**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 log_{10}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?