Showing 5 of total 5 results (show query)
tidyverse
tidyr:Tidy Messy Data
Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).
Maintained by Hadley Wickham. Last updated 25 days ago.
1.4k stars 22.88 score 168k scripts 5.5k dependents
markfairbanks
tidytable:Tidy Interface to 'data.table'
A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax.
Maintained by Mark Fairbanks. Last updated 2 months ago.
460 stars 11.39 score 732 scripts 11 dependents
elbersb
tidylog:Logging for 'dplyr' and 'tidyr' Functions
Provides feedback about 'dplyr' and 'tidyr' operations.
Maintained by Benjamin Elbers. Last updated 10 months ago.
dplyrtidyrtidyversewrapper-functions
593 stars 10.23 score 1.7k scripts
hope-data-science
tidyft:Fast and Memory Efficient Data Operations in Tidy Syntax
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'.
Maintained by Tian-Yuan Huang. Last updated 6 months ago.
35 stars 6.25 score 34 scripts
biostats-dev
ggsurveillance:Tools for Outbreak Investigation/Infectious Disease Surveillance
Create epicurves or epigantt charts in 'ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation. Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the 'ggplot2'. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).
Maintained by Alexander Bartel. Last updated 26 days ago.
epidemiologyinfectious-disease-surveillanceinfectious-diseasesoutbreaks
2 stars 5.31 score