Hands on Exercise 2

Getting Started

Load and Install the required R packages

pacman::p_load(ggrepel, patchwork, 
               ggthemes, hrbrthemes,
               tidyverse) 

Import Data

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

      ID               CLASS              GENDER              RACE          
 Length:322         Length:322         Length:322         Length:322        
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
    ENGLISH          MATHS          SCIENCE     
 Min.   :21.00   Min.   : 9.00   Min.   :15.00  
 1st Qu.:59.00   1st Qu.:58.00   1st Qu.:49.25  
 Median :70.00   Median :74.00   Median :65.00  
 Mean   :67.18   Mean   :69.33   Mean   :61.16  
 3rd Qu.:78.00   3rd Qu.:85.00   3rd Qu.:74.75  
 Max.   :96.00   Max.   :99.00   Max.   :96.00  

Annotation: ggrepel

Compare when using annotation

without ggrepel

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              linewidth=0.5) +  
  geom_label(aes(label = ID), 
                   fontface = "bold") +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")

with ggrepel

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5, 
              formula = y~x) +  
  geom_label_repel(aes(label = ID), 
                   fontface = "bold") +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")

ggplot2 Themes

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="yellow2", 
                 fill="grey60") +
  theme_classic() +
  ggtitle("Distribution of Maths scores") 

ggtheme package

Using different themes

economist

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_economist()

wsj

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_wsj()

solarised

ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  ggtitle("Distribution of Maths scores") +
  theme_solarized()

Combining Graphs

Step 1: Create single graphs

1

p1 <- ggplot(data=exam_data, 
             aes(x = MATHS)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") + 
  coord_cartesian(xlim=c(0,100)) +
  ggtitle("Distribution of Maths scores")

2

p2 <- ggplot(data=exam_data, 
             aes(x = ENGLISH)) +
  geom_histogram(bins=20, 
                 boundary = 100,
                 color="grey25", 
                 fill="grey90") +
  coord_cartesian(xlim=c(0,100)) +
  ggtitle("Distribution of English scores")

3

p3 <-  
  ggplot(data=exam_data, 
             aes(x= MATHS, 
                 y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5) +  
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("English scores versus Maths scores for Primary 3")

Step 2: Combining

(p1 / p2) | p3

p3 + inset_element(p2, 
                   left = 0.02, 
                   bottom = 0.7, 
                   right = 0.5, 
                   top = 1)

patchwork <- (p1 / p2) | p3
patchwork & theme_economist()