The goal of prenoms is to give the names of babies born
in Quebec between 1980 and 2020.
You can install prenoms from github with:
# install.packages("devtools")
devtools::install_github("desautm/prenoms")Here is the graph of the first names of the four members of my family, between 1980 and 2020.
library(tidyverse)
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.3 v purrr 0.3.4
#> v tibble 3.1.1 v dplyr 1.0.5
#> v tidyr 1.1.3 v stringr 1.4.0
#> v readr 1.4.0 v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(prenoms)family <- prenoms %>%
filter(
name == "Marc-Andre" & sex == "M" |
name == "Laurent" & sex == "M" |
name == "Melanie" & sex == "F" |
name == "Anna" & sex == "F"
) %>%
group_by(name, year, sex) %>%
summarise(n = sum(n)) %>%
arrange(year)
#> `summarise()` has grouped output by 'name', 'year'. You can override using the `.groups` argument.
ggplot(data = family, aes(x = year, y = n, color = name))+
geom_line()+
scale_x_continuous( breaks = seq(1980, 2020, by = 5))
The five most popular female names in 2020.
prenoms %>%
filter(year == 2020 & sex == "F") %>%
select(year, sex, name, n) %>%
arrange(desc(n)) %>%
head(5)
#> # A tibble: 5 x 4
#> year sex name n
#> <int> <chr> <chr> <int>
#> 1 2020 F Olivia 543
#> 2 2020 F Alice 491
#> 3 2020 F Emma 491
#> 4 2020 F Charlie 488
#> 5 2020 F Charlotte 449The five most popular male names in 2020.
prenoms %>%
filter(year == 2020 & sex == "M") %>%
select(year, sex, name, n) %>%
arrange(desc(n)) %>%
head(5)
#> # A tibble: 5 x 4
#> year sex name n
#> <int> <chr> <chr> <int>
#> 1 2020 M Liam 661
#> 2 2020 M William 644
#> 3 2020 M Noah 639
#> 4 2020 M Thomas 594
#> 5 2020 M Leo 572