dplyr v0.7.3


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A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.



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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.


# The easiest way to get dplyr is to install the whole tidyverse:

# Alternatively, install just dplyr:

# Or the the development version from GitHub:
# install.packages("devtools")

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.



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 %>% 
#> # 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) %>%
    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

Functions in dplyr

Name Description
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.
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Vignettes of dplyr

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Type Package
URL http://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr
BugReports https://github.com/tidyverse/dplyr/issues
Encoding UTF-8
VignetteBuilder knitr
LinkingTo Rcpp (>= 0.12.0), BH (>= 1.58.0-1), bindrcpp, plogr
LazyData yes
License MIT + file LICENSE
RoxygenNote 6.0.1
NeedsCompilation yes
Packaged 2017-09-05 23:09:09 UTC; muelleki
Repository CRAN
Date/Publication 2017-09-09 14:04:29 UTC

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