- Trending Categories
Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
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_y
Output
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
Advertisements