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    <p>This was previously <a href="http://markmail.org/message/zeew2gr2x77cglcv#query:r-project%20quickly%20read%20large%20file+page:1+mid:bovcytjhdkzmefru+state:results" rel="noreferrer">asked on <b>R-Help</b></a>, so that's worth reviewing.</p> <p>One suggestion there was to use <code>readChar()</code> and then do string manipulation on the result with <code>strsplit()</code> and <code>substr()</code>. You can see the logic involved in readChar is much less than read.table.</p> <p>I don't know if memory is an issue here, but you might also <a href="http://cran.r-project.org/web/packages/HadoopStreaming/index.html" rel="noreferrer">want to take a look at the <b>HadoopStreaming</b> package</a>. This <a href="http://en.wikipedia.org/wiki/Hadoop" rel="noreferrer">uses Hadoop</a>, which is a MapReduce framework designed for dealing with large data sets. For this, you would use the hsTableReader function. This is an example (but it has a learning curve to learn Hadoop):</p> <pre><code>str &lt;- "key1\t3.9\nkey1\t8.9\nkey1\t1.2\nkey1\t3.9\nkey1\t8.9\nkey1\t1.2\nkey2\t9.9\nkey2\" cat(str) cols = list(key='',val=0) con &lt;- textConnection(str, open = "r") hsTableReader(con,cols,chunkSize=6,FUN=print,ignoreKey=TRUE) close(con) </code></pre> <p>The basic idea here is to break the data import into chunks. You could even go so far as to use one of the parallel frameworks (e.g. snow) and run the data import in parallel by segmenting the file, but most likely for large data sets that won't help since you will run into memory constraints, which is why map-reduce is a better approach.</p>
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    1. POQuickly reading very large tables as dataframes
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    1. USRoman Luštrik
    1. USShane
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    1. COI just did a quick test and readChar does seem to be much faster than even readLines for some inexplicable reason. However, it is still slow as sin compared to a simple C test. At the simple task of reading 100 megs, R is about 5 - 10x slower than C
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      1. USJonathan Chang
    2. COBringing Hadoop to bear here is like bringing a cannon to a knife fight.
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      1. USmedriscoll
    3. CODon't understand your point. The point of Hadoop is to handle very large data, which is what the question was about.
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