Description
Dataset (located in your assignment prompt in Canvas).
● The dataset is customer data related to health insurance. The data set file
name is “custdata.tsv”. You will use this data set to answer the questions in the
assignment. Field names (in order): custid, sex, is.employed, income,
marital.stat, health.ins, housing.type, recent.move, num.vehicles.
1. Using the customers data (custdata.tsv). Like histogram, you can also plot the density of
a variable.
● 1.1: Figure out how to plot density of income. (10points)
● 1.2: Provide a couple of sentences of description along with the plot. Imagine you
are explaining this to your manager or a senior leader. (10points)
2. Create a bar chart for housing type (x axis) and income (y axis) using the customers
data. Make sure to remove the “NA” type. [Hint: Remove “NA” type from the
“housing.type” and “income” columns by using the subset function with an appropriate
condition on the “income” and “housing type” field. Use ggplot and add
“+geom_bar(stat=’identity’)” after the initial ggplot command.] Provide your commands
and theplot. (10 points)
3. Using the customers data(custdata.tsv):
● 3.1: Extract a subset of customers that are married and have an income more
than $50,000. (5 points)
● 3.2: What percentage of these customers have health insurance? (10points)
● 3.3: How does this percentage differ from that for the whole data set? (10 points)
4. Using the customers data (custdata.tsv):
● 4.1: In the customers data, do you think there is any correlation between age,
income, and number of vehicles? Explain why or why not. To determine this:
• Remove any age outliers. Assume age > 100 is an outlier.
• Then plot a scatterplot between 2 of the 3 variables mentioned
where the color of each point is the 3rd variable. [Hint: use
ggplot for this].
(10 points)
● 4.2: Report the best correlation coefficient between “income” and
“num.vehicles”, and “num.vehicles” and “age”. To determine this:
• Remove “NA” type from the “housing.type” and “income” columns
by using the subset function
• Then determine the correlation coefficient between the columns
“income” and “num.vehicles”, and “num.vehicles” and “age”
(15 points)
