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scripts/figures.qmd

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+---
+title: "figures"
+format: html
+editor: visual
+---
+
+```{r}
+data = read.csv("full_data.csv")
+rubriques_lemonde = c("international","culture","politique","société","économie","sport","science/technologie","inclassable")
+
+z = data$rubrique %in% rubriques_lemonde
+data$rubrique[!z] = "inclassable"
+```
+
+```{r}
+library(dplyr)
+d = filter(data,sexe_prenom %in% c("Femme","Homme"))
+d = d %>% group_by(year,sexe_prenom) %>% summarise(
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women,na.rm=T)
+) 
+d$mentions_total = d$mentions_men + d$mentions_women
+ggplot(d %>% filter(mentions_women > 20),aes(year,mentions_women/mentions_total,color=sexe_prenom)) + geom_point() + ylab("Masculinité") + labs(color="Author sex")
+
+```
+```{r}
+library(dplyr)
+d = filter(data)
+d = d %>% group_by(year) %>% summarise(
+  mentions_men = sum(citations_men,na.rm=T),
+  mentions_women = sum(citations_women,na.rm=T)
+) 
+d$mentions_total = d$mentions_men + d$mentions_women
+ggplot(d %>% filter(mentions_women > 20),aes(year,mentions_women/mentions_total)) + geom_line() + ylab("% women mentionned") + labs(color="Author sex") + ylim(c(0,.25))
+
+```
+
+
+```{r}
+library(dplyr)
+d = filter(data,sexe_prenom %in% c("Femme","Homme"),!rubrique %in% c("inclassable","sport"))
+d = d %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women,na.rm=T)
+) 
+d$mentions_total = d$mentions_men + d$mentions_women
+ggplot(d %>% filter(mentions_women > 50),aes(year,mentions_women/mentions_total,color=sexe_prenom)) + geom_point() + ylab("% women") + facet_wrap(.~rubrique,scales="free_y") + labs(color="Author sex")
+
+```
+
+
+```{r}
+d = filter(data,sexe_prenom %in% c("Femme","Homme"),!rubrique %in% c("inclassable","sport")) %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
+  mentions_men = sum(mentions_men,na.rm=T),
+  mentions_women = sum(mentions_women,na.rm=T)
+) 
+d$mentions_total = d$mentions_men + d$mentions_women
+d$feminity = d$mentions_women/d$mentions_total
+
+#ggplot(d %>% filter(mentions_total > 200),aes(year,mentions_women/mentions_total,color=sexe_prenom)) + geom_point() + ylab("Masculinité")
+
+d_bis = d %>% filter(mentions_women > 100) %>% select(year,sexe_prenom,feminity,rubrique) %>% pivot_wider(names_from = sexe_prenom, values_from = feminity, id_cols = c(rubrique, year))
+ggplot(d_bis,aes(year,Femme-Homme)) + geom_point() + geom_smooth(se=F,color="black") + facet_wrap(.~rubrique,scales = "free_y")
+
+
+```
+
+
+
+
+
+```{r}
+#Full corpus
+
+d = filter(data,sexe_prenom %in% c("Femme","Homme")) %>% group_by(year,sexe_prenom,rubrique) %>% summarise(
+  mentions_men = sum(citations_men,na.rm=T),
+  mentions_women = sum(citations_women,na.rm = T)
+) 
+d$mentions_total = d$mentions_men + d$mentions_women
+d$feminity = d$mentions_women/d$mentions_total
+
+#ggplot(d %>% filter(mentions_total > 200),aes(year,mentions_women/mentions_total,color=sexe_prenom)) + geom_point() + ylab("Masculinité")
+
+d_bis = d %>% filter(mentions_total > 200) %>% select(year,sexe_prenom,feminity,rubrique) %>% pivot_wider(names_from = sexe_prenom, values_from = feminity, id_cols =  c(year,rubrique))
+ggplot(d_bis,aes(year,Femme/Homme)) + geom_point() + geom_smooth(se=F,color="black") + facet_wrap(.~rubrique,scales = "free_y&")
+
+
+```
+
+
+Enseignements :
+* Pas trop d'effet de structure rubrique (pas de grosse diff entre les sexes, à part en sport).
+* De 1970 à 2000, convergence des rôles de genre ? Ou estompement d'un journalisme féministe (Nicole-Lise Bernheim) ?