BUA 455, Data Management for Business, is a relatively new course in Whitman in our new major, Business Analytics.
Students are required to use R and RStudio in BUA 455.
You will learn how to use and modify provided R code to complete assigned tasks and your final group project.
I have developed some pre-class videos and also list useful text sections to help you prepare before class begins.
All students taking this course are required to have their own functional laptop for this course.
Minimum RAM: 4 GB (but more is better)
Minimum Storage: 256 GB
This course can not be completed on a borrowed or loaner laptop.
You will need your own laptop with full administrative access.
If your laptop breaks, and you can not replace it within a few days, you will fall behind and may have to take an incomplete.
HW 1 will include a question that verifies that you understand this requirement which is not negotiable.
NOTES for Mac OS users:
I am a Windows OS user and I know only limited information about using R and RStudio on a Mac OS.
I will try to help you if you have a Mac OS question, but you are expected to also do your own problem-solving (Google is very helpful.).
I will augment lectures with sections from this FREE required electronic textbook by Rafael A. Irizarry (2022).
I will not assign exercises from this book, but you are responsible for reviewing and understanding sections that I assign.
There many other excellent free textbooks and other resources available online.
In this short video, I demonstrate how to uninstall and re-install an R and RStudio.
Linkedin Learning - Learning R
Linkedin Learning - Learning R
In this course, lectures, assignments, etc. will be in the form of an R project.
I will go over how to create an R Project and to save and document files within a project during Week 1.
Below is some material to help get you started.
The base R software is very streamlined, but still useful.
In order to use R and RStudio more efficiently, we need to install and load R packages.
This aspect of R is why you are required to use your own laptop for which you have administrative permissions.
Packages only have to be installed once after R and RStudio are installed or re-installed.
Packages have to be loaded every time you start a new R session.
In this video I explain the standard way for installing and loading packages and the more efficient method I will use in this course.
Linkedin Learning - Learning R
To access Linkedin Learning videos (free to SU students), log on here.
Packages for R in 2. Getting Started of Learning R (halfway down the list of topics)
If you have used R in the past, you may be accustomed to using R scripts.
In this course, we will use R markdown files which can include:
R markdown files can be rendered (knitted) as
The course textbook is written in Bookdown which is a R markdown file format for writing books.
R. A. Irizarry (2022). Introduction to Data Science - Data Analysis and Prediction Algorithms with R. GitHub repository. URL: https://github.com/charlespwd/project-title
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/.
RStudio Team (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. URL: http://www.rstudio.com/.
Yihui Xie (2021). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.36.
CRAN (Comprehensive R Archive Network) also has documentation on the installation process. It is a little technical, but it may be useful if you are having a more complex installation issue.