Hands On Exercise 1

Getting Started

Installing R Packages

The code below uses p_load() of pacman package to check if tidyverse packages are installed in the computer. If they are, then they will be launched into R.

pacman::p_load(tidyverse)

Importing Data

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

knitr::kable(head(exam_data))

ID	CLASS	GENDER	RACE	ENGLISH	MATHS	SCIENCE
Student321	3I	Male	Malay	21	9	15
Student305	3I	Female	Malay	24	22	16
Student289	3H	Male	Chinese	26	16	16
Student227	3F	Male	Chinese	27	77	31
Student318	3I	Male	Malay	27	11	25
Student306	3I	Female	Malay	31	16	16

What is ggplot2?

Applied to visualizations, grammar of graphics is a grammar used to describe and create a wide range of statistical graphics. This layered approach is implemented in ggplot2, an R package for creating data-driven graphics. It has become the go-to tool for making plots flexibly in R.

• Data: The dataset being plotted.

• Aesthetics take attributes of the data and use them to influence visual characteristics, such as position, colours, size, shape, or transparency.

• Geometrics: The visual elements used for our data, such as point, bar or line.

• Facets split the data into subsets to create multiple variations of the same graph (paneling, multiple plots).

• Statistics, statiscal transformations that summarise data (e.g. mean, confidence intervals).

• Coordinate systems define the plane on which data are mapped on the graphic.

• Themes modify all non-data components of a plot, such as main title, sub-title, y-aixs title, or legend background.

Essential Grammatical Elements in ggplot2

Let’s examine the first three essential layers for making a plot - Data, Aesthetics and Geometries.

Data

First, let’s call the ggplot() function using the below code.

ggplot(data=exam_data)

Aesthetic mappings

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

Geom

Geometric objects are the actual marks we put on a plot. A plot must have at least one geom and there is no upper limit. You can add a geom to a plot using the + operator.

Examples include:

• geom_point() for drawing individual points (e.g., a scatter plot)

• geom_line() for drawing lines (e.g., for a line charts)

• geom_bar() for drawing bars (e.g., for bar charts)

• geom_jitter() to add a small amount of random variation to the location of each point

• geom_map() for drawing polygons in the shape of a map!

Plotting a Bar Chart

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

Plotting a Dot Plot

ggplot(data = exam_data,
       aes(x = RACE,
           y = SCIENCE)) + 
  geom_point()

Plotting a Histogram

ggplot(data=exam_data, 
       aes(x= ENGLISH)) +
  geom_histogram(bins=20,            
                 color="black",      
                 fill="#630e27") 

Plotting a Jitter Plot

ggplot(data=exam_data, 
       aes(x= GENDER, 
           y= SCIENCE)) +
  geom_jitter(width = 0.5, height = 0.5,
              bins=20, 
              aes(colour = "#630e27"))

Plotting a Scatter Plot Smoothing

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

Plotting a Box Plot

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

Combining geom objects

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