For this lab, let’s explore the mtcars dataset that is included within R.

As we are about halfway through the course, this activity description does not provide the same level of code prompts as previous labs – it is assumed that you remember or can lookup the necessary code. The overall goal of this activity is to use the package called ggplot2 to show different attributes of the mtcars data set. More information about making visualizations using ggplot() can be found here: ggplot().

Please be sure to first install ggplot2 using install.packages("ggplot2") and include both the code and the images that were generated with your assignment.

• Histogram of MPG
• Boxplots of mpg by cyl (i.e. 3 box plots, one for all cars with 4 cylinders, one for all cars with 6 cylinders and one with all the cars with 8 cylindars).
• MultiLine chart of wt on the x-axis, mpg for the y-axis. With a line for each am (i.e. two lines). Also be sure to show the each point on the chart.
• Barchart with the x-axis being the name of each car, and the height being wt. Make sure to rotate the x-axis labels, so we can actually read the car name.

Note: A common issue with base R datasets is record names are included as index values and not columns in the dataframe. Run this code to place car names in a column and add numeric values to the index

This code takes the rownames from mtcars and column binds cbind() the names and the dataset
mtcars <- cbind(car = rownames(mtcars), mtcars)
This code creates new rownames and places then in the index of mtcars
rownames(mtcars) <- 1:nrow(mtcars)
• Scatter chart with the x-axis being the mpg and the y-axis being the wt of the car. Have the color and the size of each “sybol” (i.e., circle) represent the how fast the car goes (based on the qsec attribute)
• Heatmap with the weight (‘wt’) on the x-axis, number of cylinders on the y-axis, and the color representing mpg Note you might have to rotate the x-axis text so you can actually read the different weights.

If you finish early do some research about the melt() function in R.

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