similar to: do I need plyr, apply or something else?

All, I have a package BondLab, all variables are passed to the models via Cusip objects. ? The only variables input by the user are? settlement date,? price or yield or spread. ? Further price may be passed as 100.125, 100-8 (1/8), or 100-2 (2/64) or 100-2+ (5/128).? Once passed these variables must have a steady state (in decimal). ?After reading the R documentation and Hadley's book I

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))) }

# 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

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

Dear all, I?m having trouble getting a list of regression variables back into a dataframe. mydf <- data.frame(x1=rnorm(100), x2=rnorm(100), x3=rnorm(100)) mydf$fac<-factor(sample((0:2),replace=T,100)) mydf$y<- mydf$x1+0.01+mydf$x2*3-mydf$x3*19+rnorm(100) dlply(mydf,.(fac),function(df) lm(y~x1+x2+x3,data=df))->dl here I?d like to use ldply(dl,coef(summary)) or something

I have a set of wind speeds read at different locations. The data is a data frame with two columns: site and wind speed. I want to split the data on site and call a function to find the shape and scale parameters of a weibull distribution fit. The end result is a plot with x-axis = shape and y-axis = scale. Currently my code looks like: fit_wind_speed<-function(x){

Dear R forum, Please refer to my query regarding "Error with function". I forgot to mention that I am using "plyr" library. Sorry for inconvenience. Regards Katherine --- On Tue, 23/4/13, Katherine Gobin <katherine_gobin@yahoo.com> wrote: From: Katherine Gobin <katherine_gobin@yahoo.com> Subject: [R] Error with function To: r-help@r-project.org Date:

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

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 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) #

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

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 have a dataset that looks like: set.seed(144) sam<-sample(1000,100) dat<-data.frame(id=letters[1:10],value=rnorm(1000),day=c(rep(1,100),rep(2,100),rep(3,100),rep(4,100),rep(5,100))) I want to "normalise" it using the following function (unless you have a better idea...): adj.values<-function(dframe){ value_mean<-mean(dframe$value) value_sd<-sd(dframe$value)

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

# 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