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 supported
install.packages("data.table")
# latest development version that has passed all tests:
data.table::update_dev_pkg()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 group
library(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 5.552000example(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.