dplyr v0.4.1


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by Hadley Wickham

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 the next iteration of plyr, focussed on tools for working with data frames (hence the d in the name). It has three main goals:

  • Identify the most important data manipulation tools needed for data analysis and make them easy to use from R.

  • Provide blazing fast performance for in-memory data by writing key pieces in C++.

  • Use the same interface to work with data no matter where it's stored, whether in a data frame, a data table or database.

You can install:

  • the latest released version from CRAN with

  • the latest development version from github with

    if (packageVersion("devtools") < 1.6) {

You'll probably also want to install the data packages used in most examples: install.packages(c("nycflights13", "Lahman")).

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.

Learning dplyr

To get started, read the notes below, then read the intro vignette: vignette("introduction", package = "dplyr"). To make the most of dplyr, I also recommend that you familiarise yourself with the principles of tidy data: this will help you get your data into a form that works well with dplyr, ggplot2 and R's many modelling functions.

If you need more, help I recommend the following (paid) resources:

  • dplyr on datacamp, by Garrett Grolemund. Learn the basics of dplyr at your own pace in this interactive online course.

  • Introduction to Data Science with R: How to Manipulate, Visualize, and Model Data with the R Language, by Garrett Grolemund. This O'Reilly video series will teach you the basics oto be an effective analyst in R.

Key data structures

The key object in dplyr is a tbl, a representation of a tabular data structure. Currently dplyr supports:

You can create them as follows:

library(dplyr) # for functions
library(nycflights13) # for data

# Caches data in local SQLite db
flights_db1 <- tbl(nycflights13_sqlite(), "flights")

# Caches data in local postgres db
flights_db2 <- tbl(nycflights13_postgres(), "flights")

Each tbl also comes in a grouped variant which allows you to easily perform operations "by group":

carriers_df  <- group_by(flights, carrier)
carriers_db1 <- group_by(flights_db1, carrier)
carriers_db2 <- group_by(flights_db2, carrier)

Single table verbs

dplyr implements the following verbs useful for data manipulation:

  • select(): focus on a subset of variables
  • filter(): focus on a subset of rows
  • mutate(): add new columns
  • summarise(): reduce each group to a smaller number of summary statistics
  • arrange(): re-order the rows

They all work as similarly as possible across the range of data sources. The main difference is performance:

system.time(summarise(carriers_df, delay = mean(ArrDelay, na.rm = TRUE)))
#   user  system elapsed
#  0.010   0.002   0.012
system.time(summarise(collect(carriers_db1, delay = mean(ArrDelay))))
#   user  system elapsed
#  0.402   0.058   0.465
system.time(summarise(collect(carriers_db2, delay = mean(ArrDelay))))
#   user  system elapsed
#  0.386   0.097   0.718

The data frame methods are all at least an order of magnitude faster than the plyr equivalent. The database methods are slower, but can work with data that don't fit in memory.

system.time(ddply(hflights, "UniqueCarrier", summarise,
  delay = mean(ArrDelay, na.rm = TRUE)))
#   user  system elapsed
#  0.527   0.078   0.604


As well as the specialised operations described above, dplyr also provides the generic do() function which applies any R function to each group of the data.

Let's take the batting database from the built-in Lahman database. We'll group it by year, and then fit a model to explore the relationship between their number of at bats and runs:

batting_db <- tbl(lahman_sqlite(), "Batting")
batting_df <- collect(batting_db)

years_db <- group_by(batting_db, yearID)
years_df <- group_by(batting_df, yearID)

system.time(do(years_db, failwith(NULL, lm), formula = R ~ AB))
system.time(do(years_df, failwith(NULL, lm), formula = R ~ AB))

Note that if you are fitting lots of linear models, it's a good idea to use biglm because it creates model objects that are considerably smaller:

mod1 <- do(years_df, lm, formula = R ~ AB)
mod2 <- do(years_df, biglm, formula = R ~ AB)
print(object.size(mod1), unit = "MB")
print(object.size(mod2), unit = "MB")

Multiple table verbs

As well as verbs that work on a single tbl, there are also a set of useful verbs that work with two tbls at a time: joins and set operations.

dplyr implements the four most useful joins from SQL:

  • inner_join(x, y): matching x + y
  • left_join(x, y): all x + matching y
  • semi_join(x, y): all x with match in y
  • anti_join(x, y): all x without match in y

And provides methods for:

  • intersect(x, y): all rows in both x and y
  • union(x, y): rows in either x or y
  • setdiff(x, y): rows in x, but not y

Plyr compatibility

You'll need to be a little careful if you load both plyr and dplyr at the same time. I'd recommend loading plyr first, then dplyr, so that the faster dplyr functions come first in the search path. By and large, any function provided by both dplyr and plyr works in a similar way, although dplyr functions tend to be faster and more general.

Functions in dplyr

Name Description
bench_compare Evaluate, compare, benchmark operations of a set of srcs.
desc Descending order.
group_size Calculate group sizes.
chain Chain together multiple operations.
grouped_dt A grouped data table.
join.tbl_sql Join sql tbls.
ranking Windowed rank functions.
glimpse Get a glimpse of your data.
knit_print.trunc_mat knit_print method for trunc mat
copy_to Copy a local data frame to a remote src.
lahman Cache and retrieve an
join Join two tbls together.
funs Create a list of functions calls.
as_data_frame Coerce a list to a data frame.
data_frame Build a data frame.
cumall Cumulativate versions of any, all, and mean
dplyr-cluster Cluster management.
tbl_dt Create a data table tbl.
order_by A helper function for ordering window function output.
join.tbl_df Join data frame tbls.
grouped_df A grouped data frame.
failwith Fail with specified value.
nth Extract the first, last or nth value from a vector.
rbind_all Efficiently bind multiple data frames by row and column.
compute Compute a lazy tbl.
src_local A local source.
backend_src Source generics.
select_vars Select variables.
tbl_sql Create an SQL tbl (abstract)
explain Explain details of an tbl.
slice Select rows by position.
n_distinct Efficiently count the number of unique values in a vector.
src_sqlite Connect to a sqlite database.
rowwise Group input by rows
setops Set operations.
join.tbl_dt Join data table tbls.
make_tbl Create a "tbl" object
summarise_each Summarise and mutate multiple columns.
group_by Group a tbl by one or more variables.
translate_sql Translate an expression to sql.
do Do arbitrary operations on a tbl.
as.tbl_cube Coerce an existing data structure into a
partial_eval Partially evaluate an expression.
filter Return rows with matching conditions.
backend_db Database generics.
mutate Add new variables.
nycflights13 Database versions of the nycflights13 data
nasa NASA spatio-temporal data
top_n Select top n rows (by value).
progress_estimated Progress bar with estimated time.
build_sql Build a SQL string.
src_postgres Connect to postgresql.
sql_quote Helper function for quoting sql elements.
sample Sample n rows from a table.
print.tbl_df Tools for describing matrices
lead-lag Lead and lag.
groups Get/set the grouping variables for tbl.
tbl_df Create a data frame tbl.
backend_sql SQL generation.
copy_to.src_sql Copy a local data frame to a sqlite src.
id Compute a unique numeric id for each unique row in a data frame.
select Select/rename variables by name.
src_mysql Connect to mysql/mariadb.
tbl_vars List variables provided by a tbl.
n The number of observations in the current group.
all.equal.tbl_df Provide a useful implementation of all.equal for data.frames.
base_scalar Create an sql translator
dplyr dplyr: a grammar of data manipulation
tally Counts/tally observations by group.
tbl_cube A data cube tbl.
arrange Arrange rows by variables.
same_src Figure out if two sources are the same (or two tbl have the same source)
tbl Create a table from a data source
add_rownames Convert row names to an explicit variable.
with_order Run a function with one order, translating result back to original order
between Do values in a numeric vector fall in specified range?
summarise Summarise multiple values to a single value.
src_sql Create a "sql src" object
group_indices Group id.
src Create a "src" object
query Create a mutable query object.
type_sum Provide a succint summary of a type
location Print the location in memory of a data frame
src_tbls List all tbls provided by a source.
sql SQL escaping.
temp_srcs Connect to temporary data sources.
distinct Select distinct/unique rows.
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Type Package
URL https://github.com/hadley/dplyr
BugReports https://github.com/hadley/dplyr/issues
VignetteBuilder knitr
LazyData yes
LinkingTo Rcpp (>= 0.11.3), BH (>= 1.51.0-2)
License MIT + file LICENSE
Collate 'RcppExports.R' 'all-equal.r' 'bench-compare.r' 'chain.r' 'cluster.R' 'colwise.R' 'compute-collect.r' 'copy-to.r' 'data-lahman.r' 'data-nasa.r' 'data-nycflights13.r' 'data-temp.r' 'data.r' 'dataframe.R' 'dbi-s3.r' 'desc.r' 'distinct.R' 'do.r' 'dplyr.r' 'explain.r' 'failwith.r' 'funs.R' 'glimpse.R' 'group-by.r' 'group-indices.R' 'group-size.r' 'grouped-df.r' 'grouped-dt.r' 'id.r' 'inline.r' 'join.r' 'lead-lag.R' 'location.R' 'manip.r' 'nth-value.R' 'order-by.R' 'over.R' 'partial-eval.r' 'progress.R' 'query.r' 'rank.R' 'rbind.r' 'rowwise.r' 'sample.R' 'select-utils.R' 'select-vars.R' 'sets.r' 'sql-escape.r' 'sql-star.r' 'src-local.r' 'src-mysql.r' 'src-postgres.r' 'src-sql.r' 'src-sqlite.r' 'src.r' 'tally.R' 'tbl-cube.r' 'tbl-df.r' 'tbl-dt.r' 'tbl-sql.r' 'tbl.r' 'top-n.R' 'translate-sql-helpers.r' 'translate-sql-base.r' 'translate-sql-window.r' 'translate-sql.r' 'type-sum.r' 'utils-dt.R' 'utils-format.r' 'utils.r' 'view.r' 'zzz.r'

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