Judging from recent R-Bloggers posts, it appears that many data scientists are concerned with scraping data from various media sources (Wikipedia, twitter, etc.). However, one should be aware that well structured and high quality datasets are available through state’s and country’s bureau of statistics. Increasingly these are offered to the public through direct database access, e.g., using a REST like API. We illustrate the usefulness of such an approach by accessing data from Statistics Sweden.

Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. The markdown+Rknitr source code of this blog is available under a GNU General Public License (GPL v3) license from github.


Scandinavian countries are world-class when it comes to public registries. So when in need for reliable population data, this is the place to look. As an example, we access Statistics Sweden data by their API using the pxweb package developed by @MansMeg, @antagomir and @LCHansson. Love was the first speaker at a Stockholm R-Meetup some years ago, where I also gave a talk. Funny how such R-Meetups become useful many years after!


By browsing the Statistics Sweden (in Swedish: Statistiska Centralbyrån (SCB)) data using their web interface one sees that they have two relevant first name datasets: one containing the tilltalsnamn of newborns for each year during 1998-2016 and one for the years 2004-2016. Note: A tilltalsnamn in Sweden is the first name (of several possible first names) by which a person is usually addressed. About 2/3 of the persons in the Swedish name registry indicate which of their first names is their tilltalsnamn. For the remaining persons it is automatically implied that their tilltalsnamn is the first of the first names. Also note: For reasons of data protection the 1998-2016 dataset contains only first names used 10 or more times in a given year, the 2004-2016 dataset contains only first names used 2 or more times in a given year.

Downloading such data through the SCB web-interface is cumbersome, because the downloads are limited to 50,000 data cells per query. Hence, one has to do several manual queries to get hold of the relevant data. This is where their API becomes a real time-saver. Instead of trying to fiddle with the API directly using rjson or RJSONIO we use the specially designed pxweb package to fetch the data. One can either use the web-interface to determine the name of the desired data matrix to query or navigate directly through the api using pxweb:

d <- interactive_pxweb(api = "api.scb.se", version = "v1", lang = "en")

and select Population followed by Name statistics and then BE0001T04Ar or BE0001T04BAr, respectively, in order to obtain the relevant data and api download url.

Downloading the first name data with pxweb

This leads to the following R code for download:

names10 <- get_pxweb_data(
  url = "http://api.scb.se/OV0104/v1/doris/en/ssd/BE/BE0001/BE0001T04Ar",
  dims = list(Tilltalsnamn = c('*'),
              ContentsCode = c('BE0001AH'),
              Tid = c('*')),
  clean = TRUE) %>% as.tbl

For better usability we rename the columns a little and replace NA counts to be zero. For visualization we pick 5 random lines of the dataset.

names10 <- names10 %>% select(-observations) %>%
  rename(firstname=`first name normally used`,counts=values) %>%
  mutate(counts = ifelse(is.na(counts),0,counts))
##Look at 5 random lines
names10 %>% slice(sample(seq_len(nrow(names10)),size=5))
## # A tibble: 5 × 3
##   firstname   year counts
##      <fctr> <fctr>  <dbl>
## 1     Livia   1998      0
## 2   Elicia    2010     21
## 3     Amie    2000      0
## 4    Albert   2011    108
## 5       Kim   2008     14

Note: Each spelling variant of a name in the data is treated as a unique name. In similar fashion we download the BE0001AL dataset as names2.

Imputing first names with only one use

Since the names2 dataset contains all first names with 2 or more uses in a given year it is possible to deduce how many first names are used only once by subtracting the total number of names in names2 from the total number of live births per year.

##Total number of newborns in the data per year
names2_year <- names2 %>% group_by(year) %>% summarise(names=sum(counts))

##Fetch number of live births per year from the corresponding data set
births <- get_pxweb_data(
  url = "http://api.scb.se/OV0104/v1/doris/en/ssd/BE/BE0101/BE0101H/FoddaK",
  dims = list(Region = c('00'),
              AlderModer = c('tot'),
              Kon = c('*'),
              ContentsCode = c('BE0101E2'),
              Tid = c('*')), clean = TRUE)
##Aggregate per year
births_year <- births %>% group_by(year) %>% summarise(births=sum(values))

##Join and compute difference, which is the number of names used exactly once.
(names_join <- inner_join(names2_year, births_year, by="year") %>%
  mutate(nNames1=births-names, propNames1=nNames1/births))
## # A tibble: 13 × 5
##     year  names births nNames1  propNames1
##    <chr>  <dbl>  <dbl>   <dbl>       <dbl>
## 1   2004  99958 100928     970 0.009610812
## 2   2005  98718 101346    2628 0.025930969
## 3   2006 101944 105913    3969 0.037474153
## 4   2007 101262 107421    6159 0.057335158
## 5   2008 101356 109301    7945 0.072689179
## 6   2009 102989 111801    8812 0.078818615
## 7   2010 108405 115641    7236 0.062572963
## 8   2011 103480 111770    8290 0.074170171
## 9   2012 104448 113177    8729 0.077126978
## 10  2013 104465 113593    9128 0.080357064
## 11  2014 106021 114907    8886 0.077332103
## 12  2015 105012 114870    9858 0.085818752
## 13  2016 107536 117425    9889 0.084215457

We notice that the proportion of names occurring exactly once increases substantially over the years - going from 1.0% in 2004 to 8.4% in 2016. It’s not clear if the imputation by subtraction is 100% exact, but it does sound plausible that names have become more unique.

Merging it all

Based on the above we can create a data.frame containing the correct number of names used once.

missing1names <- names_join %>% group_by(year) %>% do({
}) %>% ungroup

We now join the three datasets into one large data.frame by

names <- rbind(data.frame(names10,type="min10"),
               data.frame(rbind(names2,missing1names),type="min01")) %>% as.tbl

Collision probabilities

We thus got everything in place to compute the name collision probability over time using the birthdayproblem package (as shown in previous posts).

collision <- names %>% group_by(year,type) %>% do({
  data.frame(p=pbirthday_up(n=26L, prob= .$counts / sum(.$counts),method="mase1992")$prob, gini= ineq::Gini(.$counts))
}) %>% ungroup %>% mutate(year=as.numeric(as.character(year)))

And the resulting probabilities based on the three datasets min02 (at least two instances of the name in a given year), min10 (at least ten instances of the name in a given year) and the complete dataset min01 can easily be visualized over time.

ggplot( collision, aes(x=year, y=p, color=type)) + geom_line(size=1.5) +
  scale_y_continuous(label=scales::percent,limits=c(0,1)) +
  xlab("Year") + ylab("Probability") +
  ggtitle("Probability of a name collision in a class of 26 kids born in year YYYY") +
  scale_colour_discrete(name = "Dataset")

As seen in similar plots for other countries, there is a decline in the collision probability over time. Note also that the two curves are upper limits to the true collision probabilities. The true probabilities, i.e. taking all tilltalsnamn into account, have been computed based on the min01 data set, which has been created by finding the difference between the total number of live births in a year and the total number of names in names2.


With all due respect for the need to anonymise the name statistics, it’s hard to understand why the summary figures of how many names with less than X uses, which have been removed due to privacy concerns, are not automatically reported. This would allow one to immediately compute correct totals and collision probabilities. The same problem occurs, e.g., in the corresponding UK and Wales data. Here, Table 6 is listing all first names with 3 or more uses, but not stating how many newborns have a name occurring once and twice, respectively.

Luckily, the missing number could be obtained for the Swedish data with a bit of data dancing and a simple subtraction. Based on these data the collision probabilities are - opposite to some of my previous blog analyses - exact. Have a look at the SCB data website, maybe the data you are looking for can be found here!