Nothing
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 449
The 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
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.