The materials made available here are not intended as a standalone educational product. The intended use scenario is with a course leader walking through the materials, adding information to what is on the slides and the data import/analysis files, and interacting with course participants.
With that said, you can browse the content and see if you find it useful. See the section Structure below.
Install R (you need version 4.1 or later) for your platform:
Install the latest version of Rstudio, which includes Quarto:
Note: If you have an older version of R (not Rstudio) and need to upgrade, I find that the easiest way is to uninstall R and reinstall everything including packages from scratch. Unfortunately, it is not sufficient to use the regular uninstall functions, but you will find instructions on removal here: https://www.delftstack.com/howto/r/uninstall-r-and-all-its-packages-on-windows/
Start up Rstudio, and go to Tools -> Global options. In the window that appears, make sure your settings match those in the image below. You do not want to save or restore workspace .RData - ever.
If you like, you can change the visual theme under the Appearance tab.
Below is a list of all the packages planned for use during the course. In Rstudio, in the bottom right quadrant (under the tab "Files"), create a "New Blank File" of type "R script". Then copy & paste the code below into the new file, or just run it from the Console tab in Rstudio.
install.packages(c("ggrepel","formattable","kableExtra","ggdist","ggrain",
"modelsummary","mice","GGally","easystats","patchwork",
"ggplot2","broom.mixed","nlme","lme4","psych","janitor",
"lubridate","skimr","car","styler","grateful","arrow","glue",
"showtext","readxl","foreign","tidyverse","visdat",
"gtsummary","scales","marginaleffects","ggeffects",
"sjPlot","haven"))
And for convenience here are the packages in another format, with some brief explanations.
# these are mostly for data management/wrangling and visualization
library(tidyverse) # for most things
library(haven) # for reading SPSS files and other formats
library(foreign) # also for reading SPSS files and other formats
library(readxl) # read MS Excel files
library(showtext) # get fonts
library(glue) # simplifies mixing text and code in figures and tables
library(arrow) # support for efficient file formats
library(grateful) # create table+references for packages used in a project
library(styler) # only a one-time installation (it is an Rstudio plugin)
library(car) # for car::recode only
library(skimr) # data skimming
library(lubridate) # for handling dates in data
library(janitor) # for many things in data cleaning
# these are mostly for data analysis and visualization
library(gtsummary)
library(scales)
library(visdat)
library(psych)
library(lme4) # linear mixed models
library(nlme) # non-linear models
library(broom.mixed) # get dataframe-formatted summaries of statistical models
library(ggplot2)
library(patchwork)
library(easystats) # lots of convenient statistical functions, see <https://easystats.github.io/>
library(GGally) # simple and nice correlation matrix
library(mice) # impute data
library(modelsummary) # easy summary of statistical models
library(ggrain) # raincloud plots
library(ggdist) # create plots of different distributions
library(kableExtra) # tables in different formats
library(formattable) # tables in HTML format
library(ggrepel) # automatic flexible positioning of text to avoid overlap
library(marginaleffects)
library(ggeffects)
library(sjPlot)
There is a zip file with all the course files:
- https://github.com/pgmj/RstudioQuartoIntro/blob/main/RstudioQuartoIntro.zip
- Click the downward arrow button to the far right to download the file
Make sure that you extract the zip file into a folder.
Note: Windows users beware of double-clicking the file since this may open the zip file in a way that looks like it already is a folder. Either right-click the file and select "Extract to..." or make sure to click the Extract button if you did double-click the file.
If you want to, you can to install Git and clone the repository instead. Download links: https://git-scm.com/downloads
Then you are going to "clone" this code repository to a folder on your
computer. There are two ways to go about this. Either you start up a terminal/shell/command prompt and navigate to where you would like to put the folder (a subfolder will automatically be created) and run the command git clone https://github.com/pgmj/RstudioQuartoIntro.git, or you can use a graphical user interface for git. I have no experience with the GUI, so you will have to figure that out for yourself.
If you are new to navigating a file system with a terminal/shell/command prompt, here are some links that I hope are useful:
- MacOS:
- Windows:
The core course files are the Quarto *.qmd files in the root directory of the repository. Rendered revealjs/HTML outputs from the .qmd files are available in the /docs folder and hosted with GitHub Pages. You can reach them directly at
- https://pgmj.github.io/RstudioQuartoIntro/start.html
- https://pgmj.github.io/RstudioQuartoIntro/data_import.html
- https://pgmj.github.io/RstudioQuartoIntro/data_analysis.html
All code is also available in the HTML-files. You should be able to re-create the HTML files from the .qmd files.
The datasets used: Mindfulness-integrated cognitive behaviour therapy (MiCBT) randomised controlled trial dataset.xlsx. (2020). [Data set]. Monash University. https://doi.org/10.26180/13240304
And for questionnaire item data: https://pgmj.github.io/PreventOSA/ With the data file available here: https://github.com/pgmj/PreventOSA/tree/main/data
Thanks to Emil Hvitfeldt for blog posts on revealjs design and the use of iframes.
It is not expected that you look at these before starting the course.
-
Introduction to R with Tidyverse by Sophie Lee.
-
Hadley Wickham's book "R for data science" is a great place to learn about R, no matter which level of prior knowledge you possess.
-
A super useful free online book on research data management and organization by Crystal Lewis: https://datamgmtinedresearch.com/
-
DeclareDesign is a book and an R package with tools to plan, implement, analyze, and communicate about empirical research: https://declaredesign.org/
-
For a nice collection of helpful materials on "research design, causal inference, and econometric tools to measure the effects of social programs", see Andrew Heiss' materials (and also check out his blog): https://evalsp23.classes.andrewheiss.com/
-
The documentation for the amazing
marginaleffectspackage includes helpful "case studies" to illustrate the use of the package in a lot of different types of analysis (click on "Case studies" in the left hand menu on the website): https://marginaleffects.com/
Magnus Johansson is a licensed psychologist with a PhD in behavior analysis from Oslo Metropolitan University. He works as a research scientist at RISE Research Institutes of Sweden, Department of System Transition, and is an affiliated researcher at Karolinska Institutet.
- ORCID: 0000-0003-1669-592X
- Mastodon: @[email protected]
- Bluesky: @pgmj
- Twitter: @pgmjoh
This work is licensed under the MIT License.