search for: llpli

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

Hi All I want to import several .dat files every day of the week from the same folder. Let say, on day 1, I have about 100 files in the folder. By using this code, everything works perfectly (maybe there is a more efficient way to do it): filenames <-list.files(path="pathtofile", full.names=TRUE) library(plyr) import.list <- llply(filenames, read.table, header=TRUE,

Hi,I am using the amazing package 'plyr". I have one problem. I would appreciate help to fix the following error: Thanks. ______________________________ > library(plyr) > data(baseball) > summarise(baseball, + duration = max(year) - min(year), + nteams = length(unique(team))) Error: could not find function "summarise" > ddply(baseball, "id", summarise, +

Hi group, I imported 16 data frames using the function "list.files" temp <- list.files(path="...........") myfiles = lapply(temp, read.table,sep = "") Now I have 16 data set imported in R window. I want to combine them by row and tried some thing like (Here I am considering only 20 columns) for(i in 1:16){ data<- cbind(myfiles[[i]][,1:20]) } but it

Dear all, Trying to extract a few rows for each element of a list of data.frames, I'm puzzled by the following behaviour, > d <- lapply(1:4, function(i) data.frame(x=rnorm(5), y=rnorm(5))) > str(d) > > lapply(d, "[", i= c(1)) # fine, this extracts the first columns > lapply(d, "[", j= c(1, 3)) # doesn't do nothing ?! > > library(plyr)

Dear all, Trying to extract a few rows for each element of a list of data.frames, I'm puzzled by the following behaviour, > d <- lapply(1:4, function(i) data.frame(x=rnorm(5), y=rnorm(5))) > str(d) > > lapply(d, "[", i= c(1)) # fine, this extracts the first columns > lapply(d, "[", j= c(1, 3)) # doesn't do nothing ?! > > library(plyr)

Hola a todos! Estoy en un proyecto de text mining y por razones de los recursos con que cuento tuve que separar los archivos de texto de input del proyecto en muchos archivos pequeños. Luego de transformar cada uno de estos archivos en un corpus separado, puedo aplicar limpieza sobre cada corpus, buscar n-gramas, construir cada termDocumentMatrix y finalmente reunir todo en una sola TDM. Pero

plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together. It's already possible to do this with split and the apply functions, but plyr just makes it all a bit easier with: * consistent names, arguments and outputs * input from and output to data.frames,

plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together. It's already possible to do this with split and the apply functions, but plyr just makes it all a bit easier with: * consistent names, arguments and outputs * input from and output to data.frames,

Dear list, I'm just getting started learning how to use remote supercomputers for execution of parallelized code. I got a lot of initial help from this <http://stackoverflow.com/questions/14553357/parallelizing-on-a-supercomputer-and-then-combining-the-parallel-results-r> previous post, as well as one particularly helpful and patient XSEDE guy. I'm only using one node (for the

Hi, I have a list of 100 data frames, each data frame has 50 obs of 377 variables. I would like to replace all the NAs with 0 in all the dataframes. Should I have a for loop for every data frame? Below is an extract of how the data looks like. List of 100 $ :'data.frame': 50 obs. of 377 variables: ..$ ACHRPO: int [1:50] NA NA NA NA NA NA NA NA NA NA ... ..$ ACTEEX: int [1:50] NA

Dear all, I have 9 data frames, and I'm simply trying to sum the values of column 3 (on a row-by-row basis). However, there are a slightly different number of rows in each data frame, so I'm receiving the following error: "Error in Ops.data.frame(mrunoff_207101[3], mrunoff_207102[3]) : ? + only defined for equally-sized data frames". Here is what I'm attempting to do:

Hello, I am using the following script to run an anova for numerous species in a table that I have: SiteSpp <-

Hi all, I have the next problem: I have a matrix with size 8,000,000x18. My personal computer...blocks...so I have cut my original file into 100 different file. I have written a function that should be run on each of this file. So imagine I need to read data from q1 to q100 file data<-read.table("q1.txt",sep="") and each time I read 1 file execute my personal function

I know that this has been asked before in other variations but I just can't seem to figure out my particular application from previous posts. My apologies if I have missed the answer to this question somewhere in the archives. I have indeed looked. I am running Ubuntu 11.04, with R 2.12.1 and ESS+Emacs. For journal formatting requirements, I need to italicize all the greek letters in any

Folks, I am trying to get a loop to run which increments the object name as part of the loop. Here "fit1" "fit2" "fit3" and "fit4" are linear regression models that I have created. > for (ii in c(1:4)){ + SSE[ii]=rbind(anova(fit[ii])$"Sum Sq") + dfe[ii]=rbind(summary(fit[ii])$df) + } Error in anova(fit[ii]) : object 'fit' not found

Jorge Gracias por el consejo. Aparentemente no lo estoy aplicando bien, pues el objeto que obtengo no contiene lo que quiero. Me explico, al ejecutar txt <- vector('list', length = length(names)) #names el el vector donde ya tenía almacenada la lista de txt's for(i in seq_along(txt)){ txt[[i]] <- Corpus(VectorSource(names[i])) } obtengo el objeto txt: > class(txt) [1]

I have a list of dataframes i.e. each list element is a dataframe with three columns and differing number of rows. The third column takes on only two values. I wish to split the list into two sublists based on the value of the third column of the list element.  Second issue with lists as well. I would like to reduce each of the sublist based on the range of the second column, i.e. if the range of

Jorge, estimados colaboradores de R-help Estuve tratando de utilizar un script para uno de los pasos en mi análisis, que es transformar cada uno de los corpus en mi espacio de trabajo en un objeto TermDocumentMatrix Tengo un vector llamado bNames que lista todos los corpus que quiero pasar a TDM, y construí los siguientes comandos: tdm.n1 <- vector('list', length = length(bNames))