ππ Introducing https://t.co/URvELhULtf ππ
Discover & Explore R Packages, Functions, and Datasets like never before! π
β¨ 20k+ Packages
β¨ 400k+ Functions
β¨ 40k+ Datasets
The ultimate resource for R developers! ππ
π https://t.co/KO0PK3TkHA
#RStats
[Tip of the day]
Write your R scripts like others will read them, because future you is someone else
Comment generously, name clearly
#rstats#CodeStyle
π‘ Struggling to get help with your R code online?
Try {reprex} package to create a reproducible example that shows exactly what your code does
Just write your code, run reprex(), and paste the result. Easy, clean, helpful.
π https://t.co/ovD3UuXVym
π Try rainbow parentheses!
They color each level of nesting so you can spot mismatches instantly.
A simple trick, a huge difference for debugging π
#rstats#rstatsTips#DataScience#Productivity
π§΅ How to clean data like a pro with dplyr and tidyr in R
If you're still struggling with messy datasets or spending hours on manual cleanupβ¦
This thread is your shortcut to clean, tidy, analysis-ready data π
#rstats#DataCleaning#DataScience
π’ Si eres de EspaΓ±a o LATAM y te interesa el mundo de la ciencia de datosβ¦
En mi LinkedIn personal (en espaΓ±ol) comparto ideas, herramientas y reflexiones sobre IA, data science y emprendimiento π€
ΒΏConectamos?
π https://t.co/V0OwJUjpfS
Found a great cheatsheet with different ways to visualize percentages and parts of a whole π
(Pie charts are just the beginningβ¦)
And if you work in R, Iβve gathered real code examples here:
π https://t.co/Je24jZTfA5
#rstats#DataViz#ggplot2#DataScience
π Want to write faster and more efficient R code?
Check out Efficient R Programming β a practical guide packed with tips on writing clean, optimized, and production-ready R code.
π https://t.co/n28l0DlKNs
#rstats#DataScience#Rprogramming#Productivity
π― Ever wondered what really sets apart a Data Analyst, Data Scientist, Data Engineer, and ML Engineer?
They might sound similar, but each role has a different focus, skill set, and mission
Letβs break it down π§΅π
#DataScience#rstats#Statistics
π If you enjoy the R content I share here, you might also like what I post on my (new) personal account: data science, AI, and entrepreneurship
Follow me at π [@josecarlossoage ]
Or on LinkedIn (in Spanish) π https://t.co/V0OwJUiRqk
#rstats#datascience
π Drowning in forgotten TODOs in your R scripts?
The {todor} package scans your code for #TODO, #FIXME, #NOTE, and more β so you can track your tasks like a pro β
π Great for big projects
π§ Never miss a fix
π» Lightweight and easy to use
π install.packages("todor")
π¦ΈββοΈ Meet the newest superhero in town β Captain R!
Fighting messy data, one line of code at a time πͺπ
Powered by tidyverse. Shielded by ggplot2
#rstats#DataScience#SuperCoder
[FREE R BOOK]
π "Tidy Modeling with R" by Max Kuhn & Julia Silge is a must-read! π Learn how to streamline your ML workflow using the tidymodels framework
π https://t.co/SJNYdhY9CP
#RStats#MachineLearning
π This #ggplot2 tutorial by @CedScherer never gets old!
π One of the best resources to master beautiful plotting in R
π https://t.co/H4sh7xxwKF
[FREE ONLINE BOOK] π
R Markdown: The Definitive Guide
Learn how to create dynamic reports, presentations, and interactive documents with R Markdown!
π https://t.co/UBdFtAQi9l
#rstats#DataScience
I've been trying out different ways of creating dumbbell charts with {ggplot2}. For this one I used a 'wide' summary table for the source data (which feels more intuitive to me). The method owes a lot to this tutorial from @RCoderWeb:
https://t.co/Tufr5yUqIz
#rstats#ggplot2