Tidy-style data manipulation with H2O as the only data
backend. The only non-base runtime dependency is
h2o. knitr and rmarkdown are
optional vignette-building dependencies. No dplyr, tidytable or
data.table is used.
# Install h2o first, then this package from its source archive.
install.packages("h2oplyr_0.2.6.tar.gz", repos = NULL, type = "source")
h2o::h2o.init(nthreads = 2, max_mem_size = "2G")
library(h2oplyr)
p <- h_penguins()
h_summarize(p, n = n(), mass = mean(body_mass_g, na.rm = TRUE), .by = species)
vignette("h2oplyr-penguins")The matching H2O server and Java runtime are external system requirements. This package never starts or shuts down a cluster automatically.
See the vignette for the supported expression language, examples and
limits. Help pages have independently executable examples for all 73
exports. Additional asserted examples and regression tests are installed
under system.file("examples", package = "h2oplyr").
h2o_tbl() requires an existing H2OFrame; upload local
files with H2O first.h_collect() returns the H2OFrame; it does not collect
rows locally.h_nest_by() returns remote keys and a list of remote
handles.Start a matching H2O cluster explicitly on a local port, then set
H2OPLYR_TEST_PORT when running R CMD check.
Without that variable, static tests run and integration tests state that
they were skipped. Live integration tests verify that H2O namespace
function bindings remain unchanged. Rebuilding vignettes against a live
server can be requested with H2OPLYR_VIGNETTE_PORT.
Copyright 2026 Carlos Ortega. Code: MIT. Included Palmer penguin
data: CC0; see inst/extdata/PROVENANCE.md.