CRAN Task View: Databases with R
|Maintainer:||Yuan Tang, James Joseph Balamuta|
|Contact:||terrytangyuan at gmail.com|
|Contributions:||Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.|
|Citation:||Yuan Tang, James Joseph Balamuta (2022). CRAN Task View: Databases with R. Version 2022-08-24. URL https://CRAN.R-project.org/view=Databases.|
|Installation:||The packages from this task view can be installed automatically using the ctv package. For example, |
ctv::install.views("Databases", coreOnly = TRUE) installs all the core packages or
ctv::update.views("Databases") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.
This CRAN task view contains a list of packages related to accessibility of different databases. This does not include data import/export or data management. Moreover, the task view on HighPerformanceComputing and MachineLearning might provide useful information.
As datasets become larger and larger, it is impossible for people to save them in traditional file formats such as spreadsheet, raw text file, etc., which could not fit on devices with limited storage and could not be easily shared across collaborators. Instead, people nowadays tend to store data in databases for more scalable and reliable data management.
Database systems are often classified based on the database models that they support. Relational databases became dominant in the 1980s. The data in relational databases is modeled as rows and columns in a series of tables with the use of SQL to express the logic for writing and querying data. The tables are relational, e.g. you have a user who users your softwares and those softwares have creators and contributors. Non-relational databases became popular in recent years due to huge demand in storing unstructured data with the use of NoSQL as the query language. Users generally don’t need to define the data schema up front. If there are changing requirements in the applications, non-relational databases can be much easier to use and manage.
The content presented in this task view is undergoing rapid changes in industries and academia. Please send any suggestions to the maintainer via e-mail or submit an issue or pull request in the GitHub repository linked above. All suggestions and corrections by others are gratefully acknowledged.
This section includes packages that provides access to relational databases within R.
- The DBI package provides a database interface definition for communication between R and relational database management systems. It’s worth noting that some packages try to follow this interface definition (DBI-compliant) but many existing packages don’t.
- The RODBC package provides access to databases through an ODBC interface. This package is maintained by the R Core Team and depends only on base R. See alternative odbc package below.
- The odbc package provides a DBI-compliant interface to ODBC drivers. This package is maintained by RStudio and has a number of package dependencies. See alternative RODBC package above.
- The RMariaDB package provides a DBI-compliant interface to MariaDB and MySQL.
- The RMySQL package provides the interface to MySQL. Note that this is the legacy DBI interface to MySQL and MariaDB based on old code ported from S-PLUS. A modern MySQL client based on Rcpp is available from the RMariaDB package we listed above.
- Packages for PostgreSQL, an open-source relational database:
- The RPostgreSQL package and RPostgres package both provide fully DBI-compliant Rcpp-backed interfaces to PostgreSQL.
- The rpostgis package provides the interface to its spatial extension PostGIS.
- The RGreenplum provides a fully DBI-compliant interface to Greenplum, an open-source parallel database on top of PostgreSQL.
- The ROracle package is a DBI-compliant Oracle database driver based on the OCI. The ora package provides convenience functions to query and browse a database through the ROracle connection.
- Packages for SQLite, a self-contained, high-reliability, embedded, full-featured, public-domain, SQL database engine:
- The RSQLite package embeds the SQLite database engine in R and provides an interface compliant with the DBI package.
- The filehashSQLite package is a simple key-value database using SQLite as the backend.
- The liteq package provides temporary and permanent message queues for R, built on top of SQLite.
- The bigrquery package provides the interface to Google BigQuery, Google’s fully managed, petabyte scale, low cost analytics data warehouse.
- The RDruid package on GitHub provides the interface to Apache Druid, a high performance analytics data store for event-driven data.
- The RH2 package provides the interface to H2 Database Engine, the Java SQL database.
- The influxdbr package provides the interface to InfluxDB, a time series database designed to handle high write and query loads.
- The RPresto package implements a DBI-compliant interface to Presto, an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
- The RJDBC package is an implementation of R’s DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
- The implyr package provides the back-end for Apache Impala, which enables low-latency SQL queries on data stored in the Hadoop Distributed File System (HDFS), Apache HBase, Apache Kudu, Amazon Simple Storage Service (S3), Microsoft Azure Data Lake Store (ADLS), and Dell EMC Isilon.
- The dbx package provides intuitive functions for high performance batch operations and safe inserts/updates/deletes without writing SQL on top of DBI. It is designed for both research and production environments and supports multiple database backends such as Postgres, MySQL, MariaDB, and SQLite.
- The sparklyr package provides provides a dplyr interface to Apache Spark DataFrames as well as an R interface to Spark’s distributed machine learning pipelines.
- The Hmisc provides a wrapper function
Hmisc::mdb.get() that uses the mdbtools utility to read from Microsoft Access database on Unix-alike systems.
This section includes packages that provides access to non-relational databases within R.
- Packages for Redis, an open-source, in-memory data structure store that can be used as a database, cache and message broker:
- The RcppRedis package provides interface to Redis using hiredis.
- The redux package provides a low-level interface to Redis, allowing execution of arbitrary Redis commands with almost no interface, and a high-level generated interface to more than 200 redis commands.
- Packages for Elasticsearch, an open-source, RESTful, distributed search and analytics engine:
- The elastic package provides a general purpose interface to Elasticsearch.
- The uptasticsearch package is a Elasticsearch client tailored to data science workflows.
- The mongolite package provides a high-level, high-performance MongoDB client based on mongo-c-driver, including support for aggregation, indexing, map-reduce, streaming, SSL encryption and SASL authentication.
- The R4CouchDB package provides a collection of functions for basic database and document management operations in CouchDB.
- The aws.dynamodb package on GitHub provides access to Amazon DynamoDB.
- The rrocksdb package on GitHub provides access to RocksDB.
This section includes packages that provides tools for working and testing with databases, database table manipulations, etc.
- The MSSQL package extends the functionality of the RODBC package to work with Microsoft SQL Server databases. Makes it easier to browse the database and examine individual tables and views.
- The pool package enables the creation of object pools, which make it less computationally expensive to fetch a new object.
- The DBItest package is a helper that tests DBI back ends for conformity to the interface.
- The dbplyr package is a dplyr back-end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a DBI back-end; more advanced features require SQL translation to be provided by the package author.
- The sqldf package provides functionalities to manipulate R Data Frames Using SQL.
- The pointblank package provides tools to validate data tables in databases such as PostgreSQL and MySQL.
- The dittodb package provides functionality to test database interactions with any DBI compliant database backend. It includes functionality to use fixtures instead of direct database calls during testing as well as functionality to record those fixtures when interacting with a real database for later use in tests.
- The tfio package provides the ability to use Apache Ignite, which handles distributed database management for high-performance computing with in-memory speed.
- The dbr package on GitHub provides convenient database connections and queries from R using YAML configuration files and templates.
- The rocker package provides a R6 class interface for handling relational database connections using DBI as backend. The purpose is having an intuitive object allowing straightforward handling of SQL databases.
- The SQRL package streamlines exploratory and interactive sessions on ODBC databases, and allows R code within SQL scripts.
|Core:||DBI, odbc, RODBC.|
|Regular:||bigrquery, DBItest, dbplyr, dbx, dittodb, dplyr, elastic, filehashSQLite, Hmisc, implyr, influxdbr, liteq, mongolite, MSSQL, ora, pointblank, pool, R4CouchDB, R6, RcppRedis, redux, RGreenplum, RH2, RJDBC, RMariaDB, RMySQL, rocker, ROracle, rpostgis, RPostgres, RPostgreSQL, RPresto, RSQLite, sparklyr, sqldf, SQRL, tfio, uptasticsearch.|