In astamm/teachr: Tutorials for Teaching Tidyverse-Based R Basics

library(learnr)
library(testwhat)
library(magrittr)

options(repos = "https://cloud.r-project.org")
tutorial_options(
  exercise.timelimit = 60,
  exercise.checker = testwhat::testwhat_learnr
)
knitr::opts_chunk$set(comment = NA)

Outline

Teaching modeling with the tidyverse principles
modelr: provides functions that help you create elegant pipelines when modelling; it is designed primarily to support teaching the basics of modelling within the tidyverse.
broom: takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy tibbles.
The tidymodels ecosystem, a collection of modeling packages designed with common APIs and a shared philosophy:
- parsnip: provides common API for common statistical inferential procedures.
- recipes: is a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools.
- rsample: has infrastructure for resampling data so that models can be assessed and empirically validated.
- yardstick: contains tools for evaluating models (e.g. accuracy, RMSE, etc.).
- infer: is a modern approach to statistical inference.
- Full list here
Parallel computing
- future: provides a very simple and uniform way of evaluating R expressions asynchronously using various resources available to the user.
- furrr: simplify the combination of purrr’s family of mapping functions and future’s parallel processing capabilities.