Facets

• One way to add a variable, particularly useful for categorical variables, is to use facets to split our plot into facets, subplots that each display one subset of the data.

Facets

facet_wrap( VAR ~ . )

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy), 
             alpha = .5) + 
  facet_wrap( class ~ .)

• To facet our plot by a single variable, we can use facet_wrap().

Facets

facet_wrap( VAR ~ . )

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy), 
             alpha = .5) + 
  facet_wrap( class ~ . , nrow = 2)

• nrow (ncol) determines the number of rows (columns) to use when laying out the facets.

Facets

facet_grid( VAR_ROW ~ VAR_COL )

• To facet our plot on the combination of two variables, add facet_grid( VAR_ROW ~ VAR_COL ) to our plot call.

• The first argument of facet_grid() is also a formula.

  • This time the formula should contain two variable names separated by a ~.

Facets

facet_grid( VAR_ROW ~ VAR_COL )

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .5) + 
  facet_grid(drv ~ cyl)

Facets

scales in Facetting

• Option scales in facet_*() is whether scales is
  • fixed ("fixed", the default),
  • free in one dimension ("free_x", "free_y"), or
  • free in two dimensions ("free").

Facets

scales in Facetting

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .5) + 
  facet_grid(drv ~ cyl, 
             scales = "free_x")

Data Visualization

Facets

• Question 3 in Classwork 6

Making Discoveries from Data Visualization

Geometric Objects

How are these two plots similar?

Making Discoveries from Data Visualization

Geometric Objects

• A geom_*() is the geometrical object that a plot uses to represent data.
  • Bar charts use geom_bar() or geom_col();
  • Histograms use geom_histogram() or geom_freqpoly();
  • Line charts use geom_line();
  • Boxplots use the geom_boxplot();
  • Scatterplots use the geom_point();
  • Fitted lines use the geom_smooth();
  • and many more!
• We can use different geom_*() to plot the same data.

Making Discoveries from Data Visualization

Relationship

• From the plots with two or more variables, we want to see co-variation, the tendency for the values of two or more variables to vary together in a related way.

• What type of co-variation occurs between variables?

  • Are they positively associated?
  • Are they negatively associated?
  • Are there no association between them?

Making Discoveries from Data Visualization

Describing a Relationship with Scatterplots

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .3)

Making Discoveries from Data Visualization

Describing a Relationship with Fitted lines

ggplot(data = mpg) + 
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy))

Making Discoveries from Data Visualization

Describing a Relationship with Scatterplots plus Fitted lines

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .3) +
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy))

Making Discoveries from Data Visualization

Distribution

• A distribution of a variable refers to the way its values are spread or arranged across a data.frame.
  • What type of variation occurs within a variable?
• Variation is the tendency of the values of a variable to change from measurement to measurement.
  • We can see variation easily in real life; if we measure any continuous variable twice, we will be likely to get two different values.
  • Which values are the most common? Why?
  • Which values are rare? Why? Does that match your expectations?

Making Discoveries from Data Visualization

Describing a Dristirubion with Histograms

ggplot(data = mpg) + 
  geom_histogram(mapping = 
                aes(x = displ))

Making Discoveries from Data Visualization

Describing a Dristirubion with Boxplots

ggplot(data = mpg) + 
  geom_boxplot(mapping = 
                aes(x = class, 
                    y = hwy))

• geom_boxplot() is used to create box plots (also known as box-and-whisker plots).
  • Box plots provide a visual summary of the distribution of a variable, showing the median, quartiles, and potential outliers.

Making Discoveries from Data Visualization

Describing a Dristirubion with Bar charts

ggplot(data = diamonds) + 
  geom_bar(mapping = 
             aes(x = cut))

• The diamonds data.frame comes in ggplot2 and contains information about ~54,000 diamonds, including cut variable.

Making Discoveries from Data Visualization

Describing a Dristirubion with Bar charts

Making Discoveries from Data Visualization

Time Trend

• A time trend plot, (also known as a time series plot), is used to visualize trends, patterns, and fluctuations in a variable over a specific time period.

  • The x-axis typically represents time, while the y-axis represents the variable being measured.

• We can check the overall direction in which the time-series variable are moving—upwards, downwards, or staying relatively constant over time.

Making Discoveries from Data Visualization

Describing Time Trend with Line charts

path <- 
  "https://bcdanl.github.io/data/NVDA.csv"
nvda <- read_csv(path)

ggplot(data = nvda) + 
  geom_line(mapping = 
                aes(x = Date, 
                    y = Close))

• The nvda data.frame includes NVIDIA’s stock information from 2019-01-02 to 2024-03-04.

Making Discoveries from Data Visualization

Data Visualization with ggplot()

• Step 1. Figure out whether variables of interests are categorical or continuous.

• Step 2. Think which geometric objects, aesthetic mappings, and faceting are appropriate to visualize distributions and relationships.

• Step 3. If needed, transform a given data.frame (e.g., filtered observations, new variables, summarized data) and try new visualizations.

Making Discoveries from Data Visualization

Geometric objects

• A distribution of a categorical variable (e.g., geom_bar() and more)
• A distribution of a continuous variable (e.g., geom_histogram() and more)
• A relationship between two categorical variables (e.g., geom_bar() and more)
• A relationship between two continuous variables (e.g., geom_point() with geom_smooth() and more)
• A relationship between a categorical variable and a continuous variable (e.g., geom_boxplot() and more)
• A time trend of a categorical variable (e.g., geom_bar() and more)
• A time trend of a continuous variable (e.g., geom_line() and more)

Geometric Objects

• Every geom function in ggplot2 takes a mapping argument.

• However, not every aesthetic works with every geom.

  • We could set the shape of a point, but we could not set the shape of a line;
  • We could set the linetype of a line.

Geometric Objects

geom_smooth()

ggplot( data = mpg ) + 
  geom_smooth( mapping = 
                 aes( x = displ, 
                      y = hwy, 
                      linetype = drv) )

Geometric Objects

geom_smooth(method = lm)

ggplot( data = mpg ) + 
  geom_smooth( mapping = 
                 aes( x = displ, 
                      y = hwy),
               method = lm)

Geometric Objects

geom_smooth(group = CATEGORICAL_VAR)

• We can set the group aesthetic to a categorical variable to draw multiple objects.
  • ggplot2 will draw a separate object for each unique value of the grouping variable.

Geometric Objects

geom_smooth(group = CATEGORICAL_VAR)

ggplot(data = mpg) +
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy))

Geometric Objects

geom_smooth(group = CATEGORICAL_VAR)

ggplot(data = mpg) +
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy, 
                    group = drv))

Geometric Objects

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, 
                  y = hwy, 
                  color = drv),
    show.legend = FALSE
  )

Geometric Objects

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .3) +
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy)) +
  geom_smooth(mapping = 
                aes(x = displ, 
                    y = hwy), 
              method = lm, 
              color = 'red')

Geometric Objects

Layers

• Using geom_point(), geom_smooth(), and geom_smooth(method = lm) together is an excellent option to visualize the relationship between the two variables.

Geometric Objects

Layers

ggplot(data = mpg, 
       mapping = 
         aes(x = displ, 
             y = hwy)) + 
  geom_point(mapping = 
               aes(color = class),
             alpha = .3) + 
  geom_smooth()

Geometric Objects

Multiple data.frames

df_subcompact <- filter(mpg, class == "subcompact")

• We can use the same idea to specify different data for each layer.

• Here, our smooth line displays just a subset of the mpg data.frame, the subcompact cars.

  • filter() is the tidyverse-way to filter observations in a data.frame.

• The local data argument in geom_smooth() overrides the global data argument in ggplot() for that layer only.

Geometric Objects

Multiple data.frames

ggplot(data = mpg, 
       mapping = 
         aes(x = displ, 
             y = hwy)) + 
  geom_point(mapping = 
               aes(color = class), 
             alpha = .3) + 
  geom_smooth(data = df_subcompact, 
              se = FALSE)

• The standard error (se) tells us how much the predicted values from a model might differ from the actual values we’re trying to predict.

Data Visualization

Geometric Objects

• Let’s do Classwork 7