Néhémie’s Travelogue

I am a lucky worker of the German capital that recognizes March 8th − International Women’s Day − as a public holiday. Berlin is the first of Germany’s 16 federal states and the first jurisdiction in the European Union to make this day a holiday.

Crashes do not records gender so I took a look at the data about given name. The city of Berlin provides the distribution of given names by district (Liste der häufigen Vornamen 2022). The data are provided for 2021 in the form

given name,count,gender,position

The position refers to the order of the name if a child has several given names. It is stated that the position designates the position of in the list of names but that the position makes “no statement about the main used name”. However, for simplicity, I only kept the name in position one.

Picture of the day

Most popular “female” name (as indicated in the data) ranked first in the list. Plot made with r and ggplot2

Show the code of the exhibit

library(dplyr)
library(ggplot2)

sets <-
paste0("https://raw.githubusercontent.com/berlinonline/haeufige-vornamen-berlin/master/data/cleaned/2021/",
       c( "charlottenburg-wilmersdorf.csv",
         "friedrichshain-kreuzberg.csv",
         "lichtenberg.csv",
         "marzahn-hellersdorf.csv",
         "mitte.csv",
         "neukoelln.csv",
         "pankow.csv",
         "reinickendorf.csv",
         "spandau.csv",
         "steglitz-zehlendorf.csv",
         "tempelhof-schoeneberg.csv",
         "treptow-koepenick.csv"
         )
       )

 do.call(rbind, lapply(sets, function(path) {
    df <- read.csv(path)
    df[["source"]] <- rep(sub(".csv", "", basename(path)), nrow(df))
    return(df)
} )) -> vorname
vorname$vorname <- as.factor(vorname$vorname)
vorname$source <- as.factor(vorname$source)
vorname$geschlecht <- as.factor(vorname$geschlecht)



vorname %>%
  filter( position == 1) %>%
  group_by(vorname, geschlecht) %>%
  summarise(gesamt = sum(anzahl)) %>%
  filter(gesamt > 100) -> vns


ggplot(data = vns,
             aes(x = reorder(vorname, gesamt),
                 y = gesamt)) +
        geom_col(data = subset(vns, geschlecht == "w")) +
#        geom_col(data = subset(vns, geschlecht == "m"),
#                 aes(y = gesamt*(-1))) +
        labs(title = "Most popular first baby names for women in Berlin",
             subtitle = "Year 2021, Total Birth = 34450, Birth women = 17005 (49.4%)",
             caption = "Data: Liste der häufigen Vornamen 2021 \nCC BY Landesamt für Bürger- und Ordnungsangelegenheiten − Berlin ")+
 xlab("Given name") +
 ylab("Given times (as first ranked name only)") +
        coord_flip() +
        theme_bw()
ggsave("2022-03-08_berlin_vorname.jpg", height = 10, width = 6)