Understanding the Limitations of Plotly with ggplot2

Plotly is a popular data visualization library that can be used to create interactive plots. However, when using Plotly with ggplot2, there are some limitations and quirks that can affect the appearance of the plot.

In this article, we will explore one such limitation: the issue of scaling commands not being applied to the plot. Specifically, we will examine how to create a plot with custom x-axis tick labels and a y-axis scale that ranges from -6 to 3.

Background on ggplot2 and Plotly

ggplot2 is a popular data visualization library for R that provides a consistent and powerful way to create high-quality plots. It uses a grammar of graphics approach, where each layer of the plot is added separately using functions such as aes(), geom_point(), etc.

Plotly, on the other hand, is a separate library that can be used to create interactive plots. When used with ggplot2, Plotly can extend the capabilities of ggplot2 by providing additional features such as zooming and panning.

However, when using Plotly with ggplot2, there are some limitations and quirks that can affect the appearance of the plot. In this article, we will explore one such limitation: the issue of scaling commands not being applied to the plot.

The Problem with Scaling Commands

When creating a plot with Plotly, you may find that certain scaling commands do not work as expected. For example, when using scale_x_date() with ggplot2, you may expect the x-axis tick labels to change automatically when the data range is adjusted. However, in some cases, this does not happen.

In fact, the issue is more complex than just this. When Plotly is used with ggplot2, the date fields in the data become character fields, rather than date fields. This means that when creating a plot with custom x-axis tick labels, you may need to use additional code to format the dates correctly.

A Solution using ggplotly

One way to overcome this limitation is to use the ggplotly() function from the ggplot2 package. This function allows you to create an interactive Plotly plot directly from a ggplot2 object.

When using ggplotly(), you can specify custom x-axis tick labels and y-axis scale using the xaxis and yaxis arguments, respectively.

Here is an example of how you might use ggplotly() to create a plot with custom x-axis tick labels and a y-axis scale that ranges from -6 to 3:

library(ggplot2)
library(plotly)

# Create sample data
set.seed(353)
website <- data.frame(
  Periode = sample(seq(as.Date("1998-03-31"), as.Date("2022-06-30"), by = "week"), 50),
  Index = sample(runif(100, -4.5, 2.5), 50)) %>% 
  arrange(Periode)

# Create plot
plot_ly(type = "scatter", mode = "markers",
        marker = list(color = "#7AA489", size = 8,
                      line = list(width = 2, color = "#003478")),
        data = website, x = ~Periode, y = ~Index) %>% 
  add_lines(line = list(width = 5, color = "#7AA489")) %&gt;% 
  layout(xaxis = list(dtick = "M18",         # every 18 months
                      tickformat = "%b %Y"), # Abbrev Month, 4-digit year
         yaxis = list(range = c(-6, 3),      # show this range
                      zeroline = F,          # remove horizontal line at 0
                      showline = T),         # vertical line along y-axis
         showlegend = F) %&gt;%                 # no legend

# Use ggplotly() to create interactive plot
ggplotly(plot, dynamicTicks = TRUE) %&gt;%
  rangeslider(borderwidth = 1)

Note that when using ggplotly(), you will need to specify custom x-axis tick labels and y-axis scale using the xaxis and yaxis arguments, respectively.

Specifying the Range on the y-axis

One way to achieve a y-axis scale that ranges from -6 to 3 is to use the range argument in the yaxis layer:

layout(yaxis = list(range = c(-6, 3)))

However, this will not change the appearance of the plot. To actually change the range of the y-axis, you need to use the limits argument.

Here is an example of how you might use the limits argument to specify a custom y-axis scale:

layout(yaxis = list(range = c(-6, 3), limits = c(-6,4)))

Non-Dynamic Date x-axis Tick Text Formatting

When using Plotly with ggplot2, the date fields in the data become character fields, rather than date fields. This means that when creating a plot with custom x-axis tick labels, you may need to use additional code to format the dates correctly.

One way to do this is by using the tickformat argument in the xaxis layer:

layout(xaxis = list(dtick = "M18",         # every 18 months
                    tickformat = "%b %Y")) # Abbrev Month, 4-digit year

This will format the x-axis tick labels as month and year.

Bold Axis Labels

Unfortunately, there is no parameter you can set to make the axis labels bold. However, you can use HTML widgets to achieve this effect.

Here is an example of how you might use HTML widgets to make the axis labels bold:

htmlwidgets::onRender("function(el, x){
    gimmeX = document.querySelector('g.infolayer g.g-xtitle text');
    gimmeY = document.querySelector('g.infolayer g.g-ytitle text');
    gimmeX.style.fontWeight = 'bold';
    gimmeY.style.fontWeight = 'bold';
  }")

This code uses the htmlwidgets::onRender() function to execute a JavaScript code that sets the font weight of the axis labels to bold.

Conclusion

When using Plotly with ggplot2, there are some limitations and quirks that can affect the appearance of the plot. In this article, we explored one such limitation: the issue of scaling commands not being applied to the plot.

We also examined how to create a plot with custom x-axis tick labels and a y-axis scale that ranges from -6 to 3 using ggplotly(). Additionally, we discussed how to format the dates correctly when creating a plot with custom x-axis tick labels. Finally, we looked at how to make the axis labels bold using HTML widgets.

We hope this article has been helpful in understanding the limitations of Plotly with ggplot2 and how to work around them.

Last modified on 2024-06-03