从 R 中的 csv 创建分组箱线图
Creating a grouped box plot from csv in R
已编辑：
我正在尝试制作分组箱线图。我使用的数据以 csv + 长格式粘贴在下面：三组（个人、多编写器、列），每组有两个变量（PK、美国）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
以下是我如何使用来自该论坛和其他地方的帮助来创建分组箱线图。
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()
但是在执行最后一条命令时出现以下错误：
Error in +aes(x = Blog, y = Dim1, colour = Region): invalid argument
to unary operator
看来我在使用我创建的数据框时弄错了。
有任何想法吗？
更新
我写了一个 R 脚本来保存图表，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")
现在我正在寻找方法来计算每个箱形图并添加均值。
将您的数据转化为长格式，然后进行分析。
下面是一些示例代码（请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')
查看数据
> 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到一个新的数据框中，但我发现它有助于为您的数据框列命名。
这是您尝试过的方法
> 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"
对于情节，有两个潜在的错误原因：i）你只为第一个函数调用ggplot2::，你需要为其他函数调用做同样的事情（ggplot2::aes, ggplot2::geom_boxplot), 或者只是在绘图之前加载包，并且 ii) 如果您直接复制并粘贴我的代码，您最终会得到两个加号 ('++') 分隔绘图的每个步骤.这是终端输出的人工产物。将代码添加到编辑器时，请确保步骤之间只有一个加号。后一个问题是错误消息的最可能原因。
修改后的剧情
> library(ggplot2)
>
> ggplot(data = dim1) +
+ aes(x = Blog, y = Dim1, colour = Region) +
+ geom_boxplot()
>
已编辑： 我正在尝试制作分组箱线图。我使用的数据以 csv + 长格式粘贴在下面：三组（个人、多编写器、列），每组有两个变量（PK、美国）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
以下是我如何使用来自该论坛和其他地方的帮助来创建分组箱线图。
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()
但是在执行最后一条命令时出现以下错误：
Error in +aes(x = Blog, y = Dim1, colour = Region): invalid argument to unary operator
看来我在使用我创建的数据框时弄错了。 有任何想法吗？ 更新 我写了一个 R 脚本来保存图表，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")
现在我正在寻找方法来计算每个箱形图并添加均值。
将您的数据转化为长格式，然后进行分析。
下面是一些示例代码（请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')
查看数据
> 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到一个新的数据框中，但我发现它有助于为您的数据框列命名。
这是您尝试过的方法
> 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"
对于情节，有两个潜在的错误原因：i）你只为第一个函数调用ggplot2::，你需要为其他函数调用做同样的事情（ggplot2::aes, ggplot2::geom_boxplot), 或者只是在绘图之前加载包，并且 ii) 如果您直接复制并粘贴我的代码，您最终会得到两个加号 ('++') 分隔绘图的每个步骤.这是终端输出的人工产物。将代码添加到编辑器时，请确保步骤之间只有一个加号。后一个问题是错误消息的最可能原因。
修改后的剧情
> library(ggplot2)
>
> ggplot(data = dim1) +
+ aes(x = Blog, y = Dim1, colour = Region) +
+ geom_boxplot()
>