This vignette is an introduction the
The package has several functions for getting real estate data for Denmark. The data is retrieved from the API of "Boligmarkedsstatistikken", which you can find here: http://rkr.statistikbank.dk/.
library(realestateDK); library(dplyr); library(statsDK); library(ggplot2)
Lets see what functions are available in the package:
If we inspect the
BM010() function then we see that it is a function for retrieving "Property prices in housing market". More specifically it gets property prices in housing market by area, property category, prices of completed transactions and time.
The help information for the function show us the following parameters:
EJKAT20 is the property category. Call realestateDK::table_vars("BM010") to see all available parameter settings.
OMR20 is the area. Call realestateDK::table_vars("BM010") to see all available parameter settings.
PRIS20 is the prices of completed transactions. Call realestateDK::table_vars("BM010") to see all available parameter settings.
Tid is the time. Call realestateDK::table_vars("BM010") to see all available parameter settings.
lang whether to return the data in english or danish.
They state that we can call
table_vars("BM010") to see all available parameter settings. Lets try that:
This produces a table that shows us all the possibilities that we have when we call this function to retrieve property prices in housing market.
You can call the function yourself on the other function names to get the paramenters for them as well.
Lets get data for
Owner-occupied flat in
All Denmark at the
Transaction price realised for all available times.
my_data <- BM010(EJKAT20 = "2", OMR20 = "00", PRIS20 = "REAL", Tid = "*")
Notice the use of the asterix ("*"). This tells the API to get all available data for that parameter.
Lets look at the data:
We can see that the time (TID) column is in Quarters. We can change that with help from the
statsDK package and the
my_data$TID <- fix_time(my_data$TID) glimpse(my_data)
Now we can plot the data:
ggplot(my_data) + geom_line(aes(x = TID, y = INDHOLD)) + labs(x = "", y = "Apartment prices in Denmark") + theme_minimal()
Wow, what a development. We also see the effect of the financial crisis and the current rebound.
The table with the data also contains the meta data as attribute.
You can access the meta data this way:
metadata <- attributes(my_data)$metadata glimpse(metadata)
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