kibior: easy scientific data handling, searching and sharing with Elasticsearch

Version: 0.1.1

TL;DR

Main features

This package allows:

• Pushing, pulling, joining, sharing and searching tabular data between an R session and one or multiple Elasticsearch instances/clusters.
• Massive data query and filter with Elasticsearch engine.
• Multiple living Elasticsearch connections to different addresses.
• Method autocompletion in proper environments (e.g. R cli, RStudio).
• Import and export datasets from an to files.
• Server-side execution for most of operations (i.e. on Elasticsearch instances/clusters).

How

Install

# Get from CRAN
install.packages("kibior")

# or get the latest from Github
devtools::install_github("regisoc/kibior")

Run

# load
library(kibior)

# Get a specific instance
kc <- Kibior$new("server_or_address", port)

# Or try something bigger...
kibio <- Kibior$get_kibio_instance()
kibio$list()

Examples

Here is an extract of some of the features proposed by KibioR. See Introduction vignette for more advanced usage.

Example: push datasets

# Push data (R memory -> Elasticsearch)
dplyr::starwars %>% kc$push("sw")
dplyr::storms %>% kc$push("st")

Example: pull datasets

# Pull data with columns selection (Elasticsearch -> R memory)
kc$pull("sw", query = "homeworld:(naboo || tatooine)", 
              columns = c("name", "homeworld", "height", "mass", "species"))
# see vignette for query syntax

Example: copy datasets

# Copy dataset (Elasticsearch internal operation)
kc$copy("sw", "sw_copy")

Example: delete datasets

# Delete datasets
kc$delete("sw_copy")

Example: list, match dataset names

# List available datasets
kc$list()

# Search for index names starting with "s"
kc$match("s*")

Example: get columns names and list unique keys in values

# Get columns of all datasets starting with "s"
kc$columns("s*")

# Get unique values of a column
kc$keys("sw", "homeworld")

Example: some Elasticsearch basic statistical methods

# Count number of lines in dataset
kc$count("st")

# Count number of lines with query (name of the storm is Anita)
kc$count("st", query = "name:anita")

# Generic stats on two columns
kc$stats("sw", c("height", "mass"))

# Specific descriptive stats with query
kc$avg("sw", c("height", "mass"), query = "homeworld:naboo")

Example: join

# Inner join between:
#   1/ a Elasticsearch-based dataset with query ("sw"), 
#   2/ and a in-memory R dataset (dplyr::starwars) 
kc$inner_join("sw", dplyr::starwars, 
              left_query = "hair_color:black",
              left_columns = c("name", "mass", "height"),
              by = "name")