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https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists

Lesson files for Practical Applications in R for Psychologists.
https://github.com/mattansb/Practical-Applications-in-R-for-Psychologists

data-analysis easystats psychologists regression rstats statistics tidyverse

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Lesson files for Practical Applications in R for Psychologists.

Lists

README 

        
---

output: github_document

---






```{r setup, include=FALSE}

library(knitr)



opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)



extract_pkgs <- function(fl) {

  if (length(fl) == 1) {

    txt <- read.delim(fl, header = FALSE)[[1]] |> 

      paste0(collapse = "\n")

    

    pkg_lib <- stringr::str_extract_all(txt, pattern = "(?<=library\\().{1,}?(?=\\))")

    

    pkg_req <- stringr::str_extract_all(txt, pattern = "(?<=require\\().{1,}?(?=\\))")

    

    pkg_name <- stringr::str_extract_all(txt, pattern = "[a-z|A-Z|0-9]{1,}(?=\\:\\:)")

    

    pkgs <- c(pkg_lib, pkg_req, pkg_name)

    

  } else if (length(fl) > 1) {

    pkgs <- sapply(fl, extract_pkgs)

  }

  

  pkgs |>

    unlist(recursive = TRUE) |> 

    as.vector() |> 

    unique()

}



make_pkg_table <- function(pkgs) {

  # pkgs <- pkgs[sapply(pkgs, function(x) length(x) > 0)]

  

  new_pkgs <- lapply(seq_along(pkgs), function(i) {

    lesson_pkgs <- pkgs[[i]]

    prev_lesson_pkgs <- unlist(head(pkgs,i-1))

    

    !lesson_pkgs %in% prev_lesson_pkgs

  })

  

  

  ps <- sapply(seq_along(pkgs), function(idx) {

    x <- pkgs[[idx]]

    is_new <- ifelse(new_pkgs[[idx]], "**", "")

    paste0(

      glue::glue("[{is_new}`{x}`{is_new}](https://CRAN.R-project.org/package={x})"),

      collapse = ", "

    )

  })

  

  c("|Lesson|Packages|\n|----|----|\n", # header

    glue::glue("|[{folder}](/{folder})|{ps}|\n\n",

             folder = names(pkgs))) |> 

    paste0(collapse = "")

}



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 <- "Local version"

    }

  }

  return(src)

}

```



# Practical Applications in R 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/)



*Last updated `r Sys.Date()`.*



This Github repo contains all lesson files for *Practical Applications in R for Psychologists*. The goal is to impart students with the basic tools to process data, describe data (w/ summary statistics and plots), and the foundations of **building, evaluating and comparing statistical models in `R`**, focusing on linear regression modeling (using both frequentist and Bayesian approaches).



These topics were taught in the graduate-level course ***Advanced Research Methods for Psychologists*** (Psych Dep., Ben-Gurion University of the Negev), laying the foundation for the following topic-focused courses:



- [Hierarchical linear models (*HLM*)](https://github.com/mattansb/Hierarchical-Linear-Models-foR-Psychologists)

- [Machine Learning (*ML*)](https://github.com/mattansb/Machine-Learning-foR-Psychologists)

- [Structural equation modelling (*SEM*)](https://github.com/mattansb/Structural-Equation-Modeling-foR-Psychologists)

- [Analysis of Factorial Designs (*ANOVA*)](https://github.com/mattansb/Analysis-of-Factorial-Designs-foR-Psychologists)



**Notes:**  



- This repo contains only materials relating to *Practical Applications in R*. Though statistics are naturally discussed in many lessons, the focus is generally on the application and not on the theory.  

- Please note that some code does not work *on purpose* and without warning, to force students to learn to debug.



## Setup



You will need:



1. A fresh installation of [**`R`**](https://cran.r-project.org/) (preferably version 4.1.1 or above).

2. [RStudio IDE](https://www.rstudio.com/products/rstudio/download/) (optional, but recommended).

3. The following packages, listed by lesson:



```{r, echo=FALSE}

r_list <- list.files(pattern = ".(R|r)$", recursive = TRUE, full.names = TRUE) |> 

  Filter(f = \(x) !stringr::str_detect(x, pattern = "(SOLUTION|logo)"))



lesson_names <- stringr::str_extract(r_list, pattern = "(?<=(/)).{1,}(?=(/))")



r_list <- split(r_list, lesson_names)



pkgs <- lapply(r_list, extract_pkgs)



print_pkgs <- make_pkg_table(pkgs)

```



`r print_pkgs`



*(Bold denotes the first lesson in which the package was used.)*



You can install all the packages used by running:



```{r echo=FALSE, comment = "", warning=FALSE}

unique_pkgs <- pkgs |> 

  unlist(recursive = TRUE) |> 

  unique() |> sort()



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

# unique_pkgs[!is.element(unique_pkgs, rownames(packinfo))]

V <- packinfo[unique_pkgs,"Version"]

src <- sapply(unique_pkgs, get_src)



cat("# in alphabetical order:")



cat("pkgs <-", dput(unique_pkgs) |> capture.output(), fill = 80) |> 

  capture.output() |> 

  styler::style_text()



cat('install.packages(pkgs, repos = c("https://easystats.r-universe.dev", getOption("repos")))')

```



Package Versions



Run on `r osVersion`, with R version `r packageVersion("base")`.



The packages used here:



```{r, echo=FALSE}

v_info <- paste0(glue::glue(" - `{unique_pkgs}` {V} (*{src}*)"), collapse = "\n")

```



`r v_info`