I teach social science methods, and students take my courses because it's required, and would celebrate if these courses were removed from the curriculum. I remember my struggles when I started learning `R`

, and thanks to the ever-increasing amount of teaching resources (yay R4DS, moderndive, etc.) and tips, am constantly updating and looking for ways to improve my instruction and student engagement. Unfortunately, I seem to have hit a road block this semester.

Most students seem to enjoy learning `ggplot2`

, and following sage advice from Hadley, Jenny, Mine, and others, I introduce `R`

through visualisation with real data (thanks to the `fivethirtyeight`

package). However, when I get to `dplyr`

, most of them seem to shut down; in fact, I had one student actually walk out of my class midway and never came back. The remaining students, save a couple of the most committed (thank goodness for them), look like they're at the dentist waiting for a wisdom tooth extraction without anaesthesia. I used to have students type `read_csv(file_path)`

, learning about file paths and how to use a computer, but that created such an uproar that I now make them point-and-click Import Data in RStudio.

This is rather discouraging, and makes me question all the work I put in when I can have an easier time just teaching research design without code/math. I am constantly selling `R`

and statistical/computational thinking, linking these skills to getting a job and what I believe is more important: being a good citizen in a world of noisy data, but to no avail. To be sure, I get about two students in a class of 30 each semester super excited about `R`

and data science, but I see at best indifference, and at worst, anger in the rest.

Has anyone encountered this? Is there something about `dplyr`

that makes students balk? I don't get it...base `R`

is so much worse! How do you teach (or learn) `dplyr`

? Are there any Lego illustrations? I'd love to hear the community's thoughts.

Thanks!