Most Popular Baby Name in Belgium (2024)

Vikram Ranga

2024-08-18

Inspiration

I was about to do tidytuesday plots. When I downloaded the data, it was on marriages of United Kingdom’s monarchs. Here is the data:

#tuesdata <- tidytuesdayR::tt_load(2024, week = 34)
#tuesdata <- tuesdata$english_monarchs_marriages_df
#saveRDS(tuesdata, '2024-08-20/english_monarachs_marriages.rds')
#tuesdata <- readRDS(paste0(getwd(), '/2024-08-20/english_monarachs_marriages.rds'))
tuesdata <- readRDS('~/Documents/R Scripts/tidyTuesdays/2024-08-20/english_monarachs_marriages.rds')
DT::datatable(tuesdata)

see in the second and third consorts are from Flanders, a region in Belgium. So I thought of doing an analysis on the nationality of consorts. However, I thought of what kind of names are there in Belgium? I searched Google and in no time I could find data.

The data

fNames <- readxl::read_xlsx('~/Documents/R Scripts/tidyTuesdays/Belgian Names/TA_POP_FEMALE_2024.xlsx')
mNames <- readxl::read_xlsx('~/Documents/R Scripts/tidyTuesdays/Belgian Names/TA_POP_MALE_2024.xlsx')

fNames$Gender <- 'Female'
mNames$Gender <- 'Male'

# bind the rows
belNaam <- rbind(fNames, mNames)

# rename the absurdities 
belNaam <- belNaam |> 
  rename(UID = CD_REFNIS,
         CityName_NL = tx_descr_nl,
         CityName_FR = tx_descr_fr,
         Names = TX_FST_NAME,
         frequency = MS_FREQUENCY)

After downloading the data, I quickly plotted the top ten names for male and female. See the plot below:

The plot

belNaam |> 
  group_by(Names, Gender) |> 
  summarise(SFreq = sum(frequency)) |> 
  arrange(desc(SFreq)) |>
  ungroup() |> 
  group_by(Gender) |> 
  top_n(10, wt = SFreq) |> 
  ungroup() |> 
  mutate(Names = reorder(Names, SFreq)) |> 
  ggplot() +
  geom_col(mapping = aes(x = Names,
                         y = SFreq,
                         fill = Gender)) +
  coord_flip() +
  scale_fill_discrete(name = "") +
  facet_grid(rows = vars(Gender), scales = 'free', switch = "y", space = "free_y") +
  theme_minimal(base_family = "Roboto Condensed") +
  labs(
    title = "Most Popular Names in Belgium (Year 2024)",
    subtitle = "Data spread across the country",
    caption = 'Source: https://statbel.fgov.be/\n First names of the total population by municipality',
    x = "",
    y = ""
  ) +
  geom_hline(yintercept = 0) +
  theme(
    legend.position = 'none',
    plot.margin = margin(0.5, 0.5, 0.5, 0.5, unit = "cm"),
    plot.title = element_text(size = 20, face = "bold"),
    strip.text.y = element_text(angle = 270, face = "bold", size = 12),
    strip.placement = "outside",
    axis.title.x = element_text(margin = margin(t = 0.5, b = 0.5, unit = "cm")),
    axis.title.y = element_blank(),
    axis.text = element_text(size = 10),
    plot.background = element_rect(fill = '#E5E4E2')
)

## `summarise()` has grouped output by 'Names'. You can override using the
## `.groups` argument.

There are few names I was expecting to see such as Els in female category and Bert in male category because I encountered them the most. However, here I could see Maria and Marie are most common for female and Marc and Jean are for males. Interesting thing is Maria and Marie are both sound the same! and Jean sounds like Zhon (with fading n) its actually french version of John.

This plotting just for fun! If you find something wrong do not hesitate to comment of laat maar weten!

Ik zou iets in nederlands ook scrijven. Ik vind the land (belgie) zeer mooie en mensen zijn veel gaastvrij! :-D

xxx

dag!