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dplyr

Overview

dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

  • mutate() adds new variables that are functions of existing variables
  • select() picks variables based on their names.
  • filter() picks cases based on their values.
  • summarise() reduces multiple values down to a single summary.
  • arrange() changes the ordering of the rows.

These all combine naturally with group_by() which allows you to perform any operation "by group". You can learn more about them in vignette("dplyr"). As well as these single-table verbs, dplyr also provides a variety of two-table verbs, which you can learn about in vignette("two-table").

dplyr is designed to abstract over how the data is stored. That means as well as working with local data frames, you can also work with remote database tables, using exactly the same R code. Install the dbplyr package then read vignette("databases", package = "dbplyr").

If you are new to dplyr, the best place to start is the data import chapter in R for data science.

Installation

# The easiest way to get dplyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just dplyr:
install.packages("dplyr")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/dplyr")

If you encounter a clear bug, please file a minimal reproducible example on github. For questions and other discussion, please use the manipulatr mailing list.

Usage

library(dplyr)

starwars %>% 
  filter(species == "Droid")
#> # A tibble: 5 x 13
#>    name height  mass hair_color  skin_color eye_color birth_year gender
#>   <chr>  <int> <dbl>      <chr>       <chr>     <chr>      <dbl>  <chr>
#> 1 C-3PO    167    75       <NA>        gold    yellow        112   <NA>
#> 2 R2-D2     96    32       <NA> white, blue       red         33   <NA>
#> 3 R5-D4     97    32       <NA>  white, red       red         NA   <NA>
#> 4 IG-88    200   140       none       metal       red         15   none
#> 5   BB8     NA    NA       none        none     black         NA   none
#> # ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>

starwars %>% 
  select(name, ends_with("color"))
#> # A tibble: 87 x 4
#>             name hair_color  skin_color eye_color
#>            <chr>      <chr>       <chr>     <chr>
#> 1 Luke Skywalker      blond        fair      blue
#> 2          C-3PO       <NA>        gold    yellow
#> 3          R2-D2       <NA> white, blue       red
#> 4    Darth Vader       none       white    yellow
#> 5    Leia Organa      brown       light     brown
#> # ... with 82 more rows

starwars %>% 
  mutate(name, bmi = mass / ((height / 100)  ^ 2)) %>%
  select(name:mass, bmi)
#> # A tibble: 87 x 4
#>             name height  mass      bmi
#>            <chr>  <int> <dbl>    <dbl>
#> 1 Luke Skywalker    172    77 26.02758
#> 2          C-3PO    167    75 26.89232
#> 3          R2-D2     96    32 34.72222
#> 4    Darth Vader    202   136 33.33007
#> 5    Leia Organa    150    49 21.77778
#> # ... with 82 more rows

starwars %>% 
  arrange(desc(mass))
#> # A tibble: 87 x 13
#>                    name height  mass hair_color       skin_color
#>                   <chr>  <int> <dbl>      <chr>            <chr>
#> 1 Jabba Desilijic Tiure    175  1358       <NA> green-tan, brown
#> 2              Grievous    216   159       none     brown, white
#> 3                 IG-88    200   140       none            metal
#> 4           Darth Vader    202   136       none            white
#> 5               Tarfful    234   136      brown            brown
#> # ... with 82 more rows, and 8 more variables: eye_color <chr>,
#> #   birth_year <dbl>, gender <chr>, homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

starwars %>%
  group_by(species) %>%
  summarise(
    n = n(),
    mass = mean(mass, na.rm = TRUE)
  ) %>%
  filter(n > 1)
#> # A tibble: 9 x 3
#>    species     n     mass
#>      <chr> <int>    <dbl>
#> 1    Droid     5 69.75000
#> 2   Gungan     3 74.00000
#> 3    Human    35 82.78182
#> 4 Kaminoan     2 88.00000
#> 5 Mirialan     2 53.10000
#> # ... with 4 more rows

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Version

Install

install.packages('dplyr')

Monthly Downloads

1,541,718

Version

0.7.3

License

MIT + file LICENSE

Issues

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Stars

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Maintainer

Last Published

September 9th, 2017

Functions in dplyr (0.7.3)

add_rownames

Convert row names to an explicit variable.
all_equal

Flexible equality comparison for data frames
all_vars

Apply predicate to all variables
arrange

Arrange rows by variables
common_by

Extract out common by variables
compute

Force computation of a database query
backend_dbplyr

Database and SQL generics.
band_members

Band membership
check_dbplyr

dbplyr compatibility functions
coalesce

Find first non-missing element
filter

Return rows with matching conditions
filter_all

Filter within a selection of variables
arrange_all

Arrange rows by a selection of variables
as.table.tbl_cube

Coerce a tbl_cube to other data structures
bench_compare

Evaluate, compare, benchmark operations of a set of srcs.
between

Do values in a numeric vector fall in specified range?
distinct

Select distinct/unique rows
do

Do anything
id

Compute a unique numeric id for each unique row in a data frame.
ident

Flag a character vector as SQL identifiers
lead-lag

Lead and lag.
explain

Explain details of a tbl
failwith

Fail with specified value.
group_by_all

Group by a selection of variables
group_by_prepare

Prepare for grouping.
n

The number of observations in the current group.
n_distinct

Efficiently count the number of unique values in a set of vector
order_by

A helper function for ordering window function output
progress_estimated

Progress bar with estimated time.
select_var

Select variable
select_vars

Select variables.
src_dbi

Source for database backends
src_local

A local source.
as.tbl_cube

Coerce an existing data structure into a tbl_cube
auto_copy

Copy tables to same source, if necessary
copy_to

Copy a local data frame to a remote src
cumall

Cumulativate versions of any, all, and mean
dplyr-package

dplyr: a grammar of data manipulation
tally

Count/tally observations by group
tbl

Create a table from a data source
bind

Efficiently bind multiple data frames by row and column
case_when

A general vectorised if
desc

Descending order
dim_desc

Describing dimensions
group_indices

Group id.
dr_dplyr

Dr Dplyr checks your installation for common problems.
grouped_df

A grouped data frame.
groups

Return grouping variables
make_tbl

Create a "tbl" object
mutate

Add new variables
location

Print the location in memory of a data frame
pull

Pull out a single variable
ranking

Windowed rank functions.
rowwise

Group input by rows
same_src

Figure out if two sources are the same (or two tbl have the same source)
funs

Create a list of functions calls.
group_by

Group by one or more variables
join

Join two tbls together
join.tbl_df

Join data frame tbls
na_if

Convert values to NA
recode

Recode values
reexports

Objects exported from other packages
sample

Sample n rows from a table
scoped

Operate on a selection of variables
sql

SQL escaping.
group_size

Calculate group sizes.
if_else

Vectorised if
init_logging

Enable internal logging
near

Compare two numeric vectors
nth

Extract the first, last or nth value from a vector
src

Create a "src" object
tbl_vars

List variables provided by a tbl.
top_n

Select top (or bottom) n rows (by value)
select_all

Select and rename a selection of variables
select_helpers

Select helpers
storms

Storm tracks data
src_tbls

List all tbls provided by a source.
starwars

Starwars characters
vars

Select variables
nasa

NASA spatio-temporal data
tally_

Deprecated SE versions of main verbs.
select

Select/rename variables by name
setops

Set operations
slice

Select rows by position
tbl_cube

A data cube tbl
tbl_df

Create a data frame tbl.
with_order

Run a function with one order, translating result back to original order
summarise

Reduces multiple values down to a single value
summarise_all

Summarise and mutate multiple columns.
summarise_each

Summarise and mutate multiple columns.