Question

我试图绘制堆积的条形图，并用我计算出的值标记每个条形。我有eah酒吧的价值向量。如果按原样使用矢量，则会收到警告“错误：美学的长度必须为1或与数据相同（156）：标签”，因此我将矢量添加到数据中，并对每个行使用rep条，但是当我使用geom_text时，我为每个条获得了许多标签（每个条中的堆栈数）。

我的代码：

g <- ggplot(df, aes(x = RNA, y = Value))
g + geom_bar(aes(fill=fct_reorder(Cancer, Value, sum, desc=TRUE)), width = 0.5, stat="identity") + 
   geom_text(aes(label = lbls), vjust = -0.5, position = position_dodge(0.9))+ 
  theme(axis.text.x = element_text(angle=90, vjust=0.6),
        axis.text.y = element_blank()) +
  scale_fill_manual(values=c("#df4a7a",
                            "#c97b7a",
                            "#de5137",
                            "#d08935",
                            "#a78d57",
                            "#d2d23e",
                            "#cfd88d",
                            "#67993f",
                            "#76d854",
                            "#66db9f",
                            "#529477",
                            "#81dacf",
                            "#6bb2d5",
                            "#6387d7",
                            "#777ba7",)) +
  labs(title="Cancer Types", 
       subtitle="")

我的数据框带有要标记的值列：

                              Cancer                  RNA       Value  Tabs
mp.117                        Breast                  snoRNA     3 0.268
tmp.118   Digestive/Gastrointestinal                  snoRNA     0 0.268
tmp.119 Endocrine and Neuroendocrine                  snoRNA     1 0.268
tmp.120                          Eye                  snoRNA     0 0.268
tmp.121                Genitourinary                  snoRNA     0 0.268
tmp.122                    Germ Cell                  snoRNA     0 0.268
tmp.123                  Gynecologic                  snoRNA     0 0.268
tmp.124                Head and Neck                  snoRNA     0 0.268
tmp.125            Hematologic/Blood                  snoRNA     0 0.268
tmp.126              Musculoskeletal                  snoRNA     1 0.268
tmp.127                   Neurologic                  snoRNA     0 0.268
tmp.128         Respiratory/Thoracic                  snoRNA     0 0.268
tmp.129                         Skin                  snoRNA     0 0.268
tmp.143                       Breast circRNA | Circular RNA      3 0.005
tmp.144   Digestive/Gastrointestinal circRNA | Circular RNA      1 0.005
tmp.145 Endocrine and Neuroendocrine circRNA | Circular RNA      1 0.005
tmp.146                          Eye circRNA | Circular RNA      0 0.005
tmp.147                Genitourinary circRNA | Circular RNA      1 0.005
tmp.148                    Germ Cell circRNA | Circular RNA      0 0.005
tmp.149                  Gynecologic circRNA | Circular RNA      4 0.005
tmp.150                Head and Neck circRNA | Circular RNA      3 0.005
tmp.151            Hematologic/Blood circRNA | Circular RNA      0 0.005
tmp.152              Musculoskeletal circRNA | Circular RNA      0 0.005
tmp.153                   Neurologic circRNA | Circular RNA      0 0.005
tmp.154         Respiratory/Thoracic circRNA | Circular RNA      1 0.005
tmp.155                         Skin circRNA | Circular RNA      0 0.005

带有标签的向量：

lbl = c(0.821, 0.899, 0.410, 0.028, 0.257, 0.217, 0.474, 0.220, 0.210, 0.268,   NaN, 0.005)

谢谢！

Answer 1

library(ggplot2)
library(dplyr)
library(forcats)


#your plot without geom_text
g <- ggplot(df, aes(x = RNA, y = Value)) + 
  geom_bar(aes(fill=fct_reorder(Cancer, Value, sum, desc=TRUE)), 
           width = 0.5, stat="identity") + 
  theme(axis.text.x = element_text(angle=90, vjust=0.6),
        axis.text.y = element_blank()) +
  scale_fill_manual(values=c("#df4a7a", "#c97b7a","#de5137","#d08935",
                             "#a78d57","#d2d23e","#cfd88d","#67993f",
                             "#76d854","#66db9f","#529477","#81dacf",
                             "#6bb2d5","#6387d7","#777ba7")) +
  labs(title="Cancer Types", subtitle="")



tabdf <- df %>% group_by(RNA) %>% summarise_at(vars(Tabs,Value), list(~mean(.)))

g +  geom_text(data = tabdf ,aes(y=Value, label = Tabs, fill = NULL))

^{由reprex package（v0.2.1）于2019-05-16创建}

用ggplot2标签堆叠的条形图

1 个答案: