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[SPARK-11715][SPARKR] Add R support corr for Column Aggregration #9680

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felixcheung wants to merge 4 commits into from
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[SPARK-11715][SPARKR] Add R support corr for Column Aggregration #9680

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91f86f9
Add R support corr for Column Aggregration
felixcheung Nov 13, 2015
7ec83db
fix doc text
felixcheung Nov 13, 2015
b8090f3
updating generic signature from feedback
felixcheung Nov 17, 2015
659f805
fix merge error in test
felixcheung Dec 1, 2015
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15 changes: 15 additions & 0 deletions R/pkg/R/functions.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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"),
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@sun-rui sun-rui Nov 18, 2015

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There are two versions of corr():

def corr(column1: Column, column2: Column): Column
def corr(columnName1: String, columnName2: String): Column

We'd better support both. Something like:

setMethod("corr", signature(x = "characterOrColumn"),

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@felixcheung felixcheung Nov 18, 2015

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That's the same for count, max, mean and so on, so change we would need to change every function here - should we do that?

function(x, col2) {
stopifnot(class(col2) == "Column")
jc <- callJStatic("org.apache.spark.sql.functions", "corr", x@jc, col2@jc)
column(jc)
})

#' cos
#'
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2 changes: 1 addition & 1 deletion R/pkg/R/generics.R
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Expand Up @@ -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
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9 changes: 5 additions & 4 deletions R/pkg/R/stats.R
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Expand Up @@ -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
Expand All @@ -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)
})
Expand All @@ -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
#'
Expand All @@ -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
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2 changes: 1 addition & 1 deletion R/pkg/inst/tests/test_sparkSQL.R
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Expand Up @@ -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
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