tidyverse VS drake

Compare tidyverse vs drake and see what are their differences.

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tidyverse drake
2 1
1,602 1,330
0.9% 0.1%
4.9 6.1
5 months ago 2 months ago
R R
GNU General Public License v3.0 or later GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

tidyverse

Posts with mentions or reviews of tidyverse. We have used some of these posts to build our list of alternatives and similar projects.

drake

Posts with mentions or reviews of drake. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-05-02.
  • Your impression of {targets}? (r package)
    3 projects | /r/Rlanguage | 2 May 2021
    The targets package is the official successor to Drake, and has the same primary author (Will Landau). He has explained why he created targets, which includes stronger guardrails for users and better UX.

What are some alternatives?

When comparing tidyverse and drake you can also consider the following projects:

targets - Function-oriented Make-like declarative workflows for R

tidyqpcr - quantitative PCR analysis with the tidyverse

easystats - :milky_way: The R easystats-project

janitor - simple tools for data cleaning in R

tabulapdf - Bindings for Tabula PDF Table Extractor Library

tidyquery - Query R data frames with SQL

ncaahoopR - An R package for working with NCAA Basketball Play-by-Play Data

tidytext - Text mining using tidy tools :sparkles::page_facing_up::sparkles:

fiery - A flexible and lightweight web server

desctable - An R package to produce descriptive and comparative tables

causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning