Create a report that shows, for each problem:
o A short description of how you addressed the question
o The resulting tables and figures, if applicable
o Your R code, either in the body of the text or in an appendix
You may be interested in trying RMarkdown for this assignment, but you are not required to do so.
Part 1: Visualization
Load the “Midwest.csv” data set and inspect it. You can find the data set on Carmen, in the “Data” folder. These are population data on 437 counties in Illinois, Indiana, Michigan, Ohio, and Wisconsin. There is no data dictionary, but the column names should be self-explanatory (except the last one). Hint: If R is showing any columns in your data set using scientific notation (e.g., 1E2), you can turn off the scientific number notation by entering: options(scipen=999).
1. Using ggplot, generate a scatter plot of percent college graduates (percollege) vs. percent of the population below the poverty line (percbelowpoverty)
o Color the points according to the state they are located in
o Make the point size a function of the total population of the county
o Properly label your axes, using xlab() and ylab()
o Briefly interpret the scatter plot. Do you see a relationship between these two variables? Is it linear? Do you see any points you would consider outliers?
2. Using the same variables as above (percollege and percbelowpoverty), use faceting to create one scatterplot per state. Does the relationship between percent college graduates and percent population below the poverty line seem to differ by state?
3. Generate a set of box and whisker plots showing the distribution of the percentage of college degrees (as a percentage of the population in a county) by state
o In the same plot, add box plots for the percent of high school graduates. Each state should have two box plots. Color-code them.
o Plot the raw data over the box plots using geom_jitter
o Manipulate the transparency of the data points empty so that they don’t distract from the box plots
Part 2: Transformation
Load the “OhioData.csv” data set and inspect it. You can find the data set on Carmen. This contains demographic information on all communities in Ohio. Be careful about missing values – there are many. You need to make sure that R correctly recognizes the missing values and document what you decide to do with them.
4. Inspect the data set. What do you think is the cause of the missing values?
5. Using mutate(), create a new column showing the average number of people living in a housing unit. Don’t include vacant homes.
6. Generate a scatter plot of the average number of people per housing unit vs. the median gross rent. Visually assess whether you see a relationship between the two variables.
Tip: You can adjust the scale of the y axis by adding “+ scale_y_continuous(limits = c(y_min, y_max))”, where you replace y_min and y_max with the axis limits of your choice.
7. Using filter(), create a smaller dataset in which you only retain the rows where you have information on the unemployment rate.
8. Using group_by() and summarize(), create a data frame where you show the average Gini Index by county.
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