What is tidymodels Getting set up Split into train/test Define a recipe Specify the model Put it all together in a workflow Tune the parameters Finalize the workflow Evaluate the model on the test set Fitting and using your final model Variable importance There’s a new modeling pipeline in town: tidymodels. Over the past few years, tidymodels has been gradually emerging as the tidyverse’s machine learning toolkit.
Tip 1: Tidy evaluation Tip 2: Pipe into later arguments of a function using . Tip 3: Function conflicts workaround (no more dplyr::select()) Tip 4: geom_col(): you’ll never have to specify “stat = identity” for your bar plots ever again! Tip 5: Using show_col() for viewing colour palettes This was my second year attending rstudio::conf() as a diversity scholar (and my first time as a speaker), and I was yet again blown away by the friendliness of the community and the quality of the talks.
Why start a blog? How to choose a topic Use interesting and easily accessible data examples Keep it simple Creating and hosting your blog Spreading the word Go forth and blog! This year I was given the opportunity to talk at rstudio::conf(2020), which, if you’ve never been, is one of those rare conferences where every person you meet is both extremely friendly and excited about R, and you learn a million fun and useful things that you can actually use.