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Create a data.table object of combination of correlation coefficients.
To create a data.table object of combination of correlation coefficients, we first need to find the correlation matrix then the combination of variables for which the correlation matrix is created then data.table will be used to combine the combination of variables and the correlation coefficients.
Check out the below Examples to understand how it can be done.
Example 1
Following snippet creates a sample data frame −
x1<-rnorm(20) x2<-rnorm(20,2,0.05) x3<-rnorm(20,2,0.78) x4<-rnorm(20,5,1) df1<-data.frame(x1,x2,x3,x4) df1
The following dataframe is created
x1 x2 x3 x4 1 0.77994570 2.044288 1.2866822 6.139428 2 -0.57165011 1.931588 2.3060454 4.156558 3 -2.41470804 1.980485 2.0120719 5.928814 4 -1.25221041 2.059422 2.6341341 4.449050 5 -0.07042562 1.989987 0.6263381 4.378990 6 0.97821341 1.998165 2.4876630 3.473989 7 -0.99143292 1.980309 0.8280679 4.561764 8 0.98428356 2.046979 2.5538780 6.288159 9 0.32649034 1.987681 1.2260120 6.088381 10 -0.43238724 1.960947 1.5529283 4.425975 11 0.53374978 2.023177 2.7411385 6.853143 12 1.47847428 2.012680 2.2124862 5.552493 13 -1.27691863 1.977755 0.7198475 5.564577 14 -0.82864337 2.095960 1.9234651 4.903183 15 0.13440338 2.010327 2.6039556 3.557598 16 0.12110980 2.038554 1.3294818 4.130975 17 -0.03789286 1.971120 1.2343970 4.714649 18 1.60358705 1.930691 2.7022694 5.498787 19 0.22071213 1.976550 2.0997674 3.553371 20 -0.77764431 1.961341 0.8304568 5.664547
To find the correlation matrix for data stored in df1 on the above created data frame, add the following code to the above snippet −
x1<-rnorm(20) x2<-rnorm(20,2,0.05) x3<-rnorm(20,2,0.78) x4<-rnorm(20,5,1) df1<-data.frame(x1,x2,x3,x4) Corr_M_df1<-cor(df1) Corr_M_df1
Output
If you execute all the above given snippets as a single program, it generates the following Output −
x1 x2 x3 x4 x1 1.00000000 0.02822366 0.34313832 0.07829032 x2 0.02822366 1.00000000 0.17955473 0.13063972 x3 0.34313832 0.17955473 1.00000000 -0.03920003 x4 0.07829032 0.13063972 -0.03920003 1.00000000
To find the combination of variables in correlation matrix on the above created data frame, add the following code to the above snippet −
x1<-rnorm(20) x2<-rnorm(20,2,0.05) x3<-rnorm(20,2,0.78) x4<-rnorm(20,5,1) df1<-data.frame(x1,x2,x3,x4) Corr_M_df1<-cor(df1) Comb_Vars_df1<-t(combn(colnames(Corr_M_df1),2)) Comb_Vars_df1
Output
If you execute all the above given snippets as a single program, it generates the following Output −
[,1] [,2] [1,] "x1" "x2" [2,] "x1" "x3" [3,] "x1" "x4" [4,] "x2" "x3" [5,] "x2" "x4" [6,] "x3" "x4"
To load data.table package and create a data.table object for the combination of correlation coefficients on the above created data frame, add the following code to the above snippet −
x1<-rnorm(20) x2<-rnorm(20,2,0.05) x3<-rnorm(20,2,0.78) x4<-rnorm(20,5,1) df1<-data.frame(x1,x2,x3,x4) Corr_M_df1<-cor(df1) Comb_Vars_df1<-t(combn(colnames(Corr_M_df1),2)) library(data.table) DT1<-data.table(Comb_Vars_df1,Corr_M_df1[Comb_Vars_df1]) DT1
Output
If you execute all the above given snippets as a single program, it generates the following Output −
V1 V2 V2 1: x1 x2 0.02822366 2: x1 x3 0.34313832 3: x1 x4 0.07829032 4: x2 x3 0.17955473 5: x2 x4 0.13063972 6: x3 x4 -0.03920003
Example 2
Following snippet creates a sample data frame −
y1<-rpois(20,1) y2<-rpois(20,2) y3<-rpois(20,5) y4<-rpois(20,2) df2<-data.frame(y1,y2,y3,y4) df2
The following dataframe is created
y1 y2 y3 y4 1 0 2 4 0 2 1 1 3 0 3 0 5 5 1 4 2 0 3 2 5 1 0 5 3 6 0 0 5 1 7 1 1 8 1 8 0 1 3 4 9 0 1 2 1 10 1 3 8 1 11 1 1 4 3 12 1 2 2 3 13 1 1 2 4 14 0 2 8 3 15 1 2 8 2 16 1 2 6 4 17 3 1 4 2 18 3 2 3 0 19 0 1 5 4 20 1 3 5 4
To find the correlation matrix for data stored in df2 on the above created data frame, add the following code to the above snippet −
y1<-rpois(20,1) y2<-rpois(20,2) y3<-rpois(20,5) y4<-rpois(20,2) df2<-data.frame(y1,y2,y3,y4) Corr_M_df2<-cor(df2) Corr_M_df2
Output
If you execute all the above given snippets as a single program, it generates the following Output −
y1 y2 y3 y4 y1 1.0000000 -0.1405368 -0.15977673 -0.14617656 y2 -0.1405368 1.0000000 0.27579791 -0.11041245 y3 -0.1597767 0.2757979 1.00000000 -0.01662246 y4 -0.1461766 -0.1104125 -0.01662246 1.00000000
To find the combination of variables in correlation matrix on the above created data frame, add the following code to the above snippet −
y1<-rpois(20,1) y2<-rpois(20,2) y3<-rpois(20,5) y4<-rpois(20,2) df2<-data.frame(y1,y2,y3,y4) Corr_M_df2<-cor(df2) Comb_Vars_df2<-t(combn(colnames(Corr_M_df2),2)) Comb_Vars_df2
Output
If you execute all the above given snippets as a single program, it generates the following Output −
[,1] [,2] [1,] "y1" "y2" [2,] "y1" "y3" [3,] "y1" "y4" [4,] "y2" "y3" [5,] "y2" "y4" [6,] "y3" "y4"
To create a data.table object for the combination of correlation coefficients on the above created data frame, add the following code to the above snippet −
y1<-rpois(20,1) y2<-rpois(20,2) y3<-rpois(20,5) y4<-rpois(20,2) df2<-data.frame(y1,y2,y3,y4) Corr_M_df2<-cor(df2) Comb_Vars_df2<-t(combn(colnames(Corr_M_df2),2)) DT2<-data.table(Comb_Vars_df2,Corr_M_df2[Comb_Vars_df2]) DT2
Output
If you execute all the above given snippets as a single program, it generates the following Output −
V1 V2 V2 1: y1 y2 -0.14053680 2: y1 y3 -0.15977673 3: y1 y4 -0.14617656 4: y2 y3 0.27579791 5: y2 y4 -0.11041245 6: y3 y4 -0.01662246
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