How can you use tree maps to visualize hierarchical data in R&D?

1 What are tree maps?

Tree maps are rectangular plots that divide the area into tiles based on a variable of interest, such as size, frequency, or value. Each tile represents a data point or a group of data points, and the tiles are nested within larger tiles that represent higher levels of hierarchy. The tiles can also be colored or shaded according to another variable, such as category, subcategory, or measure. Tree maps can help you identify patterns, trends, outliers, and proportions in your data.

2 Why use tree maps in R&D?

Tree maps can be useful in R&D for several reasons. First, they can help you explore and communicate complex and multidimensional data in a simple and effective way. For example, you can use tree maps to compare the performance, impact, or budget of different R&D projects, subprojects, or activities across different dimensions, such as time, location, or sector. Second, they can help you optimize the use of space and reduce clutter in your data visualization, especially when you have a large number of data points or categories. Third, they can help you customize and enhance your data visualization by using different shapes, colors, labels, and layouts.

3 How to create tree maps in R?

To create tree maps in R, you will need to install and load the treemap package, which provides a function called treemap() that takes a data frame as input and produces a tree map as output. You will also need to specify the variables that define the hierarchy, the size, and the color of the tiles. For example, if you have a data frame called df that contains the following columns: project, subproject, value, and category, you can create a tree map with the following code:

library(treemap)  
treemap(df,  
        index = c("project", "subproject"), # hierarchy  
        vSize = "value", # size  
        vColor = "category", # color  
        type = "value" # color palette  
)        

This will create a tree map that shows the value of each subproject within each project, and the category of each subproject as a color.

4 How to interpret tree maps in R&D?

To interpret tree maps in R&D, you will need to pay attention to the size, color, and position of the tiles. The size of the tiles indicates the relative importance or magnitude of the data points or groups. The color of the tiles indicates the category or measure of the data points or groups. The position of the tiles indicates the hierarchy or structure of the data. For example, in the previous tree map, you can see that project A has the largest value, followed by project B and C. You can also see that project A has three subprojects, while project B and C have two subprojects each. You can also see that project A has two subprojects in category 1 and one subproject in category 2, while project B has one subproject in category 1 and one subproject in category 3, and project C has two subprojects in category 2.

5 How to improve tree maps in R&D?

To make your data visualization more appealing and informative, there are some tips and tricks you can use to improve tree maps in R&D. For instance, it is important to use meaningful and consistent labels for the tiles and the legend. Additionally, contrasting and harmonious colors should be used for the tiles and the background. Different shapes or borders can be used to highlight or separate the tiles. Moreover, various layouts or algorithms can be used to arrange the tiles, such as slice-and-dice, squarified, or ordered. Finally, interactive features, such as tooltips, zooming, or filtering, can be added to provide more details or insights to the tree map.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?