ggplot bar_col中的降序

如何解决ggplot bar_col中的降序

下面是数据集，

# A tibble: 449 x 7
   `Country or Area` `Region 1`      Year   Rate MinCI MaxCI Average
   <chr>             <chr>           <chr> <dbl> <dbl> <dbl>   <dbl>
 1 Afghanistan       Southern Asia   2011    4.2   2.6   6.2    4.4 
 2 Afghanistan       Southern Asia   2016    5.5   3.4   8.1    5.75
 3 Aland Islands     Northern Europe NA     NA    NA    NA     NA   
 4 Albania           Southern Europe 2011   18.8  14.8  23     18.9 
 5 Albania           Southern Europe 2016   21.7  17    26.7   21.8 
 6 Algeria           Northern Africa 2011   24    19.9  28.4   24.2 
 7 Algeria           Northern Africa 2016   27.4  22.5  32.7   27.6 
 8 American Samoa    Polynesia       NA     NA    NA    NA     NA   
 9 Andorra           Southern Europe 2011   24.6  19.8  29.8   24.8 
10 Andorra           Southern Europe 2016   25.6  20.1  31.3   25.7

我需要使用上述数据集绘制bar_col以比较每个区域的平均肥胖率。此外，我需要按从最高到最低的顺序排序。

我还如上所述计算了平均肥胖率。

下面是我用于生成ggplot的代码，但无法弄清楚如何从最高到最低排序。

region_plot <- ggplot(continent) + aes(x = continent$`Region 1`,y = continent$Average,fill = Average) +
  geom_col() +
  xlab("Region") + ylab("Average Obesity") +
  theme(axis.text.x = element_text(angle = 90,hjust = 1)) +
  ggtitle("Average obesity rate of each region")
region_plot

解决方法

检查数据后，您将拥有多个区域，因此为了显示每个区域的平均值，您必须先计算然后绘制。您可以使用dplyr和group_by()与summarise()进行此操作。您的数据有限，但对于真实数据，NA不应该存在。这里的代码使用了部分共享数据。使用真实数据时，请小心使用名称。 reorder()函数可以排列小节。这里的代码：

library(dplyr)
library(ggplot2)
#Code
df %>% group_by(Region) %>%
  summarise(Avg=mean(Average,na.rm=T)) %>%
  filter(!is.na(Avg)) %>%
  ggplot(aes(x=reorder(Region,-Avg),y=Avg,fill=Region))+
  geom_col() +
  xlab("Region") + ylab("Average Obesity") +
  theme(axis.text.x = element_text(angle = 90,hjust = 1)) +
  ggtitle("Average obesity rate of each region")

输出：

使用了一些数据：

#Data
df <- structure(list(Region = c("Southern Asia","Southern Asia","Northern Europe","Southern Europe","Northern Africa","Polynesia","Southern Europe"),Year = c(2011L,2016L,NA,2011L,2016L),Rate = c(4.2,5.5,18.8,21.7,24,27.4,24.6,25.6),MinCI = c(2.6,3.4,14.8,17,19.9,22.5,19.8,20.1),MaxCI = c(6.2,8.1,23,26.7,28.4,32.7,29.8,31.3),Average = c(4.4,5.75,18.9,21.8,24.2,27.6,24.8,25.7)),row.names = c(NA,-10L),class = "data.frame")

,

可以通过预处理数据并按Average对结果进行排序来解决问题。然后强制Region 1进行分解。

library(ggplot2)
library(dplyr)

continent %>%
  group_by(`Region 1`) %>%
  summarise(Average = mean(Average,na.rm = TRUE)) %>%
  arrange(desc(Average)) %>% 
  mutate(`Region 1` = factor(`Region 1`,levels = unique(`Region 1`))) %>%
  ggplot(aes(x = `Region 1`,y = Average,fill = Average)) +
  geom_col() +
  xlab("Region") + ylab("Average Obesity") +
  ggtitle("Average obesity rate of each region") +
  theme(axis.text.x = element_text(angle = 90,hjust = 1)) -> region_plot

region_plot

数据

continent <- read.table(text = "
 'Country or Area' 'Region 1'      Year   Rate MinCI MaxCI Average
 1 Afghanistan       'Southern Asia'   2011    4.2   2.6   6.2    4.4 
 2 Afghanistan       'Southern Asia'   2016    5.5   3.4   8.1    5.75
 3 'Aland Islands'     'Northern Europe' NA     NA    NA    NA     NA   
 4 Albania           'Southern Europe' 2011   18.8  14.8  23     18.9 
 5 Albania           'Southern Europe' 2016   21.7  17    26.7   21.8 
 6 Algeria           'Northern Africa' 2011   24    19.9  28.4   24.2 
 7 Algeria           'Northern Africa' 2016   27.4  22.5  32.7   27.6 
 8 'American Samoa'    Polynesia       NA     NA    NA    NA     NA   
 9 Andorra           'Southern Europe' 2011   24.6  19.8  29.8   24.8 
10 Andorra           'Southern Europe' 2016   25.6  20.1  31.3   25.7
",header = TRUE,check.names = FALSE)