Visaluzing Data:
Part I

Dr. Mine Dogucu

1 / 67

Review2 / 67

How LGBTQ+ hate crime is committed by young people against young people

Why Time Flies

Mandatory Paid Vacation

Why are K-pop groups so big?

3 / 67

Data Visualizations

are graphical representations of data

4 / 67

Data Visualizations

are graphical representations of data
use different colors, shapes, and the coordinate system to summarize data

5 / 67

Data Visualizations

are graphical representations of data
use different colors, shapes, and the coordinate system to summarize data
tell a story

6 / 67

Data Visualizations

are graphical representations of data
use different colors, shapes, and the coordinate system to summarize data
tell a story
are useful for exploring data

7 / 67

Visuals with a Single Categorical Variable

8 / 67

Bar plot

9 / 67

Visuals with a Single Numeric Variable

10 / 67

Box plot

The horizontal line inside the box represents the median.
The box itself represents the middle 50% of the data with Q3 on the upper end and Q1 on the lower end.
Whiskers extend from the box. They can extend up to 1.5 IQR away from the box (i.e. away from Q1 and Q3).
The points are potential outliers that represent babies with really low or high birth weight.

11 / 67

Histogram

Bin width = 5 ounces

Bin width = 20 ounces

12 / 67

Exploring Histograms Interactively

13 / 67

There is no "best" number of bins

14 / 67

Etymology

histo comes from the Greek word histos that literally means "anything set up right".

gram: comes from the Greek word gramma which means "that which is drawn".

Online Etymology Dictionary

15 / 67

Histogram vs. Boxplot

Tail tells the tale.

16 / 67

Note: Violin plots display densities, not counts!

17 / 67

Note: Violin plots display densities, not counts!

18 / 67

Visuals with Two Categorical Variables

19 / 67

Standardized Bar Plot

20 / 67

Dodged Bar Plot

21 / 67

Visuals with a single numerical and single categorical variable.

22 / 67

Side-by-side box plots

23 / 67

Visuals with Two Numerical Variables

24 / 67

Scatter plots

Length of gestation can possibly eXplain a baby's birth weight. Gestation is the eXplanatory variable and is shown on the x-axis. Birth weight is the response variable and is shown on the y-axis.

25 / 67

ggplot is based on grammar of graphics.

26 / 67

Data

glimpse(titanic)

Rows: 891
Columns: 6
$ survived <lgl> FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TR~
$ pclass   <chr> "Third", "First", "Third", "First", "Third", "Third", "First"~
$ sex      <fct> sex, sex, sex, sex, sex, sex, sex, sex, sex, sex, sex, sex, s~
$ age      <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 14, 55,~
$ fare     <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21~
$ embarked <fct> Southampton, Cherbourg, Southampton, Southampton, Southampton~

The data frame has been cleaned for you.

27 / 67

Visualizing a Single Categorical Variable

28 / 67

3 Steps of Making a Basic ggplot

1.Pick data

2.Map data onto aesthetics

3.Add the geometric layer

29 / 67

Step 1 - Pick Data

ggplot(data = titanic)

30 / 67

Step 2 - Map Data to Aesthetics

ggplot(data = titanic,
       aes(x = pclass))

31 / 67

Step 3 - Add the Geometric Layer

ggplot(data = titanic,
       aes(x = pclass)) +
  geom_bar()

32 / 67

Create a ggplot using the titanic data frame.
Map the pclass to the x-axis.
Add a layer of a bar plot.

ggplot(data = titanic,
       aes(x = pclass)) +
  geom_bar()

33 / 67

Visualizing a Single Numeric Variable

34 / 67

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

Create a ggplot using the titanic data frame.
Map the fare to the x-axis.
Add a layer of a histogram.

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram()

35 / 67

Step 1 - Pick Data

ggplot(data = titanic)

36 / 67

Step 2 - Map Data to Aesthetics

ggplot(data = titanic,
       aes(x = fare))

37 / 67

Step 3 - Add the Geometric Layer

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram()

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

38 / 67

What is this warning?

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

39 / 67

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram(binwidth = 15)

40 / 67

41 / 67

There is no "best" number of bins

42 / 67

🌈

Pick your favorite color(s) from the list at:

bit.ly/colors-r

43 / 67

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram(binwidth = 15, 
                 color = "white")

44 / 67

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram(binwidth = 15, 
                 fill = "darkred")

45 / 67

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram(binwidth = 15, 
                 color = "white",
                 fill = "darkred")

46 / 67

Visualizing Two Categorical Variables

47 / 67

Stacked Bar-Plot

ggplot(data = titanic,
       aes(x = pclass, 
           fill = survived)) +
  geom_bar()

48 / 67

Standardized Bar Plot

ggplot(data = titanic,
       aes(x = pclass, 
           fill = survived)) + 
  geom_bar(position = "fill")

Note that y-axis is no longer count but we will learn how to change that later.

49 / 67

Dodged Bar Plot

ggplot(data = titanic,
       aes(x = pclass, 
           fill = survived)) + 
  geom_bar(position = "dodge")

Note that y-axis is no longer count but we will change that later.

50 / 67

New Data

Artwork by @allison_horst

51 / 67

New Data

glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel~
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse~
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, ~
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, ~
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186~
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, ~
$ sex               <fct> male, female, female, NA, female, male, female, male~
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007~

52 / 67

Artwork by @allison_horst

53 / 67

Visualizing a single numerical and single categorical variable.

54 / 67

Warning: Removed 2 rows containing non-finite values (stat_ydensity).

Create a ggplot using the penguins data frame.
Map the species to the x-axis and bill_length_mm to the y-axis.
Add a layer of a violin plot.

ggplot(penguins,
       aes(x = species,
           y = bill_length_mm))  +
  geom_violin()

55 / 67

Warning: Removed 2 rows containing non-finite values (stat_boxplot).

Create a ggplot using the penguins data frame.
Map the species to the x-axis and bill_length_mm to the y-axis.
Add a layer of a box plot.

ggplot(penguins,
       aes(x = species,
           y = bill_length_mm))  +
  geom_boxplot()

56 / 67

Visualizing Two Numerical Variables

57 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm))  +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

58 / 67

Linear Relationship

ggplot(babies,
       aes(x = gestation,
           y = bwt))  +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

59 / 67

Considering More Than Two Variables

60 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           color = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

61 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

62 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

63 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species)) +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

64 / 67

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species,
           color = species,
           size = body_mass_g)) +
  geom_point()

Warning: Removed 2 rows containing missing values (geom_point).

65 / 67

66 / 67

Practice

Using either the babies, titanic or penguins data frame ask a question that you are interested in answering. Visualize data to get a visual answer to the question. What is the visual telling you? Note all of this down in your lecture notes.

↑, ←, Pg Up, k	Go to previous slide
↓, →, Pg Dn, Space, j	Go to next slide
Home	Go to first slide
End	Go to last slide
Number + Return	Go to specific slide
b / m / f	Toggle blackout / mirrored / fullscreen mode
c	Clone slideshow
p	Toggle presenter mode
t	Restart the presentation timer
?, h	Toggle this help

Visaluzing Data: Part I

Dr. Mine Dogucu

Review

Bar plot

Box plot

Histogram

Etymology

Histogram vs. Boxplot

Standardized Bar Plot

Dodged Bar Plot

Side-by-side box plots

Scatter plots

Data

Step 1 - Pick Data

Step 2 - Map Data to Aesthetics

Step 3 - Add the Geometric Layer

Step 1 - Pick Data

Step 2 - Map Data to Aesthetics

Step 3 - Add the Geometric Layer

What is this warning?

Stacked Bar-Plot

Standardized Bar Plot

Dodged Bar Plot

New Data

New Data

Linear Relationship

Practice

Review

Help

Visaluzing Data:
Part I