By Phil Spector
This publication offers a big selection of tools acceptable for interpreting info into R, and successfully manipulating that facts. as well as the integrated services, a couple of on hand applications from CRAN (the entire R Archive community) also are coated. all the equipment offered make the most of the middle positive factors of R: vectorization, effective use of subscripting, and the right kind use of the various capabilities in R which are supplied for universal information administration initiatives. so much skilled R clients observe that, specially whilst operating with huge info units, it can be priceless to exploit different courses, significantly databases, along side R. hence, using databases in R is roofed intimately, in addition to tools for extracting info from spreadsheets and datasets created by way of different courses. personality manipulation, whereas occasionally ignored inside of R, can be lined intimately, permitting difficulties which are characteristically solved via scripting languages to be performed fullyyt inside R. For clients with event in different languages, instructions for the powerful use of programming constructs like loops are supplied. when you consider that many statistical modeling and pictures features desire their information offered in a knowledge body, thoughts for changing the output of commonplace features to info frames are supplied in the course of the ebook.
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One restriction of subqueries is that all subquery tables must be given an alias (through the AS operator), even if you won’t be directly referring to the table. We can produce the table of family sizes with the following query: SELECT ct,COUNT(*) as n FROM (SELECT COUNT(*) AS ct FROM children GROUP BY family_id) AS x GROUP BY ct; Subqueries are also useful when the timing of database operations makes a query impossible for the database to understand. Let’s say we wanted all the available information about the tallest child in the database.
For example, suppose that we have connected to the database “test” as described in the previous section, and we wish to extract all of the observations in a table called “mydata”. After the appropriate calls to dbDriver and dbConnect, we could retrieve the data with the following command: > mydata = dbGetQuery(con,’select * from mydata’) Any valid SQL query can be passed to a database by this method. In the case where data needs to be processed in pieces, the dbSendQuery function can be used to initiate the query, and the fetch function can be passed the result from dbSendQuery to sequentially access the results of the query.
3 Aggregation The GROUP BY clause, in conjunction with some SQL-provided aggregation functions, can be useful if you wish to produce a table of counts or a data summary from a database, without bringing all of the data into R. 2. For example, suppose we wanted to create a table of means for a variable x, from a database table named table, broken down by a categorical variable called type. 2. Basic SQL aggregation commands Remember to include the grouping variable in the list of selected variables, as SQL will not do this automatically.