subset

I have an algorithm problem. I am trying to find all unique subset of values from a larger set of values. For example say I have the set {1,3,7,9} . What algorithm can I use to find these subsets of 3? {1,3,7} {1,3,9} {1,7,9} {3,7,9} Subsets should not repeat, and order is unimportant, set {1,2,3} is the same as set {3,2,1} for these purposes. Psudocode (or the regular kind) is encouraged. A brute force approach is obviously possible, but not desired. For example such a brute force method would be as follows. for i = 0 to size for j = i + 1 to size for k = j + 1 to size subset[] = {set[i],set

I have big data frame with various numbers of columns and rows. I would to search the data frame for values of a given vector and remove the rows of the cells that match the values of this given vector. I'd like to have this as a function because I have to run it on multiple data frames of variable rows and columns and I wouls like to avoid for loops. for example ff<-structure(list(j.1 = 1:13, j.2 = 2:14, j.3 = 3:15), .Names = c("j.1","j.2", "j.3"), row.names = c(NA, -13L), class = "data.frame") remove all rows that have cells that contain the values 8,9,10 I guess i could use ff[ !ff[,1] %in%

Let's say we have got a set {a_1, a_2, a_3, ..., a_n} The goal is to find a sum that we create in the following way: We find all subsets whose length is 3, then multiply each subset's elements (for the subset {b_1, b_2, b_3} the result will be b_1*b_2*b_3 ). At the end we sum up all these products. I am looking for a shortest time-execution algorithm. Example SET: {3, 2, 1, 2} Let S be our sum. S = 3*2*1 + 3*2*2 + 2*1*2 + 3*1*2 = 28 It is easier to calculate sum of multiplied triplets when repetitions are allowed (like a_1*a_1*a_1). This sum is just (sum^3) . Since repetitions are not allowed,

Function foo1 can subset a list by a requested variable (e.g., by = type == 1 ). Otherwise, foo1 will simply output the inputted list itself. For my purposes, I need to use foo1 within a new function called foo2 . In my code below, my desired output is obtained like so: foo2(data = D, by = G[[1]]) ; foo2(data = D, by = G[[2]]) ; foo2(data = D, by = G[[3]]) . But, I wonder why when I loop over G using lapply , I get an error as shown below ? foo1 <- function(data, by){ L <- split(data, data$study.name) ; L[[1]] <- NULL if(!missing(by)){ L <- lapply(L, function(x) do.call("subset", list(x, by)))

I am new to R and can't get to grips with this concept. Suppose I have a table loaded called "places" with 3 say columns - city, population and average summer temperature Say I want to "filter" - produce a new table object where population is less than 1 million and average summer temperature is greater than 70 degrees. In any other program I have used this would be pretty easy but having done some research I'm working myself up into greater confusion. Given the purpose of R and what it does this must be pretty simple stuff. How would I apply the above conditions to the table? What would the