Selecting the names (X’s)

Last compiled on May, 2025

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The second step is to find the frequencies of the selected set of names (or X’s, in NSUM terminology). We again use the Meerten Voornamenbank that shows name popularity by year. We do this via relatively straightforward crawl of that website. You can see the selected names below in the code.

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1 Initatiating R environment

Start out with a custom function to load a set of required packages.

# packages and read data
rm(list = ls())

# scraper to collect popularity lists of names in the Netherlands, per year Rense Corten, Utrecht
# University, April 2021

# ----------- LOAD THE REQUIRED PACKAGES ---------------- |

# (c) Jochem Tolsma
fpackage.check <- function(packages) {
    lapply(packages, FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
            install.packages(x, dependencies = TRUE)
            library(x, character.only = TRUE)
        }
    })
}
packages = c("tidyverse", "rvest", "polite")
fpackage.check(packages)

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2 Functions

Make a custom function that queries the Meertens

# ----------- FUNCTIONS ---------------- |

get_name_number <- function(session, name){
  #year = 2014
  name_path = paste("/nvb/naam/is/", name,sep="") # set the path for the specific name's webpage
  
  name_session <-nod(session, path = name_path) # agree changing of the path with the host (assuming I have already "bowed" for the higher-level path)
  
  name_page <- scrape(name_session) # get the page for this year
  
  names_data <-name_page %>%  # parse the page as a table. Turns out this is a list of three tables; we need numbers 2 and 3
    html_table()
  
  n_m <- names_data[[1]][["X3"]][2] %>% 
    str_replace("--","0")
  
  n_v <- names_data[[1]][["X3"]][6] %>% 
    str_replace("--","0")
  
  
  print(name)
  
  n_total <- as.numeric(n_m) + as.numeric(n_v)
  
  return(n_total)
}

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3 Scraping the specific names

The name frequencies that are queried from Meertens are found below. These are then put into a dataframe.

# ----------- IMPLEMENT THE SCRAPER ---------------- |

namelist <- c("Sophie", "Julia", "Sanne", "Lisa", "Laura", "Maria", "Linda", "Johanna", "Monique", "Ester",
    "Anna", "Elisabeth", "Cornelia", "Wilhelmina", "Amira", "Samira", "Sara", "Daan", "Sem", "Thomas",
    "Max", "Kevin", "Johannes", "Dennis", "Jeroen", "Jan", "Marcel", "Cornelis", "Hendrik", "Petrus",
    "Willem", "Ali", "Mohammed", "Noor")

name_number <- c()

# check permissions and introduce myself to the host
session <- bow("https://www.meertens.knaw.nl/nvb/", user_agent = "R. Corten, Universiteit Utrecht", delay = 1)
session


for (i in 1:length(namelist)) {
    name_number[i] <- get_name_number(session, namelist[i])
}


names_numbers <- data.frame(name = namelist, number = name_number)

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4 Saving the data

We finally save the data in a CSV file.

write.csv(names_numbers, file = "name_numbers.csv", row.names = FALSE)