similar to: Plyr 1.7

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

This might do it: > lhs=c('a','a','a','b') > rhs=c('a','b','b','b') > > > # function to determine differences > f_diff <- function(l, r){ + t_l <- table(l) + t_r <- table(r) + # compare 'l' to 'r' + sapply(names(t_l), function(x){ + if (is.na(t_r[x])) return(t_l[x])

Hi all. I found one situation, on my OS - Windows 7, where R stops working with reported error R for Windows GUI front-end has stopped working. Here is the example: library(plyr) DF <- data.frame(x=c(1:3, NA, NA), y=factor(sample(1:3,5,rep=T),levels=1:5)) DF[DF$x<3, ] #this works properly ddply(DF, .(y), nrow, .drop=FALSE) #this causes the problem ddply(DF[DF$x<3, ], .(y), nrow,

Dear all, I have a question regarding the possibility of parallel computation in plyr version 1.7. The help files of the following functions mention the argument '.parallel': ddply, aaply, llply, daply, adply, dlply, alply, ldply, laply However, the help files of the following functions do not mention this argument: ?d_ply, ?aply, ?lply Is it because parallel computation is not

Hello everyone, I was asked to repost this again, sorry for any inconvenience. I'm looking replacement for ddply function from plyr package. Function allows to apply function by category stored in any column/columns. Regular loops or lapplys slow down greatly because my unique combination count exceeds 9000. Is there any available solution which allow me to apply function by category?

I'd like to announce the availability of the new "itertools" package, which provides a variety of functions used to create iterators, as defined by REvolution Computing's "iterators" package. The package has been uploaded to CRAN and is now available under the GPL-2 license. The "itertools" package is strongly inspired by the Python itertools module, and

I'd like to announce the availability of the new "itertools" package, which provides a variety of functions used to create iterators, as defined by REvolution Computing's "iterators" package. The package has been uploaded to CRAN and is now available under the GPL-2 license. The "itertools" package is strongly inspired by the Python itertools module, and