根据评论更新

Question

编辑：我正在尝试制作一个分组的箱图。我使用的数据以csv + long格式粘贴在下面：三个组（Individual，Multiwriter，Column），每个组有两个变量（PK，US）及其数值。

Blog,Region,Dim1
Individual,PK,-4.75
Individual,PK,-5.69
Individual,PK,-0.27
Individual,PK,-2.76
Individual,PK,-8.24
Individual,PK,-12.51
Individual,PK,-1.28
Individual,PK,0.95
Individual,PK,-5.96
Individual,PK,-8.81
Individual,PK,-8.46
Individual,PK,-6.15
Individual,PK,-13.98
Individual,PK,-16.43
Individual,PK,-4.09
Individual,PK,-11.06
Individual,PK,-9.04
Individual,PK,-8.56
Individual,PK,-8.13
Individual,PK,-14.46
Individual,PK,-4.21
Individual,PK,-4.96
Individual,PK,-5.48
Multiwriter,PK,-3.31
Multiwriter,PK,-5.62
Multiwriter,PK,-4.48
Multiwriter,PK,-6.08
Multiwriter,PK,-4.68
Multiwriter,PK,-6.92
Multiwriter,PK,-11.29
Multiwriter,PK,6.66
Multiwriter,PK,1.66
Multiwriter,PK,3.39
Multiwriter,PK,0.06
Multiwriter,PK,4.11
Multiwriter,PK,-1.57
Multiwriter,PK,1.33
Multiwriter,PK,-6.91
Multiwriter,PK,4.87
Multiwriter,PK,-10.87
Multiwriter,PK,6.25
Multiwriter,PK,-0.68
Multiwriter,PK,0.11
Multiwriter,PK,0.71
Multiwriter,PK,-3.8
Multiwriter,PK,-1.75
Multiwriter,PK,-5.38
Multiwriter,PK,1.24
Multiwriter,PK,-5.59
Multiwriter,PK,4.98
Multiwriter,PK,0.98
Multiwriter,PK,7.47
Multiwriter,PK,-5.25
Multiwriter,PK,-14.24
Multiwriter,PK,-1.55
Multiwriter,PK,-8.44
Multiwriter,PK,-7.67
Multiwriter,PK,5.85
Multiwriter,PK,6
Multiwriter,PK,-7.53
Multiwriter,PK,1.59
Multiwriter,PK,-9.48
Multiwriter,PK,-3.99
Multiwriter,PK,-5.82
Multiwriter,PK,1.62
Multiwriter,PK,-4.14
Multiwriter,PK,1.06
Multiwriter,PK,4.52
Multiwriter,PK,-5.6
Multiwriter,PK,-3.38
Multiwriter,PK,4.82
Multiwriter,PK,0.76
Multiwriter,PK,-4.95
Multiwriter,PK,-2.05
Column,PK,1.64
Column,PK,5.2
Column,PK,2.8
Column,PK,1.93
Column,PK,2.36
Column,PK,4.77
Column,PK,-1.92
Column,PK,-2.94
Column,PK,4.58
Column,PK,2.98
Column,PK,9.07
Column,PK,8.5
Column,PK,1.23
Column,PK,8.97
Column,PK,4.1
Column,PK,7.25
Column,PK,0.02
Column,PK,-3.48
Column,PK,1.01
Column,PK,2.7
Column,PK,-2.32
Column,PK,3.22
Column,PK,-2.37
Column,PK,-13.28
Column,PK,-4.36
Column,PK,2.91
Column,PK,4.4
Column,PK,-5.07
Column,PK,-10.24
Column,PK,12.8
Column,PK,1.92
Column,PK,13.24
Column,PK,12.32
Column,PK,12.7
Column,PK,9.95
Column,PK,12.11
Column,PK,7.63
Column,PK,11.09
Column,PK,13.04
Column,PK,12.06
Column,PK,9.49
Column,PK,8.64
Column,PK,10.05
Column,PK,6.4
Column,PK,9.64
Column,PK,3.53
Column,PK,4.78
Column,PK,9.54
Column,PK,8.49
Column,PK,2.56
Column,PK,8.82
Column,PK,-3.59
Column,PK,-3.31
Column,PK,10.05
Column,PK,-0.28
Column,PK,-0.5
Column,PK,-6.37
Column,PK,2.97
Column,PK,4.49
Column,PK,9.14
Column,PK,4.5
Column,PK,8.6
Column,PK,6.76
Column,PK,3.67
Column,PK,6.79
Column,PK,5.77
Column,PK,10.5
Column,PK,1.57
Column,PK,9.47
Individual,US,-9.85
Individual,US,-2.73
Individual,US,-0.32
Individual,US,-0.94
Individual,US,-7.51
Individual,US,-8.21
Individual,US,-7.33
Individual,US,-5.1
Individual,US,-1.58
Individual,US,-2.49
Individual,US,-1.36
Individual,US,-5.76
Individual,US,-0.48
Individual,US,-3.38
Individual,US,2.42
Individual,US,-1.71
Individual,US,-2.17
Individual,US,-2.81
Individual,US,-0.64
Individual,US,-8.88
Individual,US,-1.53
Individual,US,-1.42
Individual,US,-17.89
Individual,US,7.1
Individual,US,-4.12
Individual,US,-0.83
Individual,US,2.05
Individual,US,-5.87
Individual,US,-0.15
Individual,US,5.78
Individual,US,-1.96
Individual,US,1.77
Individual,US,-0.67
Individual,US,-10.23
Individual,US,3.37
Individual,US,-1.18
Individual,US,6.94
Individual,US,-3.86
Individual,US,2.21
Individual,US,-11.64
Individual,US,-14.71
Individual,US,-12.74
Individual,US,-6.24
Individual,US,-13.64
Individual,US,-8.53
Individual,US,-10.4
Individual,US,-6.24
Individual,US,-12.15
Individual,US,-15.96
Multiwriter,US,11.27
Multiwriter,US,3.51
Multiwriter,US,4.05
Multiwriter,US,3.81
Multiwriter,US,8.56
Multiwriter,US,6.36
Multiwriter,US,-8.99
Multiwriter,US,3.36
Multiwriter,US,3.18
Multiwriter,US,-5.22
Multiwriter,US,-8.61
Multiwriter,US,-9.02
Multiwriter,US,-6.32
Multiwriter,US,0.53
Multiwriter,US,11.03
Multiwriter,US,-5.7
Multiwriter,US,4
Multiwriter,US,-3.55
Multiwriter,US,2.79
Multiwriter,US,4.61
Multiwriter,US,-3.8
Multiwriter,US,-9.62
Multiwriter,US,-8.37
Multiwriter,US,-2.18
Multiwriter,US,-1.64
Multiwriter,US,-9.99
Multiwriter,US,-1.44
Multiwriter,US,-4.45
Multiwriter,US,-7.84
Multiwriter,US,-11.6
Multiwriter,US,-2.71
Multiwriter,US,1.2
Multiwriter,US,-6.44
Multiwriter,US,-2.64
Multiwriter,US,-11.59
Multiwriter,US,-5.9
Multiwriter,US,-3.78
Multiwriter,US,-14.99
Multiwriter,US,1.32
Multiwriter,US,-6.55
Multiwriter,US,0.92
Multiwriter,US,-5.61
Multiwriter,US,-14.16
Multiwriter,US,-10.03
Multiwriter,US,-7.08
Multiwriter,US,0.62
Multiwriter,US,-5.43
Multiwriter,US,-1.11
Multiwriter,US,-11.37
Multiwriter,US,-13.37
Multiwriter,US,-12.71
Multiwriter,US,1.86
Multiwriter,US,14.11
Multiwriter,US,-5.24
Multiwriter,US,-6.77
Multiwriter,US,-4.79
Multiwriter,US,-6.22
Multiwriter,US,3.66
Multiwriter,US,-2.65
Multiwriter,US,-2.87
Multiwriter,US,-12.32
Multiwriter,US,-7.48
Multiwriter,US,-4.84
Multiwriter,US,0.44
Column,US,8.93
Column,US,10.29
Column,US,8.31
Column,US,5.88
Column,US,8.87
Column,US,-2.9
Column,US,3.71
Column,US,8.43
Column,US,1.47
Column,US,3.05
Column,US,-1.78
Column,US,1.14
Column,US,7.2
Column,US,5.22
Column,US,5.53
Column,US,8.14
Column,US,-2.22
Column,US,0.89
Column,US,2.5
Column,US,6.77
Column,US,3.63
Column,US,2.86
Column,US,3.7
Column,US,7.52
Column,US,3.12
Column,US,0
Column,US,0.28
Column,US,6.86
Column,US,-0.32
Column,US,2.92
Column,US,-1.14
Column,US,-1.11
Column,US,4.42
Column,US,4.37
Column,US,1.09
Column,US,-3.66
Column,US,7.09
Column,US,-11.02
Column,US,-0.78
Column,US,8.44
Column,US,4.88
Column,US,-3.9
Column,US,-0.21
Column,US,6.48
Column,US,4.49
Column,US,-8.89
Column,US,-0.73
Column,US,1.76
Column,US,-4.31
Column,US,4.63
Column,US,8.91
Column,US,3.55
Column,US,6.69
Column,US,-4.45
Column,US,9.82
Column,US,6.79
Column,US,1.84
Column,US,8.97
Column,US,2.38
Column,US,4.68
Column,US,9.23
Column,US,2.85
Column,US,4.19
Column,US,2.43
Column,US,5.48
Column,US,-1.08
Column,US,7.47
Column,US,3.13
Column,US,-0.42
Column,US,-0.71
Column,US,6.51
Column,US,6.34
Column,US,3.94
Column,US,5.46
Column,US,0.39
Column,US,8.15
Column,US,7.99
Column,US,6.26
Column,US,7.91
Column,US,14.18
Column,US,7.41
Column,US,7.16
Column,US,5.6
Column,US,7.51
Column,US,6.24
Column,US,3.67
Column,US,3.84
Column,US,2.37
Column,US,-3.5
Column,US,5.02
Column,US,-6.04
Column,US,5.36
Column,US,1.98
Column,US,7.79
Column,US,0.02
Column,US,-1.9
Column,US,-2.81
Column,US,10.69
Column,US,1.65
Column,US,8.19
Column,US,1.92

以下是我使用此论坛和其他地方的帮助创建分组框图的方法。

dim1 <- read.csv("path\to\dim1.csv", fileEncoding="UTF-8-BOM")
summary(dim1)
>>summary is show just fine.
ggplot2::ggplot(data = dim1) +
+     aes(x = Blog, y = Dim1, colour = Region) +
+     geom_boxplot()

但是在执行最后一个命令时出现以下错误：

+ aes中的错误（x =博客，y = Dim1，颜色=区域）：参数无效到一元运算符

看起来我错误地使用了我创建的数据框。有任何想法吗？的更新我已经编写了一个R脚本来保存图形，并且效果很好。

rm(list = ls())
library(ggplot2)
dims <- read.csv("DataBlogs.csv", fileEncoding="UTF-8-BOM")
attach(dims)
summary(dims)
save <- function(plot, file_name){
    ggsave(file_name, plot = plot, path = "path\\")
    unlink(file_name)
}
plotgraph <- function(x, y, colour, title)
{
    plot1 <- ggplot(dims, aes(x = x, y = y, colour = colour)) +
        geom_boxplot()
    plot1 <- plot1 + scale_y_continuous(name = "Dimension Score") + scale_x_discrete(name = "Blog Type")
    plot1 <- plot1 + ggtitle(title) + labs(color='Region') 
    return(plot1)
}
plot1 <- plotgraph (Blog, Dim1, Region, "Title")
save(plot1, "Dimension1.png")

现在我正在寻找计算和添加方法的方法。

Answer 1

将数据转换为长格式，然后进行分析。

以下是一些示例代码（请在下次提供一些可用的示例数据）：

> # Dummy data
> df <- data.frame(Group = rep(c('Individual', 'Multiwriter', 'News'), each = 20),
+                  Sub_group = rep(c('US', 'PK'), each = 10),
+                  Data = c(rnorm(9, 3, 1), NA, rnorm(10, 6, 1), # Individual
+                           rnorm(10, 5, 2), rnorm(4, 3, 1), rep(NA, 6), # Multiwriter
+                           rnorm(7, 9, 3), rep(NA, 3), rnorm(4, 7, 2), rep(NA, 6))) # News
> 
> # View
> df
         Group Sub_group       Data
1   Individual        US  3.7638540
2   Individual        US  2.3400850
3   Individual        US  2.0956558
4   Individual        US  2.3174383
5   Individual        US  3.0939467
6   Individual        US  2.4215387
7   Individual        US  2.5750679
8   Individual        US  3.4976811
9   Individual        US  4.3177465
10  Individual        US         NA
11  Individual        PK  5.4029275
12  Individual        PK  6.8948472
13  Individual        PK  5.7083497
14  Individual        PK  6.5364870
15  Individual        PK  8.6442513
16  Individual        PK  5.8469505
17  Individual        PK  5.6308073
18  Individual        PK  6.0677232
19  Individual        PK  7.5343209
20  Individual        PK  6.5114774
21 Multiwriter        US  5.2797776
22 Multiwriter        US  7.5760614
23 Multiwriter        US  5.1100036
24 Multiwriter        US  6.5244811
25 Multiwriter        US  7.2320747
26 Multiwriter        US  6.8812925
27 Multiwriter        US  4.8357571
28 Multiwriter        US  1.5791503
29 Multiwriter        US  2.3667888
30 Multiwriter        US  4.8180301
31 Multiwriter        PK  1.4981379
32 Multiwriter        PK  1.1078890
33 Multiwriter        PK  1.3776812
34 Multiwriter        PK  0.1700818
35 Multiwriter        PK         NA
36 Multiwriter        PK         NA
37 Multiwriter        PK         NA
38 Multiwriter        PK         NA
39 Multiwriter        PK         NA
40 Multiwriter        PK         NA
41        News        US 11.6376160
42        News        US 11.6902192
43        News        US  5.0811126
44        News        US 13.0634139
45        News        US  2.6509108
46        News        US  8.2467842
47        News        US 10.6461310
48        News        US         NA
49        News        US         NA
50        News        US         NA
51        News        PK  6.5860009
52        News        PK  9.4336251
53        News        PK  6.6071028
54        News        PK 10.4564444
55        News        PK         NA
56        News        PK         NA
57        News        PK         NA
58        News        PK         NA
59        News        PK         NA
60        News        PK         NA
> 
> # Plot
> library(ggplot2)
> ggplot2::ggplot(data = df) +
+     aes(x = Group, y = Data, colour = Sub_group) +
+     geom_boxplot()
Warning message:
Removed 16 rows containing non-finite values (stat_boxplot).

根据评论更新

导入数据

> data <- read.csv('DataBlogs.csv')

检查数据

> str(data)
'data.frame':   3674 obs. of  6 variables:
 $ Blog  : Factor w/ 5 levels "Column","Individual",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Region: Factor w/ 2 levels "PK","US": 1 1 1 1 1 1 1 1 1 1 ...
 $ Dim1  : num  -4.75 -5.69 -0.27 -2.76 -8.24 ...
 $ Dim2  : num  13.47 6.08 6.09 7.35 12.75 ...
 $ Dim3  : num  8.47 1.51 0.03 5.62 3.71 2.01 7.56 3.01 6.5 4.76 ...
 $ Dim4  : num  -1.29 -1.65 1.65 3.13 3.78 0.21 2.16 3.35 1.49 1.98 ...
>
> summary(data)
          Blog      Region         Dim1                 Dim2          
 Column     :1173   PK:1835   Min.   :-29.330000   Min.   :-17.55000  
 Individual :1188   US:1839   1st Qu.: -4.282500   1st Qu.: -4.41000  
 Multiwriter: 405             Median :  0.885000   Median : -0.74500  
 News       : 508             Mean   : -0.000054   Mean   :  0.00004  
 Tech       : 400             3rd Qu.:  5.147500   3rd Qu.:  3.44000  
                              Max.   : 22.020000   Max.   : 47.36000  
      Dim3                 Dim4           
 Min.   :-17.260000   Min.   :-15.050000  
 1st Qu.: -3.340000   1st Qu.: -3.280000  
 Median : -0.530000   Median : -0.460000  
 Mean   : -0.000035   Mean   :  0.000024  
 3rd Qu.:  2.845000   3rd Qu.:  2.647500  
 Max.   : 29.080000   Max.   : 29.640000

我不确定为什么要将数据子集化为新的数据帧，但我发现它有助于为您的数据帧列命名。

以下是您尝试的内容

> dim1 <- data.frame(data$Blog, data$Region, data$Dim1) 
> names(dim1)
[1] "data.Blog"   "data.Region" "data.Dim1"

...现在有了命名列。

> dim1 <- data.frame(Blog = data$Blog, Region = data$Region, Dim1 = data$Dim1) 
> names(dim1)
[1] "Blog"   "Region" "Dim1"

对于绘图，有两个可能的错误原因：i）你只为第一个函数调用ggplot2::，你也需要为其他函数调用（ggplot2::aes， ggplot2::geom_boxplot），或者只是在绘图之前加载包，以及ii）如果你直接复制并粘贴了我的代码，你最终会得到两个加号（'+ +'）来分隔图的每一步。这是终端输出的假象。将代码添加到编辑器时，请确保步骤之间只有一个加号。后一个问题是导致错误消息的最可能原因。

修订情节

> library(ggplot2)
>
> ggplot(data = dim1) + 
+     aes(x = Blog, y = Dim1, colour = Region) + 
+     geom_boxplot()
>

从R

1 个答案:

根据评论更新