Module 2: Intro to data visualization using ggplot2

  1. The grammar of graphics
  2. Datasets and mapping
  3. Geometries
  4. Statistical transformation and plotting distribution
  5. Position adjustment and scales
  6. Coordinates and themes
  7. Facets and custom plots

View slides in new window

Presentation keyboard shortcuts

  • Use ← and → to navigate through the slides
  • Use F to toggle full screen
  • Use O to view an overview of all slides
  • Use ? to see a list of other shortcuts

Class demonstration

# libraries
library(tidyverse)
library(babynames)

## select names
friends <- babynames %>% 
  filter(year >= 1950,
         name %in% c("Christopher", "Dave", "Karl"),
         sex == "M")

## plotting trends
ggplot(data = friends,
       mapping = aes(x = year, y = n, color = name)) +
  geom_line(linetype = "dashed") +
  geom_point(size = 4, alpha = 0.5) +
  scale_y_log10() +
  theme_minimal() +
  theme(axis.title = element_text(size = 24),
        axis.text = element_text(size = 16),
        legend.text = element_text(size = 16),
        legend.title = element_text(size = 18)
        )


# libraries
library(tidyverse)

## calculating mean hwy per class
mean_hwy_data <- mpg %>% 
  group_by(class) %>% 
  summarise(mean_hwy = mean(hwy, na.rm = TRUE))

ggplot(data = mpg,
       mapping = aes(x = class, y = hwy, color = class)) +
  geom_point(position = "jitter", size = 3, alpha = 0.5) +
  geom_point(data = mean_hwy_data, aes(y = mean_hwy), size = 7) +
  labs(title = "Fuel consumption per class vehicle",
       x = "Class of vehicle",
       y = "Highway fuel consumption") +
  theme_minimal() +
  theme(plot.title = element_text(size = 24),
        axis.title = element_text(size = 24),
        axis.text = element_text(size = 16),
        legend.text = element_text(size = 16),
        legend.title = element_text(size = 18)
        )


## libraries
library(tidyverse)

## histogram plot

ggplot(data = mpg, 
       mapping = aes(x = displ)) +
  geom_histogram(bins = 10, fill = "cadetblue3", alpha = 0.5) + 
  geom_text(aes(label = after_stat(count)),
            stat = "bin", 
            bins = 10, 
            nudge_y = 2) +
  theme_minimal() +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 16),
        legend.text = element_text(size = 14),
        legend.title = element_text(size = 16)
        )