plyr

I am trying to find lazy/easy ways of creating summary tables/ data.frames from wide data.frames . Assume a following data.frame, but with many more columns so that specifying the column names takes a long time: set.seed(2) x <- data.frame(Rep = rep(1:3, 4), Temp = c(rep(10,6), rep(20,6)), pH = rep(c(rep(8.1, 3), rep(7.6, 3)), 2), Var1 = rnorm(12, 5,2), Var2 = c(rnorm(6,4,1), rnorm(6,3,5)), Var3 = rt(12, 20)) x[1:3] <- as.data.frame(apply(x[1:3], 2, function(x) as.factor(x))) Now I can calculate summary statistics with plyr : (mu <- ddply(x, .(Temp, pH), numcolwise(mean))) (std <- ddply(x, .

I am trying to use ddply and summarise together from the plyr package but am having difficulty parsing through column names that keep changing...In my example i would like something that would parse in X1 programatically rather than hard coding in X1 into the ddply function. setting up an example require(xts) require(plyr) require(reshape2) require(lubridate) t <- xts(matrix(rnorm(10000),ncol=10), Sys.Date()-1000:1) t.df <- data.frame(coredata(t)) t.df <- cbind(day=wday(index(t), label=TRUE, abbr=TRUE), t.df) t.df.l <- melt(t.df, id.vars=c("day",colnames(t.df)[2]), measure.vars=colnames(t.df)

How can I get a data frame's name from a list? Sure, get() gets the object itself, but I want to have its name for use within another function. Here's the use case, in case you would rather suggest a work around: lapply(somelistOfDataframes, function(X) { ddply(X, .(idx, bynameofX), summarise, checkSum = sum(value)) }) There is a column in each data frame that goes by the same name as the data frame within the list. How can I get this name bynameofX ? names(X) would return the whole vector. EDIT: Here's a reproducible example: df1 <- data.frame(value = rnorm(100), cat = c(rep(1,50), rep(2,50))

I wonder whether there is a more straighforward way to calculate a certain type of variables than the approach i normally take.... The example below probably explains it best. I have a dataframe with 2 columns (fruit and whether the fruit is rotten or not). I would like to, for each row, add e.g. the percentage of fruit of the same category that is rotten. For example, there are 4 entries for apples, 2 of them are rotten, so each row for apple should read 0.5. The target values (purely as illustration) are included in the "desired outcome" column. I have previously approached this problem by *

I have a dataset filled with the average windspeed per hour for multiple years. I would like to create an 'average year', in which for each hour the average windspeed for that hour over multiple years is calculated. How can I do this without looping endlessly through the dataset? Ideally, I would like to just loop through the data once, extracting for each row the right month, day, and hour, and adding the windspeed from that row to the right row in a dataframe where the aggregates for each month, day, and hour are gathered. Is it possible to do this without extracting the month, day, and hour