Mapping the Biotech industry ecosystem in Denmark

Denmark has an history of innovation in life sciences, compared to it’s small size. In this project for Developing Data Products: R Markdown and Leaflet. This is of interest to me, as I current work as a Scientist in a Danish Biotech company. Here i will map the physical locations of Danish Biotechnology companies, in Denmark

Data used

The dataset for this project has been scraped from Proff.dk, which is a Danish website that shows information about all registered Danish companies. You can search companies by Industry. You have to may to get the data in a .csv file, and they do not have latitude/longitude for the companies. This means that some effort was put into retrieving and cleaning the data to make it useful, and the data gathering actually took ~10 times longer than making this simple RMD file. You can see how the data retrieval/cleaning was done in the a separate R file: DDPcourseproject_datascrape.R. We will just read the .csv file that is the output of this script:

urlfile<-'https://raw.githubusercontent.com/DavidL-H/Biotech-in-Denmark/main/Biotech_companies_Denmark.csv'
BiotechCompanies<-read.csv(urlfile)
BiotechCompanies <- BiotechCompanies[,-1]
colnames(BiotechCompanies)<-c("Company", "Address", "lng", "lat")
head(BiotechCompanies)
##                          Company                             Address       lng
## 1                   AdaptVac ApS Ole Maaløes Vej 3, 2200 København N 12.556294
## 2                 BGI EUROPE A/S Ole Maaløes Vej 3, 2200 København N 12.556294
## 3      Clinical-Microbiomics A/S    Fruebjergvej 3, 2100 København Ø 12.549398
## 4 Selskabet af 27. juni 1997 ApS        Gartnerhaven 5, 6823 Ansager  8.754698
## 5             Embark Biotech ApS Ole Maaløes Vej 3, 2200 København N 12.556294
## 6                  ALPHALYSE A/S Rødegårdsvej 209 C 1, 5230 Odense M 10.424343
##        lat
## 1 55.69883
## 2 55.69883
## 3 55.71568
## 4 55.70611
## 5 55.69883
## 6 55.37777

Mapping Danish Biotech companies:

Now we have the data we want, and we can easily map it with the package leaflet():

library(leaflet)
library(dplyr)
BiotechCompanies %>%
  leaflet()%>%
  addTiles()%>%
  addMarkers(clusterOptions = markerClusterOptions(),
             popup = BiotechCompanies$Company)