This presentation gives an introduction to our open source package RDBL, the R package for developers to interact with databases using only R.

1. 1RDBL – The open source R package for database access
RDBL
The R database layer package
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2. 2RDBL – The open source R package for database access
Overview
 RDBL is an open source package
 An abstraction layer for R developers to interact with databases
 No prior SQL knowledge required
 data.frame based syntax
 Lazy load – load the data only when absolutely necessary
 Maps tables to db.model analogue to a data.frame
Package description

3. 3RDBL – The open source R package for database access
Main functionalities
 Supports Oracle, MySql, SQLite, PostgreSQL, MS Access
 Loading data from database with column and row filtering
 as.data.frame performs loading data
 merge to create SQL joins
 logical indexing-like syntax for WHERE clauses
 aggregate for GROUP BY queries
 derived columns
What we have done already

4. 4RDBL – The open source R package for database access
Planned functionalities
 INSERT INTO statements
 Additional analytical queries (full range of standard SQL functionalities)
 UPDATE statements
 Optimizing generated SQL queries
What we are working on

5. 5RDBL – The open source R package for database access
Premium services
 RDBL is an open source package available for free
 Missing features you need can be ordered for a fee
 We provide on-site trainings
 We offer 24/7 support if required
What we can do for you

6. 6RDBL – The open source R package for database access
Detailed functionalities
library(RMySQL)
# Create a MySQL connection using DBI
# Database layout:
# Chinook database
db.connection <- dbConnect(MySQL(), dbname = "test")
# Set as the connection used
SetConnection(db.connection)
# Create several database models
artist.db <- db.model("Artist")
album.db <- db.model("Album")
# Load data
artists <- as.data.frame(artist.db)
albums <- as.data.frame(albums)
Creating and loading a db.model

7. 7RDBL – The open source R package for database access
Detailed functionalities
# Create the artist and album models
artist.db <- db.model("Artist")
album.db <- db.model("Album")
names(artist.db)
# [1] "ArtistId" "Name"
names(album.db)
# [1] "AlbumId" "Title" "ArtistId"
# Join tables together
# all.x = TRUE means LEFT JOIN
albums.of.artists.db <-
merge(artist.db,
album.db,
by = "ArtistId", # by.x ="ArtistId", by.y = "ArtistId"
all.x = TRUE)
# Load data
albums.of.artists <- as.data.frame(albums.of.artists.db)
Merging db.models

8. 8RDBL – The open source R package for database access
Detailed functionalities
# Column filtering
aoa.db <-
albums.of.artists.db[, c("Name",
"Title")]
# Row filtering
aoa.db <-
aoa.db[aoa.db$Name == "ACDC" &
aoa.db$Title == "Thunderstruck", ]
# Load data
thunder <- as.data.frame(aoa.db)
Filtering

9. 9RDBL – The open source R package for database access
Detailed functionalities
# rename columns with the same name
rename(genre.db, "Name", "GenreName")
rename(artist.db, "Name", "ArtistName")
# join tables together
# artist x album x track x genre to get the
# mapping between artists and genre
artist.genre.db <-
merge(merge(merge(artist.db, album.db,
by = "ArtistId")[,c("ArtistName",
"AlbumId")],
track.db,
by = "AlbumId")[, c("ArtistName",
"GenreId")],
genre.db,
by = "GenreId")[, c("ArtistName",
"GenreName")]
Merge – complex example

10. 10RDBL – The open source R package for database access
Detailed functionalities
# which group has the most rock songs?
rocks <- artist.genre.db[artist.genre.db$GenreName == "Rock", ]
most.rock.db <-
aggregate(rocks,
list(SongCount = count(rocks$GenreName)),
by = "ArtistName")
most.rock.db <-
most.rock.db[order.db(most.rock.db$SongCount,
decreasing = TRUE)]
rock.band <- as.data.frame(most.rock.db)[1, "ArtistName"]
Aggregation

11. 11RDBL – The open source R package for database access
Detailed functionalities
# which group has the most rock songs?
rocks <- artist.genre.db[artist.genre.db$GenreName == "Rock", ]
most.rock.db <-
aggregate(rocks,
list(SongCount = count(rocks$GenreName)),
by = "ArtistName")
most.rock.db$SongCount2 <-
most.rock.db$SongCount * 2
most.rock.db$SongCount3 <-
most.rock.db$SongCount + most.rock.db$SongCount2
# Load data
most.rock <- as.data.frame(most.rock.db)
Derived columns