多个ICC分组条形图R

问题描述

我正试图为同一定量变量的多个类内系数建立分组的条形图，在不同因素（“构造函数”，“政变” ...“音高”）标准化前后进行估算。这是我的数据集的前5行：

  _buildStoresList(AsyncSnapshot<QuerySnapshot> snapshot) {
    return snapshot.data.documents
        .map((doc) => ListTile(
          title: Center(child: Text(doc['store_name'])),//store_name is one of the fields inside my documents inside the collection on which I took the snapshots.
          subtitle: Center(child: Text(doc['about']))  //about is another such field.
    ))
        .toList();
  }

这里有一些转换，以便barplot函数可以使用方便的矩阵

ICC_intra_observ_3D <- read.csv2("~/Documents/ICC_intra_observ_3D_av_ap_H.csv")
ICC_intra_observ_3D[c(1:5),]
      Texture_Feature ICC_intra_observ_3D_av_H ICC_ap_H_constructeur ICC_ap_H_coup ICC_ap_H_detect ICC_ap_H_filter ICC_ap_H_kv ICC_ap_H_mAs ICC_ap_H_pitch
1  CONVENTIONAL_HUmin                    11.18                 22.26          11.3           13.86           22.94       11.74        18.84          14.26
2 CONVENTIONAL_HUmean                    91.16                 91.06         91.05           92.09           89.33       90.79         88.2          92.26
3  CONVENTIONAL_HUstd                    60.16                 62.09         60.34           62.26           63.64       60.22        61.94          59.96
4  CONVENTIONAL_HUmax                    76.36                 77.09         76.41           80.12           74.75       75.74        73.37          77.21
5   CONVENTIONAL_HUQ1                    88.81                 88.86          88.7           90.04           87.29       88.46        86.17          90.62

之后的结果是：

rownames(ICC_intra_observ_3D)=ICC_intra_observ_3D$Texture_Feature
ICC_intra_observ_3D=ICC_intra_observ_3D[,-1]
ICC_intra_observ_3D=t(ICC_intra_observ_3D)

但是在运行时：

CONVENTIONAL_HUmin CONVENTIONAL_HUmean CONVENTIONAL_HUstd CONVENTIONAL_HUmax CONVENTIONAL_HUQ1 CONVENTIONAL_HUQ2 CONVENTIONAL_HUQ3 ICC_intra_observ_3D_av_H "11.18" "91.16" "60.16" "76.36" "88.81" "89.91" "91.1" ICC_ap_H_constructeur "22.26" "91.06" "62.09" "77.09" "88.86" "89.89" "91.04" ICC_ap_H_coup "11.3" "91.05" "60.34" "76.41" "88.7" "89.84" "91.1" ICC_ap_H_detect "13.86" "92.09" "62.26" "80.12" "90.04" "90.96" "91.47" ICC_ap_H_filter "22.94" "89.33" "63.64" "74.75" "87.29" "88.12" "89.07" ICC_ap_H_kv "11.74" "90.79" "60.22" "75.74" "88.46" "89.62" "90.79" ICC_ap_H_mAs "18.84" "88.2" "61.94" "73.37" "86.17" "87.03" "87.92" ICC_ap_H_pitch "14.26" "92.26" "59.96" "77.21" "90.62" "91.26" "91.88"

错误信息：-0.01 *高度错误：二进制运算符的非数字参数

知道使用默认参数barplot(ICC_intra_observ_3D,beside=T)时，我有一个堆积的barplot似乎可以正常工作，但是我需要比较不同的ICC，以便将它们放在旁边比较合适。

请注意，我没有尝试使用beside = F，因为重组输入数据集似乎更加困难，但是欢迎提出任何建议。

感谢您的帮助

解决方法

我建议使用tidyverse方法。将您的最终数据用作df，代码如下：

首先数据：

#Data
df <- structure(list(Var = c("ICC_intra_observ_3D_av_H","ICC_ap_H_constructeur","ICC_ap_H_coup","ICC_ap_H_detect","ICC_ap_H_filter","ICC_ap_H_kv","ICC_ap_H_mAs","ICC_ap_H_pitch"),CONVENTIONAL_HUmin = c(11.18,22.26,11.3,13.86,22.94,11.74,18.84,14.26),CONVENTIONAL_HUmean = c(91.16,91.06,91.05,92.09,89.33,90.79,88.2,92.26),CONVENTIONAL_HUstd = c(60.16,62.09,60.34,62.26,63.64,60.22,61.94,59.96),CONVENTIONAL_HUmax = c(76.36,77.09,76.41,80.12,74.75,75.74,73.37,77.21),CONVENTIONAL_HUQ1 = c(88.81,88.86,88.7,90.04,87.29,88.46,86.17,90.62),CONVENTIONAL_HUQ2 = c(89.91,89.89,89.84,90.96,88.12,89.62,87.03,91.26),CONVENTIONAL_HUQ3 = c(91.1,91.04,91.1,91.47,89.07,87.92,91.88)),class = "data.frame",row.names = c(NA,-8L))

现在输入代码。

为了使ggplot2函数正常工作，最好重塑变量。您可以使用pivot_longer中的tidyverse将所有列移至行，然后使用geom_bar()绘制图：

df %>% pivot_longer(cols = -Var) %>%
  ggplot(aes(x=Var,y=value,fill=name))+
  geom_bar(stat = 'identity',position = position_dodge2(width = 0.9,preserve = 'single'))

输出：

那是您在x轴上使用的第一个变量，另一种方法是将所有量度移到x轴：

df %>% pivot_longer(cols = -Var) %>%
  ggplot(aes(x=name,fill=Var))+
  geom_bar(stat = 'identity',preserve = 'single'))

输出：

更新：为了按所需顺序排列Var，请使用以下代码：

#Order x-axis
df %>% pivot_longer(cols = -Var) %>%
  mutate(Var=factor(Var,levels = unique(u1$Var),ordered=T)) %>%
  ggplot(aes(x=Var,preserve = 'single'))

输出：

bar-chart ggplot2 grouped-table r r