Exercise 1. ggplot2 basics

Start by loading the dplyr and ggplot2 libraries as well as the murders data.

library(dplyr) library(ggplot2) library(dslabs) data(murders)

Note that you can load both dplyr and ggplot2, as well as other packages, by installing and loading the tidyverse package.

With ggplot2 plots can be saved as objects. For example we can associate a dataset with a plot object like this

p <- ggplot(data = murders) Because data is the first argument we don’t need to spell it out. So we can write this instead:

p <- ggplot(murders) or, if we load dplyr, we can use the pipe:

p <- murders %>% ggplot()

Remember the pipe sends the object on the left of %>% to be the first argument for the function the right of %>%.

Now let’s get an introduction to ggplot.

What is the class of the object p?

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(dslabs)
data(heights)
data(murders)
p <- ggplot(murders)
class(p)
## [1] "gg"     "ggplot"

Exercise 2. Printing

Remember that to print an object you can use the command print or simply type the object. For example, instead of

x <- 2 print(x)

you can simply type

x <- 2 x

Print the object p defined in exercise one

p <- ggplot(murders)

and describe what you see.

Possible Answers:

p <- ggplot(murders)
p

Exercise 3. Pipes

Now we are going to review the use of pipes by seeing how they can be used with ggplot.

Using the pipe %>%, create an object p associated with the heights dataset instead of with the murders dataset as in previous exercises.

data(heights)
# define ggplot object called p like in the previous exercise but using a pipe 
p <- heights %>% ggplot()

Exercise 4. Layers

Now we are going to add layers and the corresponding aesthetic mappings. For the murders data, we plotted total murders versus population sizes in the videos.

Explore the murders data frame to remind yourself of the names for the two variables (total murders and population size) we want to plot and select the correct answer.

Possible Answers:

Exercise 5. geom_point 1

To create a scatter plot, we add a layer with the function geom_point. The aesthetic mappings require us to define the x-axis and y-axis variables respectively. So the code looks like this:

murders %>% ggplot(aes(x = , y = )) + geom_point()

except we have to fill in the blanks to define the two variables x and y.

Fill out the sample code with the correct variable names to plot total murders versus population size.

## Fill in the blanks
murders %>% ggplot(aes(x = population, y = total)) +
  geom_point()

Exercise 7. geom_point text

If instead of points we want to add text, we can use the geom_text() or geom_label() geometries. However, note that the following code

murders %>% ggplot(aes(population, total)) + geom_label()

will give us the error message: Error: geom_label requires the following missing aesthetics: label

Why is this?

Possible Answers:

Exercise 8. geom_point text

You can also add labels to the points on a plot.

Rewrite the code from the previous exercise to:

add a label aesthetic to aes equal to the state abbreviation use geom_label instead of geom_point

library(dplyr)
library(ggplot2)
library(dslabs)
data(murders)
## edit the next line to add the label
murders %>% ggplot(aes(population, total,label = abb)) +
  geom_label()

Exercise 9. geom_point colors

Now let’s change the color of the labels to blue. How can we do this?

Possible Answers:

Exercise 10. geom_point colors 2

Now let’s go ahead and make the labels blue. We previously wrote this code to add labels to our plot:

murders %>% ggplot(aes(population, total, label= abb)) + geom_label()

Now we will edit this code.

Rewrite the code above to make the labels blue by adding an argument to geom_label. You do not need to put the color argument inside of an aes col. Note that the grader expects you to use the argument color instead of col; these are equivalent.

murders %>% ggplot(aes(population, total,label= abb)) +
  geom_label(color="blue")

Exercise 11. geom_labels by region

Now suppose we want to use color to represent the different regions. So the states from the West will be one color, states from the Northeast another, and so on. In this case, which of the following is most appropriate:

Possible Answers:

Exercise 12. geom_label colors

We previously used this code to make a plot using the state abbreviations as labels:

murders %>% ggplot(aes(population, total, label = abb)) + geom_label()

We are now going to add color to represent the region.

Rewrite the code above to make the label color correspond to the state’s region. Because this is a mapping, you will have to do this through the aes function. Use the existing aes function inside of the ggplot function.

murders %>% ggplot(aes(population, total,label= abb, color = region)) +
  geom_label()

Exercise 13. Log-scale

Now we are going to change the axes to log scales to account for the fact that the population distribution is skewed. Let’s start by defining an object p that holds the plot we have made up to now:

p <- murders %>% ggplot(aes(population, total, label = abb, color = region)) + geom_label()

To change the x-axis to a log scale we learned about the scale_x_log10() function. We can change the axis by adding this layer to the object p to change the scale and render the plot using the following code:

p + scale_x_log10()

Change both axes to be in the log scale on a single graph. Make sure you do not redefine p - just add the appropriate layers.

p <- murders %>% ggplot(aes(population, total, label = abb, color = region)) + geom_label()
## add layers to p here
p + scale_x_log10() + scale_y_log10()

p

Exercise 14. Titles

In the previous exercises we created a plot using the following code:

library(dplyr) library(ggplot2) library(dslabs) data(murders) p<- murders %>% ggplot(aes(population, total, label = abb, color = region)) + geom_label() p + scale_x_log10() + scale_y_log10()

We are now going to add a title to this plot. We will do this by adding yet another layer, this time with the function ggtitle.

Edit the code above to add the title “Gun murder data” to the plot.

p <- murders %>% ggplot(aes(population, total, label = abb, color = region)) +
  geom_label()
# add a layer to add title to the next line
p + scale_x_log10() + 
    scale_y_log10() +
    ggtitle("Gun murder data")

Exercise 15. Histograms

We are going to shift our focus from the murders dataset to explore the heights dataset.

We use the geom_histogram function to make a histogram of the heights in the heights data frame. When reading the documentation for this function we see that it requires just one mapping, the values to be used for the histogram.

What is the variable containing the heights in inches in the heights data frame?

Possible Answers:

Exercise 16. A second example

We are now going to make a histogram of the heights so we will load the heights dataset. The following code has been pre-run for you to load the heights dataset:

library(dplyr) library(ggplot2) library(dslabs) data(heights)

Create a ggplot object called p using the pipe to assign the heights data to a ggplot object. Assign height to the x values through the aes function.

# define p here
p <- heights %>% ggplot(aes(x=height))

Exercise 17. Histograms 2

Now we are ready to add a layer to actually make the histogram.

Add a layer to the object p (created in the previous exercise) using the geom_histogram function to make the histogram.

p <- heights %>% 
  ggplot(aes(height))
## add a layer to p
p + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Exercise 18. Histogram binwidth

Note that when we run the code from the previous exercise we get the following warning:

stat_bin() using bins = 30. Pick better value with binwidth.

Use the binwidth argument to change the histogram made in the previous exercise to use bins of size 1 inch.

p <- heights %>% 
  ggplot(aes(height))
## add the geom_histogram layer but with the requested argument
p + geom_histogram(binwidth = 1)

Exercise 19. Smooth density plot

Now instead of a histogram we are going to make a smooth density plot. In this case, we will not make an object p. Instead we will render the plot using a single line of code. In the previous exercise, we could have created a histogram using one line of code like this:

heights %>% ggplot(aes(height)) + geom_histogram()

Now instead of geom_histogram we will use geom_density to create a smooth density plot.

Add the appropriate layer to create a smooth density plot of heights.

## add the correct layer using +
heights %>% 
  ggplot(aes(height)) + geom_density()

Exercise 20. Two smooth density plots

Now we are going to make density plots for males and females separately. We can do this using the group argument within the aes mapping. Because each point will be assigned to a different density depending on a variable from the dataset, we need to map within aes.

Create separate smooth density plots for males and females by defining group by sex. Use the existing aes function inside of the ggplot function.

## add the group argument then a layer with +
heights %>% 
  ggplot(aes(x = height,group = sex)) +
  geom_density()

Exercise 21. Two smooth density plots 2

In the previous exercise we made the two density plots, one for each sex, using:

heights %>% ggplot(aes(height, group = sex)) + geom_density()

We can also assign groups through the color or fill argument. For example, if you type color = sex ggplot knows you want a different color for each sex. So two densities must be drawn. You can therefore skip the group = sex mapping. Using color has the added benefit that it uses color to distinguish the groups.

Change the density plots from the previous exercise to add color.

## edit the next line to use color instead of group then add a density layer
heights %>% 
  ggplot(aes(height, color = sex))+geom_density()

Exercise 22. Two smooth density plots 3

We can also assign groups using the fill argument. When using the geom_density geometry, color creates a colored line for the smooth density plot while fill colors in the area under the curve.

We can see what this looks like by running the following code:

heights %>% ggplot(aes(height, fill = sex)) + geom_density()

However, here the second density is drawn over the other. We can change this by using something called alpha blending.

Set the alpha parameter to 0.2 in the geom_density function to make this change.

heights %>% 
  ggplot(aes(height, fill = sex)) + 
  geom_density(alpha = 0.2)