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How to find the sequence of correlation between variables in an R data frame or matrix?
To find the sequence of correlation between variables in an R data frame or matrix, we can use correlate and stretch function from corrr package.
For example, if we have a data frame called df then we can find the sequence of correlation between variables in df by using the below given command −
df%>% correlate() %>% stretch() %>% arrange(r)
Example 1
Following snippet creates a sample data frame −
x1<-rnorm(20) x2<-rnorm(20,5,0.2) x3<-rnorm(20,5,0.005) x4<-rnorm(20,2,0.14) df1<-data.frame(x1,x2,x3,x4) df1
The following dataframe is created −
x1 x2 x3 x4 1 0.6808273 5.100547 5.008409 1.903267 2 -0.8982181 5.236966 5.003451 1.885443 3 1.7004141 4.938643 4.998515 2.078969 4 -0.5556164 5.086777 5.000830 1.836611 5 -0.3700080 5.180180 4.999621 2.066981 6 -0.6171065 5.218265 5.006482 2.012696 7 1.3167304 4.886068 5.005309 1.697461 8 -1.0291898 5.082370 4.996527 1.876241 9 0.3552824 5.354599 5.004550 2.010306 10 -0.8318924 4.943031 4.999007 2.097666 11 -0.3643198 5.287603 4.996394 2.119303 12 -0.6422962 4.864442 4.998594 2.105324 13 3.3631619 4.675183 4.999328 1.878981 14 1.7794186 4.769273 4.997484 2.027005 15 -0.5102582 5.140516 5.001077 1.830695 16 1.1652416 4.586822 4.996408 2.101790 17 -0.4535449 5.046426 5.004014 1.906526 18 -2.0166857 4.666686 4.996425 2.011478 19 -0.9543124 4.956333 5.002519 1.984997 20 1.5101443 5.273918 4.988374 1.876615
To load corrr package and find the sequence of correlation between variables in df1, add the following code to the above snippet −
library(corrr) df1%>% + correlate() %>% + stretch() %>% + arrange(r) Correlation method: 'pearson' Missing treated using: 'pairwise.complete.obs' # A tibble: 16 x 3
Output
If you execute all the above given snippets as a single program, it generates the following output −
x y r <chr> <chr> <dbl> 1 x1 x2 -0.284 2 x2 x1 -0.284 3 x3 x4 -0.263 4 x4 x3 -0.263 5 x1 x4 -0.155 6 x4 x1 -0.155 7 x1 x3 -0.137 8 x3 x1 -0.137 9 x2 x4 -0.127 10 x4 x2 -0.127 11 x2 x3 0.185 12 x3 x2 0.185 13 x1 x1 NA 14 x2 x2 NA 15 x3 x3 NA 16 x4 x4 NA
Example 2
Following snippet creates a matrix −
M1<-matrix(rpois(100,10),ncol=5) M1
The following matrix is created −
[,1][,2][,3][,4][,5] [1,] 10 4 13 4 9 [2,] 14 12 13 15 11 [3,] 11 6 15 10 16 [4,] 6 10 5 9 9 [5,] 9 6 20 10 10 [6,] 7 10 6 10 12 [7,] 13 15 6 13 8 [8,] 3 14 11 9 8 [9,] 13 10 7 9 11 [10,] 10 7 16 13 12 [11,] 8 7 9 7 14 [12,] 7 6 9 16 6 [13,] 7 14 7 7 12 [14,] 8 7 7 5 10 [15,] 4 9 13 11 7 [16,] 15 9 14 11 8 [17,] 9 12 7 9 15 [18,] 11 10 3 14 10 [19,] 8 5 13 17 11 [20,] 7 13 10 12 7
To find the sequence of correlation between variables in M1, add the following code to the above snippet −
M1%>% + correlate()%>% + stretch() %>% + arrange(r) Correlation method: 'pearson' Missing treated using: 'pairwise.complete.obs' # A tibble: 25 x 3
Output
If you execute all the above given snippets as a single program, it generates the following output −
x y r <chr> <chr> <dbl> 1 V2 V3 -0.473 2 V3 V2 -0.473 3 V4 V5 -0.233 4 V5 V4 -0.233 5 V2 V5 -0.136 6 V5 V2 -0.136 7 V1 V2 -0.0355 8 V2 V1 -0.0355 9 V3 V5 0.0261 10 V5 V3 0.0261
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