3 Week3: Data Visualization II

3.1 The Review of Key Concepts in ggplot2

  • The data is what we want to visualize. It consists of variables, which are stored as columns in a data frame.

  • Geoms are the geometric objects that are drawn to represent the data, such as bars, lines, and points.

  • Aesthetic attributes, or aesthetics, are visual properties of geoms, such as x and y position, line color, point shapes, etc.

  • There are mappings from data values to aesthetics.

  • Scales control the mapping from the values in the data space to values in the aesthetic space. A continuous y scale maps larger numerical values to vertically higher positions in space.

  • Guides show the viewer how to map the visual properties back to the data space. The most commonly used guides are the tick marks and labels on an axis.

Notes. This review came from the appendix of Winston Chang’s R Graphics Cookbook. In our class, I just introduced some key concepts and examples in ggplot2. If you want to further develop your skill for ggplot2, I strongly recommend you to read Chang’s book.

Notes. The gcookbook package contains data sets for many examples in Chang’s book.

3.2 Annotations

  • Once you create your plot using data, you can add extra contextual information (e.g., text, lines).

3.2.1 Adding Text Annotations

  • faithful data
# faithful is a built-in data in R
# ?faithful in your console will display the help documentation for the data
# faithful contains waiting time between eruptions and the duration of the eruption for the Old Faithful geyser in Yellowstone National Park, Wyoming, USA.
# head() will display the first six observations in your screen
head(faithful)
##   eruptions waiting
## 1     3.600      79
## 2     1.800      54
## 3     3.333      74
## 4     2.283      62
## 5     4.533      85
## 6     2.883      55
  • Let’s create the scatter plot between eruptions (x-axis) and waiting (y-axis)
# A variable name `p` points to (or binds or references) the ggplot object
# Simply, we just give a name `p` to the ggplot object
# <- is an assignment operator in R
# e.g., a <- 10  # a variable name `a` points to the value `10`
p <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  geom_point()
p

  • The annotate() function can be used to add any type of geometric object. In the example below, we add text to a plot.
# we are adding another layers to the `p` object
# Where do the values 3, 48, 4.5, and 66 come from? 
p +
  annotate("text", x = 3, y = 48, label = "Group 1") +
  annotate("text", x = 4.5, y = 66, label = "Group 2")

3.2.2 Adding Lines

# load the gcookbook package onto the memory
library(gcookbook)
  • heightweight data
# How do you get the information of the heighweight data? 
head(heightweight)
##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0
# How do you read `colour = sex` in the code below? 
# Explain the role of `colour = sex`
hw_plot <- ggplot(heightweight, aes(x = ageYear, y = heightIn, colour = sex)) +
  geom_point()
hw_plot

  • geom_hline(yintercept = y) adds horizontal line at y
# Add horizontal lines
# how do you get more detailed information about the geom_hline() function? 
hw_plot +
  geom_hline(yintercept = 60)

  • geom_vline(xintercept = x) adds horizontal line at x
# Add vertical lines
hw_plot +
  geom_vline(xintercept = 14)

  • You can do both
# Add horizontal and vertical lines
hw_plot +
  geom_hline(yintercept = 60) +
  geom_vline(xintercept = 14)

  • geom_abline(intercept = i, slope = s) adds horizontal line with y = i + s*x
hw_plot +
  geom_abline(intercept = 37.4, slope = 1.75)

mpg
## Warning: `...` is not empty.
## 
## We detected these problematic arguments:
## * `needs_dots`
## 
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?
## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l~ f        18    29 p     comp~
##  2 audi         a4         1.8  1999     4 manual~ f        21    29 p     comp~
##  3 audi         a4         2    2008     4 manual~ f        20    31 p     comp~
##  4 audi         a4         2    2008     4 auto(a~ f        21    30 p     comp~
##  5 audi         a4         2.8  1999     6 auto(l~ f        16    26 p     comp~
##  6 audi         a4         2.8  1999     6 manual~ f        18    26 p     comp~
##  7 audi         a4         3.1  2008     6 auto(a~ f        18    27 p     comp~
##  8 audi         a4 quat~   1.8  1999     4 manual~ 4        18    26 p     comp~
##  9 audi         a4 quat~   1.8  1999     4 auto(l~ 4        16    25 p     comp~
## 10 audi         a4 quat~   2    2008     4 manual~ 4        20    28 p     comp~
## # ... with 224 more rows

3.3 Axes

  • ggplot will display the axes with defaults that look good in most cases, but you might want to control, for example, the axis labels, the number and placement of tick marks, the tick mark labels, and so on.

3.3.1 Swapping X- and Y-Axes

  • You can’t read the tick labels
ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot()

  • coord_flip() flips the axes
ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  coord_flip()

ggplot(mpg, aes(x = hwy, y = model)) +
  geom_boxplot()

3.3.2 Setting the Position of Tick Marks

  • Often, we want to set the tick marks on the axis
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point()

  • breaks sets the tick marks.
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  scale_x_continuous(breaks = c(2,4,6)) +
  scale_y_continuous(breaks = c(15, 25, 35, 45))

3.3.3 Changing the Text of Tick Labels

  • labels sets the tick labels.
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  scale_x_continuous(breaks = c(2,4,6), labels = c("2 cylinders", "4 cylinders", "6 cylinders")) +
  scale_y_continuous(breaks = c(15, 25, 35, 45))

3.3.4 Changing the Appearance of Tick Labels

  • You can rotate your tick labels using theme().
ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot()

# rotate 30 degrees
ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 30))

# rotate 90 degrees
ggplot(mpg, aes(x = model, y = hwy)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 90))

3.4 Using Colors in Plots

3.4.1 Setting and Mapping the Colors of Objects

  • It is important to distinguish
    • setting aesthetics to a constant
    • mapping aesthetics to a variable
  • Setting aesthetics to a constant means you fix the value of aesthetics to a constant value.
# set the value of the color aesthetics to "blue" 
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(color = "blue") 

# set the value of the color aesthetics to "red" 
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(color = "red")

  • You can find many resources on the name of the color in R by typing “R color” in Google. Here is an example.

  • Mapping aesthetics to a variable means you want to use different colors depending on the value of the variable.

# map the value of the color aesthetics to the variable `drv`
# This is called an aesthetic mapping, and you need aes()
ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point(aes(color = drv))

3.4.2 Using a Different Palette for a Discrete Variable

  • To use different color scheme, color palettes are available from the RColorBrewer package.
# load RColorBrewer package to a memory
library(RColorBrewer)
# display.brewer.all() generates available palette
display.brewer.all()

library(gcookbook)
hw_splot <- ggplot(heightweight, aes(x = ageYear, y = heightIn, colour = sex)) +
  geom_point()
hw_splot

# "Oranges" palette
hw_splot + 
  scale_colour_brewer(palette = "Oranges")

# "Set3" palette
hw_splot + 
  scale_colour_brewer(palette = "Set3")

3.4.3 Using a Manually Defined Palette for a Discrete Variable

  • scale_colour_manual() sets the values of color
hw_splot +
  scale_colour_manual(values = c("red", "blue"))

3.4.4 Using a Manuallly Defined Palette for a Continuous Variable

hw_plot <- ggplot(heightweight, aes(x = ageYear, y = heightIn, colour = weightLb)) +
  geom_point(size = 3)
hw_plot

  • scale_colour_gradient() sets the low and high values of a color gradient
hw_plot +
  scale_colour_gradient(low = "yellow", high = "orange")

3.5 Legends

  • The PlantGrowth data are the results from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment conditions.
pg_plot <- ggplot(PlantGrowth, aes(x = group, y = weight, fill = group)) +
  geom_boxplot()
pg_plot

  • Use labs() and set the value of fill, colour, shape, or whatever aesthetic is appropriate for the legend
pg_plot + 
  labs(fill = "Condition")

  • In fact, labs() sets the title, subtitle, caption, x-axis label, y-axis label, and the title of the legend.
pg_plot + 
  labs(title = "Weight of Plants", 
       subtitle = "By Experimental Conditions", 
       caption = "source: PlantGrowth", 
       x = "Experimental Conditions", 
       y = "Weight (pounds)", 
       fill = "Condition")

  • Changing the position of the legend
# removing legend
pg_plot +
  theme(legend.position = "none")

# place the legend on the bottom
pg_plot +
  theme(legend.position = "bottom")

# place the legend on the left
pg_plot +
  theme(legend.position = "left")

# place the legend inside the plot
# The coordinate space starts at (0, 0) in the bottom left and goes to (1, 1) in the top right.
pg_plot +
  theme(legend.position = c(.8, .3))

3.6 Exercise

  • Replicate each plot by yourself

3.6.1 Exercise 3-1

  • Using the heightweight dataset in the gcookbook package, replicate the following plots
# Height and weight of school children
# head() displays the first six observations
head(heightweight)
##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0

  • Exercise 3-1-a. Replicate the plot above
ggplot(heightweight, aes(x = ageYear, y = heightIn, size = weightLb, color = sex)) +
  geom_point()

  • Often we want to set (or map) the transparency of points, especially when points overlaps. In that case, alpha controls the transparency of points. In this exercise, use alpha = 0.3.

  • Exercise 3-1-b. Replicate the above plot
ggplot(heightweight, aes(x = ageYear, y = heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3)

  • you can set the title, subtitle, x-axis label, y-axis label, and legend title using labs()

  • Exercise 3-1-c. Replicate the above plot
ggplot(heightweight, aes(x = ageYear, y = heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3) +
  labs(title = "Height and weight of school children", 
       subtitle = "Height vs Weight", 
       caption = "Source: heightweight", 
       x = "Age (year)", 
       y = "Height (inches)", 
       size = "Weight (Lb)", 
       color = "Gender"
  )

  • You may want to use themes. Use theme_classic().

  • Exercise 3-1-d. Replicate the above plot
ggplot(heightweight, aes(x = ageYear, y = heightIn, size = weightLb, color = sex)) +
  geom_point(alpha = 0.3) +
  labs(title = "Height and weight of school children", 
       subtitle = "Height vs Weight", 
       x = "Age (year)", 
       y = "Height (inches)", 
       size = "Weight (Lb)", 
       color = "Gender"
  ) + 
  theme_classic()

3.6.2 Exercise 3-2

  • Using the heightweight dataset in the gcookbook package, replicate the following plots
# Height and weight of school children
# head() displays the first six observations
head(heightweight)
##   sex ageYear ageMonth heightIn weightLb
## 1   f   11.92      143     56.3     85.0
## 2   f   12.92      155     62.3    105.0
## 3   f   12.75      153     63.3    108.0
## 4   f   13.42      161     59.0     92.0
## 5   f   15.92      191     62.5    112.5
## 6   f   14.25      171     62.5    112.0
  • geom_histogram() displays a histogram to display the distribution of a variable.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • Exercise 3-2-a. Replicate the above plot
ggplot(heightweight, aes(x = heightIn)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • The fill aesthetics control the inside color of a geometric object.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • Exercise 3-2-b. Replicate the above plot
ggplot(heightweight, aes(x = weightLb, fill = sex)) +
  geom_histogram() 
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • If you use color and fill aesthetics, you need scale_color_manual() and scale_fill_manual() to manually control the color and fill aesthetics. Depending on the aesthetic you used in 3-2-a, manually change the color of the female to orange and male to yellow.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

* Exercise 3-2-c. Replicate the above plot

ggplot(heightweight, aes(x = weightLb, fill = sex)) +
  geom_histogram(alpha = 0.4) + 
  scale_fill_manual(values = c("orange", "yellow"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • Again, add titles and apply theme_minimal()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • Exercise 3-2-d. Replicate the above plot
ggplot(heightweight, aes(x = weightLb, fill = sex)) +
  geom_histogram(alpha = 0.4) + 
  scale_fill_manual(values = c("orange", "yellow")) +
  labs(title = "A histogram of the weight of school children", 
       subtitle = "By gender", 
       x = "Weight (Lb)", 
       y = "Count", 
       fill = "Gender"
  ) + 
  theme_minimal()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.