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POBinning variable by set number of observations
 

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  1. POBinning variable by set number of observations
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    <p>Quick question. I am binning a variable in a number of different ways for exploratory data analysis. Let's say I have a variable called <code>var</code> in data.frame <code>df</code>.</p> <pre><code>df$var&lt;-c(1,2,8,9,4,5,6,3,6,9,3,4,5,6,7,8,9,2,3,4,6,1,2,3,7,8,9,0) </code></pre> <p>So far, I've employed the following approaches (code below):</p> <pre><code>#Divide into quartiles df$var_quartile &lt;- with(df, cut(var, breaks=quantile(var, probs=seq(0,1, by=.25)), include.lowest=TRUE)) # Values of var_quartile &gt; [0,3],[0,3],(7.25,9],(7.25,9],(3,5],(3,5],(5,7.25],[0,3],(5,7.25],(7.25,9],[0,3],(3,5],(3,5],(5,7.25],(5,7.25],(7.25,9],(7.25,9],[0,3],[0,3],(3,5],(5,7.25],[0,3],[0,3],[0,3] #Bin into increments of 2 df$var_bin&lt;- cut(df[['var']],2, include.lowest=TRUE, labels=1:2) # Values of var_bin &gt; 1 1 2 2 1 2 2 1 2 2 1 1 2 2 2 2 2 1 1 1 2 1 1 1 2 2 2 1 </code></pre> <p>The last thing that I'd like to do is bin the variable into sections of 10 observations after it has been sorted in chronological order. This is an identical approach to splitting after finding the median (counting up to the middle observation), only I want to count in 10-observation increments.</p> <p>Using my example, this would split <code>var</code> into the following sections: </p> <pre><code>0,1,1,2,2,2,3,3,3,3 4,4,4,5,5,6,6,6,6,7 7,8,8,8,9,9,9 </code></pre> <p><strong>N.B. -- I need to run this operation in very large datasets (usually 3-6 million observations in wide form).</strong></p> <p>How do I do this? Thanks!</p>
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    1. COPlease provide reproducible data.
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    2. CO@roody I think you'll find a lot of utility in the 'plyr' package for work like this. You can use the function 'ddply' with the 'mutate' function to create various new columns for your bin idexes. You can also use the rolling functions in the 'zoo' package for grouping observations by 10.
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    3. COWhat is the desired outcome if the data looks like: `var <- c( rep(1, 11), rep(2, 3), rep(3, 9), rep(4,20) )` or even `sample(1:10, 100, repl=TRUE)`, since I'm quite sure that there will be some of those values with counts greater than 10.
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