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dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
There is also an active R community around the tidyverse. For example, there is the TidyTuesday social data project organised by the Data Science Learning Community (DSLC), [ 16 ] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier. [ 17 ]
Tibbles and Tibble may refer to: Tibbles, a pet cat which is alleged to have wiped out Lyall's wren on Stephens Island in New Zealand tibble, an alternative to a dataframe or datatable in the tidyverse in the R programming language
Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.
In SQL:1999 a recursive (CTE) query may appear anywhere a query is allowed. It's possible, for example, to name the result using CREATE [ RECURSIVE ] VIEW . [ 16 ] Using a CTE inside an INSERT INTO , one can populate a table with data generated from a recursive query; random data generation is possible using this technique without using any ...
A WHERE clause in SQL specifies that a SQL Data Manipulation Language (DML) statement should only affect rows that meet specified criteria. The criteria are expressed in the form of predicates. WHERE clauses are not mandatory clauses of SQL DML statements, but can be used to limit the number of rows affected by a SQL DML statement or returned ...
A Guide to the SQL standard, 4th ed., Addison Wesley, USA 1997, ISBN 978-0-201-96426-4; What Not How: The Business Rules Approach to Application Development, 2000, ISBN 0-201-70850-7; The Database Relational Model: A Retrospective Review and Analysis, 2001, ISBN 0-201-61294-1; Temporal Data & the Relational Model, 2003, ISBN 1-55860-855-9
Pipeline: allowing the simultaneous running of several components on the same data stream, e.g. looking up a value on record 1 at the same time as adding two fields on record 2; Component: The simultaneous running of multiple processes on different data streams in the same job, e.g. sorting one input file while removing duplicates on another file