Data Visualization

Mine Doğucu

Examples

How Common Is Your Birthday?

One Dataset Visualized 25 Ways

Mandatory Paid Vacation

Why are K-pop groups so big?

We will only touch the surface of data visualization in this class. Visualization is a rich field on its own, in case some of you would consider it as a career option. It sits at the intersection of art, design, statistics, data science, psychology, cognitive science.

Data Visualizations

• are graphical representations of data

• use different colors, shapes, and the coordinate system to summarize data

• can tell a story or can be useful for exploring data

Packages for Today

library(hellodatascience)
library(tidyverse)

Note that most of the functions we will be using today comes from the {ggplot2} package within tidyverse.

Example from NYT

Image source

Data Context

data(atus_college)
glimpse(atus_college)

Rows: 312
Columns: 13
$ employment        <fct> Part Time, Full Time, Part Time, Part Time, NA, Part…
$ age               <dbl> 19, 23, 22, 21, 26, 25, 27, 36, 30, 20, 18, 20, 25, …
$ enrollment        <fct> Part Time, Full Time, Full Time, Full Time, Full Tim…
$ weekly_earnings   <dbl> 400.00, 1476.92, 561.25, 100.00, NA, 300.00, 1076.92…
$ household_size    <dbl> 6, 2, 2, 4, 2, 3, 3, 1, 3, 2, 4, 4, 2, 2, 4, 5, 4, 2…
$ time_alone        <dbl> 326, 150, 357, 22, 0, 455, 90, 340, 326, 120, 285, 5…
$ sleep_time        <dbl> 680, 180, 470, 660, 875, 765, 630, 300, 445, 630, 66…
$ work_time         <dbl> 315, 0, 0, 0, 0, 0, 0, 645, 555, 0, 0, 0, 520, 615, …
$ degree_class_time <dbl> 0, 0, 238, 0, 0, 0, 0, 0, 0, 0, 0, 228, 0, 0, 0, 0, …
$ shopping_time     <dbl> 14, 0, 0, 0, 0, 0, 0, 5, 20, 345, 0, 0, 0, 0, 0, 0, …
$ lunch_break_time  <dbl> 66, 60, 20, 115, 35, 50, 25, 30, 75, 60, 15, 15, 60,…
$ sports_time       <dbl> 0, 60, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 0, 0, 0…
$ religious_time    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0…

Documentation for Data

?atus_college

Visualizing a Single Variable

Visualizing a ___ Variable

Figure 1: A bar plot of employment status

1. What do you notice about this graph?
2. If you could talk in English to R, how would you tell R to make this plot?

Pick your data

ggplot(data = atus_college)

Figure 2: Blank coordinate system

Map your variable

ggplot(data = atus_college, aes(x = employment))

Figure 3: Mapping employment variable to the x-axis

Pick your plot type

ggplot(data = atus_college, aes(x = employment)) +
  geom_bar()

Figure 4: Creating the bars by adding the geometric layer of a barplot

Summary

The three main steps to make a plot are:

1. Call the ggplot() function on the data we want to plot.
2. Map variables to plot aesthetics using the aes() function.
3. Add a layer to specify the plot type.

code style

The tidyverse style guide has the following convention for writing ggplot2 code.

1. The plus sign for adding layers + always has a space before it and is followed by a new line.

2. The new line is indented by two spaces. RStudio does this automatically for you.

code style

ggplot(data = atus_college, aes(x = employment)) +
  geom_bar()

Both the above and below code are correct styles of writing ggplot code.

ggplot(
  data = atus_college, 
  aes(x = employment)
) +
  geom_bar()

Visualizing a Numeric Variable - Histogram

ggplot(data = atus_college, aes(x = weekly_earnings)) +
  geom_histogram() 

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Figure 5: A histogram of weekly earnings

Comparing binwidth

• binwidth = 20
• binwidth = 100
• binwidth = 500

ggplot(data = atus_college, aes(x = weekly_earnings)) +
  geom_histogram(binwidth = 20) 

ggplot(data = atus_college, aes(x = weekly_earnings)) +
  geom_histogram(binwidth = 100) 

ggplot(data = atus_college, aes(x = weekly_earnings)) +
  geom_histogram(binwidth = 500) 

Describing histograms

Figure 6: Understanding skewness of a histogram

Think 💭 - Pair 👫🏽 - Share 💬

1. The distribution of weekly_earnings is

1. left-skewed
2. symmetric
3. right-skewed

2. Looking at the distribution of the weekly_earnings which of the following can be concluded?

1. mean > median
2. mean = median
3. mean < median

Visualizing a Numeric Variable - Boxplot

ggplot(data = atus_college, aes(y = weekly_earnings)) +
  geom_boxplot() 

Figure 7: Boxplot of weekly earnings

Understanding Boxplots

Figure 8: Annotated boxplot

Understanding Boxplots

Figure 9: Boxplot overlayed with individual observation points

Visualizing Two Variables

Stacked Bar Plot

ggplot(
  data = atus_college, 
  aes(
    x = employment,
    fill = enrollment 
  )
) +
  geom_bar()

Figure 10: Stacked barplot of employment and enrollment status

Standardardized stacked barplot

ggplot(
  data = atus_college, 
  aes(
    x = employment,
    fill = enrollment
  )
) +
  geom_bar(position = "fill")

Figure 11: Standardardized stacked barplot of employment and enrollment

Dodged barplot

ggplot(
  data = atus_college, 
  aes(
    x = employment,
    fill = enrollment
  )
) +
  geom_bar(position = "dodge") 

Figure 12: Side-by-side (dodge) barplot of employment and enrollment status

Scatterplot

ggplot(
  data = atus_college,
  aes(
    x = time_alone, 
    y = weekly_earnings
  )
) +
  geom_point() 

Figure 13: Scatterplot of time spent alone and weekly earnings

Side-by-side Boxplot

ggplot(
  data = atus_college, 
  aes(
    x = employment,
    y = weekly_earnings
  )
) +
  geom_boxplot()

Figure 14: Side-by-side boxplots based on employment status

Visualizing More Than Two Variables

Use of color

ggplot(
  data = atus_college,
  aes(
    x = time_alone,
    y = weekly_earnings,
    color = employment 
  )
) +
  geom_point()

Figure 15: Grouping points by color based on employment status

If you are color-blind, depending on the type, you may possibly not be able to distinguish these colors so the next slide will make much more sense.

Use of shape

ggplot(
  data = atus_college,
  aes(
    x = time_alone,
    y = weekly_earnings,
    shape = employment
  ) 
) +
  geom_point()

Figure 16: Grouping points by shape based on employment status

Use of color and shape

ggplot(
  data = atus_college,
  aes(
    x = time_alone,
    y = weekly_earnings,
    color = employment,
    shape = employment
  )
) +
  geom_point()

Figure 17: Using both color and shape to group points

Use of size

ggplot(
  data = atus_college,
  aes(
    x = time_alone,
    y = weekly_earnings,
    color = employment,
    size = work_time
  ) 
) +
  geom_point()

Figure 18: Adding a numerical variable to differentiate points based on size

Summary

Figure 19: A summary of aesthetics and geom objects covered in this lecture