1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# stats.R - Statistic functions for DataFrames.
setOldClass("jobj")
#' crosstab
#'
#' Computes a pair-wise frequency table of the given columns. Also known as a contingency
#' table. The number of distinct values for each column should be less than 1e4. At most 1e6
#' non-zero pair frequencies will be returned.
#'
#' @param col1 name of the first column. Distinct items will make the first item of each row.
#' @param col2 name of the second column. Distinct items will make the column names of the output.
#' @return a local R data.frame representing the contingency table. The first column of each row
#' will be the distinct values of `col1` and the column names will be the distinct values
#' of `col2`. The name of the first column will be `$col1_$col2`. Pairs that have no
#' occurrences will have zero as their counts.
#'
#' @rdname statfunctions
#' @name crosstab
#' @export
#' @examples
#' \dontrun{
#' df <- jsonFile(sqlContext, "/path/to/file.json")
#' ct <- crosstab(df, "title", "gender")
#' }
setMethod("crosstab",
signature(x = "DataFrame", col1 = "character", col2 = "character"),
function(x, col1, col2) {
statFunctions <- callJMethod(x@sdf, "stat")
sct <- callJMethod(statFunctions, "crosstab", col1, col2)
collect(dataFrame(sct))
})
#' cov
#'
#' Calculate the sample covariance of two numerical columns of a DataFrame.
#'
#' @param x A SparkSQL DataFrame
#' @param col1 the name of the first column
#' @param col2 the name of the second column
#' @return the covariance of the two columns.
#'
#' @rdname statfunctions
#' @name cov
#' @export
#' @examples
#'\dontrun{
#' df <- jsonFile(sqlContext, "/path/to/file.json")
#' cov <- cov(df, "title", "gender")
#' }
setMethod("cov",
signature(x = "DataFrame", col1 = "character", col2 = "character"),
function(x, col1, col2) {
statFunctions <- callJMethod(x@sdf, "stat")
callJMethod(statFunctions, "cov", col1, col2)
})
#' corr
#'
#' 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
#' @param method Optional. A character specifying the method for calculating the correlation.
#' only "pearson" is allowed now.
#' @return The Pearson Correlation Coefficient as a Double.
#'
#' @rdname statfunctions
#' @name corr
#' @export
#' @examples
#'\dontrun{
#' df <- jsonFile(sqlContext, "/path/to/file.json")
#' corr <- corr(df, "title", "gender")
#' corr <- corr(df, "title", "gender", method = "pearson")
#' }
setMethod("corr",
signature(x = "DataFrame", col1 = "character", col2 = "character"),
function(x, col1, col2, method = "pearson") {
statFunctions <- callJMethod(x@sdf, "stat")
callJMethod(statFunctions, "corr", col1, col2, method)
})
#' freqItems
#'
#' Finding frequent items for columns, possibly with false positives.
#' Using the frequent element count algorithm described in
#' \url{http://dx.doi.org/10.1145/762471.762473}, proposed by Karp, Schenker, and Papadimitriou.
#'
#' @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`.
#' Should be greater than 1e-4. Default support = 0.01.
#' @return a local R data.frame with the frequent items in each column
#'
#' @rdname statfunctions
#' @name freqItems
#' @export
#' @examples
#' \dontrun{
#' df <- jsonFile(sqlContext, "/path/to/file.json")
#' fi = freqItems(df, c("title", "gender"))
#' }
setMethod("freqItems", signature(x = "DataFrame", cols = "character"),
function(x, cols, support = 0.01) {
statFunctions <- callJMethod(x@sdf, "stat")
sct <- callJMethod(statFunctions, "freqItems", as.list(cols), support)
collect(dataFrame(sct))
})
|
