CIS XXXXXXXXXXData Visualization Summer 2020 Exam # 2 – Part 2 Due: July 24th mid night (11:59 PM) Maximum points: 20 or 20% of the course grade Note: You must include R code along with the output...

Must include R code along with the output graph(s) for the answers.


CIS 3306 - Data Visualization Summer 2020 Exam # 2 – Part 2 Due: July 24th mid night (11:59 PM) Maximum points: 20 or 20% of the course grade Note: You must include R code along with the output graph(s) for your answer. The simplest way is to copy the R code along with the plot within a word document and submit it. You must use the ggplot function for the ggplot2 package to write the R code for visualizing data. Your work must involve using correct variables for the dataset of your choice to plot meaningful visualizations. Just producing visual plots which are incorrect will not receive any credit. Partial credit will be assigned for work demonstrating significant efforts. The first four questions are worth 3 points each and the last two are 4 points each. 1. Create the following scatter plot for mtcars dataset. Hint: Use theme_classic() and scale_color_grey() to get a black and white plot with no background. (3 points) 2. Create the following plot for the mtcars dataset. (3 points) 3. Using the heightweight data set, create a new column that indicates if the height in inches is less than the mean height value or >= mean height value. This dataset is part of the gcookbook package. Name this new column as heightgroup and store it as a new column in the heightweight data set. Name the new dataset as heightweight_mod. Now create the following plot based on this new categorical variable of heightgroup as shown below. (3 points) 4. Create the following scatter plot for the faithful dataset using eruptions > 3 to highlight two categories of eruptions. (3 points) 5. Create the following histogram for the airquality data set. Hint: Use binwidth = 5, color = “black” and aes(fill = ..count..) within geom_histogram. Also remember to include the plot title “Frequency histogram of mean ozone”. (4 points) 6. Create the following boxplot for the airquality dataset. Hint: Use theme_classic() to create the plot with no background. (4 points)