The goal of statsDK is to make it easy to call the API of Statistics Denmark.
You can install statsDK from github with:
# install.packages("devtools")
devtools::install_github("mikkelkrogsholm/statsDK")This little vignette shows you how to get started with the statsDK
package.
The package has a few “retriever”-functions that are used to retrieve
data from Statistics Denmark. Those are: sdk_retrieve_subjects(),
sdk_retrieve_tables(), sdk_retrieve_metadata() and the
sdk_retrieve_data() functions.
This function retrieves the overall subjects that are available in the API.
This function retrieves an overview of all the tables in the API. Lets use it to see what data we would like to fetch:
library(statsDK); library(dplyr); library(stringr); library(lubridate); library(ggplot2); library(tidyr)
tables <- sdk_retrieve_tables()
glimpse(tables)
#> Observations: 2,051
#> Variables: 8
#> $ id <chr> "FOLK1A", "FOLK1B", "FOLK1C", "FOLK1D", "FOLK1E", "…
#> $ text <chr> "Population at the first day of the quarter", "Popu…
#> $ unit <chr> "number", "number", "number", "number", "number", "…
#> $ updated <chr> "2019-08-09T08:00:00", "2019-08-09T08:00:00", "2019…
#> $ firstPeriod <chr> "2008Q1", "2008Q1", "2008Q1", "2008Q1", "2008Q1", "…
#> $ latestPeriod <chr> "2019Q3", "2019Q3", "2019Q3", "2019Q3", "2019Q3", "…
#> $ active <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ variables <list> [<"region", "sex", "age", "marital status", "time"…Lets say we are interested in marriages. Maybe there is an official data set about marriages that we can use?
First we unnest the variables column:
tables_long <- tables %>%
unnest(variables)Then we see if we can find something with marriage. We use the
str_detect() function from the stringr package to detect a text
pattern matching marriage:
marriage_tables <- tables_long %>%
filter(str_detect(tables_long$variables, "marriage"))
glimpse(marriage_tables)
#> Observations: 10
#> Variables: 8
#> $ id <chr> "VIEDAG", "VIEDAG", "VIE8", "VIE6", "VIE307", "VIE3…
#> $ text <chr> "Marriages", "Marriages", "Marriages between two of…
#> $ unit <chr> "number", "number", "number", "number", "number", "…
#> $ updated <chr> "2019-02-14T08:00:00", "2019-02-14T08:00:00", "2019…
#> $ firstPeriod <chr> "2007", "2007", "2007", "2012", "2006", "2006", "20…
#> $ latestPeriod <chr> "2018", "2018", "2018", "2018", "2018", "2018", "20…
#> $ active <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ variables <chr> "day of marriage", "month of the marriage", "type o…Indeed there is. There is the VIEDAG table that seems to have data on
marriage that we might be interested in. Lets therefore have a look at
the meta data for that particular table.
This function retrieves meta data for a table - like our VIEDAG table.
viedag_meta <- sdk_retrieve_metadata("VIEDAG")
#> Metadata collected succesfully
glimpse(viedag_meta)
#> List of 11
#> $ id : chr "VIEDAG"
#> $ text : chr "Marriages"
#> $ description : chr "Marriages by day of marriage, month of the marriage and time"
#> $ unit : chr "number"
#> $ suppressedDataValue: chr "0"
#> $ updated : chr "2019-02-14T08:00:00"
#> $ active : logi TRUE
#> $ contacts :'data.frame': 1 obs. of 3 variables:
#> ..$ name : chr "Connie Østberg"
#> ..$ phone: chr "+4539173384"
#> ..$ mail : chr "[email protected]"
#> $ documentation :List of 2
#> ..$ id : chr "fc512ffc-7334-4237-aab9-b776fcc6748c"
#> ..$ url: chr "https://www.dst.dk/documentationofstatistics/fc512ffc-7334-4237-aab9-b776fcc6748c"
#> $ footnote : NULL
#> $ variables :'data.frame': 3 obs. of 5 variables:
#> ..$ id : chr [1:3] "VDAG" "VIMDR" "Tid"
#> ..$ text : chr [1:3] "day of marriage" "month of the marriage" "time"
#> ..$ elimination: logi [1:3] TRUE TRUE FALSE
#> ..$ time : logi [1:3] FALSE FALSE TRUE
#> ..$ values :List of 3
#> .. ..$ :'data.frame': 32 obs. of 2 variables:
#> .. ..$ :'data.frame': 13 obs. of 2 variables:
#> .. ..$ :'data.frame': 12 obs. of 2 variables:The list of meta data has a lot of information that we can use to determine wether or not to use the data. There is an URL under documentation that we can follow to read a lot more about the data and how it is collected. There is contact information if we still have unanswered questions that need to be answered.
And there is also a part of the list called variables. This is the part
we need to determine what we can get from calling that table directly
and also how we should call it. We will use the helper function
get_variables() to get a nice tidy tibble to inspect.
variables <- sdk_get_variables(viedag_meta)
glimpse(variables)
#> Observations: 57
#> Variables: 4
#> $ param <chr> "VDAG", "VDAG", "VDAG", "VDAG", "VDAG", "VDAG", "VDA…
#> $ setting <chr> "TOT", "D01", "D02", "D03", "D04", "D05", "D06", "D0…
#> $ type <chr> "day of marriage", "day of marriage", "day of marria…
#> $ description <chr> "Total", "1.", "2.", "3.", "4.", "5.", "6.", "7.", "…Lets see if we can get a short overview of all the different options we have. Lets make a tibble for this vignette that shows the first, middle and last row of each parameter:
variable_overview <- variables %>%
group_by(param) %>%
slice(c(1, round(n()/2), n())) %>%
ungroup()
variable_overview
#> # A tibble: 9 x 4
#> param setting type description
#> <chr> <chr> <chr> <chr>
#> 1 Tid 2007 time 2007
#> 2 Tid 2012 time 2012
#> 3 Tid 2018 time 2018
#> 4 VDAG TOT day of marriage Total
#> 5 VDAG D15 day of marriage 15.
#> 6 VDAG D31 day of marriage 31.
#> 7 VIMDR TOT month of the marriage Total
#> 8 VIMDR 005 month of the marriage May
#> 9 VIMDR 012 month of the marriage DecemberFrom this overview it looks like Tid is the year, VDAG is the day of
the month and VIMDR is the month. VDAG and VIMDR also has a TOT
that is the total.
With this newfound knowledge we can now construct an API call to get the data we are interested in.
This is the function that actually retrieves the data that we need.
Lets get the total data for each month of june and december for all the available years. This forces us to construct an API call that shows different aspects.
From the variable overview we did earlier we can see that in order to
get the Total for days of marriage then we have to use the TOT
setting for the VDAG parameter. And in order to get the month of
June and December we will have to use the 006 and 012 setting
for the VIMDR parameter. But how do we call all years? Easy, we just
have to set that to be an asterix *.
Below is the call to the API:
VIEDAG <- sdk_retrieve_data("VIEDAG", Tid = "*", VDAG = "TOT", VIMDR = "006,012")
#> Getting data. This can take a while, if the data is very large.
#> Data collected succesfully
names(VIEDAG) <- c("time", "day", "month", "count")Let us have a glimpse at the data:
glimpse(VIEDAG)
#> Observations: 24
#> Variables: 4
#> $ time <dbl> 2007, 2007, 2008, 2008, 2009, 2009, 2010, 2010, 2011, 2011…
#> $ day <chr> "Total", "Total", "Total", "Total", "Total", "Total", "Tot…
#> $ month <chr> "June", "December", "June", "December", "June", "December"…
#> $ count <dbl> 4486, 2092, 3838, 1894, 3546, 1824, 3396, 1596, 3462, 1394…Finally lets plot it and see what is going on in our new marriage data set:
VIEDAG$time <- ymd(paste0(VIEDAG$time, "-01-01"))
my_y <- VIEDAG %>%
filter(time == max(time)) %>%
pull(count)
ggplot(VIEDAG) +
geom_line(aes(x = time, count, group = month)) +
annotate("text", x = max(VIEDAG$time) %m+% months(1) , y = my_y,
label = c("June", "December"), hjust = 0) +
annotate("point", x = max(VIEDAG$time), y = my_y) +
xlim(min(VIEDAG$time), max(VIEDAG$time) %m+% years(1) ) +
labs(y = "Total marriages for the given month", x = "Years") +
theme_minimal()
There is quite a spike in the data for December 2012. A lot of people got married in December in that particular year…
Can you figure out why? Make your own API call that calls all days in December for all years and see if you can figure out what made that particular year so different…
Visit the http://statbank.dk/ and http://api.statbank.dk/console for further exploration of Statistics Denmark data.