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| r4ds | badger | |
|---|---|---|
| 165 | 1 | |
| 4,349 | 187 | |
| - | - | |
| 8.7 | 4.7 | |
| 5 days ago | 3 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | - |
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.
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.
r4ds
Posts with mentions or reviews of r4ds.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-24.
- Ask HN: Learning Maths from the Ground Up
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Any suggestions on where I can learn R studio for an affordable cost?
https://r4ds.hadley.nz is free and very good
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Help with Understanding data loading/cleaning in R.
R for Data Science teaches you the tidyverse packages, which makes data wrangling so much easier!
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Learning R & statistics
One of the best free resources is the R4DS book by Hadley Wickham. You should make sure you start with the in progress second edition. https://r4ds.hadley.nz/
- Trying to learn Rstudio
- Questions as incoming PhD political science student
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First R project
The first edition of R4DS is quite old now. Check out the soon to be released second edition: https://r4ds.hadley.nz/
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Is R dead?
R for Data Science (2nd Ed), the updated guide from Hadley Wickham
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[Career] Strong Mathematics Background, Limited "Technical" Background
The big skills gap you have is in practical data exploration and transformation, which will be a large part of any data-centric role. As much as people may have distaste for it, there is no avoiding data manipulation as critical foundational enabler of all inferential and predictive modeling work. SQL is the lingua franca here and well worth picking up the basics (joins, window functions, handling dates and times, etc.), plus learning how to implement similar transformations in R and Python. With appropriately transformed data, you then need to be able to visualize it effectively using tools like Tableau or ggplot2 in R. I would not necessarily seek courses or certificates in it but expect to be evaluated on them in technical interview screenings, so self-study accordingly. R for Data Science by Hadley Wickham is a great free resource for these topics for R.
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There’s a lot of data science books out there, any recommendations for must-reads?
I just looked and there is now a second edition! https://r4ds.hadley.nz/
badger
Posts with mentions or reviews of badger.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Using lifecycle badges with Roxygen
Then I used the badger package to add different badges. You can use badger::badge_lifecycle for a lifecycle badge!
What are some alternatives?
When comparing r4ds and badger you can also consider the following projects:
swirl - :cyclone: Learn R, in R.
DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning
fasteR - Fast Lane to Learning R!
rmarkdown - Dynamic Documents for R
tidytuesday - Official repo for the #tidytuesday project
ggplot2 - An implementation of the Grammar of Graphics in R
R-vs.-Python-for-Data-Science
dplyr - dplyr: A grammar of data manipulation
lab02_R_intro - Vežbe 2: Uvod u R
awesome-R - A curated list of awesome R packages, frameworks and software.
viridis - Colorblind-Friendly Color Maps for R
ggplot2-book - ggplot2: elegant graphics for data analysis