Map with Data Labels in R

Mapping in R
Mapping in R

Mapping is one of the better features of PowerBI.  It is one of the more distinguishing feature differences between Excel and PowerBI.  You can produce a map inside an excel document using Bing maps, however, the experience has always felt a little like an after-thought.  Mapping within PowerBI has a planned, and thoughtful integration.  While the mapping functionalities within PowerBI Desktop are far improved when compared to excel, there are still some limitations to the mapping visuals.  This past week I encountered such an example.  We wanted to draw a map of the United States, add state name labels and some dimensional property like year over year percent change.

I started with the standard map visual, but this didn’t work because there is no ability to shade each state individually.  This just looked like a bubbled mess.

Globe Map Visual
Globe Map Visual

Next, I tried the Filled Map visual.  While this mapping visual provides the colored states it lacks the ability to add data labels onto the map.  Clicking on the map would filter down to the selected state, which could show a numerical value.  Alternatively, you can place your mouse over a state and the resulting tag will show the details of the state (hovering example provided below).

Filled Map Visual
Filled Map Visual

Still this did not quite meet my visual requirements.  I finally decided to build the visual in R which provided the correct amount of flexibility. See below for final result.  You can download the pbix file from the Microsoft R Script Showcase.

R Map Visual
R Map Visual

In this visual, each state is shaded with a gradient color scale.  The states with the lowest sales are grey and the states with higher sales numbers transition to dark blue.  The darker the blue the more sales the state saw.  Each state has an applied label.  The color of the label denotes the percent change in sales.  If the color is green then the sales this year were higher than last year, red means that the state sales were lower this year.  The state name is listed in the label as well as the calculation for the year over year percent change.

Alright, let’s start the tutorial.

First, before we open PowerBI we need to load the appropriate packages for R.  For this visual you will need to load both the maps and the ggplot2 packages from Microsoft R Open.

Open the R console and use the following code to install maps.

Install Maps Package
Install Maps Package

Repeat this process for installing ggplot2.


After installing the R packages we are ready to work in PowerBI Desktop.  First, we need to load our sample data.  Open up PowerBI Desktop and start a blank query.  On the View ribbon in the query editor open the Advanced Editor and enter the following M code.

Note: If you need some more help loading the data follow this tutorial about loading data using the Advanced Query Editor.  This tutorial teaches you how to copy and paste M code into the Advanced Editor.

  Source = Excel.Workbook(Web.Contents(""), null, true),
  StateData_Table = Source{[Item="StateData",Kind="Table"]}[Data],
  #"Changed Type" = Table.TransformColumnTypes(StateData_Table,{{"StateName", type text}, {"Abb", type text}, {"TY Sales", Int64.Type}, {"state", type text}, {"Latitude", type number}, {"Longitude", type number}, {"LY Sales", Int64.Type}, {"Chng", type number}}),
  #"Renamed Columns" = Table.RenameColumns(#"Changed Type",{{"TY Sales", "Sales"}})
  #"Renamed Columns"

After pasting the code into the Advanced Editor click Done to load the data.  While in the Query Editor, rename the query to be StateData, then click Close & Apply on the Home ribbon.

Load Mapping Data
Load Mapping Data

We still need to prepare the data further by adding two calculated columns.  Click the bottom half of the New Measure button on the Home ribbon and select New Column.

Add New Column
Add New Column

Enter the following code into the formula bar that appears after clicking New Column.

Change = StateData[Abb] & " " & ROUND(100*StateData[Chng],0) & "%"
Change Column Measure
Change Column Measure

Again, click on the New Column button found on the Home ribbon and add the code for a color column.

Color = if(StateData[Chng] > 0 , "Dark Green", "Dark Red")
Color Column Measure
Color Column Measure

The Fields list should now look like the following.

Fields List
Fields List

Add the R visual with the following fields.

R Visual Fields
R Visual Fields

Add the following R script into the R Script Editor.

# Load the ggplot2 and maps packages

# Load the mapping data into a dataframe called states_map
 states_map <- map_data("state")

# Start ggplot2 by sending it the dataset and setting the map_id variable to state
 ggplot(dataset, aes(map_id = state)) +

# Add the map layer, define the map as our data frame defined earlier
 # as states_map, and define the fill for those states as the Sales data
 geom_map(map = states_map, aes(fill=Sales)) +

# Add the data for the labels
 # the aes defines the x and y cordinates for longitude and latitude
 # colour = white defines the text color of the labels
 # fill = dataset$Color defines the label color according to the column labeled Color
 # label = dataset$Change defines the text wording of the label
 # size = 3 defines the size of the label text
 geom_label( aes(x=Longitude, y=Latitude), 
  label=dataset$Change, size=3
  ) +

# define the x and y limits for the map
 expand_limits(x = states_map$long, y = states_map$lat) +

# define the color gradient for the state images
 scale_fill_gradient( low = "dark grey", high = "#115a9e") +

# remove all x and y axis labels
 labs(x=NULL, y=NULL) +

# remove all grid lines
 theme_classic() +

# remove other elements of the graph
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  axis.text = element_blank()

After adding the R script press the execute button to reveal the map.

Paste R Script
Paste R Script
Final Map Product
Final Map Product

Notice how we have data included for Alaska and Hawaii but those states are not drawn.  We want to remove the Alaska and Hawaii data points.  Add the StateName field to the Page Level Filters and then click Select All.  Now, un-check the boxes next to Alaska and Hawaii.  The data is now clean and the map correctly displays only the continental United States.

Page Level Filters
Page Level Filters

Here is the filtered final map product.

Filtered Final Map
Filtered Final Map

Thanks for following along.  I hope you enjoyed this tutorial.  Please share if you liked this content.  See you next week.



  1. Hi,
    Thanks for your article!
    I was hoping you could do one with arules package for Association Rules and arulesViz. I have a table with Transaction ID and Product Name, just that simple, how it will be the Scripting in such scenario which is likely the most common an analyst will find dealing in Power BI?

    • Great suggestion. I have not had personal experience working with the arules in R. However, I have done some research on this topic since you suggested it, and it looks very interesting. I will add it to my potential topics. Thanks.

  2. I’m fairly new to R, but noticed this example only works if the dataset is summarized and there is no date dimensions. Is there a similar solution or R package to allow for slicers on dates or other dimensions?

    • Matt, thanks for the question. After producing this tutorial I learned some more about this R package. The reason this does not work with filters is that when data is passed to R to visualize each state. Each state has to have a number. Thus, when you filter your dataset you are actually removing some of the state data. In order to conduct filtering as the r script would need to be modified such that each state starts with a 0 and then only the data that is passed to the R script would provide values for each state.

  3. This is very cool. I am probably asking this question in the wrong place, but here goes anyway.

    I would like to do something like this only using a construction project’s general arrangement drawing broken out into “areas”. We are working on a Oil & Gas project that’s 1 mile long and 3/4 of a mile wide. We have electronic CAD drawings with coordinates but I am not sure how to approach getting this into Power BI so we can create reports by Unit or Area for cost, hours, performance, by discipline, etc… Any suggestions would be appreciated!

    • great question. I think you would be able to graph you sections of pipeline inside the R visual. However, the R visuals are only a view of the data. It is not interactive, meaning you can’t click on images with in the R visual to change the data. Thus, in order to be able show the high cost areas you would have to employ filters external to the R visual. I have done this before and it gets quite complicated very quickly. A better option might be to explore using a custom visual such as Synoptic Panel by OKViz. This visuals allows you to blend images and details about an image such rooms or building layouts in a way that enables filtering.

  4. Does the solution work only for the US or any country around the world? What do I need to do to make similar map for another country?

    • Great question. In the Tutorial I narrowed down the map library to only the U.S. and only used the data for the U.S. states. Here is the documentation on the maps library that I used. Read through this documentation because there is the ability to plot other regions using the maps library.

  5. The last snippet of R code is not working for me. I get an error: “Error in ggplot(dataset, aes(map_id = state)) :
    object ‘dataset’ not found”
    I am using R version 3.5.0 (2018-04-23). Any ideas? Thank you.

  6. Very helpful indeed!
    But can we group the states of America including Hawaii and Alaska into Sales Regions and have the same look as to what you have in this article using R?

    Can you give an example if it’s possible? Thanks!

    • You would want to adjust this line of code to change the x / y limits of the graph:
      expand_limits(x = states_map$long, y = states_map$lat) +
      This is where the View window is set.

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