dtplyr memo

data.table + dplyr = dtplyr

We only need use lazy_dt() to transform our data in advance, then use dplyr expression as usual, and then get results by as.data.table() or as.data.frame() or as_tibble().

library(data.table)

## data.table 1.14.2 using 6 threads (see ?getDTthreads).  Latest news: r-datatable.com

## **********
## 用中文运行data.table。软件包只提供英语支持。当在在线搜索帮助时，也要确保检查英语错误信息。这个可以通过查看软件包源文件中的po/R-zh_CN.po和po/zh_CN.po文件获得，这个文件可以并排找到母语和英语错误信息。
## **********

## 
## 载入程辑包：'data.table'

## The following objects are masked from 'package:lubridate':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday,
##     week, yday, year

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

## The following object is masked from 'package:purrr':
## 
##     transpose

## The following objects are masked from 'package:reshape2':
## 
##     dcast, melt

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

mtcars2 <- lazy_dt(mtcars)

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

Details please click below links:

• writer’s blog to introduce this R package

• writer’s article about how dtplyr translates dplyr expressions into the data.table code

dplyr also has other two backends:

• dbplyr (translates your dplyr code to SQL), you could look at here and here.

• sparklyr (for datasets stored in Spark), you could click here.