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    <p>One of the most important things when learning to program in R is to think in terms of vector operations rather than loops. You're getting <code>NA</code>s (not the same as <code>NaN</code> in R) in your two vectors because you're not filling all the locations: you're only filling locations in <code>densf</code> where you have <code>"raw"</code>, and locations in <code>densft</code> where you have <code>"cooked"</code>. What do you want R to fill the rest of the spaces with? <code>NA</code> is the default, but you can fill them with something else (zeros, for example) if you want. That's what I've done below.</p> <pre><code># your code N=1000 alpha=0.1 zerosandones = rbinom(N, 1,alpha) vector1=sample(c("raw","cooked"),1000,T,prob=c(0.12,.88)) # my code densf &lt;- ifelse(zerosandones == 1 &amp; vector1 == "raw", 1, 0) densft &lt;- ifelse(zerosandones == 0 &amp; vector1 == "raw", rbinom(N, 1,alpha*0.2), 0) </code></pre> <p>Things to note:</p> <ul> <li>The use of <code>ifelse</code>, which is a vectorized form of <code>if</code>.</li> <li>The use of <code>&amp;</code>, which is a vectorized form of <code>&amp;&amp;</code>.</li> <li>No loops! Loops in R are slow. Thinking in terms of vectors takes some adjustment, but is almost always better.</li> </ul> <p>When I ran this I got 12 ones in <code>densf</code> and 3 ones in <code>densft</code>, which is about what I would expect. If this is not what <em>you</em> expect, then you need to better explain what you're trying to do here and why you expect something different.</p>
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    1. POQuantitative risk assessment model
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    1. USJonathan Christensen
    1. USJonathan Christensen
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    1. COthank you very much. if i may ask. i actually have to produce the densft for a string of values from 0.2 right upto 0.9. Do you have tips on how should i go about doing that. Or will I have to change 0.2 to 0.25, 0.3 etc.... each time?
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    2. COLoops in R are not slow as a general rule. What is done in them can be slow, especially if you make the mistake of growing objects or subsetting data.frames. Of course, vectorized solutions are still better.
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    3. CO@user2007598 You could write a function that would calculate densft and then just call the function a bunch of times. The distribution of the entries in `densft` is easy to find analytically, though--it would probably be easier to do this with pen and paper than through simulation.
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      1. USJonathan Christensen
 

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