I’ve been asked if the adapter “let” from our R package replyr works with data.table.

My answer is: it does work. I am not a data.table user so I am not the one to ask if data.table benefits a from a non-standard evaluation to standard evaluation adapter such as replyr::let.

Using replyr::let with data.table looks like the following:

library("data.table")
library("replyr")

data("iris", package= "datasets")
iris.dt <- data.table(iris)

# non-standard evaluation, column names hard-coded
iris.dt[, mean(Sepal.Length), by=Species]

# standard evaluation, column names parameterized
let(
  list(GROUPCOL='Species', DATACOL='Sepal.Length'),
  iris.dt[, mean(DATACOL), by=GROUPCOL]
)

# alternate (development/Github) operator notations:

# "let in"
list(GROUPCOL='Species', DATACOL='Sepal.Length') %:%
  iris.dt[, mean(DATACOL), by=GROUPCOL]

# "eval over"
iris.dt[, mean(DATACOL), by=GROUPCOL] %//%
  list(GROUPCOL='Species', DATACOL='Sepal.Length')

I’ve generated some timings to show there is some overhead in the translation (especially on trivial examples):

If any data.table users want to comment if this is useful or not, I’d be happy to hear from you.

Categories: Statistics

Tagged as: data.table R replyr

jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.