worksheet-5.R

## Getting started

library(dplyr)
library(...)
animals <- read.csv(..., na.strings = '') %>%
  filter(!is.na(species_id), !is.na(sex), !is.na(weight))

## Constructing layered graphics in ggplot

ggplot(...,
       ...) +
  ...

ggplot(data = animals,
       aes(x = species_id, y = weight)) +
  ...

ggplot(data = animals,
       aes(x = species_id, y = weight)) +
  geom_boxplot() ...
  geom_point(...,
             ...,
             ...)

ggplot(data = animals,
       aes(x = species_id, y = weight, ...)) +
  geom_boxplot() +
  geom_point(stat = 'summary',
             fun.y = 'mean')

## Exercise 1

...

## Adding a regression line

levels(animals$sex) <- c('Female', 'Male')
animals_dm <- filter(animals, ...)
ggplot(...,
       aes(x = year, y = weight)) +
  geom_point(...,
             size = 3,
             stat = 'summary',
             fun.y = 'mean') +
  ...

ggplot(data = animals_dm,
       aes(x = year, y = weight)) + 
  geom_point(aes(shape = sex),
             size = 3,
             stat = 'summary',
             fun.y = 'mean') +
  geom_smooth(...)

ggplot(data = animals_dm,
       aes(...,
           ...,
           ...) + 
  geom_point(aes(shape = sex),
             size = 3,
	           stat = 'summary',
	           fun.y = 'mean') +
  geom_smooth(method = 'lm')

# Storing and re-plotting

year_wgt <- ggplot(data = animals_dm,
                   aes(x = year,
                       y = weight,
                       color = sex)) +
  geom_point(aes(shape = sex),
             size = 3,
             stat = 'summary',
             fun.y = 'mean') +
  geom_smooth(method = 'lm')

year_wgt +
  ...
                     
year_wgt <- year_wgt +
  scale_color_manual(...)
year_wgt

## Exercise 2

...

## Axes, labels and themes

histo <- ggplot(data = animals_dm,
                aes(x = weight, fill = sex)) +
  geom_...
histo

histo <- histo +
  ...(title = 'Dipodomys merriami weight distribution',
       x = 'Weight (g)',
       y = 'Count') +
  scale_x_continuous(limits = c(20, 60),
                     breaks = c(20, 30, 40, 50, 60))
histo

histo <- histo +
  theme_bw() +
  theme(legend.position = c(0.2, 0.5),
        plot.title = ...,
        ... = element_text(...),
        ... = element_text(size = 13, vjust = 0))
histo

## Facets

animals_common <- filter(animals, ...)
ggplot(data = ...,
       ...) +
  geom_histogram() +
  ...
  labs(title = "Weight of most common species",
       x = "Count",
       y = "Weight (g)")

ggplot(data = animals_common,
       aes(x = weight)) +
  geom_histogram(...,
                 ...) +
  geom_histogram() +
  facet_wrap( ~ species_id) +
  labs(title = "Weight of most common species",
       x = "Count",
       y = "Weight (g)")

ggplot(data = animals_common,
       aes(x = weight, ...)) +
  geom_histogram(...) +
  facet_wrap( ~ species_id) +
  labs(title = "Weight of most common species",
       x = "Count",
       y = "Weight (g)") +
  guides(fill = FALSE)		

## Exercise 3

...