1 Exploratory Data Analysis (Summary Figures)

1.1 Introduction.

The present session will focus on basics of data visualization. The process of visualization and creation of summary tables go hand in hand, however, we shall limit our discussion to creating beautiful figures here!

Before we start with learning how to create graphs/ plots/ figures using R, it is important to understand that the selection of type of plots is dependent entirely upon the number and type of variables selected. It is strongly recommended that the same be refreshed and strengthened by participants from time to time for advanced use of data analytics in future.

Programming Basics

Data visualization is a powerful tool for data science in epidemiology. The basics of programming for creating plots using ggplot package includes understanding of four important aspects.

  1. Use of reusable/ reproducible templates.
  2. Creation of different types of plots using geoms.
  3. Addition of variables using mapping.
  4. Customization of plots using settings.

1.2 Visualization of single variable.

1.2.1 Continous variable

For illustration, we will plot birth weights to understand the distribution pattern among all study participants in birth weight data set.

Code 
ggplot(data = df) + # Template
  geom_histogram( # geom
    mapping = aes(x = birth_weight), # mapping
    binwidth = 350, # mandatory settings
    color = "blue", # optional settings
    fill = "red",
    linetype = 2,
    alpha = 0.8,
    size = 1) 
ggplot(data = df) + # Template
  geom_freqpoly( # geom
    mapping = aes(x = birth_weight), # mapping
    binwidth = 350, # mandatory settings
    color = "blue", # optional settings
    fill = "red",
    linetype = 2,
    alpha = 0.8,
    size = 1)

Histogram

Frequency polygon

Code 
ggplot(data = df) + # Template
  geom_dotplot( # geom
    mapping = aes(x = birth_weight), # mapping
    binwidth = 350, # mandatory settings
    size = 1) 
ggplot(data = df) + # Template
  geom_boxplot( # geom
    mapping = aes(y = birth_weight), # mapping
    size = 1)

Dot plot

Box plot

1.2.2 Discrete/ Categorical variable.

Illustrative example: In the birth weight dataset, if we are interested to understand the distribution of smoking history (Present/Absent) among mothers.

Add optional arguments (settings) to enhance visualizations.
What happens if instead of writing {mapping = aes()}, we write only {aes()} in the code chunk?

Code 
ggplot(data = df) +
  geom_bar(mapping = aes(x = smoke_cat)) 
ggplot(data = df) +
  geom_bar(mapping = aes(y = smoke_cat))

Bar chart (Vertical)

Bar chart (Horizontal)

1.3 Visualization of two variables.

1.3.1 Two continous variables.

Illustrative example: In the birth weights dataset, mother’s weight at last menstrual period and birth weight of the infant are continuous variables. We might be interested in looking at how mother’s weight is associated with birth weight of an infant.

Code 
ggplot(data = df) + 
  geom_point(aes(x = maternal_weight, y = birth_weight), 
             color = "red",
             size = 1,
             shape = 1,
             stroke = 1) 
ggplot(data = df) + 
  geom_point(aes(x = maternal_weight, y = birth_weight), 
             color = "red",
             size = 1,
             shape = 1,
             stroke = 1) +
  geom_smooth(aes(x = maternal_weight, y = birth_weight),
              method = "lm")

Scatter Plot

Scatter Plot with Regression line

1.3.2 One discrete and one continous variable.

Illustrative example: In the birth weights dataset, smoking history is categorical and birth weight is a continuous variable. We might be interested to estimate if maternal smoking history has effect on infant birth weights.

Code 
ggplot(data = df) + 
  geom_bar(aes(x = smoke_cat, y = birth_weight),
           stat = "identity") +
  labs(x = "Smoking History",
       y = "Birth Weight")
ggplot(data = df) + 
  geom_boxplot(aes(x = smoke_cat, y = birth_weight),
               coef = 1.5)

Multiple bar chart

Box and Whisker plots

1.3.3 Two discrete variables

Illustrative example: In the dataset, smoking history and whether the infants birth weight was low or not are two discrete/ categorical variables. As a researcher, we would like to see the relationship between these two discrete variables.

Note

To see relationships between two discrete variables, multiple bar charts and component bar charts are used. Till now, the fill argument has been used in the setting section of the code. If you look carefully, while in the setting section, we manually placed the value/ color. The same argument can also be used in mapping section within aesthetics.

Code 
ggplot(data = df) + 
  geom_bar(aes(x = smoke_cat, fill = race_cat),
           position = "dodge")

1.4 Way forward

Now, you know how to create the basic plot. However, plot has additional components such as title, subtitle, caption, axis labels, legend, etc. The same also requires deliberation and details for the same can be learnt from multiple resources. We recommend R Graphics Cookbook https://r-graphics.org/ as a good resource for the same. We are introducing you to this important aspect of data visualization, however considering the present workshop as an introductory workshop, we shall introduce you to AI powered programming for beginners, however, we plan to cover additional aspects such as animated plots, 3D plots, spatial plots, other ggplot extensions, etc during intermediate/ advanced levels only. Enjoy plotting!!