dplyr v0.4.2

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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

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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

    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("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 needed to 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
flights
#> Source: local data frame [336,776 x 16]
#> 
#>    year month day dep_time dep_delay arr_time arr_delay carrier tailnum
#> 1  2013     1   1      517         2      830        11      UA  N14228
#> 2  2013     1   1      533         4      850        20      UA  N24211
#> 3  2013     1   1      542         2      923        33      AA  N619AA
#> 4  2013     1   1      544        -1     1004       -18      B6  N804JB
#> 5  2013     1   1      554        -6      812       -25      DL  N668DN
#> 6  2013     1   1      554        -4      740        12      UA  N39463
#> 7  2013     1   1      555        -5      913        19      B6  N516JB
#> 8  2013     1   1      557        -3      709       -14      EV  N829AS
#> 9  2013     1   1      557        -3      838        -8      B6  N593JB
#> 10 2013     1   1      558        -2      753         8      AA  N3ALAA
#> ..  ...   ... ...      ...       ...      ...       ...     ...     ...
#> Variables not shown: flight (int), origin (chr), dest (chr), air_time
#>   (dbl), distance (dbl), hour (dbl), minute (dbl)

# 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  <- flights %>% group_by(carrier)
carriers_db1 <- flights_db1 %>% group_by(carrier)
carriers_db2 <- flights_db2 %>% group_by(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(carriers_df %>% summarise(delay = mean(arr_delay)))
#>    user  system elapsed 
#>   0.036   0.001   0.037
system.time(carriers_db1 %>% summarise(delay = mean(arr_delay)) %>% collect())
#>    user  system elapsed 
#>   0.263   0.130   0.392
system.time(carriers_db2 %>% summarise(delay = mean(arr_delay)) %>% collect())
#>    user  system elapsed 
#>   0.016   0.001   0.151

Data frame methods are much much faster than the plyr equivalent. The database methods are slower, but can work with data that don't fit in memory.

system.time(plyr::ddply(flights, "carrier", plyr::summarise,
  delay = mean(arr_delay, na.rm = TRUE)))
#>    user  system elapsed 
#>   0.100   0.032   0.133

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:

by_year <- lahman_df() %>% 
  tbl("Batting") %>%
  group_by(yearID)
by_year %>% 
  do(mod = lm(R ~ AB, data = .))
#> Source: local data frame [143 x 2]
#> Groups: <by row>
#> 
#>    yearID     mod
#> 1    1871 <S3:lm>
#> 2    1872 <S3:lm>
#> 3    1873 <S3:lm>
#> 4    1874 <S3:lm>
#> 5    1875 <S3:lm>
#> 6    1876 <S3:lm>
#> 7    1877 <S3:lm>
#> 8    1878 <S3:lm>
#> 9    1879 <S3:lm>
#> 10   1880 <S3:lm>
#> ..    ...     ...

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:

by_year %>% 
  do(mod = lm(R ~ AB, data = .)) %>%
  object.size() %>%
  print(unit = "MB")
#> 22.2 Mb

by_year %>% 
  do(mod = biglm::biglm(R ~ AB, data = .)) %>%
  object.size() %>%
  print(unit = "MB")
#> 0.8 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
arrange Arrange rows by variables.
bench_compare Evaluate, compare, benchmark operations of a set of srcs.
backend_sql SQL generation.
make_tbl Create a "tbl" object
id Compute a unique numeric id for each unique row in a data frame.
between Do values in a numeric vector fall in specified range?
tbl_sql Create an SQL tbl (abstract)
build_sql Build a SQL string.
chain Chain together multiple operations.
cumall Cumulativate versions of any, all, and mean
grouped_df A grouped data frame.
as.tbl_cube Coerce an existing data structure into a
ranking Windowed rank functions.
group_by Group a tbl by one or more variables.
tbl Create a table from a data source
select_vars Select variables.
compute Compute a lazy tbl.
src Create a "src" object
join.tbl_sql Join sql tbls.
group_indices Group id.
n_distinct Efficiently count the number of unique values in a vector.
rowwise Group input by rows
desc Descending order.
partial_eval Partially evaluate an expression.
nth Extract the first, last or nth value from a vector.
dplyr dplyr: a grammar of data manipulation
top_n Select top n rows (by value).
data_frame Build a data frame.
funs Create a list of functions calls.
sample Sample n rows from a table.
base_scalar Create an sql translator
backend_src Source generics.
tally Counts/tally observations by group.
temp_srcs Connect to temporary data sources.
backend_db Database generics.
n The number of observations in the current group.
with_order Run a function with one order, translating result back to original order
tbl_cube A data cube tbl.
type_sum Provide a succint summary of a type
tbl_dt Create a data table tbl.
sql SQL escaping.
query Create a mutable query object.
summarise_each Summarise and mutate multiple columns.
tbl_df Create a data frame tbl.
same_src Figure out if two sources are the same (or two tbl have the same source)
knit_print.trunc_mat knit_print method for trunc mat
src_postgres Connect to postgresql.
src_local A local source.
src_sqlite Connect to a sqlite database.
bind Efficiently bind multiple data frames by row and column.
dplyr-cluster Cluster management.
translate_sql Translate an expression to sql.
copy_to Copy a local data frame to a remote src.
summarise Summarise multiple values to a single value.
grouped_dt A grouped data table.
join Join two tbls together.
location Print the location in memory of a data frame
tbl_vars List variables provided by a tbl.
glimpse Get a glimpse of your data.
nasa NASA spatio-temporal data
order_by A helper function for ordering window function output.
slice Select rows by position.
explain Explain details of an tbl.
copy_to.src_sql Copy a local data frame to a sqlite src.
distinct Select distinct/unique rows.
select Select/rename variables by name.
join.tbl_df Join data frame tbls.
progress_estimated Progress bar with estimated time.
lahman Cache and retrieve an
sql_quote Helper function for quoting sql elements.
add_rownames Convert row names to an explicit variable.
setops Set operations.
filter Return rows with matching conditions.
group_size Calculate group sizes.
nycflights13 Database versions of the nycflights13 data
as_data_frame Coerce a list to a data frame.
mutate Add new variables.
src_sql Create a "sql src" object
all.equal.tbl_df Provide a useful implementation of all.equal for data.frames.
do Do arbitrary operations on a tbl.
lead-lag Lead and lag.
failwith Fail with specified value.
print.tbl_df Tools for describing matrices
groups Get/set the grouping variables for tbl.
src_mysql Connect to mysql/mariadb.
join.tbl_dt Join data table tbls.
src_tbls List all tbls provided by a source.
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Details

Type Package
URL https://github.com/hadley/dplyr
BugReports https://github.com/hadley/dplyr/issues
VignetteBuilder knitr
LazyData yes
LinkingTo Rcpp (>= 0.11.6), BH (>= 1.58.0-1)
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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