Download the homework3.Rmd file from Canvas or the course website.
Open homework3.Rmd in RStudio.
Replace the “Your Name Here” text in the author: field with your own name.
Supply your solutions to the homework by editing homework3.Rmd.
When you have completed the homework and have checked that your code both runs in the Console and knits correctly when you click Knit HTML, rename the R Markdown file to homework3_YourNameHere.Rmd, and submit on Canvas (YourNameHere should be changed to your own name.)
The code chunk below appears in the Rmd file, but won’t be displayed in your html output.
For this problem we’ll use the diamonds dataset from the ggplot2 package.
Use the hist function to create a histogram of carat with bars colored steelblue.
# Edit meUse the qplot function from the ggplot2 package to create a histogram of depth. Note that geom = "histogram" is a valid geometry in qplot.
# Edit meUse the qplot function from the ggplot2 library to create violin plots showing how price varies across diamond cut. Specify fill = cut to get all the boxplots to be coloured differently.
# Edit meHint: For this exercise, it will be useful to know that violin is a geometry (geom) built into ggplot2, and that qplot can be called with the arguments:
qplot(x, y, data, geom, fill)For this exercise we’ll go back to the Cars93 data set in the MASS library
Define a ggplot object using the Cars93 data set that you can use to view Price on the y-axis, MPG.highway on the x-axis, and set the size mapping to be based on Horsepower.
Use geom_point() to create a scatterplot from your ggplot object.
# Edit meRepeat part (a), this time also setting the colour mapping to be based on Origin.
# Edit meRepeat part (b), this time using the scale_colour_manual() layer to specify that you want to use cbPalette as your color palette.
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# Edit meRepeat part 2(b), this time using stat_smooth() to add a layer showing the smoothed curve representing how Price varies with MPG.highway.
# Edit meUse your ggplot object from 2(b) along with the geom_point() and facet_grid layers to create scatterplots of Price against MPG.highway, broken down by (conditioned on) Origin.
# Edit me(Your code should produce a figure with two scatterplots, analogous to the facet_wrap example from class. Note that the example from class had a factor with 7 levels, so 7 scatterplots were produced. Origin has two levels.)
Modify your solution to part (b) to also display regression lines for each scatterplot.
# Edit meThis problem uses the Adult dataset, which we load below. The main variable of interest here is high.income, which indicates whether the individual’s income was over $50K. Anyone for whom high.income == 1 is considered a “high earner”.
adult.data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", header=FALSE, fill=FALSE, strip.white=T,
col.names=c("age", "type_employer", "fnlwgt", "education",
"education_num","marital", "occupation", "relationship", "race","sex",
"capital_gain", "capital_loss", "hr_per_week","country", "income"))
adult.data <- mutate(adult.data,
high.income = as.numeric(income == ">50K"))Use the ddply() function to produce a summary table showing how many individuals there are in each education_num bin, and how the proportion of high earners varies across education_num levels. Your table should have column names: education_num, count and high.earn.rate.
# Edit meUsing the ggplot and geom_bar commands along with your data summary from part (a) to create a bar chart showing the high earning rate on the y axis and education_num on the x axis. Specify that the color of the bars should be determined by the number of individuals in each bin.
# Edit meUse the ddply() function to produce a summary table showing how the proportion of high earners varies across all combinations of the following variables: sex, race, and marital (marital status). In addition to showing the proportion of high earners, your table should also show the number of individuals in each bin. Your table should have column names: sex, race, marital, count and high.earn.rate.
# Edit mekable()Use the kable() function from the knitr library to display the table from part (c) in nice formatting. You should use the digits argument to ensure that the values in your table are being rounded to a reasonable number of decimal places.
# Edit meUsing the table you created in 4(c), use ggplot graphics to construct a plot that looks like the one at this link
Hint You may find it useful to use the following layers: facet_grid, coord_flip (for horizontal bar charts), theme (rotating x axis text) and guides (removing fill legend).
# Edit meechoRepeat part (a), but this time set the echo argument of the code chunk in such a way that the code is not printed, but the plot is still displayed.
# Edit me