dplyr v0.3

0

Monthly downloads

0th

Percentile

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.

Readme

dplyr

Build Status

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

      install.packages("dplyr")
      `
    
  • the latest development version from github with

      if (packageVersion("devtools") < 1.6) {
        install.packages("devtools")
      }
      devtools::install_github("hadley/lazyeval")
      devtools::install_github("hadley/dplyr")
    

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

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

tbls

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(hflights) # for data
head(hflights)

# Coerce to data table
hflights_dt <- tbl_dt(hflights)

# Caches data in local SQLite db
hflights_db1 <- tbl(hflights_sqlite(), "hflights")

# Caches data in local postgres db
hflights_db2 <- tbl(hflights_postgres(), "hflights")

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

carriers_df  <- group_by(hflights, UniqueCarrier)
carriers_dt  <- group_by(hflights_dt, UniqueCarrier)
carriers_db1 <- group_by(hflights_db1, UniqueCarrier)
carriers_db2 <- group_by(hflights_db2, UniqueCarrier)

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

See ?manip for more details.

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(carriers_dt, delay = mean(ArrDelay, na.rm = TRUE)))
#   user  system elapsed
#  0.007   0.000   0.008
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 and data table methods are order of magnitude faster than plyr. The database methods are slower, but can work with data that don't fit in memory.

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

do()

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)
batting_dt <- tbl_dt(batting_df)

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

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

library(biglm)
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")

Binary 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. 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

Currently join variables must be the same in both the left-hand and right-hand sides.

Other operations

All tbls also provide head() and print() methods. The default print method gives information about the data source and shows the first 10 rows and all the columns that will fit on one screen.

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
build_sql Build a SQL string.
chain Chain together multiple operations.
all.equal.tbl_df Provide a useful implementation of all.equal for data.frames.
arrange Arrange rows by variables.
as.tbl_cube Coerce an existing data structure into a
backend_db Database generics.
bench_compare Evaluate, compare, benchmark operations of a set of srcs.
between Do values in a numeric vector fail in specified range?
backend_sql SQL generation.
backend_src Source generics.
compute Compute a lazy tbl.
copy_to Copy a local data frame to a remote src.
grouped_df A grouped data frame.
grouped_dt A grouped data table.
join Join two tbls together.
join.tbl_df Join data frame tbls.
n_distinct Efficiently count the number of unique values in a vector.
nasa NASA spatio-temporal data
lahman Cache and retrieve an
lead-lag Lead and lag.
nth Extract the first, last or nth value from a vector.
select_vars Select variables.
setops Set operations.
dplyr-cluster Cluster management.
print.tbl_df Tools for describing matrices
explain Explain details of an tbl.
location Print the location in memory of a data frame
make_tbl Create a "tbl" object
mutate Add new variables.
n The number of observations in the current group.
copy_to.src_sql Copy a local data fram to a sqlite src.
cumall Cumulativate versions of any, all, and mean
group_by Group a tbl by one or more variables.
group_size Calculate group sizes.
dplyr The dplyr package.
nycflights13 Database versions of the nycflights13 data
slice Select rows by position.
sql SQL escaping.
src_tbls List all tbls provided by a source.
data_frame Build a data frame.
desc Descending order.
funs Create a list of functions calls.
glimpse Get a glimpse of your data.
sample Sample n rows from a table.
select Select/rename variables by name.
sql_quote Helper function for quoting sql elements.
base_scalar Create an sql translator
tbl_df Create a data frame tbl.
tbl_dt Create a data table tbl.
distinct Select distinct/unique rows.
do Do arbitrary operations on a tbl.
failwith Fail with specified value.
join.tbl_dt Join data table tbls.
join.tbl_sql Join sql tbls.
progress_estimated Progress bar with estimated time.
query Create a mutable query object.
groups Get/set the grouping variables for tbl.
id Compute a unique numeric id for each unique row in a data frame.
order_by A helper function for ordering window function output.
partial_eval Partially evaluate an expression.
filter Return rows with matching conditions.
ranking Windowed rank functions.
rbind_list Efficiently rbind multiple data frames.
src_sql Create a "sql src" object
src Create a "src" object
src_local A local source.
tbl Create a table from a data source
tbl_cube A data cube tbl.
with_order Run a function with one order, translating result back to original order
src_sqlite Connect to a sqlite database.
temp_srcs Connect to temporary data sources.
top_n Select top n rows (by value).
summarise Summarise multiple values to a single value.
tbl_sql Create an SQL tbl (abstract)
tbl_vars List variables provided by a tbl.
rowwise Group input by rows
same_src Figure out if two sources are the same (or two tbl have the same source)
src_mysql Connect to mysql/mariadb.
src_postgres Connect to postgresql.
summarise_each Summarise and mutate multiple columns.
tally Counts/tally observations by group.
translate_sql Translate an expression to sql.
type_sum Provide a succint summary of a type
No Results!

Last month downloads

Details

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' 'cbind.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-size.r' 'grouped-df.r' 'grouped-dt.r' 'id.r' 'inline.r' 'join-df.r' 'join-dt.r' 'join-sql.r' 'join.r' 'lead-lag.R' 'location.R' 'manip-cube.r' 'manip-df.r' 'manip-dt.r' 'manip-sql.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-data-frame.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'

Include our badge in your README

[![Rdoc](http://www.rdocumentation.org/badges/version/dplyr)](http://www.rdocumentation.org/packages/dplyr)