Week 1 - Set up SPSS & R with RStudio

major update August 2025 - previous version (SPSS via virtual computing, R/RStudio local system install without docker) is here

1. Create a local folder for your work in this class

It can be difficult to manage everybody's individual technology issues, so it's important to make sure we all have similar set ups. Pick a location on your computer's file system, and create a new folder called psychstats596 - leave it empty for now.

2. SPSS installation/access

• If you are a Rutgers student you have access to an SPSS license now (wasn't always the case)! Follow installation and licensing instructions at https://software.rutgers.edu/product/3880 - we will be using version 31 in class. Note from the Rutgers Software site: The paperwork for the SPSS renewal is now with the vendor for processing, while we hope it will be completed prior to our expiration date. I can not confirm that. However, there is a 30-day grace period for SPSS in case the paperwork is delayed past 8/31/2025.
  

3. Install R and RStudio (two methods)

R is a statistical programming language. RStudio is an interactive development environment (IDE) built to make it easier to run, view, and document your work that uses the R language. R and Rstudio are open-source, so you can install them on your own.

• if you already have R and Rstudio installed, make sure you have an R version 4 or greater (e.g., 4.0.4 or higher version number), and RStudio 1.4 or greater. Class materials are tested in R version 4.3.3 and RStudio version 2023.12.1+402, with tidyverse version 2.0.0

  • to check your R version, run getRversion() in the RStudio console
    
  • to check your RStudio version use the RStudio -> About menu
    

If you already have R and RStudio installed, I still recommend running it from a docker container (see below). But if you are happy to manage package versions yourself and troubleshoot any conflicts that arise then I won't stop you.

3.1 Run Rstudio from a Docker container (recommended)

One of the biggest challenges in creating reproducable research is that any work you do on a computer (e.g., design, simulation, analysis) relies on the programs and environment that are particular to your computer. This means it is not always easy for someone to recreate your work A Docker container basically simulates a whole computing environment on your host computer. In this case I have created a Docker image for this class that you can download and run on your own computer. This image can be used to run a container that has the RStudio environment and most of the pckages you will need for class (I left some packages uninstalled so that you will have some experience installing packages).
Steps to get a container running:

• Download Docker Desktop for your OS (hover over the "Download Docker Desktop" button). Install it (if on windows you can leave unchecked "allow windows containers to be used with this installation"). If on Windows you probably need to restart your computer.
  

• Now, you can pull the Docker image for this class and "spin up a container" based on the image. The image is called stored on hub.docker.com (the most common location for sharing docker images).

  1. Launch the Docker Desktop application on your Mac.
     
  2. Go to the "Images" Tab. In the Docker Desktop interface, click on the "Images" tab in the left-hand sidebar.
     
  3. Search for the Image. In the "Search for images on Docker Hub" field at the top, type the name of the image you want to pull (e.g., nginx or myusername/my-app).
     
     • If you are on a mac with a silicon arm64 processor then search for jamilfelipe/psych596applearm64-rstudio_app. (to check if you have an arm64 processor, open a terminal and type uname -m if the output is arm64 then it is, otherwise it is an amd64 type chip.
       
     • If you are on a computer with a amd64 processor (all others, but talk to me if you are unsure or on a Chromebook), then search for jamilfelipe/psych596amd64-rstudio_app
       
  4. Select the Image. From the search results, locate the image you want to pull.
     
  5. Pull the Image. Click the "Pull" button next to the desired tag (latest). Docker Desktop will start downloading the image, and you can monitor the progress in the interface.
     
  6. Run the image - after it downloads, click "run" (windows) or the triangle "play" (apple) button. This is what is meant by "spinning up a container"
     
  7. Click the expand arrow next to "Optional Settings".
     
  8. Enter 8787 in the "Host port" text box. This will designate a port number that you will use to access the running container through a web browser (if you have multiple containers running , you will need to use a different port numbers for each).
     
  9. Under "Volumes" click the "..." and then select the folder you created earlier. In the "Container path" text box type /home/rstudio/psychstats596. Click "Run".
     
  10. In the message that comes up, look for "The password is set to ..." and copy the password displayed.
      
  11. Now open a web browser and go to localhost:8787 -- when the username/password box comes up enter rstudio for the user, and paste in the password that you copied. You should see the RStudio environment come up in the browser.
      

Notes on using containers:

• once you start a container running it will stay in your "running containers" list in the "Containers" tab in Docker Desktop. Even if you close the Browser window it will still be running in the background.
  
• if you encounter problems and want to restart the RStudio session, you can (increasing levels of extremity):
  
  • click "Session", "Restart R"
    
  • type quit() in the console and then click "Start New Session"
    
  • click "restart" on the container in Docker Desktop (then note the new password and refresh the browser window)
    
  • click the "trash can" icon next to the container to delete it, then spin up a new container from the original image. you will lose any packages you added in the container you deleted (plus anything you did in the container that wasn't saved within the "mapped" psychstats596 folder) -- see this guide for information about writing a new image with the changes you made in the container
    
• in the RStudio environment, you won't be able to see your whole desktop filesystem, only the folder that you created (and "mapped" into the container as a volume).
  
• my standard practice is to just leave a container running indefinitely, closing the browser tab whenever I'm done working for the day and coming back to it each time by opening a new browser. If my computer restarts then I just restart the container through Docker Desktop.
  

3.2 (do this if you can't or don't want to run Docker) Install R and RStudio, then install packages one by one.

Follow instructions here, start at #2

4. Set up your workflow for this week's lab activity

This video (linked in the Syllabus also) describes the workflow that we will use in class. These are the basic steps in the workflow:
1. Create a folder containing an RStudio project (*.Rproj file) for the lab activity each week. This week, make a folder called "intro-essentials" and then use File -> New Project -> Existing Folder to create an R project file in the "intro-essentials" folder. Open the project in your current session.
2. Inside the folder you made for the project, create new folders called "data", "r_docs", and "images". You can create the folders through the Files tab in the lower right RStudio Pane, or as you normally would in Windows or MacOS.
3. Create an R Markdown file called "lab-notes-intro" and save it in "r_docs" folder. Use File ->New File -> R Markdown... then Save (on the RStudio menu). The markdown file will open in the top left RStudio pane - this is where you will write your R code and where you will take notes. When you reach a point where you want to share the document you can use the Knit option to generate a report containing your code, notes, and visualizations.
4. Delete the template text starting from "## R Markdown" down to the end of the file.
5. For future activities, write your code inside code "chunks", and run chunks in order when writing/testing code. When you want to generate a report (e.g., an html file that you can share), use the Knit button.
- the start of a chunk is designated by a line that starts with 3 backticks ` followed by {r chunk-name}. The end of a chunk is designated by a line with 3 backticks.
- in the "setup" code chunk, add this line to set the working directory (see here for explanation):
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
6. Write your notes above or below code chunks. Characters like # and * are used for markdown-style formatting of the report as described in this pdf.
7. For this week, just write "I created an R Markdown document" as a note in your R Markdown file, then save it and submit the *.Rmd file in the Canvas lab activity assignment.

When you have set up your project, your RStudio environment should look something like this:

5. Load the first "learnr" tutorial

Run this in the Console: learnr::initialize_tutorial()

Go to the Tutorial Pane (top right) and click "start tutorial" for any tutorial that interests you. it will take a minute or two to load - use the "pop-out" button to open the tutorial in a larger view.

• If you still have time left in class, go through a little bit of the tutorial. It will be helpful to get familiar with the concepts of objects, functions, data types, assignment ("<-"), and piping ("%>%").