Sfoglia il codice sorgente

Analyse in genderequality

genderequality-alternative
Leon Burgard 4 anni fa
parent
commit
789bf57756
2 ha cambiato i file con 131 aggiunte e 2 eliminazioni
  1. +2
    -2
      README.md
  2. +129
    -0
      vignettes/genderequality.Rmd

+ 2
- 2
README.md Vedi File

@@ -14,7 +14,7 @@ tables <- read_all()
tables <- repair(tables) tables <- repair(tables)
write_to_csv(tables) write_to_csv(tables)
``` ```
Wir verwenden NIEMALS source, etc.! Außerdem NIEMALD library(...) verwenden, sondern
Wir verwenden NIEMALS source, etc.! Außerdem NIEMALS library(...) verwenden, sondern
um neue pakete hinzuzufuegen (als dependency), verwende: um neue pakete hinzuzufuegen (als dependency), verwende:
```r ```r
use_package("my-good-old-package") use_package("my-good-old-package")
@@ -40,7 +40,7 @@ Bevor analysiert werden kann, muss fetch.R ausgeführt werden, um alle Protokoll


## Tabellen ## Tabellen


parse.R parsed einzelne Protokolle und erstellt 3 Tibbles
parse.R parsed einzelne Protokolle und erstellt 5 Tibbles


### Redner ### Redner




+ 129
- 0
vignettes/genderequality.Rmd Vedi File

@@ -92,5 +92,134 @@ gender <- tibble(speaker = names,
gender = gender) gender = gender)




speaker %>%
unite("speaker", vorname, nachname, sep = " ") %>%
right_join(gender, by = "speaker") ->
speaker_with_gender

```

#Analyse

First, let's look at the relative distribution of the sexes throughout the whole Bundestag.

```{r}
speaker_with_gender %>%
select(gender) %>%
group_by(gender) %>%
summarise("count" = n()) %>%
filter(gender %in% c("male", "female")) %>%
mutate(portion = 100*count/sum(count)) ->
plot1

bp <- ggplot(plot1, aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity")
pie <- bp + coord_polar("y", start=0)
pie +
scale_fill_manual(values=c("pink", "blue")) +
ggtitle("Relative distribution of sexes") +
xlab("") +
ylab("")
```

Next we look at the individual distributions between men and women in relation to the individual parties.

```{r}

speaker_with_gender %>%
select(fraction, gender) %>%
group_by(fraction, gender) %>%
summarise("count" = n()) %>%
filter(gender %in% c("male", "female")) %>%
filter(!is.na(fraction)) %>%
group_by(fraction) %>%
mutate(portion = 100*count/sum(count)) ->
plot2

plot2 %>%
filter(fraction == "AfD") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie1 <- bp + coord_polar("y", start=0) + ggtitle("AfD") + xlab("") + ylab("")
plot2 %>%
filter(fraction == "BÜNDNIS 90 / DIE GRÜNEN") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie2 <- bp + coord_polar("y", start=0) + ggtitle("DIE GRÜNEN") + xlab("") + ylab("")
plot2 %>%
filter(fraction == "CDU/CSU") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie3 <- bp + coord_polar("y", start=0) + ggtitle("CDU/CSU") + xlab("") + ylab("")
plot2 %>%
filter(fraction == "DIE LINKE") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie4 <- bp + coord_polar("y", start=0) + ggtitle("DIE LINKE") + xlab("") + ylab("")
plot2 %>%
filter(fraction == "FDP") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie5 <- bp + coord_polar("y", start=0) + ggtitle("FDP") + xlab("") + ylab("")
plot2 %>%
filter(fraction == "SPD") %>%
ggplot(aes(x = "", y = portion, fill = gender))+
geom_bar(width = 1, stat = "identity") ->
bp
pie6 <- bp + coord_polar("y", start=0) + ggtitle("SPD") + xlab("") + ylab("")

gridExtra::grid.arrange(pie1,pie2,pie3,pie4,pie5,pie6,nrow=2)
```



```{r}

speeches %>%
group_by(speaker) %>%
summarize(n = n()) %>%
ungroup() %>%
arrange(-n) %>%
left_join(speaker, by=c("speaker" = "id")) %>%
unite(name, vorname, nachname, sep = " ") %>%
inner_join(gender, by=c("name"= "speaker")) %>%
group_by(gender) %>%
summarise(absolute=sum(n)) %>%
filter(gender %in% c("female", "male")) %>%
mutate(absolute2=absolute/sum(absolute)) %>%
mutate(portion=c(0.32, 0.68)) %>%
mutate(relative=absolute*(1-portion)) %>%
mutate(relative2=relative/sum(relative)) ->
plot3
```

```{r}
barplot(plot3$absolute2,
ylab = "amount of speeches",
main = "Absolute comparison of speech shares",
las = 1,
names.arg = c("women", "men"),
col = c("pink", "darkblue"),
font.main = 4,
cex.axis = 0.7)
``` ```


```{r}
barplot(plot3$relative2,
ylab = "amount of speeches",
main = "Relative comparison of speech shares",
las = 1,
names.arg = c("women", "men"),
col = c("pink", "darkblue"),
font.main = 4,
cex.axis = 0.7)
```





Loading…
Annulla
Salva