Hands on Exercise 1

Getting Started

Load and Install R packages

pacman::p_load(tidyverse, forcats)

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  

#Introductions to ggplot The aesthetic mappings take attributes of the data and and use them to influence visual characteristics, such as position, colour, size, shape, or transparency. Each visual characteristic can thus encode an aspect of the data and be used to convey information.

Bar chart

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

Dot Plot

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot(binwidth=2.5,         
               dotsize = 0.5) +      
  scale_y_continuous(NULL,           
                     breaks = NULL)  

Histogram

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

changing aes()

ggplot(data=exam_data, 
       aes(x= MATHS, 
           fill = GENDER)) +
  geom_histogram(bins=20, 
                 color="grey30") 

ggplot2: geom

geom-density()

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_density()         

ggplot(data=exam_data, 
       aes(x = MATHS, 
           colour = GENDER)) +
  geom_density()        

geom_boxplot

ggplot(data=exam_data, 
       aes(y = MATHS,       
           x= GENDER)) +    
  geom_boxplot()  

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot(notch=TRUE)

geom_violin

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_violin()

geom_point()

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

combining geom objects

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot() +                    
  geom_point(position="jitter", 
             size = 0.5)        

Statistics Functions

The Statistics functions statistically transform data, usually as some form of summary.

Using stat_summary() function to over ride the default geom.

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot() +
  geom_point(stat="summary",        
             fun.y="mean",           
             colour ="red",          
             size=4)     

Adding best curve on a scatterplot using geom_smooth

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(size=0.5)

Without using geom_smooth

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

Overriding default smoothing method

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5)

ggplot2: facets

facet_wrap()

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
    facet_wrap(~ CLASS)

facet_grid()

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
    facet_grid(~ CLASS)

ggplot 2: coordinates

By the default, the bar chart of ggplot2 is in vertical form. The code chunk below flips the horizontal bar chart into vertical bar chart by using coord_flip().

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

Changing the y- and x- axis range

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, size=0.5)

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))

Using Themes

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