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https://github.com/ThinkR-open/prenoms

French Baby Names 1900-2020
https://github.com/ThinkR-open/prenoms

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French Baby Names 1900-2020

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  fig.align = "center",

  out.width = "100%"

)

```



[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)

[![R-CMD-check](https://github.com/ThinkR-open/prenoms/workflows/R-CMD-check/badge.svg)](https://github.com/ThinkR-open/prenoms/actions)



# {prenoms} 



# About



`{prenoms}` (namely "firstnames") allows you to explore the data on first names given to children born in metropolitan France between 1900 and 2021.



These data are available at the French level and by department.



> Source: These statistics come from the French civil status. They have been collected by the National

Institute of Statistics and Economic Studies (Insee), that collects, analyses and disseminates

information on the French economy and society. These statistics are available

[here](https://www.insee.fr/fr/statistiques/2540004#documentation).



# Installation



```{r, eval= FALSE}

# install.packages("devtools")

devtools::install_github( "ThinkR-open/prenoms" )

library("prenoms")

```



# Use package {prenoms}



Load package and its data:

```{r warning=FALSE, message=FALSE}

library(prenoms)

data("prenoms_france")

data("prenoms")

data("departements")

```



Example of study with names from current [ThinkR](https://thinkr.fr) staff through time:



* [Colin](https://github.com/colinfay)

* [Diane](https://github.com/DianeBeldame)

* [Sébastien](https://github.com/statnmap)

* [Cervan](https://github.com/Cervangirard)

* [Vincent](https://github.com/VincentGuyader)

* [Margot](https://github.com/MargotBr)

* Estelle

* [Arthur](https://github.com/ArthurData)

* [Antoine](https://github.com/ALanguillaume)

* [Florence](https://github.com/FlorenceMounier)

* [Murielle](https://github.com/MurielleDelmotte)

* [Swann](https://github.com/dagousket)

* [Yohann](https://github.com/ymansiaux)



```{r warning=FALSE, message=FALSE}

library(ggplot2)

library(dplyr)

library(tidyr)

library(purrr)

```



Let's define a dataset holding our names and genders:



```{r}

team_members <- tribble(

  ~name,      ~sex, 

  "Colin",     "M", 

  "Diane",     "F", 

  "Sébastien", "M", 

  "Cervan",    "M", 

  "Vincent",   "M", 

  "Margot",    "F",

  "Estelle",  "F",

  "Arthur",    "M", 

  "Antoine",   "M", 

  "Florence",  "F",

  "Murielle",  "F",

  "Swann",     "F",

  "Yohann",    "M"

)

```



And then craft a function that will retrieve only the names corresponding to our own names.



```{r}

get_thinkr_team_name_data <- function(

  prenoms_df, 

  team_members_df

) {

  prenoms_df %>% 

    # Get data corresponding only to team member names

    inner_join(

      team_members, 

      by = c("name", "sex")

    ) %>%

    # Add missing combination for name x year 

    complete(

      name = team_members$name,

      year = 1900:2021,

      fill = list( n = 0, prop = 0 )

    ) %>% 

    group_by(name, year, sex) %>%

    summarise(

      n = sum(n),

      .groups = "drop"

    ) %>%

    arrange(year) %>% 

    # If sex is not define (NA) we assumed it was

    # the same as the corresponding team member's

    mutate(

      sex = map2_chr(

        sex,

        name,

        function(

          sex,

          name

        ) {

          ifelse(

            is.na(sex) & name %in% team_members$name,

            team_members$sex[team_members$name == name],

            sex

          )

        }

      )

    )

}

```



### At France country scale



```{r}

# Data for the whole France

data(prenoms_france)



thinkrs <- get_thinkr_team_name_data(

  prenoms_df = prenoms_france,

  team_members_df = team_members

)

```



```{r, graph-france, fig.dim=c(7,5)}

thinkrs %>%

  ggplot() +

  aes(x = year, y = n, color = name) +

  geom_line() +

  scale_x_continuous( breaks = seq(1900, 2021, by = 10) ) +

  labs(title = "ThinkR's team names evolution in France") +

  theme_bw()

```



### In the "départment" of ThinkR's headquarters: 93 (Seine-Saint-Denis)



```{r}

# Data by "départment"

data(prenoms)



thinkrs_93 <- prenoms %>%   

  filter(dpt == 93) %>%

  get_thinkr_team_name_data(

    team_members

  )

```



```{r, graph-departement, fig.dim=c(7,5)}

thinkrs_93 %>%

  ggplot() +

  aes(x = year, y = n, color = name) +

  geom_line() +

  scale_x_continuous( breaks = seq(1900, 2021, by = 10) ) +

  labs(title = "ThinkR's team names evolution in the 93 department") +

  theme_bw()

```



## Code of Conduct



Please note that this project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/1/0/0/code-of-conduct.html). By participating in this project you agree to abide by its terms.