In this blogpost I provide a coding example in R for how to create a map-based scatterplot using the deckgl package. This can come in handy when visualising data with some spatial aspect. For example you might want to visualise the geo-spatial distribution of certain property clusters.
Before I can apply the deckgl package’s functionality I need a geocoded dataset, i.e. a dataset which contains information about longitude and latitude coordinates of the property of interest. For this I will use a geocoding function applying the Open Street Map API. I found the function on datascienceplus.com.
# osm geocoder
# source: https://datascienceplus.com/osm-nominatim-with-r-getting-locations-geo-coordinates-by-its-address/
osm_geocoder <- function(address = NULL)
{
if(suppressWarnings(is.null(address)))
return(data.frame())
tryCatch(
d <- jsonlite::fromJSON(
gsub('\\@addr\\@', gsub('\\s+', '\\%20', address),
'http://nominatim.openstreetmap.org/search/@addr@?format=json&addressdetails=0&limit=1')
), error = function(c) return(data.frame())
)
if(length(d) == 0)
return(data.frame())
return(data.frame(lon = as.numeric(d$lon), lat = as.numeric(d$lat)))
}
Next, I need to initialize the data I want to plot. I stored a list of cities in a separate csv-file. I thus read in this file and convert it into a dataframe. I then use the geocoding function to geocode all the cities in my data frame. In addition I add normally distributed values to the “entries” and “exits” column; required for determining e.g. circle design in the scatterplot.
# ensuring that required packages are loaded
library(deckgl)
## deckgl 0.1.8 wrapping deckgljs 6.2.4
## Documentation: https://crazycapivara.github.io/deckgl/
## Issues, notes and bleeding edge: https://github.com/crazycapivara/deckgl
library(magrittr)
library(jsonlite)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
# setting up the data frames
scatter_data_df_1 <- data.frame(matrix(nrow=30,ncol=6))
column_names <- c("name","code","address","entries","exits","coordinates")
colnames(scatter_data_df_1) <- column_names
city_list_1_df <- read.csv("city list 1.csv",header = FALSE, stringsAsFactors = FALSE)
# geocode cities into longitude and latitude
for(i in 1:nrow(city_list_1_df)){
dum_coord <- osm_geocoder(toString(city_list_1_df$V1[i]))
scatter_data_df_1$name[i] <- paste0("city liste 1 : ",i)
scatter_data_df_1$code[i] <- c("CL1")
scatter_data_df_1$address[i] <- toString(city_list_1_df$V1[i])
scatter_data_df_1$entries[i] <- as.integer(rnorm(1,mean=3000,sd=1000))
scatter_data_df_1$exits[i] <- as.integer(rnorm(1,mean=3000,1000))
scatter_data_df_1$coordinates[i] <- list(c(as.numeric(dum_coord[1]),as.numeric(dum_coord[2])))
}
# print head of scatter_data_df_1
head(scatter_data_df_1)
## name code address entries exits
## 1 city liste 1 : 1 CL1 Berlin Germany 5008 3112
## 2 city liste 1 : 2 CL1 Karlsruhe Germany 2002 2223
## 3 city liste 1 : 3 CL1 Stuttgart Germany 3453 3498
## 4 city liste 1 : 4 CL1 Mannheim Germany 2478 3041
## 5 city liste 1 : 5 CL1 Heidelberg Germany 3811 1003
## 6 city liste 1 : 6 CL1 Frankfurt Germany 1875 3135
## coordinates
## 1 13.38886, 52.51704
## 2 8.40342, 49.00687
## 3 9.180013, 48.778449
## 4 8.467236, 49.489591
## 5 8.694724, 49.409358
## 6 8.682092, 50.110644
I can now create the scatterplot, using the deckgl function from the deckgl R-package.
# define properties of the plot
properties_1 <- list(
getPosition = get_property("coordinates"),
getRadius = JS("data => Math.sqrt(data.exits)"),
radiusScale = 1000,
getColor = c(255, 153, 77)
)
# plot scatterplot
deckgl(zoom = 10.5, pitch = 35, longitude = 8.40342, latitude = 40.00687) %>%
add_scatterplot_layer(data = scatter_data_df_1, properties = properties_1) %>%
add_mapbox_basemap(style = "mapbox://styles/linnartsf/cjq6p9q8f8zwf2rp74qf2o3d5")
We end up with the following scatterplot:

Please feel free to to check out my other posts on spatial data analysis and spatial data visualisation in R.
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