iebabynames: Full baby name data for Ireland

Description

Full baby name data (1964–2023) for Ireland, gathered from the Central Statistics Office.

The package contains the dataset iebabynames with 78,897 observations on six variables: year, sex, name, n, rank, and prop, and prop_sex. Due to confidentiality reasons, only names with 3 or more instances in the relevant year are included.

The package can be used to explore patterns of baby names in Ireland over time. The dataset iebabynames is also very suitable for filtering, summarising and plotting variables in workshops or lectures.

The structure of the package follows the babynames package by Hadley Wickham and the ukbabynames package by Mine Çetinkaya-Rundel, Thomas Leeper, and Nicholas Goguen-Compagnoni.

Installation

The package is hosted on GitHub and not available at CRAN. To install the latest development version:

if (!require("remotes")) {
    install.packages("remotes")
}
remotes::install_github("stefan-mueller/iebabynames") 

Demonstration

# load packages
library(iebabynames)
library(dplyr)
library(ggplot2)
library(scales)

# set ggplot2 theme
theme_set(theme_bw())

head(iebabynames)
##   year    sex   name   n rank        prop   prop_sex
## 1 2023 Female  Grace 339    1 0.007325137 0.01533728
## 2 2023 Female  Fiadh 300    2 0.006482422 0.01357282
## 3 2023 Female  Emily 297    3 0.006417598 0.01343709
## 4 2023 Female Sophie 283    4 0.006115085 0.01280369
## 5 2023 Female   Lily 270    5 0.005834180 0.01221554
## 6 2023 Female  Ellie 252    6 0.005445234 0.01140117

Get most popular names in 2023

dat_top_2023 <- iebabynames %>% 
    filter(year == "2023") %>% 
    group_by(sex) %>% 
    top_n(n = 10, wt = -rank) # get top 10

dat_top_2023
## # A tibble: 20 × 7
## # Groups:   sex [2]
##     year sex    name        n  rank    prop prop_sex
##    <dbl> <chr>  <chr>   <int> <dbl>   <dbl>    <dbl>
##  1  2023 Female Grace     339     1 0.00733   0.0153
##  2  2023 Female Fiadh     300     2 0.00648   0.0136
##  3  2023 Female Emily     297     3 0.00642   0.0134
##  4  2023 Female Sophie    283     4 0.00612   0.0128
##  5  2023 Female Lily      270     5 0.00583   0.0122
##  6  2023 Female Ellie     252     6 0.00545   0.0114
##  7  2023 Female Mia       242     7 0.00523   0.0109
##  8  2023 Female Amelia    241     8 0.00521   0.0109
##  9  2023 Female Éabha     241     8 0.00521   0.0109
## 10  2023 Female Ella      231    10 0.00499   0.0105
## 11  2023 Male   Jack      561     1 0.0121    0.0232
## 12  2023 Male   Noah      473     2 0.0102    0.0196
## 13  2023 Male   James     369     3 0.00797   0.0153
## 14  2023 Male   Rían      339     4 0.00733   0.0140
## 15  2023 Male   Oisín     330     5 0.00713   0.0136
## 16  2023 Male   Fionn     306     6 0.00661   0.0127
## 17  2023 Male   Tadhg     301     7 0.00650   0.0125
## 18  2023 Male   Liam      289     8 0.00624   0.0120
## 19  2023 Male   Cillian   275     9 0.00594   0.0114
## 20  2023 Male   Daniel    256    10 0.00553   0.0106

Plotting the 10 most frequent male and female names across the entire period

iebabynames_top <- iebabynames %>% 
    group_by(sex, name) %>% 
    summarise(n_total = sum(n)) %>% 
    top_n(n = 10, wt = n_total)

ggplot(iebabynames_top, aes(x = n_total,
                            y = reorder(name, n_total), 
                            fill = sex)) +
    geom_bar(stat = "identity") +
    geom_text(aes(label = name), nudge_x = -2000, 
              hjust = "right",
              colour = "white") +
    scale_x_continuous(labels = scales::comma_format()) +
    scale_fill_manual(values = c("#83D0F5", "#71B294")) +
    labs(title = "Frequency of Baby Names in Ireland (1964–2023)", 
         x = "Number of Babies",
         y = NULL) +
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          legend.title = element_blank(),
          legend.position = "bottom") 

Inspecting the development of selected names

ggplot(data = filter(iebabynames, name %in% c("John", "Mary")),
       aes(x = year, y = prop)) +
    geom_smooth(se = FALSE) +
    scale_x_continuous(breaks = c(seq(1963, 2023, 10))) +
    scale_y_continuous(labels = scales::percent,
                       breaks = c(seq(0, 0.06, 0.01))) +
    geom_point(alpha = 0.4) +
    facet_wrap(~name) +
    labs(x = NULL, y = "Percentage of Babies") 

Explore different variants of names

iebabynames_variants <- iebabynames %>% 
    filter(name %in% c("Aoife", "Aoibhe", "Eva",
                       "Eve"))

ggplot(data = iebabynames_variants,
       aes(x = year, y = n)) +
    geom_smooth(se = FALSE) +
    scale_x_continuous(breaks = c(seq(1963, 2023, 20))) +
    geom_point(alpha = 0.4) +
    facet_wrap(~name, nrow = 1) +
    labs(x = NULL, y = "Frequency") +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

The website of the Central Statistics Office includes an interactive interface that allows you to plot the frequency and rank of custom names.

How to cite

Stefan Müller (2024). iebabynames: Ireland Baby Names, 1964–2023. R package version 0.2.4. URL: http://github.com/stefan-mueller/iebabynames.

If you use the data, please also cite the CSO website: https://www.cso.ie/en/interactivezone/visualisationtools/babynamesofireland/.

Issues

Please file an issue (with a bug, wish list, etc.) via GitHub.