This week and next we will complete our introduction to R in our text,
Part I Whole Game.
The shorter "Workflow:" chapters (2, 4, 6, and 8) are familiarizing you
with the environment of RStudio and how you can work efficiently in this
environment.
The longer "Data" chapters (1, 3, 5, and 7) are introducing you to the
fundamental issues of working with data: visualizing, transforming,
tidying, and importing.
Each chapter ends with a short summary of what has been presented. You
will not understand all of the aspects of what has been presented until
after you have used these tools regularly, but you should be able to look
at the summary and recognize what the authors are saying.
You may not remember all of the shortcuts that the authors share, but
over time they will become more natural.
Chapter 4 Workflow: Code Style presents basic advice for keeping the R code in
script files clean and readable with common conventions for the use of
names, spaces, pipes, parentheses, arguments, and sectioning
Chapter 5 Data Tidying presents the concepts of tidy data and
pivoting
tidy data
each variable is a column and each column is a variable
each observation is a row and each row is an observation
each value is a cell and each cell is a single value
pivoting
pivot_longer() helps you tidy data when column names
either contain data values that you want represented in cells or
multiple variables that you want represented in individual columns
pivot_wider() helps you tidy data when single
observations are spread across multiple rows
Assessment deadlines will be 11:59pm each Saturday.
All assessments are submitted to the Homework Folder inside your assigned
Google Drive folder.
There are no make-ups for missed assessments. Contact me before a deadline
if you have an issue meeting the deadline and we will find a mutually
agreeable solution.