting variable.
sort: Whether to sort the results.

Example:

mtcars |> count(cyl, sort = TRUE)

Alternatives: tally(), add_count(), and add_tally().

Manipulating Cases

Extracting Cases

filter(.data, ...): Extracts rows that meet specified logical criteria.

Example:

mtcars |> filter(mpg > 20)

.data: The data frame to filter.
...: Logical conditions to apply.

distinct(.data, ...): Removes rows with duplicate values.

Example:

mtcars |> distinct(gear)

slice(.data, ...): Selects rows by their position.

Example:

mtcars |> slice(10:15)

slice_sample(.data, ..., n, prop, replace = FALSE): Randomly selects rows.

Example:

mtcars |> slice_sample(n = 5, replace = TRUE)

Arranging Cases

arrange(.data, ...): Orders rows by the values of specified columns.

Example:

mtcars |> arrange(mpg)

.data: The data frame to arrange.
...: Columns to order by.

desc(): Use with arrange() to order in descending order.

Example:

mtcars |> arrange(desc(mpg))

Adding Cases

add_row(.data, ...): Adds one or more rows to a table.

Example:

cars |> add_row(speed = 1, dist = 1)

.data: The data frame to add rows to.
...: Values for the new row(s).

Manipulating Variables

Extracting Variables

pull(.data, var = -1): Extracts column values as a vector.

Example:

mtcars |> pull(wt)

.data: The data frame to extract from.
var: The column to extract (name or index).

select(.data, ...): Extracts columns as a new table.

Example:

mtcars |> select(mpg, wt)

relocate(.data, ...): Moves columns to a new position.

Example:

mtcars |> relocate(mpg, cyl, after = last_col())

Making New Variables

mutate(.data, ...): Computes new column(s) using vectorized functions.

Example:

mtcars |> mutate(gpm = 1 / mpg)

.data: The data frame to mutate.
...: Expressions that compute new columns.

rename(.data, ...): Renames columns.

Example:

mtcars |> rename(miles_per_gallon = mpg)

Manipulating Multiple Variables at Once

across(.cols, .fns, ...): Applies the same function(s) to multiple columns.

Example:

df |> summarize(across(everything(), mean))

.cols: Columns to apply the function to.
.fns: Function(s) to apply.

c_across(.cols): Computes across columns in row-wise data.

Example:

df |> rowwise() |> mutate(x_total = sum(c_across(1:2)))

Combining Tables

Combining Variables

bind_cols(...): Returns tables placed side by side.

Example:

bind_cols(df1, df2)

Ensure tables have the same number of rows and are ordered correctly.

Combining Cases

bind_rows(...): Returns tables stacked on top of each other.

Example:

bind_rows(df1, df2)

Can add a column indicating the source table using .id.

Example:

bind_rows(df1, df2, .id = 'source')

Relational Data (Joins)

left_join(x, y, by = NULL): Join matching values from y to x.

Example:

left_join(x, y, by = 'A')

right_join(x, y, by = NULL): Join matching values from x to y.

inner_join(x, y, by = NULL): Retain only rows with matches in both tables.

full_join(x, y, by = NULL): Retain all rows and values from both tables.

semi_join(x, y, by = NULL): Return rows of x that have a match in y.

anti_join(x, y, by = NULL): Return rows of x that do not have a match in y.