tidymodels has long supported parallelizing model fits across CPU cores. A couple of the modeling engines that #rstats#tidymodels supports for gradient boosting—#XGBoost and #LightGBM—have their own tools to parallelize model fits. A new blog post explores whether tidymodels users should use tidymodels' implementation, the engines', or both.
Discover essential techniques to check for column existence in R data frames!
Use %in% with names() or colnames(), explore dynamic checks with exists() and within(), or identify patterns with grepl(). Experiment with these methods in your projects.
@almenal99
In my head pipes are like comma, as they indicate that there is more to come. So it should come at the end of the line, unless similar to bash we can use escape character to escape the new line. If someone is after aesthetics, they can use indentation.
I also sometimes use -> assignment at the end of a pipe chain to store the results in a variable. So I can see that having it at the beginning of the line can provide some clarity.
Muito tempo sem usar #rstats ... desaprendi tudo e fiquei mal acostumando com python. Quanta burocracia para fazer coisas simples. credo... que delicia :) hahaha
@villares@ludovia hahaha sería isso, mas sem o -1, já que com o length(coleção) vc tem a quantidade e com esse calor acesso o último elemento. Mas repara como é burocrático?! Tenho que salvar o solit para usar o length e acessar o último valor…
Python seria só um “path/to/folde.tif”.split(“/“)[-1]. Isso para não usar o Pathlib, como vc sugeriu..
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.