##Test groups ragne

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.5     v dplyr   1.0.3
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

create random variables

x<-sample(1:100,size = 1000,replace = TRUE )
y<-sample(1:100,size = 1000,replace = TRUE )

change variables to data fram from 100 groups with equal range

df<-data.frame( x1 = table(cut_interval(x,n = 100)), y1 =  table(cut_interval(y,n = 100)))
DT::datatable(df)

correlation f

cor(df$x1.Freq,df$y1.Freq)
## [1] 0.0227463

repeat the same step for 500 times

 dff<- data.frame (number_of_groubs = 1 , cor_p = 1,cor_s=1,cor_k=1)

for ( i in 3:500) {
  #x<-sample(1:100,size = 1000,replace = TRUE )
  #y<-sample(1:100,size = 1000,replace = TRUE )
  x<-rnorm(5000,50,5)
  y<-rnorm(5000,50,5)
  df<-data.frame( x1 = table(cut_interval(x,n = i)), y1 =  table(cut_interval(y,n = i)))
 # print(paste( "correlation is ",cor(df$x1.Freq,df$y1.Freq),"  , Number of Groups = " , i))
  dff[i-2,] <-   data.frame (number_of_groubs = i , cor_p = cor(df$x1.Freq,df$y1.Freq, method = c("pearson")), cor_s = cor(df$x1.Freq,df$y1.Freq, method = "spearman"),cor_k = cor(df$x1.Freq,df$y1.Freq, method = "kendall" ))
  
}
DT::datatable(dff)
plot(dff)