In-class worksheet 4
Jan 30, 2020
We will again be working with the ggplot2 package, so we need to load it:
library(ggplot2) # load ggplot2 library
theme_set(theme_bw(base_size = 12)) # set the default plot theme for the ggplot2 library1. Bar plots
The bacteria data set contains data from tests of the presence of the bacterium H. influenzae in children with otitis media in the Northern Territory of Australia. We are interested in two columns of this data set: presence reports the presence (y) or absence (n) of the bacterium. treatment reports the treatment, which was placebo, drug, or drug+ (drug plus high adherence).
# download the bacteria data set:
bacteria <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/bacteria.csv")
head(bacteria)## presence ap hilo week ID treatment
## 1 y p hi 0 X01 placebo
## 2 y p hi 2 X01 placebo
## 3 y p hi 4 X01 placebo
## 4 y p hi 11 X01 placebo
## 5 y a hi 0 X02 drug+
## 6 y a hi 2 X02 drug+Using geom_bar(), make a bar plot that shows the absolute number of cases with or without the bacterium, stacked on top of each other, for each treatment.
ggplot(bacteria, aes(x = treatment, fill = presence)) +
geom_bar()
Now modify the plot so that bars representing the absolute number of cases with or without the bacterium are shown side-by-side. Hint: This requires the argument position='dodge' in geom_bar().
ggplot(bacteria, aes(x = treatment, fill = presence)) +
geom_bar(position = 'dodge')
Now modify the plot so that bars represent the relative number of cases with or without the bacterium. What is the appropriate position option in geom_bar() to achieve this effect?
ggplot(bacteria, aes(x = treatment, fill = presence)) +
geom_bar(position = 'fill')
2. Histograms and density plots
Make a histogram plot of sepal lengths in the iris data set, using the default histogram settings. Then make two more such plots, with different bin widths. Use geom_histogram()
# default settings
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# wider bins
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(binwidth = 0.2)
# even wider bins
ggplot(iris, aes(x = Sepal.Length)) +
geom_histogram(binwidth = 0.4)
Instead of geom_histogram(), now use geom_density() and fill the area under the curves by species identity.
ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density()
Now make the areas under the curve partially transparent, so the overlap of the various distributions becomes clearly visible.
ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density(alpha = 0.7)
3. Scales
The movies data set provided in the package ggplot2movies containes data from the internet movie database (IMDB) about 28819 different movies. It contains information such as the length of the movie, the year the movie was released, number of votes the movie has received on the IMDB, and so on. To use the data set, you first need to load it in:
library(ggplot2movies)
head(movies)## # A tibble: 6 x 24
## title year length budget rating votes r1 r2 r3 r4 r5 r6
## <chr> <int> <int> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 $ 1971 121 NA 6.4 348 4.5 4.5 4.5 4.5 14.5 24.5
## 2 $100… 1939 71 NA 6 20 0 14.5 4.5 24.5 14.5 14.5
## 3 $21 … 1941 7 NA 8.2 5 0 0 0 0 0 24.5
## 4 $40,… 1996 70 NA 8.2 6 14.5 0 0 0 0 0
## 5 $50,… 1975 71 NA 3.4 17 24.5 4.5 0 14.5 14.5 4.5
## 6 $pent 2000 91 NA 4.3 45 4.5 4.5 4.5 14.5 14.5 14.5
## # … with 12 more variables: r7 <dbl>, r8 <dbl>, r9 <dbl>, r10 <dbl>,
## # mpaa <chr>, Action <int>, Animation <int>, Comedy <int>, Drama <int>,
## # Documentary <int>, Romance <int>, Short <int>Now, using this data set, make a scatter plot of the number of votes (votes) vs. the length of the movie (length). Use a log scale for both the x and the y axis.
ggplot(movies, aes(y = votes, x = length)) +
geom_point() +
scale_x_log10() +
scale_y_log10() 
Now color the points by year and use a color gradient that goes from yellow to blue. You can change the color scale using scale_color_gradient().
ggplot(movies, aes(y = votes, x = length, color = year)) +
geom_point() +
scale_x_log10() +
scale_y_log10() +
scale_color_gradient(low = "gold", high = "blue")
Now zoom in to movies that are between 1 and 20 minutes long, using xlim() instead of scale_x_log10().
ggplot(movies, aes(y = votes, x = length, color = year)) +
geom_point() +
xlim(1, 20) +
scale_y_log10() +
scale_color_gradient(low = "gold", high = "blue")## Warning: Removed 51221 rows containing missing values (geom_point).
4. If this was easy
Take the log-log plot of votes vs. length from the movies data set and plot only movies that are between 1 and 20 minutes long, but keep the log scale.
ggplot(movies, aes(y = votes, x = length, color = year)) +
geom_point() +
scale_x_log10(limits = c(1, 20)) +
scale_y_log10() +
scale_color_gradient(low = "gold", high = "blue")## Warning: Removed 51221 rows containing missing values (geom_point).
Make a log-log plot of votes vs. length from the movies data set, faceted by year. Plot a trend line onto each facet, without confidence band.
ggplot(movies, aes(y = votes, x = length)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, size = 1.5, color = "tomato") +
scale_x_log10() +
scale_y_log10() +
facet_wrap(~year)
Go back to the bacteria dataset, make a bar plot that shows the total number of cases within each treatment, and plot the number of such cases on top of each bar.
ggplot(bacteria, aes(x = treatment)) +
geom_bar() +
stat_count(
aes(label = stat(count)),
geom = "text",
vjust = -1,
size = 5
) +
ylim(0, 120)
To plot the number of cases within each treatment, we need to count them. That count is done using stat_count(), which is used by default by geom_bar(). However, to actually plot these numbers, we need to have direct access to them, and therefore we have to call stat_count() explicitly. In the aes() call inside stat_count, the expression stat(count) indicates that we want to map the count column generated by the stat, instead of a column present in the original dataset.