similar to: data types changing

note: whole function is below- I am sure I am doing something silly. when I use it like USGS(input="precipitation") it is choking on the precip.1 <- subset(DF, precipitation!="NA") b <- ddply(precip.1$precipitation, .(precip.1$gauge_name), cumsum) DF.precip <- precip.1 DF.precip$precipitation <- b$.data part, but runs fine outside of the function: days=7

Look at the bottom of the message for my question #here is a little function that I wrote USGS <- function(input="discharge", days=7){ library(chron) library(gsubfn) #021973269 is the Waynesboro Gauge on the Savannah River Proper (SRS) #02102908 is the Flat Creek Gauge (ftbrfcms) #02133500 is the Drowning Creek (ftbrbmcm) #02341800 is the Upatoi Creek Near Columbus (ftbn) #02342500 is

I have a really simple question I have a data frame of 8 variables (the first column is the subjects' id): SubID G1 G2 G3 G4 W1 W2 W3 W4 1 6 5 6 2 6 2 2 4 2 6 4 7 2 6 6 2 3 3 5 5 5 5 5 5 4 5

Hi, I have a large matrix (SNPs) that I want to cycle over with logistic regression with interaction terms. I have made a loop but I am struggling to identify to the formula the name of the column in a way which is meaningful to the formula. It errors becasue it is not evaluated proporly. (below is a pilot with only 7 to 33 columns, my actual has 200,000 columns) My attempts: for (i in 7:33)

Hi, everybody: I asked about this in r-help last week and promised a summary of answers. Special thanks to the folks that helped me understand do.call and pointed me toward plyr. We face this problem all the time. A procedure generates a list of data frames. How to stack them together? The short answer is that the plyr package's rbind.fill method is probably the fastest method that is not

Hi All, I'm new to R and am trying to run a unit root test on the vector "y" (a time series of inflation (i.e. changes in the Consumer Price Index quarter on quarter)). I've run the Augmented-Dickey-Fuller Test below (R's URCA package). It gives me an error that it cannot find the function ur.df unless I comment out the third last line of code (see below). I try to call

Hi all, I bumped into this awkward zoo behaviour. I'd be half tempted to call it a bug, what do you think? It's annoying to work around it :( I wonder if this was the behaviour of older zoo versions, I can't remember coming across this sort of thing... > version _ platform i386-pc-solaris2.10 arch i386 os solaris2.10 system i386,

HI, Using c11<- 0.01 c12<- 0.01 c1<- 0.10 c2<- 0.10 One possible problem is that: dim(res5) #[1] 513? 20 res6<-aggregate(.~m1+n1+m+n,data=res5[,c(1:6,9:12,21:24)] ,max) #Error in `[.data.frame`(res5, , c(1:6, 9:12, 21:24)) : ?# undefined columns selected A.K. ________________________________ From: Joanna Zhang <zjoanna2013 at gmail.com> To: arun <smartpink111 at

Hi R, y1 <- zoo(matrix(1:10, ncol = 2), 1:5) colnames(y1)=c("a","b") y2 <- zoo(matrix(rnorm(10), ncol = 2), 6:10) colnames(y2)=c("b","a") > y1 a b 1 1 6 2 2 7 3 3 8 4 4 9 5 5 10 > y2 b a 6 0.9070204 0.3527630 7 1.2405943 0.8275001 8 -0.1690653 -0.1724976 9 -0.6905223 -1.1127670 10

I was running a sampling syntax based on a data frame (ago) of 160 rows and 25 columns. Below are the column names: > names(ago) [1] "SubID" "AGR1" "AGR2" "AGR3" "AGR4" "AGR5" "AGR6" "AGR7" "AGR8" [10] "AGR9" "AGR10" "WAGR1" "WAGR2"

Why, in R, does (0.1 + 0.05) > 0.15 evaluate to True? What am I missing here? How can I ensure this (ostensibly incorrect) behavior doesn't introduce bugs into my code? Thanks for your time. Dave Mitchell [[alternative HTML version deleted]]

Hi, I'm trying to execute the same R code on multiple data frames listed in a single directory. The code works fine if I use the code (below) for each file. However, I have several files and it becomes tedious to run each one, name it and then aggregate into a single dataframe. Name 0.0           1 21.15       2 2400.26   1 3222.14   2 name = read.table(file.choose(),header=F) #

Dear all, Possibly a rudimentary question, however any help is greatly appreciated. I am sorting a large matrix into an array of dim(p(i),q,3). I put each entry into a corresponding matrix (1 of the 3) based on some criteria. I figure this will assist me in condensing code as I can loop through the 3rd dimension of the array instead of generating 3 separate matrices and using the same block of

Hi! All, To find co-expressed genes from a expression matrix of dimension (9275 X 569), I used rcorr function from library(Hmisc) to calculate pearson correlation coefficient (PCC) and their corresponding p-values. From the correlation matrix (9275 X 9275) and pvalue matrix (9275 X 9275) obtained using rcorr function, I wanted to select those pairs whose PCC's are above 0.8 cut-off and then

Hi All, I have a problem with rbind. I have data that consist of weight height .. etc of 1000 patients. I would like to find the mean and the standard deviation ( for the weight , height etc) for each gender. data<-read.table("data.txt", header=T, sep='\t') fdata=NULL for (i in 1:50){ nn<-names(X)[i] m<-tapply(X[,i],data$gender,mean,na.rm=T) s<-tapply(X[,i],

Dear R experts, I got help to build a loop but there is a bug inside it that causes one part of the mechanism to fail. It should grow once, but if keep growing on rows where $ti_all is not NA. Here is a wall of code that very crudely demonstrates the problem, there is a couple of dim() outputs at the end where you can see how it the second time around keeps adds (2) rows, but this does not

I want to create a list based on the information from a data.frame, Model. So I tried the following: MyList <- list(colnames(Model)[2] = levels(Model$(colnames(Model)[2]))) but it failed with an error: Error: unexpected '=' in "list(colnames(Model)[2] =" I have the following problems with this command line: (1) I wanted to use colnames(Model)[2] as a tag for the list:

Hi List, I am looking for a variable selection procedure with a forward-backward selection method. Firstly, it is meant to work with the cophenetic correlation coefficient (CPCC) and intended to find the variable combination with the highest cophenetic correlation. Secondly, it is aimed at Gower metric with wards method (though this could be easily extended) aimed at categorical data. What I

I want to obtain a barplot with error bars and a mean plot with error bars with other scale on the left in the same plot. I need help to get the same x axis (centered) when overlay two plots (barplot2 and plotCI (or lineplot.CI) with errorbars), with diferent y axis. I use par (new=T) but the X axis names are not centered with the figures. An example of my data:

Hi group, I am trying to convert the organization of a data frame so I can do some correlations between stocks, I have something like this: stock.returns <- data.frame(rbind(c("MSFT","20110301",0.05),c("MSFT","20110302",0.01),c("GOOG","20110301",-0.01),c("GOOG","20110302",0.04))) colnames(stock.returns) <-