For an advanced undergraduate analytics class for business school students, I use a textbook by Saltz and Stanton called
Data Science for Business with R (Amazon link)
This textbook teaches R and analytics at the same time. The professor does not have to provide a separate R curriculum or require students to buy a second book.
The running example in the textbook is an airline business scenario that is interesting and builds with the complexity of the subject matter. The authors provide the dataset that students can work with for the airline case study. Many examples in the textbook use data that is available online and therefor can be imported to R with just a few lines of code.
One semester is not enough time to cover every chapter in the book. I emphasize predictive analytics, so I skip the chapters on maps and shiny apps.
I do some supplemental lectures on concepts in predictive analytics before students reach the chapters on regression and decision trees. For example, overfitting is a new concept to undergraduates. I want them to have a more intuitive grasp of that subject before learning the R code to separate data into training and validation sets.
Note that these students have already taken what has traditionally been called Business Statistics, so they already understand basic descriptive statistics and graphing. The book is no substitute for that primary class.
There are free supplementary materials online for learning R. Students find message boards especially helpful in pinpointing answers for questions that come up while coding.