Scatter Plot using Plotly in R

A scatter plot in R is a graphical tool used to display the relationship between two continuous variables. Each point represents one observation, with its position determined by values on the x and y axes.

Uses of Scatter Plot

We can use a scatter plot for the following

• To observe the relationship between two variables.
• To identify trends or patterns in data.
• To detect outliers.

Scatter Plots in R using Plotly

To create interactive scatter plots in R, we use the Plotly package, which offers interactive and customizable visualizations.

1. Installing the Required Library

First, we have to install the Plotly package.

install.packages("plotly")

2. Loading the package

Now load the Plotly library to use its functions.

library(plotly)

3. Creating Basic Scatter Plot

Let's define two simple numeric vectors and create a basic scatter plot.

• plot_ly(): This is the main function used to create interactive plots in R using the Plotly package.

x <- c(1, 2, 3, 4, 5)
y <- c(2, 3, 5, 4, 7)

plot_ly(x = x, y = y, type = 'scatter', mode = 'markers')

Output:

4. Customizing Scatter Plots

We can enhance the plot by adding axis labels and a title.

• x = x, y = y: These arguments specify the data to be plotted. The values in vector x are mapped to the x-axis and the values in vector y are mapped to the y-axis.
• type = 'scatter': This tells Plotly to create a scatter plot, which displays individual data points.
• mode = 'markers': This sets the plot to display only the data points (as dots or circles), without connecting lines or text labels.

plot_ly(x = x, y = y, type = 'scatter', mode = 'markers') %>%
  layout(
    xaxis = list(title = 'X-axis'),
    yaxis = list(title = 'Y-axis'),
    title = 'Customized Scatter Plot'
  )

Output:

5. Changing Marker Color and Size

We adjust marker color and size to make the plot visually appealing.

plot_ly(x = x, y = y, type = 'scatter', mode = 'markers', 
        marker = list(color = 'red', size = 10))

Output:

6. Adding Regression Line

We overlay a regression line to show the trend in the data.

• add_trace(): Adds a new layer to the existing plot. In this case, we’re adding a line to represent the regression model.
• x = x: The x-values for the regression line match those of the original data.
• y = lm(y ~ x)$fitted.values: Fits a simple linear regression model (y as a function of x) and extracts the predicted values to plot the trend.
• mode = 'lines': Ensures the new trace is drawn as a continuous line, not as points or markers.
• line = list(color = 'blue'): Sets the color of the regression line to blue.
• name = 'Regression Line': Adds a legend label for the line trace.

plot_ly(x = x, y = y, type = 'scatter', mode = 'markers') %>%
  add_trace(
    x = x,
    y = lm(y ~ x)$fitted.values,
    mode = 'lines',
    line = list(color = 'blue'),
    name = 'Regression Line'
  )

Output:

7. Multiple Scatter Plots

To create the first scatter plot, we start by plotting the first dataset (x1 and y1). In the code, plot_ly(x = x1, y = y1, type = 'scatter', mode = 'markers', name = 'Dataset 1'):

• x = x1: Specifies the values of x1 will be plotted on the x-axis.
• y = y1: Specifies the values of y1 will be plotted on the y-axis.
• type = 'scatter': This defines the plot as a scatter plot.
• mode = 'markers': This ensures that the points are plotted as individual markers (no connecting lines).
• name = 'Dataset 1': Assigns a name to this trace for the legend, which will display as "Dataset 1".

Next, to add a second dataset (x2 and y2), we use add_trace(x = x2, y = y2, type = 'scatter', mode = 'markers', name = 'Dataset 2'):

• x = x2: Specifies that x2 values will be plotted on the x-axis for the second dataset.
• y = y2: Specifies that y2 values will be plotted on the y-axis for the second dataset.
• type = 'scatter': Again, this defines the second trace as a scatter plot.
• mode = 'markers': Ensures that the second dataset’s points are displayed as markers.
• name = 'Dataset 2': Assigns the name "Dataset 2" to this second trace for the legend.

By using add_trace(), the second dataset is added to the same plot, allowing both datasets to appear on the same graph with distinct labels.

x1 <- c(1, 2, 3, 4, 5)
y1 <- c(2, 3, 5, 4, 7)

x2 <- c(1, 2, 3, 4, 5)
y2 <- c(3, 4, 2, 6, 5)


plot_ly(x = x1, y = y1, type = 'scatter', mode = 'markers', name = 'Dataset 1')%>%
  add_trace(x = x2, y = y2, type = 'scatter', mode = 'markers', name = 'Dataset 2')

Output:

8. 3D Scatter Plot

We can also visualize data in three dimensions using scatter3d.

• x, y, and z: Represent the coordinates of each data point in 3D space.
• type = 'scatter3d': Indicates that the plot will be three-dimensional.
• mode = 'markers': Tells Plotly to display the data using markers.
• marker = list(color = ...): Specifies how the markers should be colored.

x <- c(1, 2, 3, 4, 5)
y <- c(2, 3, 5, 4, 7)
z <- c(10, 8, 12, 9, 15)
categories <- c("A", "B", "A", "C", "B") 

category_colors <- c("red", "blue", "green")

plot_ly(x = x, y = y, z = z, type = 'scatter3d', mode = 'markers',
        marker = list(color = factor(categories, levels = unique(categories), 
                                     labels = category_colors)))

Output:

The 3D scatter plot makes it easy to see how the data points are spread out in space, with different colors showing different categories.