diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index b30331c61c9a..23a67750f289 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -259,6 +259,21 @@ setMethod("column",
           function(x) {
             col(x)
           })
+#' corr
+#'
+#' Computes the Pearson Correlation Coefficient for two Columns.
+#'
+#' @rdname corr
+#' @name corr
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{corr(df$c, df$d)}
+setMethod("corr", signature(x = "Column"),
+          function(x, col2) {
+            stopifnot(class(col2) == "Column")
+            jc <- callJStatic("org.apache.spark.sql.functions", "corr", x@jc, col2@jc)
+            column(jc)
+          })
 
 #' cos
 #'
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 711ce38f9e10..709309260895 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -411,7 +411,7 @@ setGeneric("cov", function(x, col1, col2) {standardGeneric("cov") })
 
 #' @rdname statfunctions
 #' @export
-setGeneric("corr", function(x, col1, col2, method = "pearson") {standardGeneric("corr") })
+setGeneric("corr", function(x, ...) {standardGeneric("corr") })
 
 #' @rdname summary
 #' @export
diff --git a/R/pkg/R/stats.R b/R/pkg/R/stats.R
index f79329b11540..d17cce9c756e 100644
--- a/R/pkg/R/stats.R
+++ b/R/pkg/R/stats.R
@@ -77,7 +77,7 @@ setMethod("cov",
 #' Calculates the correlation of two columns of a DataFrame.
 #' Currently only supports the Pearson Correlation Coefficient.
 #' For Spearman Correlation, consider using RDD methods found in MLlib's Statistics.
-#' 
+#'
 #' @param x A SparkSQL DataFrame
 #' @param col1 the name of the first column
 #' @param col2 the name of the second column
@@ -95,8 +95,9 @@ setMethod("cov",
 #' corr <- corr(df, "title", "gender", method = "pearson")
 #' }
 setMethod("corr",
-          signature(x = "DataFrame", col1 = "character", col2 = "character"),
+          signature(x = "DataFrame"),
           function(x, col1, col2, method = "pearson") {
+            stopifnot(class(col1) == "character" && class(col2) == "character")
             statFunctions <- callJMethod(x@sdf, "stat")
             callJMethod(statFunctions, "corr", col1, col2, method)
           })
@@ -109,7 +110,7 @@ setMethod("corr",
 #'
 #' @param x A SparkSQL DataFrame.
 #' @param cols A vector column names to search frequent items in.
-#' @param support (Optional) The minimum frequency for an item to be considered `frequent`. 
+#' @param support (Optional) The minimum frequency for an item to be considered `frequent`.
 #'                Should be greater than 1e-4. Default support = 0.01.
 #' @return a local R data.frame with the frequent items in each column
 #'
@@ -131,7 +132,7 @@ setMethod("freqItems", signature(x = "DataFrame", cols = "character"),
 #' sampleBy
 #'
 #' Returns a stratified sample without replacement based on the fraction given on each stratum.
-#' 
+#'
 #' @param x A SparkSQL DataFrame
 #' @param col column that defines strata
 #' @param fractions A named list giving sampling fraction for each stratum. If a stratum is
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 92ec82096c6d..4743eb835044 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -887,7 +887,7 @@ test_that("column functions", {
   c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c)
   c12 <- variance(c)
   c13 <- lead("col", 1) + lead(c, 1) + lag("col", 1) + lag(c, 1)
-  c14 <- cume_dist() + ntile(1)
+  c14 <- cume_dist() + ntile(1) + corr(c, c1)
   c15 <- dense_rank() + percent_rank() + rank() + row_number()
 
   # Test if base::rank() is exposed