How to Change Trace Visibility When Restyling Multiple Data Attributes in R Plotly

Introduction:

Explore the fascinating world of data visualization with R and Plotly. In this tutorial, we will learn how to change trace visibility and restyle multiple data attributes in R Plotly charts. Specifically, we’ll focus on a scenario where we want to demonstrate the steps of a mathematical function that finds primes. By leveraging Plotly’s interactive features, we’ll create a button interface that allows users to explore each step’s contribution to the final output. Let’s dive in!

Generating the Data Frame

Subheading: Creating the Mathematical Function

To begin, we need to generate the necessary data frame for our mathematical function. The code snippet below demonstrates how we can create the data frame and calculate each step

x_values <- seq(0.01, 10, by = 0.01)

df <- data.frame(x = x_values)

# Calculate each step
df$factorial <- factorial(df$x - 1) + 1
df$division <- df$factorial / df$x
df$multiplication <- df$division * pi
df$cosine <- cos(df$multiplication)
df$squaring <- df$cosine ^ 2
df$y <- floor(df$squaring)

Formatting the Plot

Subheading: Marking Holes and Jumps

Now that we have our data frame ready, let’s focus on formatting the plot to properly mark the holes and jumps present within the floored output. To achieve this, we can make use of the plot_ly function and customize the trace visibility. Consider the following code snippet:

# Create plot
p <- plot_ly(df, x = ~x, y = ~y, type = 'scatter', mode = 'lines') %>%
  
  # Add traces for the continuous segments
  add_trace(data = df[-(jump_indices), ] 






,
            x = ~x, y = ~y,
            type = 'scatter', mode = 'lines',
            line = list(color = '#1f77b4', width = 1),
            showlegend = FALSE) %>%

  # Add closed circles for the jumps
  add_trace(data = df[jump_indices, ] 








,
            x = ~x, y = ~y,
            type = 'scatter', mode = 'markers',
            marker = list(symbol = "circle", size = 8, color = "#1f77b4",
                          line = list(color = "#1f77b4", width = 1),
                          opacity = 1),
            showlegend = FALSE) %>%

  # Add open circles for the holes
  add_trace(data = df[jump_indices, ] 








,
            x = ~x, y = ~y - 1,
            type = 'scatter', mode = 'markers',
            marker = list(symbol = "circle-open", size = 8, color = "#1f77b4",
                          line = list(color = "#1f77b4", width = 1),
                          opacity = 1),
            showlegend = FALSE) %>%

  # Apply theme
  {do.call(layout, c(list(.), plotly_theme))}

Implementing Button Logic

Subheading: Swapping Y Values and Adjusting Trace Visibility

Now comes the interesting part—implementing the necessary button logic to swap the plotted y values and change the visibility of traces when the corresponding button is pressed. Let’s take a look at the code snippet below:

# Add buttons for each step of the function
p <- p %>% 
  config(displayModeBar = F)