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tidyfst is a toolkit of tidy data manipulation verbs with data.table as the backend . Combining the merits of syntax elegance from dplyr and computing performance from data.table, tidyfst intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of data.table, while enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations. Also, tidyfst would introduce more tidy data verbs from other packages, including but not limited to tidyverse and data.table. If you are a dplyr user but have to use data.table for speedy computation, or data.table user looking for readable coding syntax, tidyfst is designed for you (and me of course). For further details and tutorials, see vignettes. Both Chinese and English tutorials could be found there.

Till now, tidyfst has an API that might even transcend its predecessors (e.g. select_dt could accept nearly anything for super column selection). Enjoy the efficient data operations in tidyfst !

PS: For extreme performance in tidy syntax, try tidyfst’s mirror package tidyft.


  • Receives any data.frame (tibble/data.table/data.frame) and returns a data.table.
  • Show the variable class of data.table as default.
  • Never use in place replacement (also known as modification by reference, which means the original variable would not be modified without notification).
  • Use suffix (“_dt”) rather than prefix to increase the efficiency (especially when you have IDE with automatic code completion).
  • More flexible verbs (e.g. pairwise_count_dt) for big data manipulation.
  • Supporting data importing and parsing with fst, which saves both time and memory. Details see parse_fst/select_fst/filter_fst and import_fst/export_fst.
  • Low and stable dependency on mature packages (data.table, fst, stringr)





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
#>        <fctr> <num>
#> 1:     setosa 4.4
#> 2: versicolor 3.4
#> 3:  virginica 3.8

iris %>%
  count_dt(Species) %>%
#>       Species     n      prop prop_label
#>        <fctr> <int>     <num>     <char>
#> 1:     setosa    50 0.3333333      33.3%
#> 2: versicolor    50 0.3333333      33.3%
#> 3:  virginica    50 0.3333333      33.3%

iris[3:8,] %>%
  mutate_when(Petal.Width == .2,
              one = 1,Sepal.Length=2)
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species one
#>          <num>       <num>        <num>       <num>  <fctr> <num>
#> 1:          2.0         3.2          1.3         0.2  setosa   1
#> 2:          2.0         3.1          1.5         0.2  setosa   1
#> 3:          2.0         3.6          1.4         0.2  setosa   1
#> 4:          5.4         3.9          1.7         0.4  setosa  NA
#> 5:          4.6         3.4          1.4         0.3  setosa  NA
#> 6:          2.0         3.4          1.5         0.2  setosa   1

Future plans

tidyfst will keep up with the updates of data.table , in the next step would introduce more new features to improve the performance and flexibility to facilitate fast data manipulation in tidy syntax.

Cheat sheet

Suggested citation

Huang et al., (2020). tidyfst: Tidy Verbs for Fast Data Manipulation. Journal of Open Source Software, 5(52), 2388,


The author of maditr, Gregory Demin and the author of fst, Marcus Klik have helped me a lot in the development of this work. It is so lucky to have them (and many other selfless contributors) in the same open source community of R.