similar to: Fwd: CRAN problem with plyr-1.4.1

I want to learn how to use the reshape package. The reshape package is not included in the Ubuntu repositories, so I attempted to install reshape with: install.packages("reshape") This is what I got for output: Warning in install.packages("reshape") : argument 'lib' is missing: using '/home/andy/R/i486-pc-linux-gnu-library/2.7'

Issue with PLYR. Now using R 2.14 and this data and plyr command line worked with 2.13 I am also loading the same saved data that worked previously, but now some issue. > library(plyr) > UNESCO <- dget('C:/Carbon-GJ/BZE_ecosys.robj') > df2 <- ddply(df, "UNESCO", summarise, total_ha = sum(Ha)) *Error in if (empty(.data)) return(.data) : missing value where

I apologize in advance for not having done more homework in advance, but thought I would send this along to see if anyone else was seeing this. I am having some sort of ggplot2/plyr/very-recent-R-devel dependency issues. Just installed R Under development (unstable) (2014-09-01 r66509) -- "Unsuffered Consequences" from source. > packageVersion("ggplot2") [1]

Hi all, has anyone tried and possibly succeeded in installing "reshape" package on Debian Lenny? I have a permanent failure because i cannot find the "plyr" package. I tried 10 mirrors. I contacted Hadley Wickham and James Long who seemed to have been closed to do it but it didn't work (meaning so far I haven't the answer from them). So has anyone any ideas as how to

Dear list and Hadley, The new plyr package seems to provide a clean and consistent way to apply a function on several arguments. However, I don't understand why the following example does not work like the standard mapply, library(plyr) df <- data.frame(a=1:10 , b=1:10) foo1 <- function(a, b, cc=0, d=0){ a + b + cc + d } mdply(df, foo1, cc=1) # fine mdply(df, foo1, d=1) #

Hi, I have been trying to use the new .parallel argument with the most recent version of plyr [1] to speed up some tasks. I can run the example in the NEWS file [1], and it seems to be working correctly. However, R will only use a single core when I try to apply this same approach with ddply(). 1. http://cran.r-project.org/web/packages/plyr/NEWS Watching my CPUs I see that in both cases

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, +

Hello everyone, I have got the same problem, with the same error message. Using R 2.14.1, plyr 1.7.1, R.Studio 0.94.110, Windows XP The plyr mailing list does not provide any help until now. >require(plyr) >c(sample(c(1:100), 50, replace=TRUE))->V1 >c(rep( 1:5, 10))->f1 #variable to group V1 >data.frame(cbind(V1, f1))->DF >str(DF) >ddply(DF$V1, DF$f1,

Dear all, I'm stuck with a problem using plyr to process a rather large junk of data. What I'm trying to do is applying a moving average to all the subparts of the dataframe (the example data can be found here https://dl.dropboxusercontent.com/u/2414056/testData.Rdata). require(plyr) load("testData.Rdata") applyfilter<-function(x){ return(filter(x,rep(1/5, times=5))) }

Hello, This is my first time trying to use plyr, and I'm getting nowhere. I have teacher ratings data (1:4), on 10 components, by external observers and internal observers, in schools in areas. I want to calculate the percentage of each rating given on each component, by each type of observer, within each school, within each area. The data look like this: unit area ext.obs rating comp 11

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?

Hello. The data.table package is very helpful in terms of speed. But I am having trouble actually using the output from linear regression. Is there any way to get the data.table output to be as pretty/useful as that from the plyr package? Below is an example. library('data.table'); library('plyr'); REG <-

If I have data: dat<-data.frame(a=rnorm(20),b=rnorm(20),c=rnorm(20),d=rnorm(20),site=rep(letters[5:8],each=5)) And want to plot like this: ctr<-1 for(i in c('a','b','c','d')){ png(file=paste('/tmp/plot_number_',ctr,'.png',sep=''),height=8.5, width=11,units='in',pointsize=9,res=300) print(ggplot(dat[,names(dat) %in%

Hello, Apologies for a similar earlier post. I didn't include enough details in that one. I am having a little trouble subsetting some data based on a grouping variable. I am using an instrument that does depth profiles of a water column. The instrument records on the way down as well as the way up. So thanks to an off-list reply I can subset the data so that all data collected at the

Dear all Is there a clean plyr version of the following by() and do.call(rbind, ...) construct: > df<-data.frame(a=1:10,b=11:20,c=21:30,grp1=c("x","y"),grp2=c("x","y"),grp3=c("x","y")) > dfsum<-by(df[c("a","b","c")], df[c("grp1","grp2","grp3")], range) >

I am trying to extract the shape and scale parameters of a wind speed distribution for different sites. I can do this in a clunky way, but I was hoping to find a way using data.table or plyr. However, when I try I am met with the following: set.seed(144) weib.dist<-rweibull(10000,shape=3,scale=8) weib.test<-data.table(cbind(1:10,weib.dist))

Dear R experts. I might be missing something obvious. I have been trying to fix this problem for some weeks. Please help. #data ped <- c(rep(1, 4), rep(2, 3), rep(3, 3)) y <- rnorm(10, 8, 2) # variable set 1 M1a <- sample (c(1, 2,3), 10, replace= T) M1b <- sample (c(1, 2,3), 10, replace= T) M1aP1 <- sample (c(1, 2,3), 10, replace= T) M1bP2 <- sample (c(1, 2,3), 10, replace= T)

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )

My data frame consists of character variables, factors, and proportions, something like c1 <- c("A", "B", "C", "C") c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N")) x <- c(0.5234, 0.6919, 0.2307, 0.1160) y <- c(0.9251, 0.7616, 0.3624, 0.4462) df <- data.frame(c1, c2, x, y) pct <- function(x) round(100*x, 1) I want to