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POGrouping functions (tapply, by, aggregate) and the *apply family
 

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    <p>Whenever I want to do something "map"py in R, I usually try to use a function in the <code>apply</code> family. </p> <p>However, I've never quite understood the differences between them -- how {<code>sapply</code>, <code>lapply</code>, etc.} apply the function to the input/grouped input, what the output will look like, or even what the input can be -- so I often just go through them all until I get what I want.</p> <p>Can someone explain how to use which one when?</p> <p>My current (probably incorrect/incomplete) understanding is...</p> <ol> <li><p><code>sapply(vec, f)</code>: input is a vector. output is a vector/matrix, where element <code>i</code> is <code>f(vec[i])</code>, giving you a matrix if <code>f</code> has a multi-element output</p></li> <li><p><code>lapply(vec, f)</code>: same as <code>sapply</code>, but output is a list?</p></li> <li><code>apply(matrix, 1/2, f)</code>: input is a matrix. output is a vector, where element <code>i</code> is f(row/col i of the matrix)</li> <li><code>tapply(vector, grouping, f)</code>: output is a matrix/array, where an element in the matrix/array is the value of <code>f</code> at a grouping <code>g</code> of the vector, and <code>g</code> gets pushed to the row/col names</li> <li><code>by(dataframe, grouping, f)</code>: let <code>g</code> be a grouping. apply <code>f</code> to each column of the group/dataframe. pretty print the grouping and the value of <code>f</code> at each column.</li> <li><code>aggregate(matrix, grouping, f)</code>: similar to <code>by</code>, but instead of pretty printing the output, aggregate sticks everything into a dataframe.</li> </ol> <p>Side question: I still haven't learned plyr or reshape -- would <code>plyr</code> or <code>reshape</code> replace all of these entirely?</p>
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    1. COto your side question: for many things plyr is a direct replacement for `*apply()` and `by`. plyr (at least to me) seems much more consistent in that I always know exactly what data format it expects and exactly what it will spit out. That saves me a lot of hassle.
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    2. COAlso, I'd recommend adding: `doBy` and the selection & apply capabilities of `data.table`.
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    3. CO`sapply` is just `lapply` with the addition of `simplify2array` on the output. `apply` does coerce to atomic vector, but output can be vector or list. `by` splits dataframes into sub-dataframes, but it doesn't use `f` on columns separately. Only if there is a method for 'data.frame'-class might `f` get column-wise applied by `by`. `aggregate` is generic so different methods exist for different classes of the first argument.
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