On Thursday, October 11, 2024, at about 6 pm, I attended an insightful introduction to R workshop organized by Zachary Lloyd and Chen Zou, fellows at the Graduate Center Digital Initiatives (GCDI). The session was aimed at beginners and focused on the essentials of using R, an open-source programming language widely utilized for statistical analysis, data transformation, survey analysis, Machine Learning, etc.

While the w/shop was ongoing, I took minutes of the session, noting down highlights which made me miss some initial concepts in setting up the software for practice. I privately chatted with Chen during the workshop who was truly patient in carrying me along irrespective of my slow-paced learning curve of the tool. We were asked to create a posit account, which enabled us to access R on a cloud for practice.

The session began by distinguishing between R and RStudio, where R serves as the engine – the core programming language, while RStudio serves as the user-friendly interface (UI) where coding, data visualization, and manipulation take place that allows users to interact with R seamlessly. The instructor also introduced foundational concepts like Boolean operations _{(1/0, TRUE/FALSE, YES/NO)}, arithmetic functions _{(+,-, /,*),} and vectors – which she mentioned are lists of items or variables of the same type. We also learned basic R syntax such as how to assign values to variables using the “<-” operator and how logical operations like `==` (equal to) and `!=` (not equal to) work in R.

One of the key takeaways was the instructor’s reiteration of the importance of practicing typing out codes instead of often falling for the temptation of copying and pasting, as it helps build muscle memory. The workshop also emphasized the value of making mistakes, which is a major part of the learning process in programming. As she mentioned, “Every programming language takes time to understand. It just takes consistent practice to become the best at it”.

We explored libraries like Tidyverse, a collection of packages designed to simplify data wrangling and visualization in R. Functions such as `view()` and `head()` were demonstrated for examining data, with the former opening a new page in RStudio and the latter displaying default the first six rows of a dataset in the console.

After being shown several examples, we did some practice exercises that enabled us to get a glimpse of how R functions. Most of my colleagues in the workshop seemed to be getting it, as there were multiple correspondences on the Zoom chat bar, which also meant that the workshop was hands-on.

On a final note, the workshop was a good introduction to R, emphasizing the importance of continuous practice and exploration. Whether for data visualization, machine learning, or statistical analysis, R offers a flexible and powerful toolset for data professionals.