我一直在制作条形图。我想在堆积的条形图中显示不同变量的百分比。我到目前为止已经绘制了情节，但我无法在酒吧获得百分比。 到目前为止，我所看到的所有示例始终都有某种y变量用于构建百分比，但事实并非如此。 我认为我的问题与以下事实有关：我必须将数据转换为字符才能完成绘图，这样我才能获得百分比。
有人有什么想法吗，我该怎么办？
这是我的数据：
dput(wissen2[,1:8]) structure(list(Offenburg_W1 = c(2, 2, 3, 4, 3, 2, 4, 2, 3, 2, 3, 3, 3, 3, 3, 4, 2, 1, 3, 2, 1, 2, 4, 4, 2, 3, 3, 3, 3, 3, 4, 4, 4, 1, 4, 4, 2, 2, 2, 4, 4, 2, 2, 3, 4, 1, 2, 3, 4, 4, 3, 4, 3, 2, 4, 4, 3, 2, 2, 4, 4, 3, 4, 2, 2, 2, 2, 2, 3, 2, 2, 3, 4, 2, 2, 2, 2, 3, 2, 4, 2, 2, 2, 2, 1, 4, 2, 3, 2, 2, 3, 3, 3, 3, 2, 3, 4, 3, 3, 4, 4, 3, 3, 4, 3, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 3, 4, 2, 3, 2, 3, 3, 3), Offenburg_W2 = c(3, 3, 2, 4, 3, 3, 4, 4, 2, 2, 3, 3, 3, 3, 3, 4, 2, 4, 4, 4, 4, 2, 4, 4, 2, 4, 3, 2, 4, 4, 4, 3, 2, 1, 4, 1, 2, 2, 2, 3, 3, 2, 2, 4, 2, 2, 2, 3, 4, 4, 1, 3, 3, 3, 3, 4, 4, 2, 4, 4, 3, 4, 4, 2, 2, 3, 3, 2, 1, 2, 3, 1, 4, 2, 4, 3, 1, 3, 3, 2, 4, 3, 1, 3, 4, 4, 1, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 4, 1, 3, 4, 1, 4, 4, 3, 3, 4, 2, 2, 3, 4, 4, 2, 2, 4, 2, 3, 3, 4, 3, 3, 3, 3, 1, 2), Heilbronn_W1 = c(3, 3, 4, 2, 4, 3, 3, 4, 1, 3, 1, 2, 2, 2, 2, 1, 3, 3, 2, 4, 2, 4, 2, 3, 3, 2, 1, 2, 2, 4, 3, 2, 2, 3, 2, 2, 3, 2, 3, 1, 2, 3, 1, 1, 2, 3, 3, 1, 3, 2, 4, 2, 2, 3, 3, 1, 4, 3, 4, 3, 2, 4, 2, 3, 4, 3, 3, 3, 4, 3, 3, 4, 3, 3, 1, 1, 3, 2, 3, 2, 2, 4, 4, 3, 2, 1, 2, 1, 3, 4, 1, 1, 2, 2, 4, 3, 3, 2, 1, 1, 3, 1, 4, 2, 2, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 3, 3, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2), Heilbronn_W2 = c(3, 2, 3, 2, 4, 2, 2, 3, 4, 4, 1, 2, 2, 2, 1, 1, 4, 2, 2, 2, 1, 4, 1, 3, 4, 2, 1, 3, 2, 3, 3, 2, 3, 2, 2, 3, 3, 3, 3, 2, 2, 3, 1, 3, 3, 4, 1, 1, 2, 2, 4, 2, 2, 4, 1, 1, 3, 3, 3, 2, 2, 3, 2, 3, 3, 2, 2, 1, 2, 3, 2, 4, 2, 3, 3, 1, 3, 2, 2, 2, 2, 2, 3, 2, 1, 2, 3, 1, 4, 1, 1, 1, 2, 2, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3, 2, 2, 2, 1, 2, 1, 3, 3, 1, 3, 3, 1, 4, 2, 2, 2, 2, 2, 1, 1, 4, 2, 2, 3), Mannheim_W1 = c(1, 4, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, 4, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 3, 4, 3, 1, 1, 3, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4, 4, 3, 1, 1, 1, 4, 4, 1, 1, 1, 3, 2, 2, 2, 1, 3, 2, 2, 4, 1, 1, 4, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 4, 1, 1, 1), Mannheim_W2 = c(4, 1, 1, 1, 1, 1, 1, 2, 3, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 3, 2, 1, 2, 1, 1, 4, 4, 1, 1, 4, 2, 1, 1, 3, 2, 1, 1, 3, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 4, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 3, 1, 1, 4, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 4, 1, 1, 2, 2, 2, 2, 2, 1, 4, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 4, 1, 1, 4, 1, 1, 1, 2, 2, 1, 4, 3, 1), Tuebingen_W1 = c(4, 1, 2, 3, 2, 4, 2, 1, 4, 4, 4, 4, 4, 4, 1, 3, 4, 4, 4, 3, 4, 3, 3, 2, 4, 4, 4, 4, 4, 1, 2, 2, 3, 4, 3, 3, 4, 3, 1, 2, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 2, 3, 4, 4, 2, 3, 2, 4, 3, 2, 1, 1, 3, 4, 3, 3, 4, 2, 2, 4, 4, 2, 2, 1, 3, 4, 4, 4, 4, 2, 4, 3, 3, 4, 4, 3, 2, 4, 4, 1, 4, 4, 1, 4, 3, 2, 2, 4, 4, 3, 2, 4, 2, 3, 4, 3, 3, 3, 4, 4, 2, 4, 4, 3, 3, 2, 4, 3, 3, 4, 4, 2, 1, 4, 1, 4, 4, 4), Tuebingen_W2 = c(2, 4, 4, 3, 2, 4, 3, 1, 1, 3, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 4, 4, 3, 1, 2, 4, 2, 4, 3, 4, 4, 4, 1, 1, 4, 4, 3, 1, 4, 3, 4, 4, 3, 3, 2, 4, 4, 2, 4, 3, 2, 4, 2, 3, 4, 1, 3, 4, 4, 4, 4, 4, 3, 4, 4, 3, 3, 4, 2, 4, 4, 4, 4, 3, 3, 4, 2, 4, 3, 3, 4, 4, 3, 4, 4, 3, 4, 4, 4, 2, 4, 4, 4, 4, 1, 4, 4, 2, 3, 2, 3, 4, 4, 3, 4, 4, 4, 2, 2, 4, 3, 3, 3, 4, 4, 3, 4, 4, 2, 1, 4, 4)), class = "data.frame", row.names = c(NA, -128L))
代码如下：
wissen2 <- as_tibble(wissen2) %>%
transmute_all(as.character)
wissen2 <- wissen2 %>%
pivot_longer(cols = OffenburgW1:TuebingenW2,
names_to = "Befragungszeitpunkt",
values_to = "Antwort")
wissen2$Befragungszeitpunkt = forcats::fct_inorder(wissen2$Befragungszeitpunkt) #Reorder factor levels by sorting along another variable
wissen2$Befragungszeitpunkt = forcats::fct_rev(wissen2$Befragungszeitpunkt) #Reverse order of factor levels
wissen2_plot <- ggplot(wissen2, aes(x = Befragungszeitpunkt)) +
geom_bar(aes(fill = Antwort), position = position_fill(reverse = T)) +
#scale_y_continuous(labels = scales::percent) +
labs(x = "Befragungszeitpunkt", y= "Häufigkeit") +
ggtitle("Schätzung der Höhe des Anteils der Bevölkerung mit Migrationshintergrund", subtitle = "in unterschiedlichen Städten (vor und nach der Befragung)") +
theme(plot.title = element_text(hjust = 0.8)) +
scale_fill_manual(values = c("#999933", "#E69F00", "#999999", "#CC6600"),
breaks=c(1, 2, 3, 4),
labels=c("Platz 1", "Platz 2", "Platz 3", "Platz 4")) +
coord_flip() +
theme(legend.position = "bottom") +
guides(fill = "legend") +
theme(axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10)) +
scale_x_discrete(labels = c ("OffenburgW1" = "Offenburg Welle 1", "OffenburgW2" = "Offenburg Welle 2", "HeilbronnW1" = "Heilbronn Welle 1", "HeilbronnW2" = "Heilbronn Welle 2", "MannheimW1" = "Mannheim Welle 1", "MannheimW2" = "Mannheim Welle 2", "TübingenW1" = "Tübingen Welle 1", "TuebingenW2" = "Tübingen Welle 2"))
wissen2_plot