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Welcome to Large objects for R project! Package 'ff' supports fast on-disk objects: standard and packed atomics, ffdf data.frames, csv import/export, chunking and indexing. Integrated package 'bit' supports fast in-memory booleans: 'bit' (1-bit boolean), 'bitwhich' (asymetric booleans). The project summary page. You can find here. Presentations on available functionality. Oehlschlägel (2010) Managing large datasets in R - ff examples and concepts. 2009, Agrocampus, Rennes.

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Welcome to Large objects for R project! Package 'ff' supports fast on-disk objects: standard and packed atomics, ffdf data.frames, csv import/export, chunking and indexing. Integrated package 'bit' supports fast in-memory booleans: 'bit' (1-bit boolean), 'bitwhich' (asymetric booleans). The project summary page. You can find here. Presentations on available functionality. Oehlschlägel (2010) Managing large datasets in R - ff examples and concepts. 2009, Agrocampus, Rennes.

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ff | ff.r-forge.r-project.org Reviews

https://ff.r-forge.r-project.org

Welcome to Large objects for R project! Package 'ff' supports fast on-disk objects: standard and packed atomics, ffdf data.frames, csv import/export, chunking and indexing. Integrated package 'bit' supports fast in-memory booleans: 'bit' (1-bit boolean), 'bitwhich' (asymetric booleans). The project summary page. You can find here. Presentations on available functionality. Oehlschlägel (2010) Managing large datasets in R - ff examples and concepts. 2009, Agrocampus, Rennes.

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Installing GHC 7.4.1 and Cabal. Sat 10 March 2012 by Adam Drake. I wanted to try out the newest version of GHC. And decided it may be a good time to get rid of all the things install by the Haskell Platform, and any associated libraries, and start from scratch. Installing GHC 7.4.1 was easy because there is a MacOS binary available, but that leaves you with no package management via Cabal. You can download the Cabal-Install. Be advised that this script deletes everything. It shows no mercy. Cd into the d...

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ff.r-forge.r-project.org

ff

Welcome to Large objects for R project! Package 'ff' supports fast on-disk objects: standard and packed atomics, ffdf data.frames, csv import/export, chunking and indexing. Integrated package 'bit' supports fast in-memory booleans: 'bit' (1-bit boolean), 'bitwhich' (asymetric booleans). The project summary page. You can find here. Presentations on available functionality. Oehlschlägel (2010) Managing large datasets in R - ff examples and concepts. 2009, Agrocampus, Rennes.

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