# Aggregate numeric and categorical variables by an ID

### Description

The `Aggregate`

function (not to be confounded with `aggregate`

) prepares a data frame for merging by computing the sum, mean and variance of all continuous (integer and numeric) variables by a given ID variable.
It also creates dummies for all categorical variables (character and factor) and subsequently computes the sum by a given ID variable. This functions aims at maximal information extraction with a minimal amount of code.

### Usage

1 |

### Arguments

`x` |
A data frame without the ID. Categorical variables have to be of type character or factor and continuous variables have to be of type integer or numeric. |

`by` |
A vector containing ID”s. |

### Value

A data frame with the aforementioned variables aggregated by the given ID variables.

### Author(s)

Dirk Van den Poel, Michel Ballings, Andrey Volkov, Jeroen D”haen, Michiel Van Herwegen

Maintainer: Michel Ballings <Michel.Ballings@GMail.com>

### References

Van den Poel, D., Ballings, M., Volkov, A., D”haen, J., Van Herwegen, M., Predictive Analytics for analytical Customer Relationship Management using SAS, Oracle and R, Springer, Forthcoming.

### See Also

Other functions in this package:
`imputeMissings`

, `Aggregate`

, `cocktailEnsemble`

, `predict.cocktailEnsemble`

### Examples

1 2 3 4 5 6 | ```
#Create some data
data <- data.frame(V1=as.factor(c('yes','no','no','yes','yes','yes','yes')),
V2=as.character(c(1,2,3,4,4,4,4)),V3=c(1:7),V4=as.numeric(c(7:1)))
ID=as.character(c(1,1,1,1,2,2,2))
#Demonstrate function
Aggregate(x=data,by=ID)
``` |

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