# Project B: Visualizing Communities

Full Draft Due: 2018-03-06 (Tuesday, start of class)

Final Due: 2018-03-08 (Thursday, start of class)

Starter code: project-b.Rmd

Data dictionary: project-b-data-dictionary

Rubric: project-b-rubric.csv

Questions deadline: 2018-03-05; 5pm (Monday) - If you would like help with the project, please see/e-mail me before this deadline. After this time I will only help with technical issues, such as R crashing or GitHub being down.

The overarching goal of this project is to tell an interesting narrative about the demographics of a particular metropolitan area in the United States. The structure of the report is much more open ended compared to the first project.

For this project we will all be working off of the same master dataset. The data gives demographic information about census tracts. You will each, however, be looking at different metropolitan areas in the United States. We will discuss the metropolitan area assignments in class on 2018-02-22.

Your task is to write a short essay in the style of a 538 news article. The essay should describe one or more interesting elements you discovered while investigating the metropolitan area that you have been assigned. Keep in mind that you will want to draw on one or more of the following tasks from exploratory data analysis:

• anomaly detection: identify areas that seem to behave differently than the rest of the data
• perspective: pick a particular area of interest and compare it to the rest of the data
• pattern recognition: understand the basic patterns present in your dataset

The final report should contain exactly three visualizations. This means that you should take care to make each visualization as information dense as possible. Aim to have a final report around 750-1000 words. The word length is not a hard limit; it is just a guidelines to indicate the expected length of the report. All of the plots should be integrated into the essay in a meaningful way rather than all included at the start or end of the essay. Finally, you must have a completed draft ready in class on 2018-03-06. We will be reviewing drafts in class; the drafts must have completely satisfied the requirements of the assignment. Students who have not completed these will receive deductions in the grading rubric. Also, note that the project must be properly named as: project-b.Rmd and project-b.html.

The grade for the assignment depends primarily on the effectiveness of the graphics in conveying information, the quality of the writing, and execution of how the writing and visualizations are integrated together. Also, half of the class will be selected to present their report to the class on 2018-03-08. The remainder of the class will present on Project C.

## Code Examples

You may find it very helpful to make maps of the data from your tract. These are great for exploratory work, but don’t overuse them in the report. To make a nice map, use the ggmap package

library(ggmap)
acs_rva <- filter(acs, cbsa == "Richmond, VA")

qmplot(lon, lat, data = acs_rva, geom = "blank")


The qmplot function replaces the typical ggplot() function in the first line of a graphic. You can add other layers just as before. The code here adds points to the plot (notice that I set the alpha parameter to make sure the points do not cover up the rest of the plot).

qmplot(lon, lat, data = acs_rva, geom = "blank") +
geom_point(aes(color = median_rent), alpha = 0.8) +
scale_color_viridis()


You may also find it useful to construct new variables that aggregate the granular ones I have provided. For example, if you want to find what percentage of people have a commute of over 45 minutes you can do this:

acs$ctime_45_plus <- acs$ctime_45_59 + acs$ctime_60_89 + acs$ctime_90_99


The name of the new variable (here, ctime_45_plus) is entirely up to you.

Also, some students have wanted to create a variable that shows, for each tract, the maximum category from a group of variables. You can do this by the following code (replace acs with the name of your dataset) for the race variables:

temp <- select(acs, starts_with("race_"))
acs\$max_race_category <- names(temp)[apply(temp, 1, which.max)]


It should be clear how to modify this for other variables (but if not, please ask!).