dplyr v0.4.0.9000


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