## In-class worksheet 4

Jan 25, 2018

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 library

## 1. 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:
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.

# R code goes here

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().

# R code goes here

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?

# R code goes here

## 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()

# R code goes here

Instead of geom_histogram(), now use geom_density() and fill the area under the curves by species identity.

# R code goes here

Now make the areas under the curve partially transparent, so the overlap of the various distributions becomes clearly visible.

# R code goes here

## 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
##                      <chr> <int>  <int>  <int>  <dbl> <int> <dbl> <dbl>
## 1                        $1971 121 NA 6.4 348 4.5 4.5 ## 2$1000 a Touchdown  1939     71     NA    6.0    20   0.0  14.5
## 3   $21 a Day Once a Month 1941 7 NA 8.2 5 0.0 0.0 ## 4$40,000  1996     70     NA    8.2     6  14.5   0.0
## 5 $50,000 Climax Show, The 1975 71 NA 3.4 17 24.5 4.5 ## 6$pent  2000     91     NA    4.3    45   4.5   4.5
## # ... with 16 more variables: r3 <dbl>, r4 <dbl>, r5 <dbl>, r6 <dbl>,
## #   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.

# R code goes here

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().

# R code goes here

Now zoom in to movies that are between 1 and 20 minutes long, using xlim() instead of scale_x_log10().

# R code goes here

## 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.

# R code goes here

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.

# R code goes here

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.

# R code goes here