Why does R use so much memory when using read.csv()?

Question

I'm running R on linux (kubuntu trusty). I have a csv file that's nearly 400MB, and contains mostly numeric values:

$ ls -lah combined_df.csv 
-rw-rw-r-- 1 naught101 naught101 397M Jun 10 15:25 combined_df.csv

I start R, and df <- read.csv('combined_df.csv') (I get a 1246536x25 dataframe, 3 int columns, 3 logi, 1 factor, and 18 numeric) and then use the script from here to check memory usage:

R> .ls.objects()
         Type  Size    Rows Columns
df data.frame 231.4 1246536      25

Bit odd that it's reporting less memory, but I guess that's just because CSV isn't an efficient storage method for numeric data.

But when I check the system memory usage, top says that R is using 20% of my available 8GB of RAM. And ps reports similar:

$ ps aux|grep R
USER       PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
naught1+ 32364  5.6 20.4 1738664 1656184 pts/1 S+   09:47   2:42 /usr/lib/R/bin/exec/R

1.7Gb of RAM for a 379MB data set. That seems excessive. I know that ps isn't necessarily an accurate way of measuring memory usage, but surely it isn't out by a factor of 5?! Why does R use so much memory?

Also, R seems to report something similar in gc()'s output:

R> gc()
           used  (Mb) gc trigger  (Mb)  max used  (Mb)
Ncells   497414  26.6    9091084 485.6  13354239 713.2
Vcells 36995093 282.3  103130536 786.9 128783476 982.6

Answer 1

As noted in my comment above, there is a section in the documention ?read.csv entitled "Memory Usage" that warns that anything based on read.table may use a "surprising" amount of memory and recommends two things:

1. Specify the type of each column using the colClasses argument, and
2. Specifying nrows, even as a "mild overestimate".

Answer 2

Not sure if you just want to know how R works or if you want an alternative to read.csv, but try fread from data.table, it is much faster and I assume it uses much less memory:

library(data.table)
dfr <- as.data.frame(fread("somecsvfile.csv"))