如果数据帧包含因子变量作为R中的字符串,如何将数据帧转换为矩阵?
矩阵仅包含数字值,因此,如果我们将转换具有因子变量作为字符串的数据框,则因子水平将转换为数字。这些编号基于因子级别的第一个字符,例如,如果字符串以A开头,则它将得到1,依此类推。如果数据帧包含因子变量作为字符串,则要将数据帧转换为矩阵,我们需要将数据帧读取为矩阵。
示例
请看以下数据帧-
x1<-1:10x2<-10:1
x3<-letters[1:10]
x4<-LETTERS[1:10]
x5<-letters[10:1]
x6<-LETTERS[10:1]
x7<-rnorm(10)
x8<-rnorm(10,0.2)
x9<-rnorm(10,0.5)
x10<-rnorm(10,1)
df<-data.frame(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)
str(df)
输出结果
'data.frame': 10 obs. of 10 variables:$ x1 : int 1 2 3 4 5 6 7 8 9 10
$ x2 : int 10 9 8 7 6 5 4 3 2 1
$ x3 : Factor w/ 10 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10
$ x4 : Factor w/ 10 levels "A","B","C","D",..: 1 2 3 4 5 6 7 8 9 10
$ x5 : Factor w/ 10 levels "a","b","c","d",..: 10 9 8 7 6 5 4 3 2 1
$ x6 : Factor w/ 10 levels "A","B","C","D",..: 10 9 8 7 6 5 4 3 2 1
$ x7 : num 0.526 -0.795 1.428 -1.467 -0.237 ...
$ x8 : num 0.0362 0.9085 -0.068 -1.2639 0.9444 ...
$ x9 : num 1.395 0.779 1.508 -1.573 1.69 ...
$ x10: num 1.482 1.758 -1.319 0.54 -0.105 ...
df
x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
1 1 10 a A j J 0.5264481 0.03624433 1.3949372 1.4824588
2 2 9 b B i I -0.7948444 0.90852210 0.7791520 1.7582138
3 3 8 c C h H 1.4277555 -0.06798055 1.5078658 -1.3193274
4 4 7 d D g G -1.4668197 -1.26392176 -1.5731065 0.5404952
5 5 6 e E f F -0.2366834 0.94443582 1.6898534 -0.1053837
6 6 5 f F e E -0.1933380 -1.21039018 -0.2243742 1.4029283
7 7 4 g G d D -0.8497547 0.66706761 0.6679838 1.5689349
8 8 3 h H c C 0.0584655 0.08067989 1.4203352 0.2939167
9 9 2 i I b B -0.8176704 0.66723896 -1.1716048 0.7099094
10 10 1 j J a A -2.0503078 0.69813556 0.9484691 -0.4838781
将数据帧df转换为矩阵-
示例
matrix(as.numeric(unlist(df)),nrow=nrow(df))
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9][1,] 1 10 1 1 10 10 0.5264481 0.03624433 1.3949372
[2,] 2 9 2 2 9 9 -0.7948444 0.90852210 0.7791520
[3,] 3 8 3 3 8 8 1.4277555 -0.06798055 1.5078658
[4,] 4 7 4 4 7 7 -1.4668197 -1.26392176 -1.5731065
[5,] 5 6 5 5 6 6 -0.2366834 0.94443582 1.6898534
[6,] 6 5 6 6 5 5 -0.1933380 -1.21039018 -0.2243742
[7,] 7 4 7 7 4 4 -0.8497547 0.66706761 0.6679838
[8,] 8 3 8 8 3 3 0.0584655 0.08067989 1.4203352
[9,] 9 2 9 9 2 2 -0.8176704 0.66723896 -1.1716048
[10,] 10 1 10 10 1 1 -2.0503078 0.69813556 0.9484691
[,10]
[1,] 1.4824588
[2,] 1.7582138
[3,] -1.3193274
[4,] 0.5404952
[5,] -0.1053837
[6,] 1.4029283
[7,] 1.5689349
[8,] 0.2939167
[9,] 0.7099094
[10,] -0.4838781
让我们看另一个例子-
示例
y1<-c("Age","Sex","Salary","Education","Ethnicity")y2<-1:5
y3<-c(24,15,48,72,29)
df_y<-data.frame(y1,y2,y3)
df_y
输出结果
y1 y2 y31 Age 1 24
2 Sex 2 15
3 Salary 3 48
4 Education 4 72
5 Ethnicity 5 29
示例
matrix(as.numeric(unlist(df_y)),nrow=5)
输出结果
[,1] [,2] [,3][1,] 1 1 24
[2,] 5 2 15
[3,] 4 3 48
[4,] 2 4 72
[5,] 3 5 29
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