Reference

• Dplyr Cheat Sheet - Cheat Sheet Maker

Syntax

dplyr::tbl_df(iris)

Converts data to tbl class. tbl’s are easier to examine than data frames. R displays only the data that fits onscreen:

dplyr::glimpse(iris)

Information dense summary of tbl data.

utils::View(iris)

View data set in the spreadsheet-like display (note capital V).

dplyr::%>%

Passes object on left hand side as the first argument (or .argument) of function on the righthand side.

x %>% f(y) is the same as f(x, y)
y %>% f(x, ., z) is the same as f(x, y, z )

"Piping" with %>% makes code more readable, e.g.

iris %>%
    group_by(Species) %>%
    summarise(avg = mean(Sepal.Width)) %>%
    arrange(avg)

Reshaping Data

  tidyr::gather(cases, "year", "n", 2:4)

Gather columns into rows.

  tidyr::unite(data, col, ..., sep)

Unite several columns into one.

  dplyr::data_frame(a = 1:3, b = 4:6)

Combine vectors into data frame (optimized).

  dplyr::arrange(mtcars, mpg)

Order rows by values of a column (low to high).

  dplyr::arrange(mtcars, desc(mpg))

Order rows by values of a column (high to low).

  dplyr::rename(tb, y = year)

Rename the columns of a data frame.

  tidyr::spread(pollution, size, amount)

Spread rows into columns.

  tidyr::separate(storms, date, c("y", "m", "d"))

Separate one column into several.

Subset Observations (Rows)

dplyr::filter(iris, Sepal.Length > 7)

Extract rows that meet logical criteria.

dplyr::distinct(iris)

Remove duplicate rows.

dplyr::sample_frac(iris, 0.5, replace = TRUE)

Randomly select fraction of rows.

dplyr::sample_n(iris, 10, replace = TRUE)

Randomly select n rows.

dplyr::slice(iris, 10:15)

Select rows by position.

dplyr::top_n(storms, 2, date)

Select and order top n entries (by group if grouped data).

Logic in R

Subset Variables (Columns)

dplyr::select(iris, Sepal.Width, Petal.Length, Species)

Select columns by name or helper function.

Helper functions for select

select(iris, contains("."))

Select columns whose name contains a character string.

select(iris, ends_with("Length"))

Select columns whose name ends with a character string.

select(iris, everything())

Select every column.

select(iris, matches(".t."))

Select columns whose name matches a regular expression.

select(iris, num_range("x", 1:5))

Select columns named x1, x2, x3, x4, x5.

select(iris, one_of(c("Species", "Genus")))

Select columns whose names are in a group of names.

select(iris, starts_with("Sepal"))

Select columns whose name starts with a character string.

select(iris, Sepal.Length:Petal.Width)

Select all columns between Sepal.Length and Petal.Width (inclusive).

select(iris, -Species)

Select all columns except Species.

Make New Variables

dplyr::mutate(iris, sepal = Sepal.Length + Sepal. Width)

Compute and append one or more new columns.

dplyr::mutate_each(iris, funs(min_rank))

Apply window function to each column.

dplyr::transmute(iris, sepal = Sepal.Length + Sepal. Width)

Compute one or more new columns. Drop original columns.

Window function

Mutate uses window functions, functions that take a vector of values and return another vector of values, such as:

dplyr::lead

Copy with values shifted by 1.

dplyr::lag

Copy with values lagged by 1.

dplyr::dense_rank

Ranks with no gaps.

dplyr::min_rank

Ranks. Ties get min rank.

dplyr::percent_rank

Ranks rescaled to [0, 1].

dplyr::row_number

Ranks. Ties got to first value.

dplyr::ntile

Bin vector into n buckets.

dplyr::between

Are values between a and b?

dplyr::cume_dist

Cumulative distribution.

dplyr::cumall

Cumulative all

dplyr::cumany

Cumulative any

dplyr::cummean

Cumulative mean

cumsum

Cumulative sum

cummax

Cumulative max

cummin

Cumulative min

cumprod

Cumulative prod

pmax

Element-wise max

pmin

Element-wise min

Summarise Data

dplyr::summarise(iris, avg = mean(Sepal.Length))

Summarise data into single row of values.

dplyr::summarise_each(iris, funs(mean))

Apply summary function to each column.

dplyr::count(iris, Species, wt = Sepal.Length)

Count the number of rows with each unique value of a variable (with or without weights).

Summary function

Summarise uses summary functions, functions that take a vector of values and return a single value, such as:

dplyr::first

The first value of a vector.

dplyr::last

Last value of a vector.

dplyr::nth

Nth value of a vector.

dplyr::n

of values in a vector.

dplyr::n_distinct

of distinct values in a vector.

IQR

IQR of a vector.

min

The minimum value in a vector.

max

The maximum value in a vector.

mean

Mean value of a vector.

median

The median value of a vector.

var

The variance of a vector.

sd

The standard deviation of a vector

Combine Data Sets

Mutating Joins

dplyr::lef_join(a, b, by = "x1")

Join matching rows from b to a.

dplyr::right_join(a, b, by = "x1")

Join matching rows from a to b.

dplyr::inner_join(a, b, by = "x1")

Join data. Retain only rows in both sets.

dplyr::full_join(a, b, by = "x1")

Join data. Retain all values, all rows.

Filtering Joins

dplyr::semi_join(a, b, by = "x1")

All rows in a that have a match in b.

dplyr::anti_join(a, b, by = "x1")

All rows in a that do not have a match in b.

Set Operations

dplyr::intersect(y, z)

Rows that appear in both y and z.

dplyr::union(y, z)

Rows that appear in either or both y and z.

dplyr::setdiff(y, z)

Rows that appear in y but not z.

Binding

dplyr::bind_rows(y, z)

Append z to y as new rows.

dplyr::bind_cols(y, z)

Append z to y as new columns.

Caution: matches rows by position.

Group Data

null

Group data into rows with the same value of Species.

dplyr::group_by(iris, Species)

Remove grouping information from data frame.

dplyr::ungroup(iris)

Compute separate summary row for each group.

iris %>% group_by(Species) %>% summarise(…)

Compute new variables by group.

iris %>% group_by(Species) %>% mutate(…)

Usage

library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)

Installation

null

You can install from CRAN with

install.packages("dtplyr")

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