Summary and Setup

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DCDC Network

A hands-on workshop that builds on what you already know from SPSS to get you productive in R. No programming experience required. By the end, you will be running analyses, creating publication-quality visualizations, and working in a fully reproducible workflow.

This course is developed by Rendell de Kort (University of Aruba / DCDC Network) and is open and freely reusable under a CC-BY 4.0 license.

Schedule

The workshop runs on two days at the University of Aruba, 22 and 24 April 2026, 9:00 to 16:00 on both days. Lunch and coffee breaks are built in. Each topic below links to the corresponding episode so you can jump directly to the material.

Day 1 · Tuesday 22 April

Time Topic
09:00 – 09:10 Welcome and introductions
09:10 – 09:55 Episode 1 — The case for switching
09:55 – 10:10 Coffee break
10:10 – 11:40 Episode 2 — Your first R session
11:40 – 12:00 Questions and consolidation
12:00 – 13:00 Lunch
13:00 – 14:30 Episode 3 — Data manipulation
14:30 – 14:45 Afternoon break
14:45 – 15:45 Episode 4 — Your first visualization
15:45 – 16:00 Wrap-up and homework brief
Callout

Between Day 1 and Day 2: the homework

The 48 hours between the two days is when the course content becomes a skill. A short practice assignment is waiting for you on a dedicated page, so you can reopen it from any device overnight:

→ Day 1 homework brief

Pick a dataset you already use, write a short R script that imports it, transforms it, summarises it, and charts it. Thirty to sixty minutes is plenty. Bring the script to the Day 2 open lab.

Day 2 · Thursday 24 April

Time Topic
09:00 – 09:30 Review and troubleshooting
09:30 – 09:45 Short break
09:45 – 10:45 Episode 5 — Statistical analysis, part 1
10:45 – 11:00 Coffee break
11:00 – 12:00 Episode 5 — Statistical analysis, part 2
12:00 – 13:00 Lunch
13:00 – 14:00 Episode 6 — Reproducible reporting
14:00 – 14:15 Afternoon break
14:15 – 15:00 Episode 7 — Where to go from here
15:00 – 15:45 Open lab
15:45 – 16:00 Wrap-up and next steps

Who is this for?

This course is designed for anyone who currently uses SPSS and wants to transition to a free, more powerful alternative. You do not need any programming experience. If you understand basic statistical concepts like means, standard deviations, and hypothesis testing, you have everything you need to start.

Students
Using SPSS for coursework or thesis research and looking for a cost-free alternative
Lecturers
Teaching research methods and interested in integrating open-source tools into courses
Researchers
Seeking reproducible analysis workflows and better visualization capabilities
Institutional analysts
Working with data in government, healthcare, or policy and paying for software licenses

You need to install R and RStudio before the first session on April 22. Both are free. Follow the instructions below for your operating system.

Callout

Using a work-issued laptop? Check with IT first

Many universities and employers lock their laptops so that users cannot install software themselves. If your laptop was issued by an institution, check with your IT department before following the steps below, otherwise the installer will simply fail.

University of Aruba laptops require a ticket through TopDesk. When you submit the ticket, explicitly ask IT to install both R and RStudio. Asking for only one of them is a common cause of a half-working setup that wastes course time.

Give IT at least a week to process the request so your laptop is ready before the first session.

Pre-workshop installation clinic

We will not spend course time on installation troubleshooting. To make sure everyone starts ready to go, there is an optional drop-in session before the course:

When: Thursday, April 16, 2026, 9:00 – 12:00 Where: University of Aruba Research Center What: Bring your laptop. We help you install R and RStudio, verify everything works, and install the required packages. Walk in, walk out.

If you cannot attend the clinic, follow the instructions below and test your setup by opening RStudio and typing 1 + 1 in the console. If it returns 2, you are ready.

Software Setup

Discussion

Install R and RStudio

You need both R (the language) and RStudio (the interface). Think of R as the engine and RStudio as the dashboard — you will work in RStudio, but it needs R installed to run.

  1. Download R from CRAN — click “Download R for Windows”, then “base”, then the download link.
  2. Run the installer with default settings.
  3. Download RStudio from Posit — click “Download RStudio Desktop”.
  4. Run the RStudio installer with default settings.
  5. Open RStudio — if you see a console panel with the R version number, you are ready.
  1. Download R from CRAN — choose the .pkg file that matches your Mac (Apple Silicon or Intel).
  2. Open the .pkg file and follow the installer.
  3. Download RStudio from Posit.
  4. Drag RStudio to your Applications folder.
  5. Open RStudio — if you see a console panel with the R version number, you are ready.
  1. Follow the instructions for your distribution at CRAN.
  2. Download RStudio from Posit — choose the .deb or .rpm file for your distribution.
  3. Install and open RStudio.

R Packages

During the course, we will install packages together. If you want to get ahead, open RStudio and run this command in the console:

R 

install.packages(c("tidyverse", "haven", "rmarkdown"))
  • tidyverse includes dplyr (data manipulation), ggplot2 (visualization), readr (reading data), and more
  • haven reads SPSS .sav files directly into R
  • rmarkdown creates reproducible reports

Verify your setup

Open RStudio and paste this into the console:

R 

install.packages(c("tidyverse", "haven", "rmarkdown"))
library(tidyverse)
ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()

If a scatter plot appears in the Plots pane, everything is working. Bring any errors you see to the installation clinic or email the instructor.

Download the workshop data

From Episode 2 onwards you will load the same small dataset in two different formats — first as a CSV file, then as an Excel workbook with multiple sheets. Download both files now and keep them together.

Open each link in your browser, then click the Download raw file button near the top right of the preview and save the file. Do not open the CSV in Excel and re-save — that can silently change the encoding.

Where to put the files

Create a folder for the workshop, for example Documents/r-workshop/, and inside it create a subfolder called data. Drop both files into data. Your structure should look like this:

r-workshop/
└── data/
    ├── aruba_visitors.csv
    └── aruba_visitors.xlsx

When you open RStudio during the course, use File → Open Project to open r-workshop (or set your working directory to the folder). The code in the lessons assumes this layout, so read_csv("data/aruba_visitors.csv") and read_excel("data/aruba_visitors.xlsx") both find their files without any extra path work.

If you cannot download the files in advance, we will walk through this step together at the start of Episode 2. Bring the two links.