- Read the income dataset, “zipIncomeAssignment.csv”, into R. (You can find the csv file in iLearn under the Content -> Week 2 folder.)
- Change the column names of your data frame so that zcta becomes zipCode and meanhouseholdincome becomes income.
- Analyze the summary of your data. What are the mean and median average incomes?
- Plot a scatter plot of the data. Although this graph is not too informative, do you see any outlier values? If so, what are they?
- In order to omit outliers, create a subset of the data so that:
$7,000 < income < $200,000 (or in R syntax , income > 7000 & income < 200000)
- What’s your new mean?
- Create a simple box plot of your data. Be sure to add a title and label the axes.
HINT: Take a look at: https://www.tutorialspoint.com/r/r_boxplots.htm (specifically, Creating the Boxplot.) Instead of “mpg ~ cyl”, you want to use “income ~ zipCode”.
In the box plot you created, notice that all of the income data is pushed towards the bottom of the graph because most average incomes tend to be low. Create a new box plot where the y-axis uses a log scale. Be sure to add a title and label the axes. For the next 2 questions, use the ggplot library in R, which enables you to create graphs with several different types of plots layered over each other.
- Make a ggplot that consists of just a scatter plot using the function geom_point() with position = “jitter” so that the data points are grouped by zip code. Be sure to use ggplot’s function for taking the log10 of the y-axis data. (Hint: for geom_point, have alpha=0.2).
- Create a new ggplot by adding a box plot layer to your previous graph. To do this, add the ggplot function geom_boxplot(). Also, add color to the scatter plot so that data points between different zip codes are different colors. Be sure to label the axes and add a title to the graph. (Hint: for geom_boxplot, have alpha=0.1 and outlier.size=0).
- What can you conclude from this data analysis/visualization?