Create H2OFrame

Import R object to the H2O cluster.

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

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

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

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

# S3 method for 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)

References

https://h2o.ai/blog/2016/fast-csv-writing-for-r/

See also

use.package

Examples

if (FALSE) {
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))
}
}