In-Class Exercise 1

Getting Started

1. Using p_load() of pacman package to load tidyverse

Code

pacman::p_load(tidyverse)

2. Importing data

Code

exam_data <- read_csv("data/Exam_data.csv")

Working with theme

Plot a horizontal bar chart

• Change the colours of plot panel background of theme_minimal() to light blue and the color of gridlines to white

Code

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  coord_flip() +
  theme_minimal() + 
  theme(
    panel.background = element_rect(fill = "lightblue", colour = "lightblue", 
                                    linewidth = 0.5, linetype = "solid"),
    panel.grid.major = element_line(linewidth = 0.5, linetype = 'solid', colour = "white"), 
    panel.grid.minor = element_line(linewidth = 0.25, linetype = 'solid', colour = "white"))

Designing Data-drive Graphics for Analysis I

In this example, we will revise the vertical bar chart to provide additional information.

The left tab panel shows the original vertical bar chart, while the right panel shows the revised version of the vertical bar chart.

Original

A simple vertical bar chart for frequency analysis.

Critics:

• y-axis label is not clear (i.e. count)
• To support effective comparison, the bars should be sorted by their respective frequencies.
• For static graph, frequency values should be added to provide addition information.

Code

ggplot(data=exam_data,
       aes(x = RACE)) +
  geom_bar()

Revised

The revised vertical bar chart will show:

• The y-axis will be changed to the Total Number of Pupils
• In order to support effective comparison, we will sort the bars by their respective frequencies
• Since this is a static graph, we will also include the frequency values to provide additional information.

Code

ggplot(data = exam_data,
       aes(x = reorder(RACE, RACE,
                       function(x)-length(x)))) +
  geom_bar() + 
  ylim(0,220) +
  geom_text(stat="count", 
      aes(label=paste0(after_stat(count), ", ", 
      round(after_stat(count)/sum(after_stat(count))*100, 1), "%")),
      vjust=-1) +
  xlab("RACE") +
  ylab("NO. OF\nPUPILS") +
  theme(axis.title.y=element_text(angle = 0))

Designing Data-drive Graphics for Analysis II

In this example, we will improve the aesthetics of the histogram.

The left tab panel shows the original version of the histogram plotted, while the right panel shows the revised version, with added information of the mean and median lines.

Original

A simple histogram.

Code

ggplot(data=exam_data,
       aes(x = MATHS)) +
  geom_histogram(bins=30)

Revised

We will

• Change the fill color to light blue and line color to black
• Include the mean and median lines onto the histogram plot

Code

ggplot(data = exam_data,
       aes(x=MATHS)) +
  geom_histogram(bins=20,
                 fill="light blue",
                 color="black") +
  geom_vline(aes(xintercept=mean(MATHS, na.rm=T)),
             color="red", 
             linetype="dashed", 
             linewidth=1) +
  geom_vline(aes(xintercept=median(MATHS, na.rm=T)),
             color="grey30",
             linetype="dashed", 
             linewidth=1)

Designing Data-drive Graphics for Analysis III

The original histograms on the left tab (Original) are elegantly designed but not informative. This is because they only reveal the distribution of English scores by gender but without context such as all pupils.

In this example, on the right tab (Revised), we will show the distribution of English scores by gender and also include the histogram of all pupils at the background.

Original

Code

ggplot(data=exam_data, aes(x = ENGLISH)) + 
  geom_histogram(bins=30) +
    facet_grid(~ GENDER)

Revised

Code

backgd_data <- exam_data   
d_bg <- backgd_data[, -3]

ggplot(backgd_data, aes(x = ENGLISH, fill = GENDER)) +
  geom_histogram(data = d_bg, fill = "grey", alpha = .5, bins=30) +
  geom_histogram(colour = "black", bins=30) +
  facet_wrap(~ GENDER) +
  guides(none) +  
  theme_bw()

Designing Data-drive Graphics for Analysis IV

In this example, we will beautify (makeover) the scatterplot from left tab (Original) to right tab (Revised)

Original

Code

ggplot(data=exam_data, 
       aes(x=MATHS, y=ENGLISH)) +
  geom_point() 

Revised

Code

ggplot(data=exam_data, 
       aes(x=MATHS, y=ENGLISH)) +
  geom_point() +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  geom_hline(yintercept=50,
             linetype="dashed",
             color="grey60",
             linewidth=1) + 
  geom_vline(xintercept=50, 
             linetype="dashed",
             color="grey60",
             linewidth=1)