ggplot(penguins)

ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm,
           y = body_mass_g))

ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm, 
           y = body_mass_g)) +
  geom_point()

ggplot(data = penguins, 
       mapping = aes(x = bill_length_mm, 
           y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm")

ggplot(data = penguins)

ggplot(data = penguins,
       aes(x = bill_length_mm,
           y = body_mass_g,
           color = island))

We can leave out the "mapping" word and just use aes .

Why is there no plot?

🤔 💭

Right !! We have not used a geom command yet!!

ggplot(data = penguins,
  aes(x = bill_length_mm,
      y = body_mass_g,
      color = island)) +  
geom_point() +
  ggtitle("A point geom with position, color aesthetics")

Note that the points are located by position coordinates on both x and y axis, and coloured by the island variable.

ggplot(data = penguins,
  aes(x = bill_length_mm,
      y = body_mass_g,
      color = island)) +  
geom_point(size = 4) +
  ggtitle("A point geom with position color and size aesthetics")

Note that the points are located by position coordinates on both x and y axis, and coloured by the island variable.

And we've fixed size = 4!

diamonds %>% 
  # Sample some 20% of the data
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat,
                 y = price))

Are the points all overlapping? Can we see them better?

diamonds %>% 
  # Sample some 20% of the data
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price),
             # alpha outside the aes() !!!
             alpha = 0.2) +
  labs(title = "Points plotted with Alpha")

Are the points all overlapping? Can we see them better?

ggplot(diamonds) + 
  geom_boxplot(aes(x = cut, y = price)) +
  labs(title = "Box Plot")

ggplot(diamonds) + 
  geom_boxplot(aes(x = cut,
                   y = price,
                   fill = cut)) +
  labs(title = "Box Plot")

ggplot(data = penguins)

ggplot(data = penguins) +  
       aes(x = species)

ggplot(data = penguins) +  
       aes(x = species) +
  geom_bar() +
  ggtitle("A bar geom with position and height aesthetics")

The bars are plotted with positions on the x-axis, defined by the species variable, and heights mapped to the y-axis.

How did the graph "know" the heights of the bars?

geom_bar has an internal count statistic computation. Many geom_s have internal computation that are accessible to programmers.

When using more than a pair of variables with a bar chart, we have a few more position options:

  ggplot(penguins,
           aes(x = species,
               fill = island))

When using more than a pair of variables with a bar chart, we have a few more position options:

ggplot(penguins,
           aes(x = species,
               fill = island)) + 
  geom_bar() +
  ggtitle(label = "A stacked bar chart")

The bars are coloured by the island variable and are stacked in position.

And here we use the dodge option:

ggplot(penguins,
           aes(x = species,
               fill = island)) + 
  geom_bar(position ="dodge") +
  ggtitle(label =
            "A dodged bar chart")

ggplot(penguins)

ggplot(penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g)

ggplot(penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  geom_point()

ggplot(penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  geom_point() +
  facet_wrap(~island) +
  ggtitle("A point geom graph with facets")

The graph has split into multiples, based on the number of islands.

ggplot(penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  geom_point()

What if we have even more "factor" variables? We have island and species...can we split further?

ggplot(penguins) +
  aes(x = flipper_length_mm,
      y = body_mass_g) +
  geom_point() +
  facet_grid(species~island) +
  ggtitle("A point geom graph with grid facets")

The graph has split into multiples, based on the number of islands and the number of species.

diamonds %>% 
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price))

diamonds %>% 
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price, colour = cut), size = 3) +
  scale_colour_brewer(palette = "Set3") +
  labs(title = "Brewer Colour Pallette (Set3)")

We are using the RColorBrewer package here. Type RColorBrewer::display.brewer.all() in your Console and see what palettes are available.

diamonds %>% 
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price, colour = cut), size = 3) +
               scale_colour_viridis_d() +
  labs(title = "Viridis Palette",
       subtitle = "The Default in ggplot")

diamonds %>% 
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price, colour = cut), size = 3) +
               scale_colour_viridis_d(option = "magma") +
  labs(title = "Viridis Palette, Option Magma")

diamonds %>% 
  slice_sample(prop = 0.2) %>% 
  ggplot(.) +
  geom_point(aes(x = carat, y = price, colour = cut), size = 3) +
               scale_colour_viridis_d(option = "inferno") +
  labs(title = "Viridis Palette, Option Inferno")