Data Analysis Mathematics Linear Algebra Statistics
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# Grouping and Aggregation QuickStart Sample (Visual Basic)

Illustrates how to group data and how to compute aggregates over groups and entire datasets. in Visual Basic.

```Option Infer On

Imports Extreme.DataAnalysis
Imports Extreme.Mathematics

Namespace Extreme.Numerics.QuickStart.CSharp

' <summary>
' Illustrates how to group data And how to compute aggregates
' over groups And entire datasets.
' </summary>
Module GroupingAndAggregation

Sub Main()

' We work with the Titanic dataset
' We'll use these columns often:
Dim age = titanic.GetColumn("Age")
Dim survived = titanic("Survived").As(Of Boolean)()
' We want to group by the passenger class,
' so we make this a categorical vector.
Dim pclass = titanic("Pclass").AsCategorical()

'
' Aggregators And Aggregation
'

' The Aggregators class defines all common aggregator functions.
' The Aggregate method applies the aggregator
' to every column in the data frame
Dim means = titanic.Aggregate(Aggregators.Mean)
Console.WriteLine(means.Summarize())

' We can create custom aggregators. Here we compute
' the fraction of true values of a boolean vector
Dim trueFraction = Aggregators.Create("survived",
Function(b As Vector(Of Boolean)) CDbl(b.CountTrue()) / b.Count)
Dim pctSurvived = survived.Aggregate(trueFraction)

' We can also compute more than one aggregate
Dim descriptives = titanic.Aggregate(
Aggregators.Count,
Aggregators.Mean,
Aggregators.StandardDeviation)
Console.WriteLine(descriptives.Summarize())

' Aggregations can be applied to individual vectors
Dim meanAge = age.Aggregate(Aggregators.Mean)

' Or to rows Or columns of a matrix
Dim m = Matrix.CreateRandom(5, 8)
Dim meanByRow = m.AggregateRows(Aggregators.Mean)
Dim meanByColumn = m.AggregateColumns(Aggregators.Mean)

'
' Groupings
'

' By defining a grouping, we can compute the aggregate
' for each group.

' The simplest grouping Is by value, similar to
' GROUP BY clauses in database queries.

' Let's get the average age by class:
Dim ageByClass = age.AggregateBy(pclass, Aggregators.Mean)

' Grouping by quantile means we sort the values
' And divide the result into groups of the same size.
Dim byQuantile = Grouping.ByQuantile(age, 5)
Dim survivedByAgeGroup = survived.AggregateBy(byQuantile, trueFraction)
Console.WriteLine("Survival rate by age group:")
Console.WriteLine(survivedByAgeGroup.Summarize())

' For the remainder we will use a vector with a DateTime index
Dim x = Vector.CreateRandom(200)
Dim dates = Index.CreateDateRange(New DateTime(2016, 1, 1), x.Length)
x.Index = dates

' A partition Is a straight division of the data into equal groups
Dim partition = Grouping.Partition(dates, 10,
alignToEnd:=True, skipIncomplete:=True)
Dim partitionAvg = x.AggregateBy(partition, Aggregators.Mean)
Console.WriteLine("Avg. by partition:")
Console.WriteLine(partitionAvg)

'
' Moving And expanding windows
'

' Moving Or rolling averages And related statistics
' can be computed efficiently by using moving windows
Dim window = Grouping.Window(dates, 20)
Dim ma20 = x.AggregateBy(window, Aggregators.Mean)
Console.WriteLine("ma20:")
Console.WriteLine(ma20.GetSlice(0, 20))
' Moving standard deviation Is just as simple
Dim mstd20 = x.AggregateBy(window, Aggregators.StandardDeviation)
Console.WriteLine("mstd20:")
Console.WriteLine(mstd20.GetSlice(0, 20))

' Moving windows can have a fixed number of elements, as above,
' Or a fixed maximum width
Dim window2 = Grouping.RangeWindow(dates, TimeSpan.FromDays(20))
Dim ma20_2 = x.AggregateBy(window2, Aggregators.Mean)

' Expanding windows keep the starting point And move the end point
' forward in time
Dim expanding = Grouping.ExpandingWindow(dates)
Dim expAvg = x.AggregateBy(expanding, Aggregators.Mean)
Console.WriteLine("expAvg:")
Console.WriteLine(expAvg.GetSlice(0, 10))

'
' Resampling
'

' Resampling means computing values for a series
' with longer periods by aggregating over the values
' for shorter periods.

' We start by creating an index with the boundaries,
' in this case the 10th of each month.
Dim months = Index.CreateDateRange(New DateTime(2016, 1, 10),
12, Recurrence.Monthly)
' We then create the resampling grouping from this:
' Giving the Direction argument as Backward means that
' the last value in the time period Is used as the key
' for the group.
Dim resampling1 = Grouping.Resample(dates, months, Direction.Backward)
' We can also obtain this grouping in one step
Dim resampling2 = Grouping.Resample(dates,
Recurrence.Monthly.Day(10), Direction.Backward)
Dim resampled = x.AggregateBy(resampling2, Aggregators.Mean)

'
' Pivot tables
'

' A pivot table Is a 2-dimensional grouping on two key columns.
' For this, we go back to the Titanic dataset, And we compute
' the survival rate per class in a different way. We group
' by class And by whether the passenger survived
Dim Pivot = Grouping.Pivot(
titanic("Pclass").As(Of Integer)(),
titanic("Survived").As(Of Boolean)())
' We can then get the # of elements in each group
' as a matrix, with rows indexed by class And columns
' indexed by survived
Dim counts = Pivot.CountsMatrix()
' Scaling by the row sums gives us the fraction
' of survived/did Not survive for each class
Dim fractions = counts.UnscaleRowsInPlace(counts.GetRowSums())
Console.WriteLine(fractions.Summarize())

Console.Write("Press any key to exit.")