Create H2OFrame

Source: R/frame.R

Import R object to the H2O cluster.

as.h2o(x, destination_frame = "", skipped_columns = NULL, ...)

# Default S3 method
as.h2o(x, destination_frame = "", skipped_columns = NULL, ...)

# S3 method for class 'H2OFrame'
as.h2o(x, destination_frame = "", skipped_columns = NULL, ...)

# S3 method for class 'data.frame'
as.h2o(
  x,
  destination_frame = "",
  skipped_columns = NULL,
  use_datatable = TRUE,
  ...
)

# S3 method for class 'Matrix'
as.h2o(
  x,
  destination_frame = "",
  skipped_columns = NULL,
  use_datatable = TRUE,
  ...
)

Arguments

x

An R object.

destination_frame

A string with the desired name for the H2OFrame

skipped_columns

A list of integer containing columns to be skipped and not parsed into the final frame

...

arguments passed to method arguments.

use_datatable

allow usage of data.table

Details

Method as.h2o.data.frame will use fwrite if data.table package is installed in required version.

To speedup execution time for large sparse matrices, use h2o datatable. Make sure you have installed and imported data.table and slam packages. Turn on h2o datatable by options("h2o.use.data.table"=TRUE)

See also

use.package

Examples

if (FALSE) { # \dontrun{
library(h2o)
h2o.init()
iris_hf <- as.h2o(iris)
euro_hf <- as.h2o(euro)
letters_hf <- as.h2o(letters)
state_hf <- as.h2o(state.x77)
iris_hf_2 <- as.h2o(iris_hf)
stopifnot(is.h2o(iris_hf), dim(iris_hf) == dim(iris),
          is.h2o(euro_hf), dim(euro_hf) == c(length(euro), 1L),
          is.h2o(letters_hf), dim(letters_hf) == c(length(letters), 1L),
          is.h2o(state_hf), dim(state_hf) == dim(state.x77),
          is.h2o(iris_hf_2), dim(iris_hf_2) == dim(iris_hf))
if (requireNamespace("Matrix", quietly=TRUE)) {
  data <- rep(0, 100)
  data[(1:10) ^ 2] <- 1:10 * pi
  m <- matrix(data, ncol = 20, byrow = TRUE)
  m <- Matrix::Matrix(m, sparse = TRUE)
  m_hf <- as.h2o(m)
  stopifnot(is.h2o(m_hf), dim(m_hf) == dim(m))
}
} # }