AGGREGATES-Object {RGMQL} | R Documentation

## AGGREGATES object class constructor

### Description

This class constructor is used to create instances of AGGREGATES object, to be used in GMQL functions that require aggregate on value.

### Usage

    SUM(value)

    COUNT()

    COUNTSAMP()

    MIN(value)

    MAX(value)

    AVG(value)

    MEDIAN(value)

    STD(value)

    BAG(value)

    BAGD(value)

    Q1(value)

    Q2(value)

    Q3(value)

### Arguments

`value` | string identifying name of metadata or region attribute

### Details

- SUM: It prepares input parameter to be passed to the library function sum, performing all the type conversions needed

- COUNT: It prepares input parameter to be passed to the library function count, performing all the type conversions needed

- COUNTSAMP: It prepares input parameter to be passed to the library function countsamp, performing all the type conversions needed. It is used only with group_by functions

- MIN: It prepares input parameter to be passed to the library function minimum, performing all the type conversions needed

- MAX: It prepares input parameter to be passed to the library function maximum, performing all the type conversions needed

- AVG: It prepares input parameter to be passed to the library function mean, performing all the type conversions needed

- MEDIAN: It prepares input parameter to be passed to the library function median, performing all the type conversions needed

- STD: It prepares input parameter to be passed to the library function standard deviation, performing all the type conversions needed

- BAG: It prepares input parameter to be passed to the library function bag; this function creates comma-separated strings of attribute values, performing all the type conversions needed

- BAGD: It prepares input parameter to be passed to the library function bagd; this function creates comma-separated strings of distinct attribute values, performing all the type conversions needed

- Q1: It prepares input parameter to be passed to the library function fist quartile, performing all the type conversions needed

- Q2: It prepares input parameter to be passed to the library function second quartile, performing all the type conversions needed

- Q3: It prepares input parameter to be passed to the library function third quartile, performing all the type conversions needed

### Value

Aggregate object

### Examples

    ## This statement initializes and runs the GMQL server for local execution
    ## and creation of results on disk. Then, with system.file() it defines
    ## the path to the folder "DATASET" in the subdirectory "example"
    ## of the package "RGMQL" and opens such folder as a GMQL dataset
    ## named "exp" using CustomParser

    init_gmql()
    test_path <- system.file("example", "DATASET", package = "RGMQL")
    exp = read_gmql(test_path)

    ## This statement copies all samples of exp dataset into res dataset, and
    ## then calculates new metadata attribute sum_score for each of them:
    ## sum_score is the sum of score values of the sample regions.

    res = extend(exp, sum_score = SUM("score"))

    ## This statement copies all samples of exp dataset into res dataset,
    ## and then calculates new metadata attribute min_pvalue for each of them:
    ## min_pvalue is the minimum pvalue of the sample regions.

    res = extend(exp, min_pvalue = MIN("pvalue"))

    ## This statement copies all samples of exp dataset into res dataset,
    ## and then calculates new metadata attribute max_score for each of them:
    ## max_score is the maximum score of the sample regions.

    res = extend(exp, max_score = MAX("score"))

    ## The following cover operation produces output regions where at least 2
    ## and at most 3 regions of exp dataset overlap, having as resulting region
    ## attribute the average signal of the overlapping regions;
    ## the result has one sample for each input cell value.

    res = cover(exp, 2, 3, groupBy = conds("cell"), avg_signal = AVG("signal"))

    ## This statement copies all samples of 'exp' dataset into 'out' dataset,
    ## and then for each of them it adds another metadata attribute, allScore,
    ## which is the aggregation comma-separated list of all the values
    ## that the region attribute score takes in the sample.

    out = extend(exp, allScore = BAG("score"))

    ## This statement counts the regions in each sample and stores their number
    ## as value of the new metadata RegionCount attribute of the sample.

    out = extend(exp, RegionCount = COUNT())

    ## This statement copies all samples of exp dataset into res dataset,
    ## and then calculates new metadata attribute std_score for each of them:
    ## std_score is the standard deviation of the score values of the sample
    ## regions.

    res = extend(exp, std_score = STD("score"))

    ## This statement copies all samples of exp dataset into res dataset,
    ## and then calculates new metadata attribute m_score for each of them:
    ## m_score is the median score of the sample regions.

    res = extend(exp, m_score = MEDIAN("score"))

[Package RGMQL version 1.2.0 Index]