r - ggplot - 具有多个 y 变量的条形图

Question

我正在尝试使用 ggplot2 创建一个包含三个 y 变量（、、、）的Total_us_received条形图Total_us_required。Total_us_received_from.CERF所有三个 y 变量均以相同的比例（美元）进行测量。

到目前为止，我已经使用以下代码创建了一个条形图，其中 Total_us_received 作为 y 变量，Disaster_category 作为 x 变量：

ggplot(Template.2006.2017.text, 
       aes(Disaster_category, y=Total_US_received)) + 
  geom_bar(stat ="identity", fill="lightblue") + 
  coord_flip()

但是，我将其他两个 y 变量包含到图中的每一次尝试都失败了。如何将其他两个变量包含在图表中？

一个后续问题：我可以让图表显示没有 NA:s 而不是总和的 x 变量 (Disaster_subtype) 的每个类别的平均值吗？

这是我在 dput 中的数据（压缩版本）：

structure(list(Disaster_category = structure(c(1L, 15L, 17L, 
15L, 5L, 8L, 13L, 8L, 2L, 8L, 2L, 3L, 8L, 2L, 8L, 2L, 10L, 5L, 
7L, 8L, 15L, 2L, 8L, 2L, 15L, 15L, 8L, 15L, 2L, 17L, 2L, 7L, 
2L, 8L, 2L, 3L, 2L, 8L, 8L, 2L, 8L, 17L, 2L, 3L, 8L, 8L, 2L, 
8L, 8L, 8L, 2L, 8L, 3L, 2L, 3L, 2L, 8L, 2L, 3L, 8L, 2L, 8L, 2L, 
15L, 5L, 8L, 13L, 8L, 15L, 2L, 8L, 2L, 3L, 2L, 3L, 15L, 8L, 3L, 
2L, 3L, 8L, 2L, 3L, 2L, 8L, 2L, 8L, 15L, 2L, 8L, 8L, 5L, 2L, 
8L, 2L, 3L, 2L, 17L, 2L, 17L, 2L, 4L, 5L, 8L, 8L, 2L, 8L, 15L, 
2L, 15L, 15L, 7L, 2L, 8L, 2L, 15L, 15L, 7L, 8L, 17L, 2L, 15L, 
8L, 2L, 17L, 2L, 3L, 8L, 2L, 5L, 2L, 8L, 2L, 8L, 8L, 15L, 2L, 
8L, 2L, 15L, 8L, 2L, 15L, 8L, 7L, 8L, 15L, 2L, 8L, 8L, 7L, 13L, 
8L, 2L, 8L, 2L, 8L, 8L, 3L, 2L, 13L, 2L, 3L, 8L, 2L, 15L, 15L, 
8L, 15L, 2L, 5L, 3L, 3L, 8L, 3L, 2L, 8L, 8L, 3L, 2L, 8L, 2L, 
15L, 2L, 17L, 2L, 5L, 2L, 8L, 2L, 15L, 2L, 3L, 8L, 8L, 2L, 8L, 
8L, 2L, 3L), .Label = c("", " ", "Disease", "Disease related disaster", 
"Drought", "Drought & storm", "Extreme temperature / fire", "Flood", 
"Flood & drought", "Insect infestation", "Insect infestation & drought", 
"Landslide & flood", "Landslide / mudslide", "Other", "Storm", 
"Storm & flood", "Winter"), class = "factor"), Total_US_received_from.CERF = c(NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 678307.8333, 
678307.8333, 678307.8333, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1110469.5, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1905355, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2493246, 
2493246, 2493246, 2493246, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 333333.3333, 333333.3333, 333333.3333, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9365420, 
NA, NA, 14321419, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), 
    Total_US_received = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 15507224.5, 15507224.5, 15507224.5, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 333333.3333, 333333.3333, 
    333333.3333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), Total_US_required = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, 20502064.83, 20502064.83, 20502064.83, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, 3070192, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, 49955895.25, 49955895.25, 49955895.25, 49955895.25, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 333333.3333, 
    333333.3333, 333333.3333, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA, 
200L), class = "data.frame")

标签： rggplot2

Answer 1

您可以将数据转换为长格式，然后绘制它们：

library(tidyr)
library(ggplot2)

my_data %>% 
 gather(Total_US_category, Total_US, Total_US_received, Total_US_required, Total_US_received_from.CERF) %>% 
  ggplot(aes(Disaster_category, y = Total_US, fill = Total_US_category)) + 
  geom_col(position = position_dodge()) + 
  coord_flip()

---

如果您想绘制每次灾难的平均值，您可以首先使用以下方式汇总数据dplyr：

library(dplyr)

my_data_sum <- my_data %>% 
  gather(Total_US_category, Total_US, Total_US_received, Total_US_required, Total_US_received_from.CERF) %>% 
  group_by(Disaster_category, Total_US_category) %>% 
  summarize(Total_US_mean = mean(Total_US, na.rm = T)) 

my_data_sum
# A tibble: 33 x 3
# Groups:   Disaster_category [?]
#    Disaster_category        Total_US_category           Total_US_mean
#    <fct>                    <chr>                               <dbl>
#  1 ""                       Total_US_received                     NaN
#  2 ""                       Total_US_received_from.CERF           NaN
#  3 ""                       Total_US_required                     NaN
#  4 " "                      Total_US_received                     NaN
#  5 " "                      Total_US_received_from.CERF           NaN
#  6 " "                      Total_US_required                     NaN
#  7 Disease                  Total_US_received                     NaN
#  8 Disease                  Total_US_received_from.CERF           NaN
#  9 Disease                  Total_US_required                     NaN
# 10 Disease related disaster Total_US_received                     NaN
# ... with 23 more rows

然后绘制数据：

ggplot(my_data_sum, aes(Disaster_category, y = Total_US_mean, fill = Total_US_category)) +
  geom_col(position = position_dodge()) + 
  coord_flip()