A #glfintech R turorial on statistics discussing hypothesis tests - parametric tests (z-test, t-test and ANOVA) and non-parametric tests (Mann-Whitney Test, Wilcoxon Rank-sum test, Wilcoxon Signed-rank Test, Kruskal Wallis Test).

R Tutorials | Non-parametric Tests in R | #rstatistics

#non-parametric tests in R
dengue.fewer <-c(3000,3200,3500,5068,5679,6200,6300,7020)
scrub.typhus<-c(4400,4500,5900,6839,7561,9047,12300,14000)

wilcox.test(dengue.fewer,scrub.typhus)
t.test(dengue.fewer,scrub.typhus,var.equal=T)


ttest.wilcox.examples <- function(x,y,z,k)
{
  subjects <- x
  mean1 <- y
  mean2 <- z
  standarddev <- k
  print( c("Number of measurements:   ", x))
  print( c("Mean of group 1: ", y))
  print( c( "Mean of group 2: ",z))
  print( c("Standard deviation:  ", k))
  group1 <- rnorm(x, y, k)
  group2 <- rnorm(x, z, k)
  framedata <- cbind(group1, group2)
  print(framedata)
  print( list (t.test(group1, group2, var.equal = T), wilcox.test(group1, group2)))
}

set.seed(570)
ttest.wilcox.examples(13,90,100,10)


#try

A <- c(91,91,93,106,97,108,97,105,106,103,105,96,105,95,90,101)
B <- c(90,89,85,99,93,104,89,103,102,95,103,95,105,87,86,101)
t.test(A,B,paired=T)
wilcox.test(A,B, paired=T)