plyr

I am (probably) NOT referring to the "all other variables" meaning like var1~. here. I was pointed to plyr once again and looked into mlply and wondered why parameters are defined with leading dot like this: function (.data, .fun = NULL, ..., .expand = TRUE, .progress = "none", .parallel = FALSE) { if (is.matrix(.data) & !is.list(.data)) .data <- .matrix_to_df(.data) f <- splat(.fun) alply(.data = .data, .margins = 1, .fun = f, ..., .expand = .expand, .progress = .progress, .parallel = .parallel) } <environment: namespace:plyr> What's the use of that? Is it just personal preference, naming

I'm going through Machine Learning for Hackers, and I am stuck at this line: from.weight <- ddply(priority.train, .(From.EMail), summarise, Freq = length(Subject)) Which generates the following error: Error in attributes(out) <- attributes(col) : 'names' attribute [9] must be the same length as the vector [1] This is a traceback(): > traceback() 11: FUN(1:5[[1L]], ...) 10: lapply(seq_len(n), extract_col_rows, df = x, i = i) 9: extract_rows(x$data, x$index[[i]]) 8: `[[.indexed_df`(pieces, i) 7: pieces[[i]] 6: function (i) { piece <- pieces[[i]] if (.inform) { res <- try(.fun(piece, ...)) if

I have a dataframe with the lengths and widths of various arthropods from the guts of salamanders. Because some guts had thousands of certain prey items, I only measured a subset of each prey type. I now want to replace each unmeasured individual with the mean length and width for that prey. I want to keep the dataframe and just add imputed columns (length2, width2). The main reason is that each row also has columns with data on the date and location the salamander was collected. I could fill in the NA with a random selection of the measured individuals but for the sake of argument let's

I have a problem with split column value when element of column has different number of strings. I can do it in plyr e.g.: library(plyr) column <- c("jake", "jane jane","john john john") df <- data.frame(1:3, name = column) df$name <- as.character(df$name) df2 <- ldply(strsplit(df$name, " "), rbind) View(df2) As a result, we have data frame with number of column related to maximum number of stings in given element. When I try to do it in dplyr, I used do function: library(dplyr) df2 <- df %>% do(data.frame(strsplit(.$name, " "))) but I get an error: Error in data.frame("jake", c("jane", "jane"

I often need to apply a function to each pair of columns in a dataframe/matrix and return the results in a matrix. Now I always write a loop to do this. For instance, to make a matrix containing the p-values of correlations I write: df <- data.frame(x=rnorm(100),y=rnorm(100),z=rnorm(100)) n <- ncol(df) foo <- matrix(0,n,n) for ( i in 1:n) { for (j in i:n) { foo[i,j] <- cor.test(df[,i],df[,j])$p.value } } foo[lower.tri(foo)] <- t(foo)[lower.tri(foo)] foo [,1] [,2] [,3] [1,] 0.0000000 0.7215071 0.5651266 [2,] 0.7215071 0.0000000 0.9019746 [3,] 0.5651266 0.9019746 0.0000000 which works, but is