r language sweep

The fact that i can reproduce sweep's effect using a simpler function suggests that i don't understand sweep's core use cases, and the fact that this function is used so often suggests that it's quite useful.
If the second argument is 1, then the vector has to have the same length as row.
Optional arguments to FUN.
The last argument is a binary operator, such as , , *, /, and.Here, you are interested in knowing where the product sells best in which department, for example.As in the default, binary operators can be supplied if"d or bac"d.Note that to get this, you only needed very few lines of code.The output returned is a list (which explains the l in the function name which has the same number of elements as the object passed.If it is 2, it is applied in column-wise manner.Sometimes it's used when a simpler function would have sufficed (e.g., 'apply other times, it's impossible to know exactly what it's is doing without spending a fair amount of time to step through the code block it's.In the right-hand side of figure 2, you can see an alternative extraction: this time you omit the first parameter and you get the first row from each of the matrices.Value, an array with the same shape as x, but with the summary statistics swept out.You do this to avoid repeating the aggregate instruction within the call to the plotting for readability: so you could have posed different questions to the data in a vectorized way like with aggregate and this you often do in conjunction with a handy plotting.Please, do not recite or link to the R Documentation, mailing lists, or any of the 'primary' R sources-assume i've read them.
This is the same as apply function.
Examples require(stats) # for median t - apply(attitude, 2, median) sweep(trix(attitude 2, t) # subtract the column medians # More sweeping: A - array(1:24, dim 4:2) # no warnings in normal use sweep(A, 1, 5) (A.min - apply(A, 1, min) # 1:4 sweep(A, 1,.min).
For example, you want to subtract 3, 4,5,6 from each value in the first, 2nd, 3rd and the last column.
The difference is that: It can be used for other objects like dataframes, lists or vectors; and.
When you execute?lapply, you see that the syntax looks like the apply function.For example, you can use mean function to calculate mean values for each column and row.As you can see, the method signature is similar to 'apply' though 'sweep' requires one more parameter, 'stats'.Arguments x an array.In the last two posts, I described useful commands either to expand or reduce your data.If true (the default warn if the length or dimensions of stats do not match the specified dimensions.FUN should be a function of two arguments: it will be called with arguments x and an array of the same dimensions generated from stats by aperm.Details, fUN is found by a call to match.If it is 2, the length of vector needs to be the same length as column.