Using data.table the tidy way

Introduction

For most functions, just use it like tidyverse, but add a _dt as the suffix. This is mostly true for verbs in dplyr. Nevertheless, the data.table has different features, and some API varies, but only varies for more convenience. Below are the supported verbs:

• basic manipulation: select_dt/slice_dt/filter_dt/mutate_dt/arrange_dt/summarise_dt/rename_dt/dist inct_dt
• count family: count_dt/add_count_dt/top_n_dt/top_frac_dt
• join: left_join_dt/right_join_dt/inner_join_dt/full_join_dt/anti_join_dt/semi_join_dt
• nest: nest_dt/unnese_dt
• wide-long transformation: longer_dt/wider_dt
• mising values: drop_na_dt/replace_na_dt
• fst: parse_fst/select_fst

I’ll give only one example so far, just explore yourself and enjoy the fun!

library(tidydt)
#> Registered S3 methods overwritten by 'tidydt':
#>   method                  from  
#>   cube.data.frame         maditr
#>   groupingsets.data.frame maditr
#>   rollup.data.frame       maditr
#> 
#> Life's short, use R.

iris %>%
  mutate_dt(group = Species,sl = Sepal.Length,sw = Sepal.Width) %>%
  select_dt(group,sl,sw) %>%
  filter_dt(sl > 5) %>%
  arrange_dt(group,sl) %>%
  distinct_dt(sl,keep_all = T) %>%
  summarise_dt(sw = max(sw),by = group)
#>         group  sw
#> 1:     setosa 4.4
#> 2: versicolor 3.3
#> 3:  virginica 3.8

Story

I am a big fan of tidyverse, however, when I have to deal with a data.frame larger than 1G, I turned to data.table. But data.table is not easy, and is not so readable as tidyverse. Even today, I still need to explore for long time before I could use data.table correctly for specific occations. Therefore, when the data is smaller than 1G, I would always use tidyverse. If the file is between 1G and 10G, I would use data.table to do some major work and turned back to tidyverse when possible. This has been annoying acutually.

Until one day, I met maditr package. It told me that the manipulation of data.table does not have to be in this way. This could be done in a tidy way! And, you don’t even need to use other packages to build this elegant API, you just need data.table and magrittr! I was shocked! To use a fast tidyverse is what I dream for long, and it could!

By now, Hadley has completed the dtplyr, which facilitate the workflow. However, many verbs are still not supported. I think it’s my time to contribute. I used a lot of functions from maditr, but with a different API. It has a specific goal: provide users with state-of-the-art data manipulation tools with least pain and fastest speed.