In a previous post I demonstrated how one can generate map-based heatmap distribution plots using the Leaflet package in R. In that example I implemented a function that sends locations as strings to a Open Street Map API and then receives latitude and longitude coordinates as return values in JSON-format.

In this post I will demonstrate how one can use the osmdata package to realize the geocoding part, without having to implement a “complicated” function.

First I define a set of cities and towns to use as locations; I add them to an empty data frame template with pre-defined header names as well as row and column counts

```
# defining a data frame tempalte to populate throughut geocoding process
data_df = as.data.frame(matrix(nrow=20,ncol=4))
colnames(data_df) = c("location","lat","long","values")
# adding city and town names as locations
data_df$location = c("Siegen, Germany", #1
"Kreuztal, Germany", #2
"Netphen, Germany", #3
"Olpe, Germany", #4
"Freudenberg, Germany", #5
"Hilchenbach, Germany", #6
"Wilnsdorf, Germany", #7
"Betzdorf, Germany", #8
"Dillenburg, Germany", #9
"Marburg, Germany", #10
"Biedenkopf, Germany", #11
"Koblenz, Germany", #12
"Erndtebrück, Germany", #13
"Herborn, Germany", #14
"Cölbe, Germany", #15
"Haiger, Germany", #16
"Krombach, Germany", #17
"Wenden, Germany", #18
"Betzdorf, Germany", #19
"Münchhausen, Germany" #20
)
```

Next, I geocode the locations using the osmdata package in R.

```
# importing osmdata package in R
library(osmdata)
# using getbb() function to geocode locations
for(i in 1:nrow(data_df)){
coordinates = getbb(data_df$location[i])
data_df$long[i] = (coordinates[1,1] + coordinates[1,2])/2
data_df$lat[i] = (coordinates[2,1] + coordinates[2,2])/2
}
```

Now the data frame contains location names and geocoded longitude and latitude coordinates; in addition, I add some randomly distributed observation value per location (this will define the color intensity of the heatmap plot).

```
# adding randomly distributed values by location (uniform distribution)
data_df$values = runif(n = 20, min = 0, max = 100)
```

In a final step I generate a heatmap displaying the distribution of observation values by location, using Leaflet in R.

```
# importing leaflet, leaflet.extras and magrittr
library(leaflet)
library(leaflet.extras)
library(magrittr)
# creating a heat map for the burger search intensity according to Google trends
data_df %>%
leaflet() %>%
addTiles() %>%
addProviderTiles(providers$OpenStreetMap.DE) %>%
setView(mean(data_df$long),mean(data_df$lat),8) %>%
addHeatmap(lng=~long,lat=~lat,intensity=~values,max=100,radius=30,blur=20)
```

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