Retrieve and visualise "Swiss Newsreel" data

This R script fallows you to extract Swiss Newsreel data from the Research API and visualise it.

Load the required libraries

In order for the required libraries to load correctly, install them to your R environment if you have not done so already. Please note that the script requires R 4.0.x or above, as well as the data.table package 1.12.x or above. All libraries can therafter be loaded with the following code:


corenum <- detectCores()


The last line detects the number of computing cores on your machine. This will be later used in parallellized processes manged by the packages "foreach" and "doParallel".

Extract data by requests to the Search API

Data retrieval can take time, since only 100 results can be retrieved per request. For over 20'000 results, this implies over 200 distinct calls to the Search API. Web requests are computationaly inexpensive but each of them is inevitably delayed by web traffic. To make the procedure much faster, we parallelize the process by using the foreach package. Also note that the Search API currently limits requests beyond the 9999th search result. Therefore, we make distinct data retrieval loops for each decade. Thus, we have a parallelised nested for-loop. At the end of this codeblock, the retrieved data is saved to a local file called FilmWochenSchau.RData for later use.


# The request function ----
makerequest <- function(skip,take,decade) {
  jsonrequest <- paste0('{
          {"key": "creationPeriod","value": "',decade,'-',decade+10,'"}
    "paging":{"skip":', skip ,',"take":', take ,',"orderBy":"","sortOrder":""},
    "facetsFilters": [
        "filters": ["level:\\"Dokument\\""],
        "facet": "level"
        "filters": [
          "aggregationFields.bestand:\\"Stiftung Schweizer Filmwochenschau (1942-1975)\\""
        "facet": "aggregationFields.bestand"
    body = minify(jsonrequest), 
    encode = "raw",

url <- ""

# Fetch all data by bunches of 100 ----
decadecounts <- data.table(decade = seq(1940,1970,by=10))
decadecounts[,count:= sapply(decade, function(x){
    res <- makerequest(1,1,x)
    fws <- content(res)
cl <- parallel::makeCluster(corenum) 
fws.datatable <- foreach(
  .packages = c("jsonlite","httr","data.table","magrittr"),
  .verbose = TRUE
  ) %:%
      i=seq(1, ceiling(count/100)*100, by = 100), 
    ) %dopar% {
      res <- makerequest(i,100,decade)
      fws <- content(res)
        refCode = sapply(fws$entities$items,function(x) return(x$archiveRecordId)),
        archiveID = sapply(fws$entities$items,function(x) return(x$referenceCode)),
        date = sapply(fws$entities$items,function(x) return(x$creationPeriod$text)),
        title = sapply(fws$entities$items,function(x) return(x$title)),
        dauer = sapply(fws$entities$items,function(x) return(x$customFields$format %>% unlist)),
        url = sapply(fws$entities$items,function(x) return(x$customFields$digitaleVersion %>% unlist %>% .["url"])),
        thema = sapply(fws$entities$items, function(x) x$customFields$thema %>% unlist)
} %>% rbindlist(fill=TRUE)




Extract variables with regular expressions

The field "thema" provided by the Search API contains multiple values. You can employ regular expressions (RegEx) to extract these values and assign them to individual columns. At this stage, we also clean the retrieved data structure.


unlistColumn <- function(column) {
  sapply(column, function(x) {
    if (!is.null(unlist(x))) {return(unlist(x))} else return(NA)
fws.datatable$refCode <- unlistColumn(fws.datatable$refCode)
fws.datatable$archiveID <- unlistColumn(fws.datatable$archiveID)
fws.datatable$date <- unlistColumn(fws.datatable$date)
fws.datatable$title <- unlistColumn(fws.datatable$title)
fws.datatable$dauer <- unlistColumn(fws.datatable$dauer)
fws.datatable$url <- unlistColumn(fws.datatable$url)
fws.datatable$thema <- unlistColumn(fws.datatable$thema)

# Filter duplicates
fws.datatable <- unique(fws.datatable, by="refCode") 

# Then apply RegEx
fws.datatable[,thema_orte := sapply(thema, function(x) {str_match(x, "Orte:[\\r\\n ]{1,3}([^\\r\\n]*)") %>% .[2]})]
fws.datatable[,thema_schlagworte := sapply(thema, function(x) {str_match(x, "Schlagworte:[\\r\\n ]{1,3}([^\\r\\n]*)") %>% .[2]})]
fws.datatable[,dauer_dauer := sapply(dauer, function(x) {str_match(x, "Dauer: ([0-9:]*)") %>% .[2]})]
fws.datatable[,dauer_seconds := sapply(dauer_dauer, function(x) {
    as.difftime(x, format = "%H:%M:%S", units = "secs") %>% strtoi,
    as.difftime(x, format = "%M:%S", units = "secs") %>% strtoi



Visualise the data

The following script counts the number of emissions per month and visualises them with the help of "rose charts" introduced by Florence Nightingale (nurse and statistician, 1820-1910) for her assessement of mortality data during the Crimea War. The visualisiation, shown at the top of this page, allows you to compare the numbers of monthly editions of the Swiss Newsreel. The visualisation is saved to an svg file.


emissions_par_date <- fws.datatable[, .N, by=date][, c("date","count","year","month", "N") := .(date,N,format(date,"%Y"),as.integer(format(date,"%m")), NULL)]
emissions_par_month <- emissions_par_date[, .(count=sum(count)), by=.(month,year)]
ggplot(emissions_par_month) +
  geom_col(aes(x=month,y=count),colour="white", fill="darkred", size=0.1) + 
    minor_breaks = c(1,2,4,5,7,8,10,11)
  ) +
  coord_polar(start=pi/12) +
  facet_wrap(~year,ncol=10) +
  labs(title = "Ciné-Journal suisse",
       subtitle = "Nombre d'émissions diffusées par année et mois",
       caption = "Source: Archives fédérales suisses", 
       x = NULL, y = NULL) 



Retrieve extra data for each record

Some data pertaining to an individual record can only be retrieved by a different API request, designed for prvoviding all details attached to a specific record ID. In this code, we retrive the "darin" field, and decompose its contents with RegEx to find out the "genre" of each record:


getDescription <- function(id) {
  resget <- GET(
    encode = "json",

sequence <- c(seq(1, ceiling(fws.datatable%>%nrow/5000)*5000, by = 5000),fws.datatable %>% nrow)
for (i in 1:(sequence%>%length-1)){
  cl <- parallel::makeCluster(corenum) 
  fws.datatable[sequence[i]:sequence[i+1], darin := foreach(
    i = refCode,
    .export = "getDescription",
    .packages = c("jsonlite","httr","magrittr"),
    .verbose = TRUE
  ) %dopar% {
    x = getDescription(i)
    x[["withinInfo"]] %>% unlist
fws.datatable[,description_genre := sapply(darin, function(x) {str_match(x, "Genre:[\\r\\n ]{1,2}([^\\r\\n]*)") %>% .[2]})]
fws.datatable[,description_inhaltsangabe := sapply(darin, function(x) {str_match(x, "Inhaltsangabe:[\\r\\n ]{1,2}([^\\r\\n]*)") %>% .[2]})]
fws.datatable[,description_inhaltsangabe_ort := sapply(description_inhaltsangabe, function(x) {str_match(x, "^([^:]*)") %>% .[2]})]
ggplot(fws.datatable[,.N,by=description_genre]) + geom_col(aes(x=description_genre,y=N)) + coord_flip()



Find the full code on GitHub and Participate

The full R code can be found in our GitHub repository.

The retrieved data allows for many other visualisations and analyses.

Do not hesitate to make your own fork!