How to find the groupwise correlation matrix for an R data frame?

To create groupwise correlation matrix for an R data frame, we can follow the below steps −

• First of all, create a data frame.
• Then, find the correlation matrix by splitting the data frame based on categorical column.

Create the data frame

Let's create a data frame as shown below −

 Live Demo

v1<-round(rnorm(25),2)
v2<-round(rnorm(25),2)
v3<-round(rnorm(25),2)
v4<-round(rnorm(25),2)
Factor<-sample(1:5,25,replace=TRUE)
df<-data.frame(v1,v2,v3,v4,Factor)
df

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

    v1    v2   v3      v4 Factor
1   0.69  1.14 -0.32 1.04  2
2  -0.79 -0.29 -1.56 -0.71 5
3   0.78  0.16 -0.17 -0.24 1
4  -0.68  0.25 -0.30 -1.22 4
5  -0.78 -1.47 0.49 -0.93  1
6  -1.00  0.96 -0.23 0.77  2
7  -0.32 -0.55 0.86 -0.45  2
8   0.30  0.73 -0.34 0.91  2
9   0.30 -0.15 -0.25 0.12  5
10 -0.75 -1.08 1.13 -0.39  3
11 -0.74  1.49 0.76 0.03   3
12 -0.62  2.16 0.75 0.59   2
13  0.97  0.71 0.00 0.10   1
14 -0.62  2.34 -1.60 0.16  3
15  0.06  1.26 2.67 -0.98  1
16  0.67  0.42 1.27 0.22   2
17 -0.71 -0.01 1.98 -1.02  1
18  1.11 -2.03 -1.07 0.81  3
19  0.76  1.50 -0.04 1.21  4
20  0.14  0.04 -0.22 -1.53 5
21  0.69 -1.02 -0.19 0.51  4
22 -1.24 -0.37 1.04 0.06   2
23 -0.24 -1.00 -0.28 1.17  1
24 -0.82 -1.29 0.64 -0.18  2
25  0.12  0.10 0.14 0.34   3

Create the groupwise correlation matrix

Using split function with lapply to create the correlation matrix for the data in df by Factor column −

 Live Demo

v1<-round(rnorm(25),2)
v2<-round(rnorm(25),2)
v3<-round(rnorm(25),2)
v4<-round(rnorm(25),2)
Factor<-sample(1:5,25,replace=TRUE)
df<-data.frame(v1,v2,v3,v4,Factor)
lapply(split(df[,1:4],df$Factor),cor)

Output

$`1`
         v1       v2       v3       v4
v1 1.0000000 0.6046591 -0.3827462 0.3293069
v2 0.6046591 1.0000000 0.4941545 -0.2506760
v3 -0.3827462 0.4941545 1.0000000 -0.7279521
v4 0.3293069 -0.2506760 -0.7279521 1.0000000

$`2`
      v1          v2       v3       v4
v1 1.0000000 0.2775834 -0.2082356 0.3783813
v2 0.2775834 1.0000000 -0.3648746 0.7787637
v3 -0.2082356 -0.3648746 1.0000000 -0.7664309
v4 0.3783813 0.7787637 -0.7664309 1.0000000

$`3`
      v1          v2          v3       v4
v1 1.0000000 -0.6659695 -0.4512594 0.9059110
v2 -0.6659695 1.0000000 -0.1832839 -0.2942610
v3 -0.4512594 -0.1832839 1.0000000 -0.6666673
v4 0.9059110 -0.2942610 -0.6666673 1.0000000

$`4`
         v1       v2          v3       v4
v1 1.00000000 0.03852878 0.8424030 0.9712250
v2 0.03852878 1.00000000 0.5709046 0.2754070
v3 0.84240302 0.57090464 1.0000000 0.9464969
v4 0.97122501 0.27540704 0.9464969 1.0000000

$`5`
      v1          v2       v3          v4
v1 1.0000000 0.7335980 0.98786495 0.13938476
v2 0.7335980 1.0000000 0.83024549 -0.57069740
v3 0.9878649 0.8302455 1.00000000 -0.01610584
v4 0.1393848 -0.5706974 -0.01610584 1.00000000

Nizamuddin Siddiqui

Updated on: 14-Aug-2021

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