Practical Applications in R for Psychologists

_{Last updated 2023-09-03.}

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:

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:

A fresh installation of R (preferably version 4.1.1 or above).
RStudio IDE (optional, but recommended).
The following packages, listed by lesson:

Lesson	Packages
01 intro
02 data wrangling	`haven`, `tidyverse`, `readxl`, `dplyr`, `datawizard`, `summarytools`, `parameters`, `psych`, `finalfit`, `Hmisc`, `mice`
03 plotting	`dplyr`, `ggplot2`, `ragg`, `tidyr`
04 hypothesis testing and power	`effectsize`, `correlation`, `BayesFactor`, `dplyr`, `pwr`, `ggplot2`
05 regression 101	`effectsize`, `parameters`, `performance`, `ggeffects`, `psychTools`
06 categorical predictors and model comparison	`dplyr`, `parameters`, `emmeans`, `ggeffects`, `bayestestR`, `performance`
07 moderation and curvilinear	`dplyr`, `datawizard`, `parameters`, `performance`, `bayestestR`, `emmeans`, `ggeffects`, `ggplot2`, `modelbased`
08 generalized linear models	`dplyr`, `parameters`, `performance`, `ggeffects`, `emmeans`, `marginaleffects`
09 assumption checks and violations	`ggeffects`, `performance`, `see`, `bayesplot`, `qqplotr`, `datawizard`, `permuco`, `parameters`, `insight`
10 ANOVA	`afex`, `emmeans`, `effectsize`, `ggeffects`, `tidyr`
11 mediation	`mediation`, `tidySEM`

_{(Bold denotes the first lesson in which the package was
used.)}

You can install all the packages used by running:

# in alphabetical order:

pkgs <- c(
  "afex", "BayesFactor", "bayesplot", "bayestestR", "correlation",
  "datawizard", "dplyr", "effectsize", "emmeans", "finalfit", "ggeffects",
  "ggplot2", "haven", "Hmisc", "insight", "marginaleffects", "mediation",
  "mice", "modelbased", "parameters", "performance", "permuco",
  "psych", "psychTools", "pwr", "qqplotr", "ragg", "readxl", "see",
  "summarytools", "tidyr", "tidySEM", "tidyverse"
)

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

Package Versions

Run on Windows 11 x64 (build 22621), with R version 4.3.1.

The packages used here:

afex 1.3-0 (CRAN)
BayesFactor 0.9.12-4.4 (CRAN)
bayesplot 1.10.0 (CRAN)
bayestestR 0.13.1.2 (Local version)
correlation 0.8.4 (CRAN)
datawizard 0.8.0.7 (Local version)
dplyr 1.1.2 (CRAN)
effectsize 0.8.5 (Local version)
emmeans 1.8.7 (CRAN)
finalfit 1.0.6 (CRAN)
ggeffects 1.3.0.5 (Github: strengejacke/ggeffects)
ggplot2 3.4.3 (CRAN)
haven 2.5.3 (CRAN)
Hmisc 5.1-0 (CRAN)
insight 0.19.3.3 (Github: easystats/insight)
marginaleffects 0.13.0 (CRAN)
mediation 4.5.0 (CRAN)
mice 3.16.0 (CRAN)
modelbased 0.8.6 (CRAN)
parameters 0.21.1 (CRAN)
performance 0.10.4 (CRAN)
permuco 1.1.2 (CRAN)
psych 2.3.6 (CRAN)
psychTools 2.3.6 (CRAN)
pwr 1.3-0 (CRAN)
qqplotr 0.0.6 (CRAN)
ragg 1.2.5 (CRAN)
readxl 1.4.3 (CRAN)
see 0.8.0.2 (Local version)
summarytools 1.0.1 (CRAN)
tidyr 1.3.0 (CRAN)
tidySEM 0.2.4 (CRAN)
tidyverse 2.0.0 (CRAN)

tableone

library(tableone)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

CreateTableOne(data = iris, strata = "Species")
#>                           Stratified by Species
#>                            setosa        versicolor    virginica     p     
#>   n                          50            50            50                
#>   Sepal.Length (mean (SD)) 5.01 (0.35)   5.94 (0.52)   6.59 (0.64)   <0.001
#>   Sepal.Width (mean (SD))  3.43 (0.38)   2.77 (0.31)   2.97 (0.32)   <0.001
#>   Petal.Length (mean (SD)) 1.46 (0.17)   4.26 (0.47)   5.55 (0.55)   <0.001
#>   Petal.Width (mean (SD))  0.25 (0.11)   1.33 (0.20)   2.03 (0.27)   <0.001
#>   Species (%)                                                        <0.001
#>      setosa                  50 (100.0)     0 (  0.0)     0 (  0.0)        
#>      versicolor               0 (  0.0)    50 (100.0)     0 (  0.0)        
#>      virginica                0 (  0.0)     0 (  0.0)    50 (100.0)        
#>                           Stratified by Species
#>                            test
#>   n                            
#>   Sepal.Length (mean (SD))     
#>   Sepal.Width (mean (SD))      
#>   Petal.Length (mean (SD))     
#>   Petal.Width (mean (SD))      
#>   Species (%)                  
#>      setosa                    
#>      versicolor                
#>      virginica


mtcars %>% 
  mutate(across(c(cyl, am, vs, gear, carb), ordered)) %>% 
  CreateTableOne(data = ., strata = "cyl") %>% 
  print(nonnormal = c("hp", "drat"))
#>                      Stratified by cyl
#>                       4                     6                      
#>   n                       11                     7                 
#>   mpg (mean (SD))      26.66 (4.51)          19.74 (1.45)          
#>   cyl (%)                                                          
#>      4                    11 (100.0)             0 (  0.0)         
#>      6                     0 (  0.0)             7 (100.0)         
#>      8                     0 (  0.0)             0 (  0.0)         
#>   disp (mean (SD))    105.14 (26.87)        183.31 (41.56)         
#>   hp (median [IQR])    91.00 [65.50, 96.00] 110.00 [110.00, 123.00]
#>   drat (median [IQR])   4.08 [3.81, 4.16]     3.90 [3.35, 3.91]    
#>   wt (mean (SD))        2.29 (0.57)           3.12 (0.36)          
#>   qsec (mean (SD))     19.14 (1.68)          17.98 (1.71)          
#>   vs = 1 (%)              10 ( 90.9)             4 ( 57.1)         
#>   am = 1 (%)               8 ( 72.7)             3 ( 42.9)         
#>   gear (%)                                                         
#>      3                     1 (  9.1)             2 ( 28.6)         
#>      4                     8 ( 72.7)             4 ( 57.1)         
#>      5                     2 ( 18.2)             1 ( 14.3)         
#>   carb (%)                                                         
#>      1                     5 ( 45.5)             2 ( 28.6)         
#>      2                     6 ( 54.5)             0 (  0.0)         
#>      3                     0 (  0.0)             0 (  0.0)         
#>      4                     0 (  0.0)             4 ( 57.1)         
#>      6                     0 (  0.0)             1 ( 14.3)         
#>      8                     0 (  0.0)             0 (  0.0)         
#>                      Stratified by cyl
#>                       8                       p      test   
#>   n                       14                                
#>   mpg (mean (SD))      15.10 (2.56)           <0.001        
#>   cyl (%)                                     <0.001        
#>      4                     0 (  0.0)                        
#>      6                     0 (  0.0)                        
#>      8                    14 (100.0)                        
#>   disp (mean (SD))    353.10 (67.77)          <0.001        
#>   hp (median [IQR])   192.50 [176.25, 241.25] <0.001 nonnorm
#>   drat (median [IQR])   3.12 [3.07, 3.22]      0.001 nonnorm
#>   wt (mean (SD))        4.00 (0.76)           <0.001        
#>   qsec (mean (SD))     16.77 (1.20)            0.002        
#>   vs = 1 (%)               0 (  0.0)          <0.001        
#>   am = 1 (%)               2 ( 14.3)           0.013        
#>   gear (%)                                     0.001        
#>      3                    12 ( 85.7)                        
#>      4                     0 (  0.0)                        
#>      5                     2 ( 14.3)                        
#>   carb (%)                                     0.007        
#>      1                     0 (  0.0)                        
#>      2                     4 ( 28.6)                        
#>      3                     3 ( 21.4)                        
#>      4                     6 ( 42.9)                        
#>      6                     0 (  0.0)                        
#>      8                     1 (  7.1)

^{Created on 2021-02-22 by the reprex package (v1.0.0)}

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practical-applications-in-r-for-psychologists's Introduction

Practical Applications in R for Psychologists

Setup

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1. Intro to R

Lesson 1

Lesson 2

2. Data wrangling

Lesson 3

Lesson 4

3. Plotting

Lesson 5

4. NHST

Lesson 6

5. Modelling

Lesson 7

Lesson 8

Lesson 9

Lesson 10

Lesson 11

6. Assumptions

Lesson 12

7. Other topics (optional)

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