https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/LinusJen_HW3.html

[giscus[bot]]

https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/LinusJen_HW3.html

Reading in Data

https://dacss.github.io/DACSS_601_Summer2023_Sec1/posts/LinusJen_HW3.html

[sp-conway]

Nice job Linus! Here are some notes I took as I read.

• Awesome job cleaning and describing these data
• Nice violin plots for education data!
  • Distribution of education received for us states
    • Re-scale y axis to show 0
    • The fact that the state plot looks so different than the county plot suggests that there are inequalities within states. That is, maybe there are counties that are less educated than others, and graphing within a state obscure that.
    • Ah, you note that!
• Nice job describing the poverty data
• RQ1
  • Careful with geom_smooth(). This assumes a particular statistical model. I would suggest only showing the raw data unless you explicitly want to use this model.
  • It makes sense that there’s a relationship between # of residents within a county and # of residents within a particular education group. However, as you note, what is important is the steepness of the relationship.
  • I would try plotting these all in the same graph, but coloring the points by educational status. Make sure to mess with the alpha value to make the points a little transparent
• RQ2
  • Nice plots.
    Overall this is a nice project, but I would caution against relying so much on the scatterplots comparing two different measure of population size. I think those are nice, but I would recommend plotting them in the same graph, rather than split up across panels.