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Data visualization in R is dominated by ggplot2, a package based on the "Grammar of Graphics". This theory breaks down graphs into independent semantic components, such as data, aesthetic mappings, and geometric elements.
The Structure of ggplot2
Every plot created with ggplot2 starts with a call to the ggplot() function, where we supply a data frame and define how columns map to visual properties using the aes() (aesthetics) function.
Next, we add geometric layers using the + operator (note: we use + and not %>% in ggplot2).
The basic template is:
ggplot(data = <DATA_FRAME>, mapping = aes(<AESTHETICS>)) +
<GEOMETRY>()
Aesthetic Mappings (aes) and Geometries (geom_*)
The aes() function defines which variables in your data control the plot's visual attributes:
x: Position on the horizontal axis.y: Position on the vertical axis.color: Color of points or lines.fill: Fill color of closed shapes (e.g. bars).size: Size of elements.
Geometries (geom_) define the type of visual representation:
1. Scatter Plot (geom_point())
Ideal for displaying the relationship between two numeric variables.
ggplot(df, aes(x = age, y = income)) +
geom_point()
2. Line Plot (geom_line())
Commonly used for time series or trend lines.
ggplot(df, aes(x = year, y = sales)) +
geom_line()
3. Bar Plot (geom_bar() and geom_col())
By default, geom_bar() counts the frequency of categories, while geom_col() represents the actual values stored in a y column directly.
# Plots the value of the 'sales' column for each 'category'
ggplot(df, aes(x = category, y = sales)) +
geom_col()
Try it yourself
Exercise 1: Create a basic scatter plot
Create a scatter plot using the df data frame. Map the height column to the x-axis and the weight column to the y-axis. Use geom_point().
הצג רמז
Assicurati di usare ggplot(df, aes(x = height, y = weight)) + geom_point()
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Exercise 2: Add color based on data
Modify the previous plot by mapping the color property to the gender column inside aes() to color the points by gender.
הצג רמז
Includi color = gender all'interno di aes(), ad esempio: aes(x = height, y = weight, color = gender).
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Exercise 3: Line plot
Create a line plot using df. Map the year column to the x-axis and the sales column to the y-axis. Add the geom_line() geometric layer.
הצג רמז
Usa ggplot(df, aes(x = year, y = sales)) + geom_line()
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Exercise 4: Bar plot with geom_col
Create a bar plot to display values directly. Map the category column to the x-axis and the value column to the y-axis. Use the geom_col() geometry.
הצג רמז
Usa ggplot(df, aes(x = category, y = value)) + geom_col()
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Exercise 5: Combine multiple geometries
In ggplot2 you can stack multiple geometric layers. Create a plot that combines both geom_point() and geom_smooth(), mapping x_val to the x-axis and y_val to the y-axis.
הצג רמז
Aggiungi sia geom_point() che geom_smooth() separati dal segno '+'.
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