More customization:

• branding
• making plots interactive
• combining plots

Easier plot automation (creating plots in scripts)

Faster (eventually)

• A package for producing graphics - gg = Grammar of Graphics

• Created by Hadley Wickham in 2005

• Belongs to “Tidyverse” family of packages

• “Make a ggplot” = Make a plot with the use of ggplot2 package

Resources:

• https://ggplot2-book.org/

• https://www.opencasestudies.org/

Based on the idea of:

plot objects are placed on top of each other with +

📉 

➕ 

📈 

• Pros: extremely powerful/flexible – allows combining multiple plot elements together, allows high customization of a look, many resources online

• Cons: ggplot2-specific “grammar of graphic” of constructing a plot

• ggplot2 gallery

To make graphics using ggplot2, our data needs to be in a tidy format

Tidy data:

1. Each variable forms a column.
2. Each observation forms a row.

Messy data:

• Column headers are values, not variable names.
• Multiple variables are stored in one column.
• Variables are stored in both rows and columns.

Each variable forms a column. Each observation forms a row.

Column headers are values, not variable names

Read more about tidy data and see other examples: Tidy Data tutorial by Hadley Wickham

It’s also helpful to have data in long format!!!

set.seed(3) 
var_1 <- seq(from = 1, to = 30)
var_2 <- rnorm(30)
my_data = tibble(var_1, var_2)
my_data

# A tibble: 30 × 2
   var_1   var_2
   <int>   <dbl>
 1     1 -0.962 
 2     2 -0.293 
 3     3  0.259 
 4     4 -1.15  
 5     5  0.196 
 6     6  0.0301
 7     7  0.0854
 8     8  1.12  
 9     9 -1.22  
10    10  1.27  
# … with 20 more rows

Will set up the plot - it will be empty!

• Aesthetic mapping (mapping= aes(x= , y =)) describes how variables in our data are mapped to elements of the plot

library(ggplot2) # don't forget to load ggplot2
# This is not code but shows the general format
ggplot({data_to plot},  mapping = aes(x = {var in data to plot},
                                      y = {var in data to plot}))

ggplot(my_data, mapping = aes(x = var_1, y = var_2))

There are many to choose from, to list just a few:

• geom_point() – points (we have seen)
• geom_line() – lines to connect observations
• geom_boxplot()
• geom_histogram()
• geom_bar()
• geom_col()
• geom_errorbar()
• geom_density()
• geom_tile() – blocks filled with color

ggplot({data_to plot},  mapping = aes(x = {var in data to plot},
                                      y = {var in data to plot})) +
  geom_{type of plot}</div>

ggplot(my_data, mapping = aes(x = var_1, y = var_2)) + 
  geom_point() 

Read as: add points to the plot (use data as provided by the aesthetic mapping)

Layer a plot on top of another plot with +

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point() + 
  geom_line()

You can change look of each layer separately.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "black", linetype = 2)

You can change the look of whole plot - specific elements, too - like changing font and font size - or even more fonts

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  theme_bw(base_size = 20, base_family = "Comic Sans MS")

The labs() function can help you add or modify titles on your plot.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2",
           x = "Variable 1",
           y = "Variable 2")

xlim() and ylim() can specify the limits for each axis

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2") + 
  xlim(0,40)

Lab document

The theme() function can help you modify various elements of your plot. Here we will adjust the horizontal justification (hjust) of the plot title.

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of var1 vs var2") + 
  theme(plot.title = element_text(hjust = 0.5, size = 20))

The theme() function always takes:

1. an object to change (use ?theme() to see - plot.title, axis.title, axis.ticks etc.)
2. the aspect you are changing about this: element_text(), element_line(), element_rect(), element_blank()
3. what you are changing:
   • text: size, color, fill, face, alpha, angle
   • position: "top", "bottom", "right", "left", "none"
   • rectangle: size, color, fill, linetype
   • line: size, color, linetype

ggplot(my_data, aes(x = var_1, y = var_2)) + 
  geom_point(size = 5, color = "red", alpha = 0.5) + 
  labs(title = "My plot of var1 vs var2", x = "Variable 1") +
  theme(plot.title = element_text(hjust = 0.5, size = 20),
        axis.title.x = element_text(size = 16))

Guide on how to: https://rpubs.com/mclaire19/ggplot2-custom-themes

First, we will generate some data frame for the purpose of demonstration.

• 2 different categories (e.g. pasta, rice)
• 4 different items (e.g. 2 of each category)
• 10 price values changes collected over time for each item

# create 4 vectors: 2x character class and 2x numeric class
item_categ <- rep(c("pasta", "rice"),each = 20)
item_ID  <- rep(seq(from = 1, to = 4), each = 10)
item_ID <- paste0("ID_", item_ID)
observation_time  <- rep(seq(from = 1, to = 10), times = 4)
item_price_change <- c(sample(0.5:2.5, size = 10, replace = TRUE),
                       sample(0:1, size = 10, replace = TRUE),
                       sample(2:5, size = 10, replace = TRUE),
                       sample(6:9, size = 10, replace = TRUE))
# use 4 vectors to create data frame with 4 columns
food  <- tibble(item_ID, item_categ, observation_time, item_price_change) 

food

# A tibble: 40 × 4
   item_ID item_categ observation_time item_price_change
   <chr>   <chr>                 <int>             <dbl>
 1 ID_1    pasta                     1               0.5
 2 ID_1    pasta                     2               2.5
 3 ID_1    pasta                     3               1.5
 4 ID_1    pasta                     4               0.5
 5 ID_1    pasta                     5               0.5
 6 ID_1    pasta                     6               0.5
 7 ID_1    pasta                     7               0.5
 8 ID_1    pasta                     8               0.5
 9 ID_1    pasta                     9               1.5
10 ID_1    pasta                    10               1.5
# … with 30 more rows

ggplot(food, aes(x = observation_time, 
                 y = item_price_change)) + 
  geom_line() 

You can use group element in a mapping to indicate that each item_ID will have a separate price line.

ggplot(food, aes(x = observation_time, 
                 y = item_price_change, 
                 group = item_ID)) + 
  geom_line() 

ggplot(food, aes(x = observation_time, 
                 y = item_price_change,
                color = item_ID)) + 
  geom_line() 

ggplot(food, aes(x = observation_time, 
                 y = item_price_change,
                color = item_categ)) + 
  geom_line() 

ggplot(food, aes(x = observation_time, 
                 y = item_price_change,
                 group = item_ID,
                color = item_categ)) + 
            geom_line() 

Two options: facet_grid()- creates a grid shape facet_wrap() -more flexible

Need to specify how you are faceting with the ~ sign.

ggplot(food, aes(x = observation_time, 
                 y = item_price_change,
                 color = item_ID)) + 
  geom_line() +
  facet_grid( ~ item_categ)

This will not work! Also don’t use pipes instead of +!

ggplot(food, aes(x = item_ID, 
                 y = item_price_change, 
                 fill = item_categ))  
 + geom_boxplot()

Can add width argument to make the jitter more narrow.

ggplot(food, aes(x = item_ID, 
                 y = item_price_change, 
                 fill = item_categ)) + 
  geom_boxplot() +
  geom_jitter(width = .06)

scale_fill_viridis_d() for discrete /categorical data scale_fill_viridis_c() for continuous data

ggplot(food, aes(x = item_ID, 
                 y = item_price_change, 
                 fill = item_categ)) + 
  geom_boxplot() +
  geom_jitter(width = .06) +
  scale_fill_viridis_d()

food_bar <-food %>% 
  group_by(item_categ) %>%
  summarize("max_price_change" = max(item_price_change)) %>%
  ggplot(aes(x = item_categ, 
             y = max_price_change,
             fill = item_categ)) + 
  scale_fill_viridis_d()+
  geom_col() +
  theme(legend.position = "none")

food_bar

Can be used to add an outline around column/bar plots.

food_bar +
   geom_col(color = "black") 

head(food)

# A tibble: 6 × 4
  item_ID item_categ observation_time item_price_change
  <chr>   <chr>                 <int>             <dbl>
1 ID_1    pasta                     1               0.5
2 ID_1    pasta                     2               2.5
3 ID_1    pasta                     3               1.5
4 ID_1    pasta                     4               0.5
5 ID_1    pasta                     5               0.5
6 ID_1    pasta                     6               0.5

food %>% group_by(item_ID) %>%
  summarize(sum = sum(item_price_change))

# A tibble: 4 × 2
  item_ID   sum
  <chr>   <dbl>
1 ID_1       10
2 ID_2        5
3 ID_3       32
4 ID_4       75

food_bar +
  theme(text = element_text(size = 20))

Great for adding labels directly onto plots

https://www.opencasestudies.org/ocs-bp-co2-emissions/

#install.packages("directlabels")
library(directlabels)
direct.label(rp_fac_plot, method = list("angled.boxes"))

#install.packages("plotly")
library("plotly")
ggplotly(rp_fac_plot)

Also check out the ggiraph package

A few options:

• RStudio > Plots > Export > Save as image / Save as PDF
• RStudio > Plots > Zoom > [right mouse click on the plot] > Save image as
• In the code

ggsave(filename = "saved_plot.png",  # will save in working directory
       plot = rp_fac_plot, 
       width = 6, height = 3.5)               # by default in inch

Lab document