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This article mainly introduces "how R language deals with the problem of memory out of memory when big data". In daily operation, I believe many people have doubts about how R language deals with memory out of memory when big data. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the question of "how R language deals with memory out of memory when big data". Next, please follow the editor to study!

R occasionally has the problem of out of memory when dealing with big data, and the relevant solutions:

1. Memory.size () to view the current work space memory usage (MB)

2. Memory.limit () to view the upper limit of memory used by the current work space.

> memory.size () [1] 94.2 > memory.limit () [1] 8081

3. Ls () to view the variables of the current work space

4. Use object.size () to check the memory occupied by the variables involved in the result returned in the previous step.

> ls () [1] "aa", "age", "bb", "diabetes", "expression", "geneID", "n", "patientData" [9], "patientID", "sample1", "sample2", "sample3"status" > object.size (geneID) 88 bytes

5. Rm () deletes some unwanted variables (Garbage collection using gc ())

> rm (geneID) > rm (patientData) > rm (patientID) > rm (age) > gc () used (Mb) gc trigger (Mb) max used (Mb) Ncells 999611 53.4 1770749 94.6 1442291 77.1Vcells 1996363 15.3 3142662 24.0 2550980 19.5

6. Memory.limit (Limit), if you increase the upper limit of memory usage

> memory.limit (100000) [1] 1e+05 > memory.limit () [1] 1e+05 > at this point, the study on "how to deal with memory out of memory in big data with R language" is over. I hope I can solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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