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...all > intends and purposes a less-than-ideal format. If you have that much data, > you do generally not want to serialize it back and forth as that is slow, > and > may drop precision. The rds format is great for R alone; we now have C > code > to read it from other apps (in the librdata repo by Evan Miller). > Different > portable serializations work too (protocol buffer, msgpack, ...), there are > databases and on and on... ] > > Dirk > > > R> df <- data.frame(replicate(100, runif(1000000, 0,1))) > R> setwd("/tmp") > R> write.cs...

Hello, I have looked on https://www.r-project.org/bugs.html , but it seems that this is the only way to do it. The issue is that the precision used by write.csv is on consistant for big files. See the following code: First I create a large dataframe filled with random uniform values. Then I write it to .csv and print out the first and last lines. df = data.frame(replicate(100, runif(1000000,

...t;> intends and purposes a less-than-ideal format. If you have that much data, >> you do generally not want to serialize it back and forth as that is slow, and >> may drop precision. The rds format is great for R alone; we now have C code >> to read it from other apps (in the librdata repo by Evan Miller). Different >> portable serializations work too (protocol buffer, msgpack, ...), there are >> databases and on and on... ] >> >> Dirk >> >> >> R> df <- data.frame(replicate(100, runif(1000000, 0,1))) >> R> setwd("/tm...

...inority) view that csv is for all intends and purposes a less-than-ideal format. If you have that much data, you do generally not want to serialize it back and forth as that is slow, and may drop precision. The rds format is great for R alone; we now have C code to read it from other apps (in the librdata repo by Evan Miller). Different portable serializations work too (protocol buffer, msgpack, ...), there are databases and on and on... ] Dirk R> df <- data.frame(replicate(100, runif(1000000, 0,1))) R> setwd("/tmp") R> write.csv(df, "temp.csv") R> system('...

...sv is for all > intends and purposes a less-than-ideal format. If you have that much data, > you do generally not want to serialize it back and forth as that is slow, and > may drop precision. The rds format is great for R alone; we now have C code > to read it from other apps (in the librdata repo by Evan Miller). Different > portable serializations work too (protocol buffer, msgpack, ...), there are > databases and on and on... ] > > Dirk > > > R> df <- data.frame(replicate(100, runif(1000000, 0,1))) > R> setwd("/tmp") > R> write.csv...

...less-than-ideal format. If you have that much > data, > >> you do generally not want to serialize it back and forth as that is > slow, and > >> may drop precision. The rds format is great for R alone; we now have C > code > >> to read it from other apps (in the librdata repo by Evan Miller). > Different > >> portable serializations work too (protocol buffer, msgpack, ...), there > are > >> databases and on and on... ] > >> > >> Dirk > >> > >> > >> R> df <- data.frame(replicate(100, runif(10...