Learn R Programming

⚠️There's a newer version (0.9.20) of this package.Take me there.

tidyft (version 0.5.7)

Fast and Memory Efficient Data Operations in Tidy Syntax

Description

Tidy syntax for 'data.table', using modification by reference whenever possible. This toolkit is designed for big data analysis in high-performance desktop or laptop computers. The syntax of the package is similar or identical to 'tidyverse'. It is user friendly, memory efficient and time saving. For more information, check its ancestor package 'tidyfst'.

Copy Link

Version

Install

install.packages('tidyft')

Monthly Downloads

297

Version

0.5.7

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Tian-Yuan Huang

Last Published

January 8th, 2023

Functions in tidyft (0.5.7)

nth

Extract the nth value from a vector
lead

Fast lead/lag for vectors
nest

Nest and unnest
group_by

Group by one or more variables
inner_join

Join tables
mutate

Create or transform variables
export_fst

Read and write fst files
fst

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

Nice printing of report the Space Allocated for an Object
slice

Subset rows using their positions
summarise

Summarise columns to single values
read_csv

Convenient file reader
pull

Pull out a single variable
replace_vars

Fast value replacement in data frame
utf8_encoding

Use UTF-8 for character encoding in a data frame
rowwise_mutate

Computation by rows
unite

Unite multiple columns into one by pasting strings together
uncount

"Uncount" a data frame
sys_time_print

Convenient print of time taken
mat_df

Conversion between tidy table and named matrix
separate

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

Select/rename variables by name
reexports

Objects exported from other packages
longer

Pivot data between long and wide
relocate

Change column order
drop_na

Drop or delete data by rows or columns
dummy

Fast creation of dummy variables
complete

Complete a data frame with missing combinations of data
arrange

Arrange entries in data.frame
as_fst

Save a data.frame as a fst table
fill

Fill in missing values with previous or next value
count

Count observations by group
filter

Filter entries in data.frame
distinct

Select distinct/unique rows in data.table
cummean

Cumulative mean