Note the following caveats regarding the course schedule:
First, this might be considered an experimental course, where student progress will largely dictate progression of material covered. Each week, I will assign readings and/or exercises that students will be expected to complete and turn in. Whether material is properly understood will dictate the following week’s readings. Further, the materials on the exercises will cover topics in more depth than the slides, so completion and understanding of the exercises is essential.
Finally–news changes quickly, so I reserve the right to remove, add, or alter any content that appears below based on current events in the world. The subjects are not mutually exclusive, so we may decide to introduce some topics prior to the scheduled date.
Why data journalism? (past, present, future)
Overview of data technologies:
The week 1 exercises cover installing R, RStudio, and how to navigate the RStudio IDE.
Also check out:
ggplot2 (part 1)
rtweet packageThis week we cover the basics of R functions and data objects, and a brief introduction to R Markdown.
fivethirtyeight data packagertweet packageThis week we will cover the basic dplyr verbs for data manipulation
select(), filter(), arrange(), mutate(), group_by() & summarize()dplyr package documentationThe slides will cover some basic data manipulation techniques, and intermediate ggplot2 graphics.
ggplot2 (part 2)
ggplot2 graphing techniques (part 3)
ggplot2 graphing techniques (part 4)
reprex
Material and project review for meetings;
Final project draft #1
Individual meetings (as needed)
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