LIS4370.001S26 - Assignment #1: Getting Started with R Programming Your first assignment - VaShayCarpenter

 

Objectives

Tasks

  1. Create a GitHub Repository ✅
    • Go to github.comLinks to an external site. and sign up (or log in).
    • Create a new public repository named r‑programming-assignments.




    • In your repository’s README.md, include:✅
      • Your name
      • Course number (e.g., LIS4370)
      • Brief description (“Repository for R Programming Assignments”)


  1. Set Up a Blog
  2. Install R and RStudio
    • Download R from cran.r-project.orgLinks to an external site. (choose your OS).
    • Download RStudio Desktop from posit.coLinks to an external site..
    • Open RStudioLinks to an external site. and take a screenshot of the console window.
    • In your blog, write a 2–3 paragraph report describing:
      • Any issues you encountered during installation.
      • How you resolved them (if applicable).
      • Your system details (OS version, R version, RStudio version).










      • Since I have utilized R and RStudio extensively in previous coursework; specifically Advanced Stats & Analytics and Intro to Data Science, the installation process for this course was a straightforward update of my existing environment. I am currently running Windows 10. For this assignment, I updated my environment to the latest RStudio 2026.01 release. My system is currently running R version 4.3.1 from a previous semester.

        The installation was seamless; the only manual step required was downloading the 2026 installer from Posit to replace the legacy version. No configuration errors were encountered, and the console (as seen in the screenshot) confirms that the English locale and 64-bit platform are correctly initialized.

        The process was seamless, as my system was already configured with the necessary compilers and dependencies for R. I did not encounter any new issues during this update. 

  3. Read Foundational Chapters
    • Read Chapter 1–2 of Matloff’s text (on R vectors).
    • Read Chapter 1–2 of Wickham’s R for Data Science.
    • On your blog, post one paragraph summarizing:
      • What an R vector is.
      • Why vectors are fundamental to data analysis in R.

        In R, a vector is the most basic data structure, serving as a sequence of data elements of the same basic type. Vectors are fundamental to data analysis in R because the language is inherently vectorized. This means functions can be applied to an entire vector at once without the need for explicit looping, allowing for highly efficient data manipulation and mathematical computations. In the context of data science, vectors serve as the building blocks for more complex structures like data frames, where each column is effectively a vector. A data frame is essentially a list whose elements are vectors. (Matloff, p. 101)

Submission Checklist

  • GitHub repository with README.md completed
  • Blog created and URL shared on Canvas
  • Screenshot of RStudio console and installation report on the blog
  • Blog post summarizing vector concepts

Grading Criteria

ItemPoints
GitHub repo and README1
Blog setup & URL on Canvas3
Installation report (clarity & completeness)1
Total5



Disclaimer:

Generative AI is integrated into my professional workflow for drafting, structural organization, and code optimization. To avoid redundancy, this statement serves as a standing disclaimer for all entries. Generative AI has been utilized to ensure technical accuracy and to facilitate the very documentation requirements mandated by the curriculum available within the course syllabus.

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