similar to: speeding up regressions using ddply

Dear WizaRds, After consulting different sources I am still unable to understand the correct use of return() in nested functions. To illustrate the problem: f <- function(x,y,type){ est1<-function(x,y){ z=x+y out(x,y,z)} est2<-function(x,y){ z=x*y out(x,y,z)} out<-function(x,y,z) return(x,y,z) if (type=="est1") est1(x,y) if (type=="est2") est2(x,y) }

Dear r-helpers, Could you please tell me what's missing: rbind(paste('txt.est',1:24, sep = '')) txt.est1, ... txt.est24 are vectors that I wish to rbind. _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400 Charlottesville, VA 22904-4400 Parcels: Room 102 Gilmer Hall McCormick Road

Dear R-mates, # Here's what I am trying to do. I have a dataset like this: id = c(rep(1,8), rep(2,8)) dur1 <- c( 17,18,19,18,24,19,24,24 ) est1 <- c( rep(1,5), rep(2,3) ) dur2 <- c(1,1,3,4,8,12,13,14) est2 <- rep(1,8) mydata = data.frame(id, estat=c(est1, est2), durat=c(dur1, dur2)) # I want to one have this: id = c(rep(1,8), rep(2,8))

I have a data.frame with ~250 observations (rows) in each of ~50 categories (columns). I would like to perform t.tests on subsets of observations within each column, with the subsets according to index vectors contained in other columns of the data.frame. My data.frame looks something like this: x<-data.frame(matrix(rnorm(200,mean=5,sd=.5),nrow=20)) colnames(x)<-c("site",

Hi, I would like to plot a bunch of tree ring width data (time series) using ggplots, but I'm having trouble figuring out how to do it. My data is in a data.frame, with years as rownames and a distinct tree ring series in each column. So, something like this: rwl<-matrix(rnorm(800), nrow = 100) colnames(rwl) <- paste('V', 1:8, sep = '')

Hi, I have a 5-paneled figure that i made using the facet function in qplot (ggplot). I've managed to arrange the panels into two rows/ three columns, but for the sake of easy visual comparisons between panels in my particular dataset, I want to have the two plots on the bottom align on the right hand side of the figure instead of the left. Here's an example: m <-

Hi, I've tried to figure this out using Intro to R and help(), to no avail - I am new at this. I'm trying to write a script that will read multiple files from a directory and then merge them into a single new data frame. The original data are in a tree-ring specific format, and so I've first used a function (read.rwl) from the dplR package to read each file, translate each into a

Estimado Carlos Ortega Muchas gracias, al ver su correo recordé por donde estaba eso muy fácil, copio y pego la solución, era tan fácil que no lo recordaba. > table(d) , , Etablecimiento = Est1 Factor Dieta A B 1 220 0 2 0 120 3 0 0 4 0 0 , , Etablecimiento = Est2 Factor Dieta A B 1 0 0 2 0 0 3 0 120 4 0 118

Dear R-Helpers: I am using trying to use *ddply* to extract min and max of a particular column in a data.frame. I am using two different forms of the function: ## var_name_to_split is a string -- something like "var1" which is the name of a column in data.frame ddply( df, .(as.name(var_name_to_split)), function(x) c(min(x[ , 3] , max(x[ , 3]))) ## fails with an error - case 1 ddply(

Hi, I am confused by results from: > ddply(aa, names(aa), colwise(sum)) I thought ddply was just calling colwise(sum)() with each column. However ddply() returns a 13 x 5 result !! The general result I expected is similar to that of apply() , or using colwise(sum)() alone. Shouldn't ddply() produce the same ? Thanks in advance for your help, - Stuart Andrews >

I have a dataframe df with two columns x and y. I want to count the number of times a unique x, y combination occurs. For example x<- c(1,2,3,4,5,1,2,3,4) y<- c(1,2,3,4,5,1,2,4,1) df<-as.data.frame(cbind(x, y)) #what is the correct way to use ddply for this example? ddply(df, c('x','y', summarize, ??) #desired output -- format and order doesn't matter # (x, y)

I'm trying to use ddply to compute summary statistics for many variables splitting on the variable site. however, it seems to work fine for mean() but if i use max() or min() things fall apart. whats going on? test.set<-data.frame(site=1:10,x=.Random.seed[1:100],y=rnorm(100)) means<-ddply(test.set,.(site),mean) means site x y 1 1 -97459496 -0.14826303 2

While putting my R code into functions, I've encountered a ddply function nesting issue and need a bit of advice on the proper way to fix it.? I've tried several approahces, but neither worked and I need to have the ability to include the "cut", "range", and "fullseq" methods within ddply.? (For a bit of that explanation refer to

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, all. I'm new to R and just beginning to learn to write functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a

Hi all, I have a data set (df, n=10 for the sake of simplicity here) where I have two continuous variables (age and weight) and I also have a grouping variable (group, with two levels). I want to run correlations for each group separately (kind of similar to "split file" in SPSS). I've been experimenting with different functions, and I was able to do this correctly using ddply

Hi, I am really confused how ddply work, so maybe you can help me. I created a function that sorts a vector etc. fn <- function(x){ x1 <- sort(x) x2 <- seq(length(x)) x3 <- x2/max(x2) df <- data.frame(x1,x2,x3) df } Probably this is not the best form of the function, but at least it produces what I want (data to plot a cumulative count curve). This function works on a

Dear group, Here is my data frame: futures <- structure(list(DESCRIPTION = c("CORN Jul/10", "CORN Jul/10", "CORN Jul/10", "CORN Jul/10", "CORN Jul/10", "LIVE CATTLE Aug/10", "LIVE CATTLE Aug/10", "SUGAR NO.11 Jul/10", "SUGAR NO.11 Jul/10", "SUGAR NO.11 Jul/10", "SUGAR NO.11

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Dear R-users, I am trying to get the plyr syntax right, without much success. Given: d<- data.frame(cbind(x=1,y=seq(20100801,20100830,1))) names(d)<-c("first", "daterep") d2<-d # I can convert the daterep column in place the classic way: d$daterep<-as.Date(strptime(d$daterep, format="%Y%m%d")) # How to do it the plyr way? ddply(d2,