nodbi

nodbi provides a single user interface for interacting with many NoSQL databases.

So far we support the following DBs:

MongoDB
Redis (server based)
CouchDB
Elasticsearch
SQLite

Currently we have support for data.frame’s for the following operations

Create - all DBs
Exists - all DBs, except MongoDB
Get - all DBs
Query - all DBs, except Redis
Delete - all DBs
Update - just CouchDB

Install

cran version

install.packages("nodbi")

dev version

install.packages("devtools")
devtools::install_github("ropensci/nodbi")

library("nodbi")

Initialize connections

Start CouchDB on the cli or with the app

src_couchdb()

Start Elasticsearch, e.g.:

cd /usr/local/elasticsearch && bin/elasticsearch

src_elastic()

If you want to use classic Redis server, we do that through the redux package, and you’ll need to start up Redis by e.g,. redis-server in your shell.

src_redis()

Start MongoDB: mongod (may need to do sudo mongod)

src_mongo()

CouchDB

src <- src_couchdb()
docout <- docdb_create(src, key = "mtcars", value = mtcars)
head( docdb_get(src, "mtcars") )

Elasticsearch

Put the iris dataset into ES

src <- src_elastic()
ff <- docdb_create(src, "iris", iris)
head( docdb_get(src, "iris") )
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          5.0         3.6          1.4         0.2  setosa
#>          4.9         3.1          1.5         0.1  setosa
#>          4.8         3.4          1.6         0.2  setosa
#>          5.4         3.9          1.3         0.4  setosa
#>          5.1         3.3          1.7         0.5  setosa
#>          5.2         3.4          1.4         0.2  setosa

Redis

src <- src_redis()
docdb_create(src, "mtcars", mtcars)

docdb_get(src, "mtcars")

MongoDB

library("ggplot2")
src <- src_mongo(verbose = FALSE)
ff <- docdb_create(src, "diamonds", diamonds)
docdb_get(src, "diamonds")

SQLite

src <- src_sqlite(dbname = ":memory:")
ff <- docdb_create(src, key = "mtcars", value = mtcars)
docdb_get(src, key = "mtcars")

Extension json1 is enabled in Package RSQLite (since version 1.1). This extension is used to emulate the behaviour of MongoDB with the methods for SQLite:

Parameter collection corresponds to the name of a table (parameter key) in the SQLite database dbname.
Json strings in parameters query and fields are translated into SQL commands, unless too complex.
Tables created by docdb_create() are defined as follows, with exactly two columns, an index column named _id and a column with json data named json:

CREATE TABLE mtcars ( _id TEXT PRIMARY_KEY NOT NULL, json JSON );
CREATE UNIQUE INDEX mtcars_index ON mtcars ( _id );

The following examples show the maximum level of complexity that can be used at this time with available json operaters (“$eq”, “$gt”, “$gte”, “$lt”, “$lte”, “$ne”); query implies AND of the comma separated expressions in the absence of a prefixed logical operator (available at this time: “$and”, “$or”).

ff <- docdb_create(src, key = "mtcars", value = contacts)
docdb_query(src, "mtcars", 
            query = '{"$or": {"eyeColor": "blue", 
                              "age": {"$lt": 22}},  
                              "name": {"$regex": "L%"} }',
            fields = '{"age": 1, "eyeColor": 1, "name": 1}')

Use with dplyr

library("dplyr")
src <- src_mongo(verbose = FALSE)

docdb_get(src, "diamonds") %>%
  group_by(cut) %>%
  summarise(mean_depth = mean(depth), mean_price = mean(price))