Intro

RAthena is dependent on data.table to read data into R. This is down to the amazing speed data.table offers when reading files into R. However a new package, with equally impressive read speeds, has come onto the scene called vroom. As vroom has been designed to only read data into R, similarly to readr, data.table is still used for all of the heavy lifting. However if a user wishes to use vroom as the file parser, RAthena_options function has been created to enable this:

library(DBI)
library(RAthena)

con = dbConnect(athena())

RAthena_options(file_parser = c("data.table", "vroom"))

By setting the file_parser to "vroom" then the backend will change to allow vroom’s file parser to be used instead of data.table.

Change back to data.table

To go back to using data.table as the file parser it is a simple as calling the RAthena_options function:

# return to using data.table as file parser
RAthena_options()

Swapping on the fly

This makes it very flexible to swap between each file parser even between each query execution:

library(DBI)
library(RAthena)

con = dbConnect(athena())

# upload data
dbWriteTable(con, "iris", iris)

# use default data.table file parser
df1 = dbGetQuery(con, "select * from iris")

# use vroom as file parser
RAthena_options("vroom")
df2 = dbGetQuery(con, "select * from iris")

# return back to data.table file parser
RAthena_options()
df3 = dbGetQuery(con, "select * from iris")

Why should you consider vroom?

If you aren’t sure whether to use vroom over data.table, I draw your attention to vroom boasting a whopping 1.40GB/sec throughput.

Statistics taken from vroom’s github readme