1. Congratulations!
Great work. You've learned many skills throughout this course to improve as a programmer, especially in programming with dplyr.
2. Chapter 1 - select() and its helper functions
In Chapter 1, we reviewed some dplyr pipelines.
Then, we dug into choosing columns based on substrings in the column names.
Lastly, we used regular expressions to look for text patterns in column names.
3. Chapter 2 - Column transformations
In Chapter 2, we rearranged the columns in our data.
We worked on multiple columns and used the across, if_any, if_all, rowwise, and c_across functions.
4. Chapter 3 - Joins and set theory
In Chapter 3, we went to The North Pole to see our old friend Set Theory Claus and the elves. While there, we also linked up with some join functions.
We then looked at many programmatic ways to work with data sources via set theory clauses.
5. Chapter 4 - Getting your feet wet with rlang
We then dove into programming with dplyr verbs in functions using the rlang package. We passed unquoted variable names into a function using the curly-curly operator, bang-bang, and enquo.
Next, we defined variable names programmatically with the walrus operator, as_name, and bang-bang in the mutate function.
Lastly, we created a function with ggplot2 code to customize our plots.
6. Other relevant courses/tracks
If you'd like to learn more about visualization, check out these courses.
If you want to accelerate and to further assess your R programming skills, check out these tracks.
7. Woohoo for you!
Awesome work on completing this course. I hope that you've gained the knowledge and skills to continue your success journey in data science. It's now time to get back to programming with dplyr in R on new projects. I can't wait to see what you accomplish!