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How to convert a data frame to a matrix if the data frame contains factor variable as strings in R?
A matrix contains only numeric values, therefore, if we will convert a data frame that has factor variables as strings then the factor levels will be converted to numbers. These numbering is based on the first character of the factor level, for example, if the string starts with an A then it will get 1, and so on. To convert a data frame to a matrix if the data frame contains factor variable as strings, we need to read the data frame as matrix.
Example
Consider the below data frame −
x1<-1:10 x2<-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)
Output
'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
Converting the data frame df to matrix −
Example
matrix(as.numeric(unlist(df)),nrow=nrow(df))
Output
[,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
Let’s have a look at another example −
Example
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_yOutput
y1 y2 y3 1 Age 1 24 2 Sex 2 15 3 Salary 3 48 4 Education 4 72 5 Ethnicity 5 29
Example
matrix(as.numeric(unlist(df_y)),nrow=5)
Output
[,1] [,2] [,3] [1,] 1 1 24 [2,] 5 2 15 [3,] 4 3 48 [4,] 2 4 72 [5,] 3 5 29
Updated on: 21-Aug-2020
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