Question

我一直在寻找一种使用boxplot中的facet_wrap并排绘制R的解决方案。尽管有很多好的解决方案，但是我没有遇到任何想要的东西。我决定画一张我想看到的两个plot的{{1}}的图片。 data.frame {strong> C 拥有我的校准数据，用于四种不同计量指标的模型（即KGE，NSE，PBIAS和R-Sq）。 } V 有我的 validation 数据。我想使用Data.frame功能的Data.frame来查看每个指标的单独plot。下面是我到目前为止所做的事情，但并没有使我更加接近。

facet_wrap

我希望看到如下图

Answer 1

因此，这是一种您可以执行所需操作的方法；

首先，我们创建您拥有的数据；

library(tidyverse)

# Creating first dataframe
C <- 
  data.frame(
    KGE_M1 = runif(3, 0, 0.5), 
    NSE_M1 = runif(3,0,0.5), 
    R_Sq_M1 = runif(3,-1,0.3), 
    PBIAS_M1 = runif(3, -0.25, 0.25),
    KGE_M2 = runif(3, 0.2, 0.7), 
    NSE_M2 = runif(3,0.2,0.7), 
    R_Sq_M2 = runif(3,-0.5,0.7), 
    PBIAS_M2 = runif(3, -0.15, 0.15),
    KGE_M3 = runif(3, 0.3, 0.8), 
    NSE_M3 = runif(3,0.3,0.8), 
    R_Sq_M3 = runif(3,0.3,0.8), 
    PBIAS_M3 = runif(3, -0.10, 0.10),
    KGE_M4 = runif(3, 0.5, 1), 
    NSE_M4 = runif(3,0.5,1), 
    R_Sq_M4 = runif(3,0.5,1), 
    PBIAS_M4 = runif(3, -0.05, 0.05),
    Cal = rep("Calibration", 3),
    stringsAsFactors = FALSE)

# Creating second dataframe
V <- 
  data.frame(
    KGE_M1 = runif(3, 0, 0.5), 
    NSE_M1 = runif(3,0,0.5), 
    R_Sq_M1 = runif(3,-1,0.3), 
    PBIAS_M1 = runif(3, -0.25, 0.25),
    KGE_M2 = runif(3, 0.2, 0.7), 
    NSE_M2 = runif(3,0.2,0.7), 
    R_Sq_M2 = runif(3,-0.5,0.7), 
    PBIAS_M2 = runif(3, -0.15, 0.15),
    KGE_M3 = runif(3, 0.3, 0.8), 
    NSE_M3 = runif(3,0.3,0.8), 
    R_Sq_M3 = runif(3,0.3,0.8), 
    PBIAS_M3 = runif(3, -0.10, 0.10),
    KGE_M4 = runif(3, 0.5, 1), 
    NSE_M4 = runif(3,0.5,1), 
    R_Sq_M4 = runif(3,0.5,1), 
    PBIAS_M4 = runif(3, -0.05, 0.05),
    Val = rep("Validation", 3),
    stringsAsFactors = FALSE)

现在我们更改数据格式并对其进行可视化；

# Rename the variable to make it same
C <- rename(C, Identifier = Cal)
V <- rename(V, Identifier = Val)

data <- 
  # First we bind the two datasets
  bind_rows(C, V) %>%
  # We convert from wide format to long format
  gather(key = "Variable", value = "Value", -Identifier) %>%
  # We separate Variable into 2 columns at the last underscore
  separate(Variable, into = c("Variable", "Number"), sep = "_(?=[^_]+$)")

data %>%
  ggplot()+
  geom_boxplot(aes(x = Number, y = Value, 
                   group  = interaction(Identifier, Number), fill = Identifier)) + 
  facet_wrap(~Variable)

Answer 2

我认为您需要在绘制之前先对Variable进行拆分，以使M1，M2，M3 M4具有一个变量，而您的条件具有一个变量：

library(tidyverse)
C2 <- C %>% pivot_longer(., -Cal, names_to = "Variable", values_to = "Value") %>%
  group_by(Variable) %>%
  mutate(Variable2 = unlist(strsplit(Variable, "_M"))[2]) %>%
  mutate(Variable2 = paste0("Cal_M",Variable2)) %>%
  mutate(Variable1 = unlist(strsplit(Variable,"_M"))[1])  %>%
  rename(., Type = Cal)

# A tibble: 6 x 5
# Groups:   Variable [6]
  Type        Variable  Value Variable2 Variable1
  <fct>       <chr>     <dbl> <chr>     <chr>    
1 Calibration KGE_M1    0.246 Cal_M1    KGE      
2 Calibration NSE_M1    0.476 Cal_M1    NSE      
3 Calibration R_Sq_M1  -0.978 Cal_M1    R_Sq     
4 Calibration PBIAS_M1  0.117 Cal_M1    PBIAS    
5 Calibration KGE_M2    0.544 Cal_M2    KGE      
6 Calibration NSE_M2    0.270 Cal_M2    NSE

现在，我们对数据集V

做同样的事情

V2 <- V %>% pivot_longer(., -Val, names_to = "Variable", values_to = "Value") %>%
  group_by(Variable) %>%
  mutate(Variable2 = unlist(strsplit(Variable, "_M"))[2]) %>%
  mutate(Variable2 = paste0("Val_M",Variable2)) %>%
  mutate(Variable1 = unlist(strsplit(Variable,"_M"))[1]) %>%
  rename(., Type = Val)

# A tibble: 6 x 5
# Groups:   Variable [6]
  Type       Variable   Value Variable2 Variable1
  <fct>      <chr>      <dbl> <chr>     <chr>    
1 Validation KGE_M1    0.459  Val_M1    KGE      
2 Validation NSE_M1    0.105  Val_M1    NSE      
3 Validation R_Sq_M1  -0.435  Val_M1    R_Sq     
4 Validation PBIAS_M1  0.0281 Val_M1    PBIAS    
5 Validation KGE_M2    0.625  Val_M2    KGE      
6 Validation NSE_M2    0.332  Val_M2    NSE

我们现在可以将它们绑定在一起：

DF <- rbind(C2,V2)

然后，我们可以绘制：

ggplot(DF, aes(x = Variable2, y = Value))+
  geom_boxplot()+
  facet_wrap(.~Variable1, scales = "free")+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

编辑：重命名x轴，添加空列以分隔校准和验证值

要在“校准”和“验证”之间添加空白，您可以像下面这样简单地为Variable1的每个条件添加空白行：

DF <- as.data.frame(DF) %>% add_row(Type = rep("Empty",4),
                     Variable = rep("Empty",4),
                     Value = rep(NA,4),
                     Variable2 = rep("Empty",4),
                     Variable1 = unique(DF$Variable1))

此外，如果要重命名x轴标签，则可以使用scale_x_discrete

ggplot(DF, aes(x = Variable2, y = Value, fill = Type))+
  geom_boxplot()+
  facet_wrap(.~Variable1, scales = "free")+
  theme(axis.text.x = element_text(angle = 45, hjust = 1))+
  scale_x_discrete(labels = c("M1","M2","M3","M4","","M1","M2","M3","M4"))

看起来像您所期望的吗？

如何在R中使用facet_wrap并排绘制箱线图？

2 个答案: