Highcharter Package in R: Customizing Data Label Format and Axis Label Angle

Introduction

The highcharter package is a popular choice for creating interactive visualizations in R, wrapping the powerful Highcharts library. In this article, we’ll delve into two essential aspects of customizing your highcharter charts: data label format and axis label angle.

Understanding Data Labels

Data labels are small text annotations that appear on each bar or point in a chart, providing additional information about the data being represented. In the context of highcharter, data labels can be customized to display specific values, such as percentages or numbers with decimal places.

Changing Data Label Format to Percentage

By default, highcharter uses a format string {point.label} to display data label values. However, this format string is not flexible enough for our needs. To change the data label format to percentage, we need to use a different format string that includes the value of point.y divided by 100.

According to the official highcharts documentation, the correct format string to use for percentages is {point.y}%. However, in highcharter’s context, we also need to specify the position and alignment of the data label.

hc_add_series(name=var,data=df$Proportion,type = "column",dataLabels = list(format='{point.y}%', align='right'), enabled = TRUE)

As you can see from the code snippet above, we’ve added two new arguments to the dataLabels list: format and align. The format argument takes a format string that includes {point.y}, which is the value of the y-coordinate of each point. By appending % to this value, we can display the percentage correctly.

The align argument specifies the horizontal alignment of the data label text within its container. In our case, we’ve set it to 'right', so the percentage value will be displayed on the right side of each bar.

Setting Axis Label Angle

To customize the axis labels in highcharter, we can use the labels argument in conjunction with the rotation option. The rotation argument allows us to specify the angle at which the label text should be rotated.

According to the official highcharts documentation, the correct way to set the axis label rotation is by adding a rotation property to the labels object. For example:

hc_xAxis(categories=df[[1]]) %>%
  hc_xAxis(labels = list(rotation = 90))

In this code snippet, we’ve added a new rotation argument to the hc_xAxis(labels) function call. This sets the axis label rotation to 90 degrees, which should display the labels on their sides rather than in a vertical orientation.

Complete Example

Let’s put everything together into a complete highcharter chart example that demonstrates how to customize data label format and axis label angle:

hcbar_categorycount_vertical <- function(data=x,var=y){
  # Create a dataframe with the proportion table
  df <- data.frame(prop.table(table(data[var])))
  names(df) <- c(var,'Proportion')
  
  # Round proportions to two decimal places and sort in descending order
  df$Proportion <- round(df$Proportion*100,2)
  df <- df%>% arrange(-Proportion)
  
  # Convert categories to factors for easier manipulation
  df[,1] <- as.character(df[,1])
  df[,1] <- factor(df[,1], levels = df[,1])
  
  # Calculate cumulative proportions
  df$Cumulative <- round(cumsum(df$Proportion),2)
  
  # Create the highcharter chart
  highchart(debug = TRUE) %>%
    hc_xAxis(categories=df[[1]]) %>%
    hc_yAxis(labels = list(format = "{value}%")) %>%
    hc_add_series(name=var,data=df$Proportion,type = "column",dataLabels = list(format='{point.y}%', align='right'), enabled = TRUE) %>%
    hc_xAxis(labels = list(rotation = 90))
}

When you run this code, it should generate a bar chart with data labels that display the percentage values and axis labels rotated to their sides.

Conclusion

Customizing data label format and axis label angle in highcharter is relatively straightforward once you understand how these properties work together. By using the correct format strings and arguments, you can create more informative and visually appealing charts for your data analysis tasks.

Last modified on 2023-08-13