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How to find the correlation matrix for rows of an R data frame?
To find the correlation matrix for rows of an R data frame, we can follow the below steps −
- First of all, create a data frame.
- Then, use cor function with transposed data frame to find the correlation matrix for rows.
Example1
Let's create a data frame as shown below −
> x1<-rnorm(5) > x2<-rnorm(5) > x3<-rnorm(5) > x4<-rnorm(5) > x5<-rnorm(5) > df1<-data.frame(x1,x2,x3,x4,x5) > df1
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
x1 x2 x3 x4 x5 1 0.23392099 -0.0919377 0.4623323 -0.5209734 0.1769501 2 0.66009528 0.8356888 -0.5394541 0.2082769 -0.1801216 3 -0.09660345 0.2935962 2.5287285 1.5602965 1.1191089 4 -0.23380750 0.3456661 0.1458570 -0.7058560 1.9192713 5 0.98615064 0.5303615 0.4312040 -0.5227473 1.8670896
Find the correlation matrix for rows
Using cor function to find the correlation matrix for rows in data frame after transposing the data frame −
> x1<-rnorm(5) > x2<-rnorm(5) > x3<-rnorm(5) > x4<-rnorm(5) > x5<-rnorm(5) > df1<-data.frame(x1,x2,x3,x4,x5) > cor(t(df1[]))
Output
[,1] [,2] [,3] [,4] [,5] [1,] 1.0000000 -0.4225284 0.14255882 0.40937315 0.6203195 [2,] -0.4225284 1.0000000 -0.89215806 -0.31996808 -0.1164585 [3,] 0.1425588 -0.8921581 1.00000000 -0.01230975 -0.3128922 [4,] 0.4093732 -0.3199681 -0.01230975 1.00000000 0.8674166 [5,] 0.6203195 -0.1164585 -0.31289217 0.86741657 1.0000000
Example2
Let’s create a data frame as shown below −
> y1<-rpois(5,1) > y2<-rpois(5,2) > y3<-rpois(5,5) > y4<-rpois(5,2) > y5<-rpois(5,10) > df2<-data.frame(y1,y2,y3,y4,y5) > df2
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
y1 y2 y3 y4 y5 1 1 2 8 2 16 2 1 4 9 4 15 3 0 2 3 3 15 4 0 2 3 2 8 5 0 2 5 2 9
Find the correlation matrix for rows
Using cor function to find the correlation matrix for rows in data frame after transposing the data frame −
> y1<-rpois(5,1) > y2<-rpois(5,2) > y3<-rpois(5,5) > y4<-rpois(5,2) > y5<-rpois(5,10) > df2<-data.frame(y1,y2,y3,y4,y5) > cor(t(df2[]))
Output
[,1] [,2] [,3] [,4] [,5] [1,] 1.0000000 0.9856531 0.9330930 0.9594638 0.9848691 [2,] 0.9856531 1.0000000 0.9188314 0.9688966 0.9997316 [3,] 0.9330930 0.9188314 1.0000000 0.9818145 0.9262269 [4,] 0.9594638 0.9688966 0.9818145 1.0000000 0.9742043 [5,] 0.9848691 0.9997316 0.9262269 0.9742043 1.0000000
Example3
Let’s create a data frame as shown below −
> z1<-runif(5,1,2) > z2<-runif(5,1,5) > z3<-runif(5,1,10) > z4<-runif(5,2,10) > z5<-runif(5,2,5) > df3<-data.frame(z1,z2,z3,z4,z5) > df3
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
z1 z2 z3 z4 z5 1 1.079122 3.430315 7.953942 6.142318 4.346613 2 1.400345 1.140915 7.714074 9.914491 2.362663 3 1.307579 1.557013 5.698641 6.810150 4.566523 4 1.452093 4.330748 4.710236 2.495453 3.531681 5 1.297150 1.628797 8.802337 5.099750 2.061406
Find the correlation matrix for rows
Using cor function to find the correlation matrix for rows in data frame after transposing the data frame −
> z1<-runif(5,1,2) > z2<-runif(5,1,5) > z3<-runif(5,1,10) > z4<-runif(5,2,10) > z5<-runif(5,2,5) > df3<-data.frame(z1,z2,z3,z4,z5) > cor(t(df3[]))
Output
[,1] [,2] [,3] [,4] [,5] [1,] 1.0000000 0.81635351 0.8600606 0.62241987 0.9144659 [2,] 0.8163535 1.00000000 0.9051244 0.09011508 0.8135256 [3,] 0.8600606 0.90512441 1.0000000 0.20627867 0.7375102 [4,] 0.6224199 0.09011508 0.2062787 1.00000000 0.4679658 [5,] 0.9144659 0.81352558 0.7375102 0.46796577 1.0000000
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