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Introduction to ggplot

Dr. Mine Dogucu

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  • This specific lecture will not necessarily show you beautiful plots.
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  • This specific lecture will not necessarily show you beautiful plots.

  • In this lecture we will focus on how ggplot works.

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  • This specific lecture will not necessarily show you beautiful plots.

  • In this lecture we will focus on how ggplot works.

  • In the next lecture we will improve the plots that we make.

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ggplot is based on grammar of graphics.

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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.

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Visualizing a Single Categorical Variable

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If you could speak to R in English, how would you tell R to make this plot for you?

OR

If you had the data and had to draw this bar plot by hand, what would you do?

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Possible ideas

  • Consider the data frame
  • Count number of passengers in each pclass
  • Put pclass on x-axis.
  • Put count on y-axis.
  • Draw the bars.

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These ideas are all correct but some are not necessary in R

  • Consider the data frame
  • Count number of passengers in each pclass
  • Put pclass on x-axis.
  • Put count on y-axis.
  • Draw the bars.

R will do some of these steps by default. Making a bar plot with another tool might look slightly different.

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3 Steps of Making a Basic ggplot

1.Pick data

2.Map data onto aesthetics

3.Add the geometric layer

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Step 1 - Pick Data

ggplot(data = titanic)

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Step 2 - Map Data to Aesthetics

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

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Step 3 - Add the Geometric Layer

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

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  • 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()
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Visualizing a Single Numeric Variable

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## `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()
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Step 1 - Pick Data

ggplot(data = titanic)

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Step 2 - Map Data to Aesthetics

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

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Step 3 - Add the Geometric Layer

ggplot(data = titanic,
       aes(x = fare)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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What is this warning?

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

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

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There is no "best" number of bins

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🌈

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

bit.ly/colors-r

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

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

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

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Visualizing Two Categorical Variables

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Stacked Bar-Plot

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

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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.

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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.

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New Data

Artwork by @allison_horst

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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…
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Artwork by @allison_horst

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Visualizing a single numerical and single categorical variable.

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## 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()
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Visualizing Two Numerical Variables

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm))  +
  geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).

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Considering More Than Two Variables

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           color = species)) +
  geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species)) +
  geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).

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ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           shape = species)) +
  geom_point()
## Warning: Removed 2 rows containing missing values (geom_point).

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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).

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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).

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  • This specific lecture will not necessarily show you beautiful plots.
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