README.qmd

---
format: gfm
execute:
  echo: false
  warning: false
---

```{r}
#| include: false

library(tidyverse)
library(glue)

extract_pkgs <- function(fl) {
  if (length(fl) == 1) {
    txt <- read.delim(fl, header = FALSE)[[1]]

    pkgs <- str_extract_all(
      string = txt,
      pattern = "((?<=(library|require)\\()[a-zA-Z0-9\\.]{1,}(?=\\))|[a-zA-Z0-9\\.]{1,}(?=\\:{2}))"
    )

    pkgs <- unlist(pkgs)
  } else if (length(fl) > 1) {
    pkgs <- sapply(fl, extract_pkgs)
  }

  pkgs |>
    unlist(recursive = TRUE) |>
    unique()
}
```

# Machine Learning foR Psychologists

[![](https://img.shields.io/badge/Open%20Educational%20Resources-Compatable-brightgreen)](https://creativecommons.org/about/program-areas/education-oer/)
[![](https://img.shields.io/badge/CC-BY--NC%204.0-lightgray)](http://creativecommons.org/licenses/by-nc/4.0/)
[![](https://img.shields.io/badge/Language-R-blue)](http://cran.r-project.org/)

<sub>*Last updated `r Sys.Date()`.*</sub>

This Github repo contains all lesson files for *Machine Learning in R*.
The goal is to impart students with the basic tools to construct, evaluate and compare various **machine learning models, using [`tidymodels`](https://www.tidymodels.org/)**,
based on [_An Introduction to Statistical Learning: with applications in R_](https://www.statlearning.com/).

These topics were taught in the graduate-level course ***Machine Learning for Psychologists*** (Psych Dep., Ben-Gurion University of the Negev; Psych Dep., Tel-Aviv University). This course assumes basic competence in R (importing, regression modeling, plotting, etc.), along the lines of [*Practical Applications in R for Psychologists*](https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists).


**Notes:**

- This repo contains only materials relating to *Practical Applications in R*, and does not contain any theoretical or introductory materials.
- Please note that some code does not work *on purpose*, to force students to learn to debug.

## Setup

You will need:

1. A fresh installation of [**`R`**](https://cran.r-project.org/) (preferably version 4.5.0 or above).
2. [RStudio IDE](https://www.rstudio.com/products/rstudio/download/) or [Positron](https://positron.posit.co/download) (optional, but recommended).
3. The following packages, listed by lesson:


```{r}
r_files <- list.files(pattern = ".R$", recursive = TRUE, full.names = TRUE)

r_files <- r_files[str_detect(r_files, "^\\./\\d{2}")]

r_lesson_names <- str_extract(r_files, pattern = "(?<=(/)).{1,}(?=(/))")

r_list <- split(r_files, r_lesson_names)

r_pkgs <- lapply(r_list, extract_pkgs)

r_df <- tibble(
  Lesson = names(r_pkgs),
  Packages = r_pkgs
)

r_df |>
  mutate(
    Lesson = glue("[{Lesson}]({Lesson}//)"),
    Packages = sapply(Packages, \(x) glue('`{x}`') |> glue_collapse(sep = ", "))
  ) |>
  knitr::kable()
```

<details><summary><i>Installing R Packages</i></summary>

You can install all the R packages used by running:

```{r}
r_pkgs <- r_pkgs |>
  unlist(recursive = TRUE) |>
  unique() |>
  setdiff(tidyverse::tidyverse_packages()) |>
  setdiff(tidymodels::tidymodels_packages()) |>
  setdiff(easystats::easystats_packages()) |>
  c("tidyverse", "tidymodels", "easystats") |>
  sort()

packinfo <- installed.packages(fields = c("Package", "Version"))

get_src <- function(pkg) {
  pd <- packageDescription(pkg)
  if (is.null(src <- pd$Repository)) {
    if (!is.null(src <- pd$GithubRepo)) {
      src <- paste0("Github: ", pd$GithubUsername, "/", src)
    } else {
      src <- "Dev"
    }
  }
  return(src)
}

r_pkg_info <- tibble(
  pkg = r_pkgs,
  source = sapply(r_pkgs, get_src),
  version = packinfo[r_pkgs, "Version"]
) |>
  filter(version != packageVersion("base")) |>
  mutate(
    pak_src = case_when(
      source == "Dev" ~ NA,
      source == "CRAN" ~ "cran",
      str_detect(source, "Github") ~ "github",
      str_detect(source, "Bioconductor") ~ "bioc"
    ),

    pak = case_match(
      pak_src,
      "cran" ~ glue("cran::{pkg}"),
      "bioc" ~ glue("bioc::{pkg}"),
      "github" ~ glue("github::{str_remove_all(source, 'Github: ')}"),
      .default = pkg
    )
  ) |>
  arrange(pkg)

cat("# in alphabetical order:\n\n")

cat("pak::pak(\n  c(")
r_pkg_info$comma <- rep(",", nrow(r_pkg_info))
r_pkg_info$comma[nrow(r_pkg_info)] <- ""
cat(
  glue_data(r_pkg_info, '    "{pak}"{comma} # {version}') |>
    glue_collapse(sep = "\n")
)
cat("  )\n)")
```

</details>

---

### Additional Versions

- Prior to 2025, this course was based on the `{caret}` package - this version can still be found [here](https://github.com/mattansb/Machine-Learning-foR-Psychologists/tree/caret).

- Partial parallel `python` lessons can be found in the [`py` folder](py/).

---

### Acknowledgements

Materials developed with Yael Bar-Shachar.