In this article I will create map-based charts in R, using the leaflet.minicharts package in R. I already introduced Leaflet for creating markers on maps and for visualizing spatial distributions with density heatmaps. In this blog post I create barcharts, piecharts and polar area charts using leaflet.minicharts in R.

Creating a basemap with Leaflet in R

First, I create a basemap for my visualization. I can do so using the Leaflet package in R. In the lines of code below I create the basemap.

library(knitr)
library(leaflet)

basemap = leaflet() %>% 
  addTiles() %>% 
  addProviderTiles(providers$OpenStreetMap.DE) %>%
  setView(-98,38.5,zoom=4)

basemap

In this case I want to visualize data for USA. For this reason I set the map view to display most of the US states. Hawaii is not shown.

Importing in sales data for leaflet.minicharts

Next, I import sales data for each relevant US state. The data has already been prepared and summarized in Excel. This is why the data is available in a csv-file that I import into memory.

data = read.csv("sales.csv",header=TRUE)
head(data)

##           state  longitude latitude sales_sensors sales_pumps sales_displays
## 1     Wisconsin  -89.50000 44.50000         13000   12002.374      5664.6262
## 2 West Virginia  -80.50000 39.00000          5000    2739.885      3774.7759
## 3       Vermont  -72.70000 44.00000          3400    1041.972      2332.2328
## 4         Texas -100.00000 31.00000         37000    6287.578     13425.8577
## 5  South Dakota -100.00000 44.50000          5600    3115.934       558.0957
## 6  Rhode Island  -71.74233 41.74233          4000    3623.527       873.5025
##   sales_motoroil sales_filters sales_tires sales_headlamps sales_rearlighting
## 1     10646.1263     3962.7175    4863.633       1921.3345           3692.084
## 2       844.2269      524.2226    4728.812        425.4734           2107.706
## 3       640.9263     1003.8087    2410.252       3056.3377           2659.093
## 4     16161.9943    30441.3494   10438.494       2196.3884          35905.807
## 5      5338.5218     4551.7116    3200.116       3823.2335           3946.851
## 6      1758.7899     3633.4848    3170.961       3211.9275           2669.132
##   sales_interiorlighting sales_total
## 1             11227.7133    66980.61
## 2               956.6307    21101.73
## 3              3177.5397    19722.16
## 4              6316.8175   158174.29
## 5               723.8857    30858.35
## 6              3967.7682    26909.09

The dataset summarizes sales revenue from last fiscal year for a automotive sparepart retail business. The retailer specializes on the European car park and currently ships all its sales orders to customers by dropshipping from Germany, France, Italy, and Spain. Sales is categorized into major product groups.

Visualizing sales data using leaflet.minichart

To begin with I create a bar chart map using Leaflet and leaflet.minichart. I use the bar charts to compare sales of pumps and sensors across USA.

library(leaflet.minicharts)
library(magrittr)
library(dplyr)

colors = c("#FF0000", "#428EF4")

basemap %>%
  addMinicharts(
    data$longitude, data$latitude,
    chartdata = select(data,sales_sensors,sales_pumps),
    colorPalette = colors,
    width = 45, height = 45
  )

How does this compare to total sales? Below bar chart map delivers the answer to this question.

colors = c("#FF0000", "#428EF4","black")

basemap %>%
  addMinicharts(
    data$longitude, data$latitude,
    chartdata = select(data,sales_sensors,sales_pumps, sales_total),
    colorPalette = colors,
    width = 45, height = 45
  )

I can make an overview like this using pie charts as well. See below code:

colors <- c("#FF0000", "#428EF4")

basemap %>%
  addMinicharts(
    data$longitude, data$latitude,
    type = "pie",
    chartdata = select(data,sales_sensors,sales_pumps), 
    colorPalette = colors, 
    width = 60 * sqrt(data$sales_total) / sqrt(max(data$sales_total)), transitionTime = 0
  )

Pie size is defined by the circle radius. It results from total sales revenue across all product categories. I now generate one more map that displays pie charts with all product categories.

basemap %>%
  addMinicharts(
    data$longitude, data$latitude,
    type = "pie",
    chartdata = select(data,sales_sensors,sales_pumps,sales_displays,sales_motoroil,sales_filters,sales_tires,sales_headlamps,sales_rearlighting,sales_interiorlighting), 
    width = 60 * sqrt(data$sales_total) / sqrt(max(data$sales_total)), transitionTime = 0
  )

By generating visualizations like this I help the retailer understand the US market from a distribution logistics point of view.

Other chart types with leaflet.minicharts

Using leaflet.minicharts I can also show material flows as directional flows on the map. For this I need to construct a dataframe that contrains “source” and “sink” coordinates in the form of latitude and longitude values. In addition, flow volume must be quantified in the dataframe to plot.

Last, but not least, I can also use leaflet.minicharts to create animated charts. This is e.g. helpful when showing some time-based trend on a map.

Summary of this leaflet.minicharts post

In this article I introduced leaflet.minicharts as a package in R for map-based chart plotting. In previous articles I had introduced Leaflet for e.g. marker plots or map-based heatmaps. This article documented how leaflet.minicharts can be used to add e.g. bar charts and pie charts to a Leaflet map.

Related content on this blog

If you are interested in spatial data visualization I recommend that you have a look at some other related articles:

• Link: deckgl in R for spatial data visualisation
• Link: Geocoding spatial data with tidygeocoder in R
• Link: Spatial SCM data visualisation in R
• Link: Spatial heatmaps in Python using Leaflet.js via Folium module
• Link: Leaflet heatmaps in R
• Link: Map-based scatterplots with deckgl in R
• Link: Single warehouse problem locating warehouse at center-of-mass using R
• Link: Free tools for spatial data visualization

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python