Just like ames, data.tables must fit inside RAM. Some users regularly work with 20 or more tables in memory, rather like a database. The MB column is useful to quickly assess memory use and to spot if any redundant tables can be removed to free up memory. It is often useful to see a list of all data.tables in memory: > tables() We have just created two data.tables: DT and MOTORS. We can easily convert existing ame objects to data.table. Observe that a data.table prints the row numbers with a colon so as to visually separate the row number from the first column. If you have created a ame before, you could recall that it is done by using the function ame(): > DF = ame(x=c("b","b","b","a","a"),v=rnorm(5))Ī data.table is created in exactly the same way: > DT = data.table(x=c("b","b","b","a","a"),v=rnorm(5)) This tutorial contains techniques to create, subset and select a data.table, following by usage of various functions and operations on rows and columns including chaining, indexing, etc. It is an ideal package for dataset handing in R. This tutorial series is about the data.table package in R that is used for Data Analysis. The syntax for using a data.table is mentioned below: DT Some of the other notable features of data.tables are its fast primary ordered indexing and its automatic secondary indexing, this is complemented by a memory efficient combined join and group by. The syntax for data.table is flexible and intuitive and therefore leads to faster development. It is widely used for fast aggregation of large datasets, low latency add/update/remove of columns, quicker ordered joins, and a fast file reader. Data.table is an extension of ame package in R.
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