R Graphics

Topics

  • R ggplot2 package
  • Geometric objects and aesthetics
  • Setup basic plots
  • Add and modify scales and legends
  • Manipulate plot labels
  • Change and create plot themes

Setup

Class Structure

  • Informal — Ask questions at any time. Really!
  • Collaboration is encouraged - please spend a minute introducing yourself to your neighbors!

Prerequisites

This is an intermediate R course:

  • Assumes working knowledge of R
  • Relatively fast-paced

Launch an R session

Start RStudio and create a new project:

  • On Windows click the start button and search for RStudio. On Mac RStudio will be in your applications folder.
  • In Rstudio go to File -> New Project.
  • Choose Existing Directory and browse to the workshop materials directory on your desktop.
  • Choose File -> Open File and select the file with the word “BLANK” in the name.

Packages

You should have already installed the tidyverse and rmarkdown packages onto your computer before the workshop — see R Installation. Now let’s load these packages into the search path of our R session.

library(tidyverse)
library(rmarkdown)

The ggplot2 package is contained within tidyverse, but we also want to install two additional packages, scales and ggrepel, which provide additional functionality.

# install.packages("scales")
library(scales)

# install.packages("ggrepel") 
library(ggrepel)

Goals

We will learn about the grammar of graphics — a system for understanding the building blocks of a graph — using the ggplot2 package. In particular, we’ll learn about:

  1. Basic plots, aesthetic mapping and inheritance
  2. Tailoring statistical transformations to particular plots
  3. Modifying scales to change axes and add labels
  4. Faceting to create many small plots
  5. Changing plot themes

Why ggplot2?

ggplot2 is a package within in the tidyverse suite of packages. Advantages of ggplot2 include:

  • consistent underlying grammar of graphics (Wilkinson, 2005)
  • very flexible — plot specification at a high level of abstraction
  • theme system for polishing plot appearance
  • many users, active mailing list

That said, there are some things you cannot (or should not) do with ggplot2:

  • 3-dimensional graphics (see the rgl package)
  • Graph-theory type graphs (nodes/edges layout; see the igraph package)
  • Interactive graphics (see the ggvis package)

What is the Grammar Of Graphics?

The basic idea: independently specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include the following (bold denotes essential elements):

  • data
  • aesthetic mapping
  • geometric object
  • statistical transformations
  • scales
  • coordinate system
  • position adjustments
  • faceting
  • themes

By the end of this workshop, you should understand what these building blocks do and how to use them to create the following plot:

ggplot2 VS base graphics

Compared to base graphics, ggplot2

  • is more verbose for simple / canned graphics
  • is less verbose for complex / custom graphics
  • does not have methods (data should always be in a data.frame)
  • has sensible defaults for generating legends

Geometric objects & aesthetics

Aesthetic mapping

In ggplot land aesthetic means “something you can see”. Examples include:

  • position (i.e., on the x and y axes)
  • color (“outside” color)
  • fill (“inside” color)
  • shape (of points)
  • linetype
  • size

Each type of geom accepts only a subset of all aesthetics; refer to the geom help pages to see what mappings each geom accepts. Aesthetic mappings are set with the aes() function.

Geometric objects (geom)

Geometric objects are the actual marks we put on a plot. Examples include:

  • points (geom_point(), for scatter plots, dot plots, etc.)
  • lines (geom_line(), for time series, trend lines, etc.)
  • boxplot (geom_boxplot(), for boxplots!)

A plot must have at least one geom; there is no upper limit. You can add a geom to a plot using the + operator.

Each geom_ has a particular set of aesthetic mappings associated with it. Some examples are provided below, with required aesthetics in bold and optional aesthetics in plain text:

geom_ Usage Aesthetics
geom_point() Scatter plot x,y,alpha,color,fill,group,shape,size,stroke
geom_line() Line plot x,y,alpha,color,linetype,size
geom_bar() Bar chart x,y,alpha,color,fill,group,linetype,size
geom_boxplot() Boxplot x,lower,upper,middle,ymin,ymax,alpha,color,fill
geom_density() Density plot x,y,alpha,color,fill,group,linetype,size,weight
geom_smooth() Conditional means x,y,alpha,color,fill,group,linetype,size,weight
geom_label() Text x,y,label,alpha,angle,color,family,fontface,size

You can get a list of all available geometric objects and their associated aesthetics at https://ggplot2.tidyverse.org/reference/. Or, simply type geom_<tab> in any good R IDE (such as Rstudio) to see a list of functions starting with geom_.

Points (scatterplot)

Now that we know about geometric objects and aesthetic mapping, we can make a ggplot(). geom_point() requires mappings for x and y, all others are optional.

Example data: housing prices

Let’s look at housing prices.

housing <- read_csv("dataSets/landdata-states.csv")
head(housing[1:5]) # view first 5 columns
## # A tibble: 6 x 5
##   State region  Date Home_Value Structure_Cost
##   <chr> <chr>  <dbl>      <dbl>          <dbl>
## 1 AK    West   2010.     224952         160599
## 2 AK    West   2010.     225511         160252
## 3 AK    West   2010.     225820         163791
## 4 AK    West   2010      224994         161787
## 5 AK    West   2008      234590         155400
## 6 AK    West   2008.     233714         157458
# create a subset for 1st quarter 2001
hp2001Q1 <- housing %>% filter(Date == 2001.25)

Step 1: create a blank canvas by specifying data:

ggplot(data = hp2001Q1)

Step 2: specify aesthetic mappings (how you want to map variables to visual aspects):

# here we map "Land_Value" and "Structure_Cost" to the x- and y-axes.
ggplot(data = hp2001Q1, mapping = aes(x = Land_Value, y = Structure_Cost))

Step 3: add new layers of geometric objects that will show up on the plot:

# here we use geom_point() to add a layer with point (dot) elements 
# as the geometric shapes to represent the data.
ggplot(data = hp2001Q1, mapping = aes(x = Land_Value, y = Structure_Cost)) +
  geom_point()

Lines (prediction line)

A plot constructed with ggplot() can have more than one geom. In that case the mappings established in the ggplot() call are plot defaults that can be added to or overridden — this is referred to as aesthetic inheritance. Our plot could use a regression line:

# get predicted values from a linear regression
hp2001Q1$pred_SC <- lm(Structure_Cost ~ log(Land_Value), data = hp2001Q1) %>%
  predict()

p1 <- ggplot(hp2001Q1, aes(x = log(Land_Value), y = Structure_Cost))

p1 + geom_point(aes(color = Home_Value)) + # values for x and y are inherited from the ggplot() call above
  geom_line(aes(y = pred_SC)) # add predicted values to the plot overriding the y values from the ggplot() call above

Smoothers

Not all geometric objects are simple shapes; the smooth geom includes a line and a ribbon.

p1 +
  geom_point(aes(color = Home_Value)) +
  geom_smooth()

Text (label points)

Each geom accepts a particular set of mappings; for example geom_text() accepts a label mapping.

p1 + 
  geom_text(aes(label = State), size = 3)

But what if we want to include points and labels? We can use geom_text_repel() to keep labels from overlapping the points and each other.

p1 + 
  geom_point() + 
  geom_text_repel(aes(label = State), size = 3)

Aesthetic mapping VS assignment

  1. Variables are mapped to aesthetics inside the aes() function.
p1 +
  geom_point(aes(size = Home_Value))

  1. Constants are assigned to aesthetics outside the aes() call
p1 +
  geom_point(size = 2)

This sometimes leads to confusion, as in this example:

p1 +
  geom_point(aes(size = 2),# incorrect! 2 is not a variable
             color="red") # this is fine -- all points red

Mapping variables to other aesthetics

Other aesthetics are mapped in the same way as x and y in the previous example.

p1 +
  geom_point(aes(color = Home_Value, shape = region))

Exercise 0

The data for the exercises is available in the dataSets/EconomistData.csv file. Read it in with

dat <- read_csv("dataSets/EconomistData.csv")

Original sources for these data are http://www.transparency.org/content/download/64476/1031428 http://hdrstats.undp.org/en/indicators/display_cf_xls_indicator.cfm?indicator_id=103106&lang=en

These data consist of Human Development Index and Corruption Perception Index scores for several countries.

  1. Create a scatter plot with CPI on the x axis and HDI on the y axis.
##
  1. Color the points in the previous plot blue.
##
  1. Map the color of the the points to Region.
##
  1. Keeping color mapped to Region, make the points bigger by setting size to 2
##
  1. Keeping color mapped to Region, map the size of the points to HDI_Rank
##
Click for Exercise 0 Solution
  1. Create a scatter plot with CPI on the x axis and HDI on the y axis.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point()

  1. Color the points in the previous plot blue.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point(color = "blue")

  1. Map the color of the the points to Region.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point(aes(color = Region))

  1. Keeping color mapped to Region, make the points bigger by setting size to 2.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point(aes(color = Region), size = 2)

  1. Keeping color mapped to Region, map the size of the points to HDI_Rank.
ggplot(dat, aes(x = CPI, y = HDI)) +
geom_point(aes(color = Region, size =  HDI_Rank))

Statistical transformations

Why transform data?

Some plot types (such as scatterplots) do not require transformations; each point is plotted at x and y coordinates equal to the original value. Other plots, such as boxplots, histograms, prediction lines etc. require statistical transformations:

  • for a boxplot the y values must be transformed to the median and 1.5(IQR)
  • for a smoother the y values must be transformed into predicted values

Each geom has a default statistic, but these can be changed. For example, the default statistic for geom_histogram() is stat_bin():

args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, na.rm = FALSE, orientation = NA, 
##     show.legend = NA, inherit.aes = TRUE) 
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL, 
##     breaks = NULL, closed = c("right", "left"), pad = FALSE, 
##     na.rm = FALSE, orientation = NA, show.legend = NA, inherit.aes = TRUE) 
## NULL

Here is a list of geoms and their default statistics https://ggplot2.tidyverse.org/reference/

Setting arguments

Arguments to stat_ functions can be passed through geom_ functions. This can be slightly annoying because in order to change them you have to first determine which stat the geom uses, then determine the arguments to that stat.

For example, here is the default histogram of Home_Value:

p2 <- ggplot(housing, aes(x = Home_Value))
p2 + geom_histogram()

We can change the binning scheme by passing the binwidth argument to the stat_bin() function:

p2 + geom_histogram(stat = "bin", binwidth = 4000)

Changing the transformation

Sometimes the default statistical transformation is not what you need. This is often the case with pre-summarized data:

housing_sum <- 
  housing %>%
  group_by(State) %>%
  summarize(Home_Value_Mean = mean(Home_Value)) %>%
  ungroup()

head(housing_sum)
## # A tibble: 6 x 2
##   State Home_Value_Mean
##   <chr>           <dbl>
## 1 AK            147385.
## 2 AL             92545.
## 3 AR             82077.
## 4 AZ            140756.
## 5 CA            282808.
## 6 CO            158176.
ggplot(housing_sum, aes(x = State, y = Home_Value_Mean)) + 
  geom_bar()

## Error: stat_count() must not be used with a y aesthetic.

What is the problem with the previous plot? Basically we take binned and summarized data and ask ggplot to bin and summarize it again (remember, geom_bar() defaults to stat = stat_count; obviously this will not work. We can fix it by telling geom_bar() to use a different statistical transformation function. The identity function returns the same output as the input.

ggplot(housing_sum, aes(x = State, y = Home_Value_Mean)) + 
  geom_bar(stat = "identity")

Exercise 1

  1. Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
##
  1. Overlay a smoothing line on top of the scatter plot using geom_smooth().
##
  1. Make the smoothing line in geom_smooth() less smooth. Hint: see ?loess.
##
  1. Change the smoothing line in geom_smooth() to use a linear model for the predictions. Hint: see ?stat_smooth.
##
  1. BONUS 1: Allow the smoothing line created in the last plot to vary across the levels of Region. Hint: map Region to the color and fill aesthetics.
##
  1. BONUS 2: Overlay a loess (method = "loess") smoothing line on top of the scatter plot using geom_line(). Hint: change the statistical transformation.
##
Click for Exercise 1 Solution
  1. Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point()

  1. Overlay a smoothing line on top of the scatter plot using geom_smooth().
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth()

  1. Make the smoothing line in geom_smooth() less smooth. Hint: see ?loess.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth(span = .4)

  1. Change the smoothing line in geom_smooth() to use a linear model for the predictions. Hint: see ?stat_smooth.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth(method = "lm")

  1. BONUS 1: Allow the smoothing line created in the last plot to vary across the levels of Region. Hint: map Region to the color and fill aesthetics.
ggplot(dat, aes(x = CPI, y = HDI, color = Region, fill = Region)) +
  geom_point() +
  geom_smooth(method = "lm")

  1. BONUS 2: Overlay a loess (method = "loess") smoothing line on top of the scatter plot using geom_line(). Hint: change the statistical transformation.
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_line(stat = "smooth", method = "loess")

Scales

Controlling aesthetic mapping

Aesthetic mapping (i.e., with aes()) only says that a variable should be mapped to an aesthetic. It doesn’t say how that should happen. For example, when mapping a variable to shape with aes(shape = x) you don’t say what shapes should be used. Similarly, aes(color = y) doesn’t say what colors should be used. Also, aes(size = z) doesn’t say what sizes should be used. Describing what colors/shapes/sizes etc. to use is done by modifying the corresponding scale. In ggplot2 scales include

  • position
  • color and fill
  • size
  • shape
  • line type

Scales are modified with a series of functions using a scale_<aesthetic>_<type> naming scheme. Try typing scale_<tab> to see a list of scale modification functions.

Common scale arguments

The following arguments are common to most scales in ggplot2:

  • name: the axis or legend title
  • limits: the minimum and maximum of the scale
  • breaks: the points along the scale where labels should appear
  • labels: the labels that appear at each break

Specific scale functions may have additional arguments; for example, the scale_color_continuous() function has arguments low and high for setting the colors at the low and high end of the scale.

Scale modification examples

Start by constructing a dotplot showing the distribution of home values by Date and State.

p4 <- ggplot(housing, aes(x = State, y = Home_Price_Index)) + 
    geom_point(aes(color = Date), alpha = 0.5, size = 1.5,
               position = position_jitter(width = 0.25, height = 0))

Now modify the breaks for the color scales

p4 + 
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"))

Next change the low and high values to blue and red:

p4 +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = "blue", high = "red")

Using different color scales

ggplot2 has a wide variety of color scales; here is an example using scale_color_gradient2() to interpolate between three different colors.

p4 +
  scale_color_gradient2(name="",
                        breaks = c(1976, 1994, 2013),
                        labels = c("'76", "'94", "'13"),
                        low = "blue",
                        high = "red",
                        mid = "gray60",
                        midpoint = 1994)

Available scales

  • Partial combination matrix of available scales
scale_ Types Examples
scale_color_ identity scale_fill_continuous()
scale_fill_ manual scale_color_discrete()
scale_size_ continuous scale_size_manual()
discrete scale_size_discrete()
scale_shape_ discrete scale_shape_discrete()
scale_linetype_ identity scale_shape_manual()
manual scale_linetype_discrete()
scale_x_ continuous scale_x_continuous()
scale_y_ discrete scale_y_discrete()
reverse scale_x_log()
log scale_y_reverse()
date scale_x_date()
datetime scale_y_datetime()

Note that in RStudio you can type scale_ followed by tab to get the whole list of available scales. For a complete list of available scales see https://ggplot2.tidyverse.org/reference/

Exercise 2

  1. Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate Region.
##
  1. Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of HDI). Hint: see ?scale_x_continous, ?scale_y_continuous, and ?scale_color_discrete.
##
  1. Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual. NOTE: you can specify color by name (e.g., “blue”) or by “Hex value” — see https://www.color-hex.com/.
##
Click for Exercise 2 Solution
  1. Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate Region.
ggplot(dat, aes(x = CPI, y = HDI, color = Region)) +
  geom_point()

  1. Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of HDI).
ggplot(dat, aes(x = CPI, y = HDI, color = Region)) +
  geom_point() +
  scale_x_continuous(name = "Corruption Perception Index") +
  scale_y_continuous(name = "Human Development Index") +
  scale_color_discrete(name = "Region of the world")

  1. Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual. NOTE: you can specify color by name (e.g., “blue”) or by “Hex value” — see https://www.color-hex.com/.
ggplot(dat, aes(x = CPI, y = HDI, color = Region)) +
  geom_point() +
  scale_x_continuous(name = "Corruption Perception Index") +
  scale_y_continuous(name = "Human Development Index") +
  scale_color_manual(name = "Region of the world",
                     values = c("red", "green", "blue", "orange", "grey", "brown"))

Faceting

What is faceting?

  • Faceting is ggplot2 parlance for small multiples
  • The idea is to create separate graphs for subsets of data
  • ggplot2 offers two functions for creating small multiples:
    1. facet_wrap(): define subsets as the levels of a single grouping variable
    2. facet_grid(): define subsets as the crossing of two grouping variables
  • Facilitates comparison among plots, not just of geoms within a plot

What is the trend in housing prices in each state?

Start by using a technique we already know; map State to color:

p5 <- ggplot(housing, aes(x = Date, y = Home_Value))
p5 + geom_line(aes(color = State))

There are two problems here; there are too many states to distinguish each one by color, and the lines obscure one another.

Faceting to the rescue

We can remedy the deficiencies of the previous plot by faceting by State rather than mapping State to color.

p5 <- p5 + geom_line() +
   facet_wrap(~ State, ncol = 10)
p5

Themes

What are themes?

The ggplot2 theme system handles non-data plot elements such as:

  • Axis label properties (e.g., font, size, color, etc.)
  • Plot background
  • Facet label background
  • Legend appearance

Built-in themes include:

  • theme_gray() (default)
  • theme_bw()
  • theme_classic()
p5 + theme_linedraw()

p5 + theme_light()

You can see a list of available built-in themes here https://ggplot2.tidyverse.org/reference/

Overriding theme defaults

Specific theme elements can be overridden using theme(). For example:

# theme(thing_to_modify = modifying_function(arg1, arg2))

p5 + theme_minimal() +
  theme(text = element_text(color = "red"))

All theme options are documented in ?theme. We can also see the existing default values using:

theme_get()

Creating & saving new themes

You can create new themes, as in the following example:

theme_new <- theme_bw() +
  theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
        text = element_text(size = 12, color = "ivory"),
        axis.text.y = element_text(colour = "purple"),
        axis.text.x = element_text(colour = "red"),
        panel.background = element_rect(fill = "pink"),
        strip.background = element_rect(fill = muted("orange")))

p5 + theme_new

Saving plots

We can save a plot to either a vector (e.g., pdf, eps, ps, svg) or raster (e.g., jpg, png, tiff, bmp, wmf) graphics file using the ggsave() function:

ggsave(filename = "myplot.pdf", plot = p5, device = "pdf", height = 6, width = 6, units = "in")

The #1 FAQ

Map aesthetic to different columns

The most frequently asked question goes something like this: I have two variables in my data.frame, and I’d like to plot them as separate points, with different color depending on which variable it is. How do I do that?

Wrong

Fixing, rather than mapping, the color aesthetic:

  1. Produces verbose code when using many colors
  2. Results in no legend being produced
  3. Means you cannot change color scales
housing_byyear <- 
  housing %>%
  group_by(Date) %>%
  summarize(Home_Value_Mean = mean(Home_Value),
            Land_Value_Mean = mean(Land_Value)) %>%
  ungroup()

ggplot(housing_byyear, aes(x=Date)) +
  geom_line(aes(y=Home_Value_Mean), color="red") +
  geom_line(aes(y=Land_Value_Mean), color="blue")

Right

To avoid these pitfalls, we need to map our data to the color aesthetic. We can do this by reshaping our data from wide format to long format. Here is the logic behind this process:

Here’s the code that implements this transformation:

home_land_byyear <- 
    housing_byyear %>%
    pivot_longer(cols = c(Home_Value_Mean, Land_Value_Mean),
                 names_to = "type",
                 values_to = "value")
                          
ggplot(home_land_byyear, aes(x = Date, y = value, color = type)) +
  geom_line()

Exercise 3

For this exercise, we’re going to use the built-in midwest dataset:

data("midwest", package = "ggplot2")
head(midwest)
## # A tibble: 6 x 28
##     PID county state  area poptotal popdensity popwhite popblack popamerindian popasian popother percwhite percblack percamerindan percasian percother popadults perchsd percollege percprof poppovertyknown
##   <int> <chr>  <chr> <dbl>    <int>      <dbl>    <int>    <int>         <int>    <int>    <int>     <dbl>     <dbl>         <dbl>     <dbl>     <dbl>     <int>   <dbl>      <dbl>    <dbl>           <int>
## 1   561 ADAMS  IL    0.052    66090      1271.    63917     1702            98      249      124      96.7     2.58          0.148    0.377     0.188      43298    75.1       19.6     4.36           63628
## 2   562 ALEXA… IL    0.014    10626       759      7054     3496            19       48        9      66.4    32.9           0.179    0.452     0.0847      6724    59.7       11.2     2.87           10529
## 3   563 BOND   IL    0.022    14991       681.    14477      429            35       16       34      96.6     2.86          0.233    0.107     0.227       9669    69.3       17.0     4.49           14235
## 4   564 BOONE  IL    0.017    30806      1812.    29344      127            46      150     1139      95.3     0.412         0.149    0.487     3.70       19272    75.5       17.3     4.20           30337
## 5   565 BROWN  IL    0.018     5836       324.     5264      547            14        5        6      90.2     9.37          0.240    0.0857    0.103       3979    68.9       14.5     3.37            4815
## 6   566 BUREAU IL    0.05     35688       714.    35157       50            65      195      221      98.5     0.140         0.182    0.546     0.619      23444    76.6       18.9     3.28           35107
## # … with 7 more variables: percpovertyknown <dbl>, percbelowpoverty <dbl>, percchildbelowpovert <dbl>, percadultpoverty <dbl>, percelderlypoverty <dbl>, inmetro <int>, category <chr>
  1. Create a scatter plot with area on the x axis and the log of poptotal on the y axis.
##
  1. Within the geom_point() call, map color to state, map size to the log of popdensity, and fix transparency (alpha) to 0.3.
##
  1. Add a smoother and turn off plotting the confidence interval. Hint: see the se argument to geom_smooth().
##
  1. Facet the plot by state. Set the scales argument to facet_wrap() to allow separate ranges for the x-axis.
##
  1. Change the default color scale to use the discrete RColorBrewer palette called Set1. Hint: see ?scale_color_brewer.
##
  1. BONUS: Change the default theme to theme_bw() and modify it so that the axis text and facet label background are blue. Hint: see ?theme and especially axis.text and strip.background.
##
Click for Exercise 3 Solution
  1. Create a scatter plot with area on the x axis and the log of poptotal on the y axis.
p6 <- ggplot(midwest, aes(x = area, y = log(poptotal))) 
p6 + geom_point()

  1. Within the geom_point() call, map color to state, map size to the log of popdensity, and fix transparency (alpha) to 0.3.
p6 <- p6 + geom_point(aes(color = state, size = log(popdensity)), alpha = 0.3)
  1. Add a smoother and turn off plotting the confidence interval. Hint: see the se argument to geom_smooth().
p6 <- p6 + geom_smooth(method = "loess", se = FALSE)
  1. Facet the plot by state. Set the scales argument to facet_wrap() to allow separate ranges for the x-axis.
p6 <- p6 + facet_wrap(~ state, scales = "free_x")
  1. Change the default color scale to use the discrete RColorBrewer palette called Set1. Hint: see ?scale_color_brewer.
p6 <- p6 + scale_color_brewer(palette = "Set1")
  1. BONUS: Change the default theme to theme_bw() and modify it so that the axis text and facet label background are blue. Hint: see ?theme and especially axis.text and strip.background.
p6 <- p6 + theme_bw() +
    theme(axis.title = element_text(color = "blue", face = "bold"),
          strip.background = element_rect(fill = "yellow"))

Here’s the complete code for the Exercise 3 plot:

p6 <- ggplot(midwest, aes(x = area, y = log(poptotal))) +
    geom_point(aes(color = state, size = log(popdensity)), alpha = 0.3) +
    geom_smooth(method = "loess", se = FALSE) +
    facet_wrap(~ state, scales = "free_x") +
    scale_color_brewer(palette = "Set1") +
    theme_bw() +
    theme(axis.title = element_text(color = "blue", face = "bold"),
         strip.background = element_rect(fill = "yellow"))

p6