diff options
| -rw-r--r-- | R/pkg/R/functions.R | 15 | ||||
| -rw-r--r-- | R/pkg/R/generics.R | 2 | ||||
| -rw-r--r-- | R/pkg/R/stats.R | 9 | ||||
| -rw-r--r-- | R/pkg/inst/tests/test_sparkSQL.R | 2 |
4 files changed, 22 insertions, 6 deletions
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R index 7432cb8e7c..25231451df 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 4b5f786d39..acfd4841e1 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 f79329b115..d17cce9c75 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 2d26b92ac7..a5a234a02d 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -892,7 +892,7 @@ 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 |