Grammar Of Graphics

ggplot2

background

Note

Visualisation is the process of representing data graphically and interacting with these representations. The objective is to gain insight into the data.

To work with ggplot2, remember that at least your R codes must

• start with ggplot()
• identify which data to plot data = Your Data
• state variables to plot for example aes(x = Variable on x-axis, y = Variable on y-axis ) for bivariate
• choose type of graph, for example geom_histogram() for histogram, and geom_points() for scatterplots

setup

• installing tidyverse package which contains dplyr and ggplot2

library(tidyverse)
loan_data <- read_csv('loan_data_cleaned.csv')
loan_data <- loan_data |>
  mutate(default=ifelse(loan_status==1,"defaulted","not defaulted")) |> 
  mutate_if(is.character,as.factor)

getting started

• Calling ggplot() along just creates a blank plot

ggplot()

next up

• I need to tell ggplot what data to use

ggplot(data=loan_data)

grammar of graphics

• And then give it some instructions using the grammar of graphics.
• Let’s build a simple scatterplot with annual income on the x-axis and loan amount on the y axis

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt))

refining…

• Let’s try representing a different dimension.
  
• What if we want to differentiate public vs. private schools?
• We can do this using the shape attribute

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt, shape=default))

not neat!!..try color.

• That’s hard to see the difference. What if we try color instead?

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt, color=default))

try size!

• I can also alter point size. Let’s do that to represent grade

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt, color=default, size=grade))

transparency

• And, lastly, let’s add some transparency so we can see through those points a bit
• Experiment with the alpha value a bit.

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt, color=default, size=grade), alpha=1)

very transparent

ggplot(data=loan_data) +
  geom_point(mapping=aes(x=annual_inc, y=loan_amnt, color=default, size=grade), alpha=1/100)

How many people defaulted?

# This calls for a bar graph!
ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=default))

Break it out by grade

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=default, color=grade))

Well, that’s unsatisfying! Try fill instead of color

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=default, fill=grade))

How about loan amount by default status?

• First, I’ll use some dplyr to create the right tibble

loan_data %>%
  group_by(default) %>%
  summarize(average_amount=mean(loan_amnt))
#> # A tibble: 2 × 2
#>   default       average_amount
#>   <fct>                  <dbl>
#> 1 defaulted              9389.
#> 2 not defaulted          9619.

And I can pipe that straight into ggplot

• but this will produce an error

loan_data %>%
  group_by(default) %>%
  summarize(average_amount=mean(loan_amnt)) %>%
  ggplot() +
  geom_bar(mapping=aes(x=default, y=average_amount))

use geom_col() instead

• But I need to use a column graph instead of a bar graph to specify my own y

loan_data %>%
  group_by(default) %>%
  summarize(average_amount=mean(loan_amnt)) %>%
  ggplot() +
  geom_col(mapping=aes(x=default, y=average_amount))

Histograms

• Let’s look at annual income
• Histograms can help us by binning results

ggplot(data=loan_data) +
  geom_histogram(mapping=aes(x=annual_inc), origin=0)

What if we want fewer groups? Let’s ask for 4 bins

ggplot(data=loan_data) +
  geom_histogram(mapping=aes(x=annual_inc), bins=4, origin=0)

Or 10 bins.

ggplot(data=loan_data) +
  geom_histogram(mapping=aes(x=annual_inc), bins=10, origin=0)

Or we can specify the width of the bins instead

ggplot(data=loan_data) +
  geom_histogram(mapping=aes(x=annual_inc), binwidth=1000, origin=0)

large binwidth

ggplot(data=loan_data) +
  geom_histogram(mapping=aes(x=annual_inc), binwidth=10000, origin=0)

changing background

Change the plot background color

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(plot.background=element_rect(fill='purple'))

Change the panel background color

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(panel.background=element_rect(fill='purple'))

Let’s be minimalist and make both backgrounds disappear

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(panel.background=element_blank()) +
  theme(plot.background=element_blank())

Add grey gridlines

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(panel.background=element_blank()) +
  theme(plot.background=element_blank()) +
  theme(panel.grid.major=element_line(color="grey"))

Only show the y-axis gridlines

ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(panel.background=element_blank()) +
  theme(plot.background=element_blank()) +
  theme(panel.grid.major.y=element_line(color="grey"))+
  scale_fill_manual(values=c("orange","blue","green","blue4"))

library(ggthemes)
ggplot(data=loan_data) +
  geom_bar(mapping=aes(x=loan_status, fill=home_ownership)) +
  theme(panel.background=element_blank()) +
  theme(plot.background=element_blank()) +
  theme(panel.grid.major.y=element_line(color="grey"))+
  scale_fill_manual(values=c("orange","blue","green","blue4"))+
  theme_solarized()+
  theme_wsj()

ggplot(data=loan_data, aes(x = loan_status)) + 
  geom_bar(fill = "chartreuse") + 
  theme(axis.text.x = element_text(angle = 90))