A package that is

• elegant and versatile
• grammar of graphics
• layered structure that lets you build block by block
• different from base graphics (base R)
  • curve could be steep
    • but looks amazing when it works!

• Understanding breakdown of your graph is important

• This is core of ggplot2!

• Will make plotting of perfect graph easier

• Process of adding layers

Options:

• tidyverse
  • includes many packages (ggplot2, dplyr)
  • time to load
• ggplot2
  

library(ggplot2)

library(tidyverse)

ggplot(diamonds) +
  geom_point(aes(x = carat, y = price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price, colour = cut))

ggplot(diamonds) +
  geom_point(aes(x = carat, y = price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price, colour = cut))

• ggplot(diamonds) loads data frame
• plus (+) tells ggplot() that there is more to add
• geom_point() / geom_smooth() defines type of plot
  • geom = geometric object
• aes(x = , y =, colour = ) defines the variables

ggplot(diamonds) +
  geom_point(aes(x = carat, y = price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price))

• Method 1
  • Delete points outside range
  • Will not be considered for further calculations!

• Method 2
  • Zoom in on area of interest
  • Maintain other points in dataset
    • Just not shown

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price)) + 
  ylim(0, 3000)

## Warning: Removed 23604 rows containing non-finite values (stat_smooth).

## Warning: Removed 23604 rows containing missing values (geom_point).

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price)) + 
  ylim(0, 3000) +
  xlim(0, 1)

## Warning: Removed 23716 rows containing non-finite values (stat_smooth).

## Warning: Removed 23716 rows containing missing values (geom_point).

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price)) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000))

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price)) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset")

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price)) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)")

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)")

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)") +
  scale_colour_brewer(palette = "Spectral")

• Many colour options available (palettes, individual colours)
• RColorBrewer package
• Can use:
  • Numbers
  • HEX value
  • RGB value

Link to colour cheatsheet

Link to colour converter and colour numbers

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)") +
  scale_colour_brewer(palette = "Spectral") +
  theme(legend.position = "bottom",
        legend.title = element_text(colour = "Steelblue4", face = "bold", size = 15))

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)") +
  scale_colour_brewer(palette = "Spectral") +
  theme(legend.position = "bottom",
        legend.title = element_text(colour = "Steelblue4", face = "bold", size = 15),
        legend.background = element_rect(fill = "Slategray2", size = 0.5, linetype = "solid"))

• To demonstrate this –> zoomed in more

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0.2, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)") +
  scale_colour_brewer(palette = "Spectral") +
  theme(legend.position = "bottom",
        legend.title = element_text(colour = "Steelblue4", face = "bold", size = 15),
        legend.background = element_rect(fill = "Slategray2", size = 0.5, linetype = "solid")) +
  scale_x_continuous(breaks = seq(0.2, 1, 0.05))

ggplot(diamonds) +
  geom_point(aes(x = carat, y= price, colour = cut)) +
  geom_smooth(aes(x = carat, y = price), colour = "firebrick", size = 2) + 
  coord_cartesian(xlim = c(0.2, 1), ylim = c(0, 3000)) + 
  labs(title = "Carat vs Price",
       subtitle = "Diamond Dataset",
       x = "Carat Diamond",
       y = "Price ($)") +
  scale_colour_brewer(palette = "Spectral") +
  theme(legend.position = "bottom",
        legend.title = element_text(colour = "Steelblue4", face = "bold", size = 15),
        legend.background = element_rect(fill = "Slategray2", size = 0.5, linetype = "solid")) +
  scale_x_continuous(breaks = seq(0.2, 1, 0.05)) +
  scale_y_continuous(breaks = seq(0, 3000, 1000),
                    labels =c("free", "expensive", "more expensive", "really expensive"))

• Possible to change theme at once
  • Pre-coded themes in ggplot
  • theme_bw()
  • theme_classic()
  • theme_gray()
  • …
• Example with + theme_classic()

• Explore theme
  • Use ?theme to find options
  • Change axis labels
  • font, size, colour
• Make graph with Midwest data set