Using apply() function to iterate over different data types doesn't work

I want to write a function that dynamically uses different correlation methods depending on the scale of measure of the feature (continuous, dichotomous, ordinal). The label is always continuous. My idea was to use the apply() function, so iterate over every feature (aka column), check it's scale of measure (numeric, factor with two levels, factor with more than two levels) and then use the appropriate correlation function. Unfortunately my code seems to convert every feature into a character vector and as consequence the condition in the if statement is always false for every column. I don't know why my code is doing this. How can I prevent my code from converting my features to character vectors?

rm(list=ls())

set.seed(42)

foo <- sample(c("x", "y"), 200, replace = T, prob = c(0.7, 0.3))

bar <- sample(c(1,2,3,4,5),200,replace = T,prob=c(0.5,0.05,0.1,0.1,0.25))

y <- sample(c(1,2,3,4,5),200,replace = T,prob=c(0.25,0.1,0.1,0.05,0.5))

data <- data.frame(foo,bar,y)

features <- data[, !names(data) %in% 'y']

dyn.corr <- function(x,y){

  # print out structure of every column
  print(str(x))

  # if feature is numeric and has more than two outcomes use corr.test
  if(is.numeric(x) & length(unique(x))>2){

    result <- corr.test(x,y)[['r']]

  } else {

    result <- "else"

  }

}

result <- apply(features,2,dyn.corr,income)