h2o (version 3.44.0.3)

h2o.group_by: Group and Apply by Column

Description

Performs a group by and apply similar to ddply.

Usage

h2o.group_by(
  data,
  by,
  ...,
  gb.control = list(na.methods = NULL, col.names = NULL)
)

Value

Returns a new H2OFrame object with columns equivalent to the number of groups created

Arguments

data

an H2OFrame object.

by

a list of column names

...

any supported aggregate function. See Details: for more help.

gb.control

a list of how to handle NA values in the dataset as well as how to name output columns. The method is specified using the rm.method argument. See Details: for more help.

Details

In the case of na.methods within gb.control, there are three possible settings. "all" will include NAs in computation of functions. "rm" will completely remove all NA fields. "ignore" will remove NAs from the numerator but keep the rows for computational purposes. If a list smaller than the number of columns groups is supplied, the list will be padded by "ignore".

Note that to specify a list of column names in the gb.control list, you must add the col.names argument. Similar to na.methods, col.names will pad the list with the default column names if the length is less than the number of colums groups supplied.

Supported functions include nrow. This function is required and accepts a string for the name of the generated column. Other supported aggregate functions accept col and na arguments for specifying columns and the handling of NAs ("all", "ignore", and GroupBy object; max calculates the maximum of each column specified in col for each group of a GroupBy object; mean calculates the mean of each column specified in col for each group of a GroupBy object; min calculates the minimum of each column specified in col for each group of a GroupBy object; mode calculates the mode of each column specified in col for each group of a GroupBy object; sd calculates the standard deviation of each column specified in col for each group of a GroupBy object; ss calculates the sum of squares of each column specified in col for each group of a GroupBy object; sum calculates the sum of each column specified in col for each group of a GroupBy object; and var calculates the variance of each column specified in col for each group of a GroupBy object. If an aggregate is provided without a value (for example, as max in sum(col="X1", na="all").mean(col="X5", na="all").max()), then it is assumed that the aggregation should apply to all columns except the GroupBy columns. However, operations will not be performed on String columns. They will be skipped. Note again that nrow is required and cannot be empty.

Examples

if (FALSE) {
library(h2o)
h2o.init()
df <- h2o.importFile(paste("https://s3.amazonaws.com/h2o-public-test-data",
                           "/smalldata/prostate/prostate.csv", 
                           sep=""))
h2o.group_by(data = df, by = "RACE", nrow("VOL"))
}