Learn R Programming

⚠️There's a newer version (1.8.3) of this package.Take me there.

tidyfst (version 0.7.7)

Tidy Verbs for Fast Data Manipulation

Description

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 inspired by 'maditr', but follows a different philosophy of design, such as prohibiting in place replacement and used a "_dt" suffix API. Also, 'tidyfst' would introduce more tidy data verbs from (and for) other packages, including but not limited to 'fst','tidyverse' and 'data.table'.

Copy Link

Version

Install

install.packages('tidyfst')

Monthly Downloads

677

Version

0.7.7

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Tian-Yuan Huang

Last Published

March 5th, 2020

Functions in tidyfst (0.7.7)

pull_dt

Pull out a single variable
group_dt

Data manipulation within groups
left_join_dt

Join table by common keys
mutate_when

Conditional update of columns in data.table
t_dt

Efficient transpose of data.frame
top_n_dt

Select top (or bottom) n rows (by value)
nest_by

Nest and unnest
slice_dt

Slice rows in data.frame
summarise_dt

Summarise columns to single values
drop_na_dt

Dump, replace and fill missing values in data.frame
export_fst

Read and write fst files
rename_dt

Rename column in data.frame
mutate_dt

Mutate columns in data.frame
reexports

Objects exported from other packages
lead_dt

Fast lead/lag for vectors
unite_dt

Unite multiple columns into one by pasting strings together
separate_dt

Separate a character column into two columns using a regular expression separator
sample_n_dt

Sample n rows from a table
longer_dt

Pivot data from wide to long
select_dt

Select column from data.frame
wider_dt

Pivot data from long to wide
in_dt

Short cut to data.table
distinct_dt

Select distinct/unique rows in data.frame
fst

Parse,inspect and extract data.table from fst file
count_dt

Count observations by group
complete_dt

Complete a data frame with missing combinations of data
all_dt

Wise mutate or summarise with "all-at-if"
arrange_dt

Arrange entries in data.frame
dummy_dt

Fast creation of dummy variables
filter_dt

Filter entries in data.frame