Provides selection, filtering, mutation, aggregation, joins, reshaping and window operations on remote 'H2O' frames. Data manipulation is delegated to 'H2O'; no local table backend is used. Includes an English tutorial and reproducible examples using Palmer penguin measurements.
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.