Muito tempo sem usar #rstats ... desaprendi tudo e fiquei mal acostumando com python. Quanta burocracia para fazer coisas simples. credo... que delicia :) hahaha
on May 16, 2024, from 4:00 pm - 6:00 pm CEST I'm giving a 2 hour online workshop on reproducibility with #Nix for #RStats users organized by the DIPF (Leibniz Institute for Research and Information in Education)
(1/2) I have been following the work of @stevensanderson and David Kum for a few years now, and I am excited to see the release of their new book 🥳- Extending Excel with Python and R 🚀.
The book focuses on the common conjunction and collaboration between data scientists and Excel users. This includes scaling and automating #Excel tasks with #RStats and #Python and core data science applications such as data wrangling, working with APIs, data visualization, and modeling.
(2/2) Here are some of the topics the book covers:
✅ Read and write Excel files with R and Python
✅ Excel automation with R and Python scripts
✅ Data visualization with ggplot2 and Matplotlib in Excel
✅ Time series analysis and forecasting
✅ Regression analysis
✅ Embading R/Python applications and functions in Excel
If you are working with Excel users or you are using Excel and want to extend your capabilities, I recommend checking this book.
So over the past year I have been using #vscode for my #rstats and #python work. my workplace is trying to move to a unified IDE, and vscode allows remote access and WSL integration for free. However, so far it fails to spark joy in me like #RStudio (despite lack of #vim mode) and #PyCharm do. Everything feels clunky, and subpar. The "intelligent" and linting things are also quite broken in R... Has there been extensions that fundamentally change the vscode experience that I should be trying?
@bentoh For R I wrote a guide for macOS users, but most of it applies to Windows too from what I can tell. It’ll probably be obvious which bits - where you set the paths in the settings json and stuff like that. I hope you find it helpful :)
In my latest blog post, I cover how to find specific strings in data columns using the str_detect function from the stringr package and base R functions. You'll see practical examples with both grepl for identifying matches and gregexpr for counting occurrences.