The Aggregate function (not to be confounded with aggregate) prepares a data.frame, tibble or data.table for merging by computing the sum, mean and variance of all continuous (integer and numeric) variables by a given variable.
For all categorical variabes (character and factor), it creates dummies and subsequently computes the sum and the mode by a given variable.
For all Date variables, it computes the recency and duration by a given variable with repsect the an end date variable.
For computational speed, all the calculations are done with data.table. This functions aims at maximum information extraction with a minimum amount of code.
The package also contains faster implementations of the dummy and categories function (comparable to the same functions in the dummy package). When using the AggregateR package, the dummy-package is deprecated and the internal dummy and categories functions are superior in terms of speed.
To install the package from CRAN:
install.packages('AggregateR')
To instal the package from github:
devtools::install_github ('MatthBogaert/AggregateR')
This code blocks shows how the Aggregate function works when confronted with a table with numeric, categorical and Date variables. Aggregate accepts a data.frame, tibble or data.table and outputs by default a data.table.
#Create some data
data <- data.frame(V1=sample(as.factor(c('yes','no')), 200000, TRUE),
V2=sample(as.character(c(1,2,3,4,5)),200000, TRUE),
V3=sample(1:20000,200000, TRUE),
V4=sample(300:1000, 200000, TRUE),
V5 = sample(as.Date(as.Date('2014-12-09'):Sys.Date()-1, origin = "1970-01-01"),200000,TRUE),
ID=sample(x = as.character(1:4), size = 200000, replace = TRUE))
Aggregate(x=data,by='ID')
## Calculating categorical variables ...
## Calculating numerical variables ...
## Calculating date variables ...
## ID V1_no_sum V1_no_mode V1_yes_sum V1_yes_mode V2_1_sum V2_1_mode V2_2_sum V2_2_mode V2_3_sum
## 1: 1 24911 0 25080 1 10006 0 9990 0 10170
## 2: 2 24938 0 25160 1 9985 0 10073 0 10030
## 3: 3 25070 1 24933 0 9845 0 9987 0 10108
## 4: 4 24926 0 24982 1 9923 0 9891 0 9901
## V2_3_mode V2_4_sum V2_4_mode V2_5_sum V2_5_mode V3_sum V3_mean V3_var V4_sum V4_mean
## 1: 0 9887 0 9938 0 498324620 9968.287 33440187 32426370 648.6442
## 2: 0 9962 0 10048 0 499201602 9964.502 33370364 32606808 650.8605
## 3: 0 9988 0 10075 0 501006529 10019.529 33208428 32535970 650.6804
## 4: 0 9939 0 10254 0 499350872 10005.427 33285590 32461104 650.4189
## V4_var V5_duration V5_recency
## 1: 40972.02 2172 1
## 2: 41186.23 2172 1
## 3: 40789.41 2172 1
## 4: 41224.02 2172 1
As mentioned, the user can also output a tibble for nicer printing.
Aggregate(x=data,by='ID', tibble = TRUE)
## Calculating categorical variables ...
## Calculating numerical variables ...
## Calculating date variables ...
##A tibble: 4 x 23
## ID V1_no_sum V1_no_mode V1_yes_sum V1_yes_mode V2_1_sum V2_1_mode V2_2_sum V2_2_mode V2_3_sum
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##1 1 25060 1 24906 0 10046 0 9847 0 9932
##2 2 25056 1 24964 0 9981 0 10010 0 9986
##3 3 24986 0 25068 1 9989 0 10057 0 10076
##4 4 25037 1 24923 0 10086 0 9955 0 10075
## ... with 13 more variables: V2_3_mode <dbl>, V2_4_sum <dbl>, V2_4_mode <dbl>, V2_5_sum <dbl>,
## V2_5_mode <dbl>, V3_sum <dbl>, V3_mean <dbl>, V3_var <dbl>, V4_sum <dbl>, V4_mean <dbl>,
## V4_var <dbl>, V5_duration <dbl>, V5_recency <dbl>
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