More water, a bit more about saints

I was lucky enough to get some nice and interesting feedback on my last post. One comment was really useful and pretty embarrassing: I had written “see” instead of “sea” in the whole post… Thanks Steve Dempsey for the correction! I also got some questions which I decided to explore.

More water?

I had chosen to look specifically for a few rivers but one commenter, actually Steve Dempsey again, asked me how it would look like if I systematically retrieved all placenames with “sur-”, “upon”, because where he lived in France there were many “sur-Dronne” or “sur l’Isle”. He actually asked the question in French, “Je me demande ce que ça donne avec tous les places avec un “-sur”. Là, où j’étais en France il y avait plein de “-sur-Dronne” ou “-sur-l’Isle”.” Let’s see! (or sea? just kidding)

library("dplyr")
library("tidyr")
library("readr")
library("purrr")
ville <- read_tsv("data/2017-01-24-kervillebourg_FR.txt", col_names = FALSE)[, c(2, 5, 6)]
names(ville) <- c("placename", "latitude", "longitude")
ville <- unique(ville)
upon <- filter(ville, grepl("sur-", placename))

If truth be told, I thought I’d just need to filter the relevant placenames and then get nice rivers on my map… nope. So I soon realized I should first find unique names of rivers, then group them by name and only draw the ones that have enough places for the map to be at least a bit pretty.

upon <- by_row(upon, function(df){
  sub(".*sur-", "", df$placename)
}, .to = "waterthing", .collate = "cols")
knitr::kable(head(upon))
placename latitude longitude waterthing
Domecy-sur-le-Vault 47.49084 3.80953 le-Vault
Yzeures-sur-Creuse 46.78609 0.87166 Creuse
Yville-sur-Seine 49.39938 0.87750 Seine
Wingen-sur-Moder 48.91900 7.37955 Moder
Willer-sur-Thur 47.84392 7.07223 Thur
Wavrans-sur-Ternoise 50.41322 2.30002 Ternoise

Let’s see what the more frequent watherthings are:

group_by(upon, waterthing) %>%
  summarize(n = n()) %>%
  arrange(desc(n)) %>%
  head(n = 20) %>%
  knitr::kable()
waterthing n
Mer 214
Loire 152
Seine 148
Marne 117
Meuse 88
Saône 70
Sarthe 50
Aube 44
Cher 41
Orne 39
Seille 39
Moselle 36
Eure 34
Yonne 33
Oise 32
Vienne 31
Garonne 30
Dordogne 28
Aisne 26
Allier 24

So I find “Sea” again, and the rivers I had chosen in the last post, and others, whose names I all knew so they must be quite important. I want to keep only the rivers with “enough”” places to make the map pretty, because at the scale of the country I prefer seeing a longer river.

upon <- group_by(upon, waterthing)
upon <- filter(upon, n() >= 25)
upon <- ungroup(upon)
library("ggplot2")
library("ggmap")
library("viridis")
library("ggthemes")
map <- ggmap::get_map(location = "France", zoom = 6, maptype = "watercolor")

This time the map will be really artsier than useful since I’ll have a color by river but no legend because of the higher numbers of rivers.

p <- ggmap(map) +
  geom_point(data = upon,
             aes(x = longitude, y = latitude,
                 col = waterthing), size = 1.3) +
  theme_map()+
  scale_colour_grey(end = 0.7)+ 
  ggtitle("French placenames containing the word 'sur'") +
  theme(plot.title = element_text(lineheight=1, face="bold"))+
  theme(text = element_text(size=14),
        legend.position = "none")
p

plot of chunk unnamed-chunk-7

I like this map because I could find the rivers again without having to enter their names, and it looks like drawing with a pencil on watercolor without making any effort. However, since I’m not very good in geography, I’d like to add labels to each river, let’s say somewhere in the middle of the river. I’ll use ggrepel to avoid having overlapping labels. My definition of “somewhere in the middle” is sorting the places of a river by latitude and longitude and then choosing a place close to the middle.

library("ggrepel")
named_upon <- arrange(upon, latitude, longitude)
named_upon <- group_by(named_upon, waterthing)
named_upon <- mutate(named_upon, index = 1:n())
named_upon <- mutate(named_upon, name = ifelse(index == floor(n()/2),
                                   waterthing, NA))
p + geom_label_repel(aes(x = longitude,
                   y = latitude, 
                   label = name,
                   max.iter = 20000),
               data = named_upon)

plot of chunk unnamed-chunk-8

Okay, I’ll now look at the map and learn the names! I’ve lived near the Loire, near the Seine and near the Marne. It might be surprising not to see the Rhine on that map, but placenames in Alsace are often more Germanic, so that would be another story.

Which saints?

I also got questions about saints, one on Twitter from Andrew Boa and a few ones from Andrew MacDonald.

Andrew Boa’s question was the easiest one: how many unique names of saints are there? The questions of the other Andrew were “I wonder, would it be possible to also obtain data on how old these towns are? It would be interesting to see if the gender of popular saints changes over time. Or, more simply, which saints have the most towns? I imagine there are tons of “Saint Sernin”s in the South, and probably lots of “Jeanne-d’Arc”s all over”. Let me tell you I am thankful for the more simply part of the questions because I have no idea where one would find information about the age of the places. Nonetheless, this would be really interesting.

Let’s answer the questions about the number of unique names and their frequencies. I am now in particular curious about André since I got questions from two Andrew’s.

Note that for finding the name I remove the “Saint-” or “Sainte-” part but also everything that could come after another hyphen and a space, e.g. in “Alban-de-Varèze” which I want as “Alban”. Also note that in this analysis I ignore homonym saints, and that Jeanne d’Arc might be a saint, the places named after her don’t contain the word “sainte”.

saints <- ville %>%
  mutate(saint = grepl("Saint-", placename))  %>%
  mutate(sainte = grepl("Sainte-", placename))  %>%
  gather("gender", "yes", saint:sainte) %>%
  filter(yes == TRUE) %>%
  select(- yes)
saints <- by_row(saints, function(df){
  if(df$gender == "saint"){
    name <- sub(".*Saint-", "", df$placename)
  }else{
    name <- sub(".*Sainte-", "", df$placename)
  }
  name <- trimws(name)
  name <- strsplit(name, "-")[[1]][1]
  name <- strsplit(name, " ")[[1]][1]
  return(name)
}, .to = "saintname", .collate = "cols")
knitr::kable(head(saints))
placename latitude longitude gender saintname
Ygos-Saint-Saturnin 43.97651 -0.73780 saint Saturnin
Canal de Vitry-le-François à Saint-Dizier 48.73333 4.60000 saint Dizier
Vitrac-Saint-Vincent 45.79585 0.49356 saint Vincent
Vineuil-Saint-Firmin 49.20024 2.49567 saint Firmin
Villotte-Saint-Seine 47.42893 4.70571 saint Seine
Villiers-Saint-Denis 49.00000 3.26667 saint Denis

How many unique names do we have?

group_by(saints, gender) %>%
  summarize(n_names = length(unique(saintname)),
            n_places = n()) %>%
  knitr::kable()
gender n_names n_places
saint 1205 10310
sainte 157 971

Let’s look at the distributions of number of places by saint name, separately for saints then saintes.

saints_freq <- group_by(saints, gender, saintname) 
saints_freq <- summarize(saints_freq, n_places = n())

filter(saints_freq, gender == "saint") %>%
ggplot() +
  geom_histogram(aes(n_places))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

plot of chunk unnamed-chunk-11

filter(saints_freq, gender == "sainte") %>%
ggplot() +
  geom_histogram(aes(n_places))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

plot of chunk unnamed-chunk-11

The information I get from looking at these ugly histograms is that there are some names that are very popular and a lot of them that are rarely used. Let’s look at the 11 more popular ones for saints and saintes. Don’t ask me why I chose 11!

arrange(saints_freq, desc(n_places)) %>%
  filter(gender == "saint") %>%
  head(n = 11) %>%
  knitr::kable()
gender saintname n_places
saint Martin 618
saint Jean 416
saint Pierre 391
saint Germain 304
saint Julien 219
saint Laurent 215
saint Georges 214
saint Hilaire 181
saint Aubin 169
saint Michel 168
saint André 164

So for saints I see names that are still common in France. And André as 11th one, which might explain why I chose the 11 most popular ones…

arrange(saints_freq, desc(n_places)) %>%
  filter(gender == "sainte") %>%
  head(n = 11) %>%
  knitr::kable()
gender saintname n_places
sainte Marie 122
sainte Croix 69
sainte Colombe 68
sainte Marguerite 52
sainte Anne 50
sainte Foy 36
sainte Eulalie 29
sainte Gemme 25
sainte Geneviève 24
sainte Catherine 21
sainte Radegonde 20

I’m fascinated by some names like Radegonde that are not common any longer.

And since looking at rare names is so fun (isn’t it?), let’s look at the 11th of the least popular names.

arrange(saints_freq, n_places) %>%
  filter(gender == "saint") %>%
  head(n = 11) %>%
  knitr::kable()
gender saintname n_places
saint Aaron 1
saint Alexis 1
saint Alvard 1
saint Amas 1
saint Amond 1
saint Anastase 1
saint Andre´ 1
saint Andrieu 1
saint Andrieux 1
saint Anne 1
saint Apoll 1

I think some rare names might be errors of accents, e.g. Andre might be André and seeing Anne in the list of saints I’m now wondering about the number of female names classified as saints in the dataset. There are 10 “Saint Mary” in the dataset and 122 “Sainte Marie” so I think the gender inbalance would still exist when accounting for this, but it’s certainly a good point to keep in mind when using this Geonames dataset! If I were to really tackle the issue, I think I’d try using the genderizer package on names, although I’m not so sure it’d perform well for French old names and the rOpensci gender package doesn’t have an historical dataset for France (yet?). I could also simply look for a very French dataset of place names, and hope no name would be translated in it.

Beside discovering this limitation of the dataset, I liked the names one can see by browsing the least popular saint names, like Eusoge or Exupère.

arrange(saints_freq, n_places) %>%
  filter(gender == "sainte") %>%
  head(n = 11) %>%
  knitr::kable()
gender saintname n_places
sainte Ame 1
sainte Assise 1
sainte Avoye 1
sainte Awawa 1
sainte Blaizine 1
sainte Clotilde 1
sainte Élisabeth 1
sainte Éturien 1
sainte Eugienne 1
sainte Eulard 1
sainte Genevieve 1

This is similarly fascinating and also shows me it might be interesting to re-analyse the all dataset without diacritical accents (Genevieve should be Geneviève).

And Saint Maël, you might ask? Well Maël or Maëlle are Breton names so there’s no place called Saint Maël in the dataset like my holy patron, but there is Maël-Carhaix and Maël-Pestivien. I can’t speak Breton, Wikipedia tells me that Pestivien means “the end of the sources” and for Carhaix it seems to be a long story. After looking at this dataset, I have the impression that the more toponomy questions one tries to answer, the more new questions one has. While this is awesome because of all the learning it implies, I’ll conclude this post (and probably my hobby career as a toponomist :)) by simply plotting the Jeanne d’Arc places for Andrew MacDonald, and Domrémy-la-Pucelle where she was born. There was no Saint-Sermin!

ville <- mutate(ville, jeanne = grepl("Jeanne [dD].[aA]rc", placename))
ville <- mutate(ville, domremy = (placename == "Domrémy-la-Pucelle"))
jeanne <- filter(ville, jeanne | domremy)
jeanne <- tidyr::gather(jeanne, "word", "value", jeanne:domremy)
jeanne <- filter(jeanne, value)
ggmap(map) +
  geom_point(data = jeanne,
             aes(x = longitude, y = latitude, col = word), size = 3) +
  theme_map()+ 
  ggtitle("French placenames containing 'Jeanne d'Arc'") +
  theme(plot.title = element_text(lineheight=1, face="bold")) +
  scale_color_viridis(discrete = TRUE)

plot of chunk unnamed-chunk-16

So there are a few Jeanne d’Arc places all over as predicted by Andrew, none very close to Domrémy-la-Pucelle but then this was called Domrémy the virgin after her already.

If you want to play with the dataset yourself, you’ll find it on Geonames, see this gist and in the data folder of this Github repo. If you do, don’t hesitate to share your findings!

 
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