awesome-R VS DataScienceR

Compare awesome-R vs DataScienceR and see what are their differences.

awesome-R

A curated list of awesome R packages, frameworks and software. (by qinwf)

DataScienceR

a curated list of R tutorials for Data Science, NLP and Machine Learning (by ujjwalkarn)
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awesome-R DataScienceR
6 1
5,781 1,959
- -
4.0 0.0
about 2 months ago about 1 year ago
R R
- MIT License
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.

awesome-R

Posts with mentions or reviews of awesome-R. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-13.

DataScienceR

Posts with mentions or reviews of DataScienceR. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-12.

What are some alternatives?

When comparing awesome-R and DataScienceR you can also consider the following projects:

fontawesome - Easily insert FontAwesome icons into R Markdown docs and Shiny apps

badger - Badge for R Package

easystats - :milky_way: The R easystats-project

aor - 🎄📦 Advent of R: Utility Functions for the Advent of Code in R

sf - Simple Features for R

design - Tidyverse design principles

lab02_R_intro - Vežbe 2: Uvod u R

viridis - Colorblind-Friendly Color Maps for R

awesome-computational-social-science - A list of awesome resources for Computational Social Science

fastverse - An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R

R-Fundamentals - D-Lab's 4 part, 8 hour introduction to R Fundamentals. Learn how to create variables and functions, manipulate data frames, make visualizations, use control flow structures, and more, using R in RStudio.