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data.table
provides a high-performance version of base R’s data.frame
with syntax and feature enhancements for ease of use, convenience and programming speed.
data.table
??fread
, see also convenience features for small data?fwrite
IRanges::findOverlaps
), non-equi joins (i.e. joins using operators >, >=, <, <=
), aggregate on join (by=.EACHI
), update on join?dcast
(pivot/wider/spread) and ?melt
(unpivot/longer/gather)list
are supportedinstall.packages("data.table")
# latest development version (only if newer available)
data.table::update_dev_pkg()
# latest development version (force install)
install.packages("data.table", repos="https://rdatatable.gitlab.io/data.table")
See the Installation wiki for more details.
Use data.table
subset [
operator the same way you would use data.frame
one, but…
DT$
(like subset()
and with()
but built-in)j
argument, not just list of columnsby
to compute j
expression by grouplibrary(data.table)
DT = as.data.table(iris)
# FROM[WHERE, SELECT, GROUP BY]
# DT [i, j, by]
DT[Petal.Width > 1.0, mean(Petal.Length), by = Species]
# Species V1
#1: versicolor 4.362791
#2: virginica 5.552000
example(data.table)
data.table
is widely used by the R community. It is being directly used by hundreds of CRAN and Bioconductor packages, and indirectly by thousands. It is one of the top most starred R packages on GitHub, and was highly rated by the Depsy project. If you need help, the data.table
community is active on StackOverflow.
Guidelines for filing issues / pull requests: Contribution Guidelines.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.