dtplyr provides a dplyr backend for data.table. The goal of dtplyr is to allow you to write dplyr code that is automatically translated to the equivalent, but usually much faster, data.table code.

Compared to the previous release, this version of dtplyr is a complete rewrite that focusses only on lazy evaluation triggered by use of lazy_dt(). This means that no computation is performed until you explicitly request it with as.data.table(), as.data.frame() or as_tibble(). This has a considerable advantage over the previous version (which eagerly evaluated each step) because it allows dtplyr to generate significantly more performant translations. This is a large change that breaks all existing uses of dtplyr. But frankly, dtplyr was pretty useless before because it did such a bad job of generating data.table code. Fortunately few people used it, so a major overhaul possible.

See vignette("translation") for details of the current translations, and table.express and rqdatatable for related work.

dtplyr is currently marked as experimental because it has had little usage in the wild. I expect that it will rapidly move towards maturing after a few weeks of feedback from users.

You can install from CRAN with:

install.packages("dtplyr")

Or try the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("tidyverse/dtplyr")

To use dtplyr, you must at least load dtplyr and dplyr. You may also want to load data.table so you can access the other goodies that it provides:

library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)

Then use lazy_dt() to create a “lazy” data table that tracks the operations performed on it.

mtcars2 <- lazy_dt(mtcars)

You can preview the transformation (including the generated data.table code) by printing the result:

mtcars2 %>% 
  filter(wt < 5) %>% 
  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
  group_by(cyl) %>% 
  summarise(l100k = mean(l100k))
#> Source: local data table [?? x 2]
#> Call:   `_DT1`[wt < 5][, `:=`(l100k = 235.21/mpg)][, .(l100k = mean(l100k)), 
#>     keyby = .(cyl)]
#> 
#>     cyl l100k
#>   <dbl> <dbl>
#> 1     4  9.05
#> 2     6 12.0 
#> 3     8 14.9 
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

But generally you should reserve this only for debugging, and use as.data.table(), as.data.frame(), or as_tibble() to indicate that you’re done with the transformation and want to access the results:

mtcars2 %>% 
  filter(wt < 5) %>% 
  mutate(l100k = 235.21 / mpg) %>% # liters / 100 km
  group_by(cyl) %>% 
  summarise(l100k = mean(l100k)) %>% 
  as_tibble()
#> # A tibble: 3 x 2
#>     cyl l100k
#>   <dbl> <dbl>
#> 1     4  9.05
#> 2     6 12.0 
#> 3     8 14.9

There are three primary reasons that dtplyr will always be somewhat slower than data.table:

• Each dplyr verb must do some work to convert dplyr syntax to data.table syntax. This takes time proportional to the complexity of the input code, not the input data, so should be a negligible overhead for large datasets. Initial benchmarks suggest that the overhead should be under 1ms per dplyr call.

• Some data.table expressions have no direct dplyr equivalent. For example, there’s no way to express cross- or rolling-joins with dplyr.

• To match dplyr semantics, mutate() does not modify in place by default. This means that most expressions involving mutate() must make a copy that would not be necessary if you were using data.table directly. (You can opt out of this behaviour in lazy_dt() with immutable = FALSE).