You will work with a partner or two to write a short blog post that contains (at least) one data graphic. Your goal is to tell us something interesting using a well-crafted, thoughtfully-prepared data graphic. One data graphic should suffice, but you may include more if you choose.
Your blog post should be short (300–500 words, as computed by the
text_stats() function in the template). We envision an introductory paragraph that explains your findings and provides some context to your data, the data graphic(s), and then a caption-like paragraph providing more detail about what to look for in the data graphic and how to interpret it. That is it. You will not earn more points by including more words or data graphics. What we are looking for is something that is insightful and well-crafted. As always, you should spend some time thinking about context, scale, color, etc.
You will compose this blog as an RStudio SDS 192 project, as we will be following a project-oriented workflow. (See
New Project... from the upper right dropdown in RStudio.) You should write your blog post in R Markdown and create your data graphic using
ggplot2. Please use the
SDS 192 assignment template provided by the
sds192 package. You will upload a ZIP file of your RStudio project directory (which contains your R Markdown (
.Rmd) file) to Moodle. Only one group member needs to upload the group’s submission. Do not forget to give your post an informative title!
sds192package by following these instructions
Here are some examples of articles that are similar in spirit to yours. Most of these are much longer than yours will be, and may contain multiple graphics, but the idea is similar: use a good data graphic to tell us something we don’t already know.
You should show your code in your blog post, but only show the minimal code that is necessary to produce your plot – not all the code that you wrote in the process of doing this assignment!
You are free to use whatever data you want. However, the purpose of this exercise is to learn how to make good data graphics – not to wrangle data (don’t worry – we’re headed there next). So we don’t want you to spend much time wrangling data. There are perfectly good data sets available through
R packages that are already well-curated:
nycflights13: data about flights leaving from the three major NYC airports in 2013
babynames: history of baby names from the Social Security Administration
Lahman: comprehensive historical archive of major league baseball data
fueleconomy: fuel economy data from the EPA, 1985–2015
fivethirtyeight: provides access to data sets that drive many articles on FiveThirtyEight.
palmerpenguins: penguins in Antarctica