similar to: Open file for reading and writing with APPEND

Hi, I want to store the output of my program to a file. However, With subsequent runs of my code, I'd like to append to the same log file. Currently, I'm using: outfile <- file("log.txt", open="w") cat(results, file=outfile) This works, but will overwrite the log file each time. Is there a way to open a file and have R append to the end? Thanks! -- Noah

Hello, I'm attempting to build a package using R 2.15.0 on OS X I am getting a generic failure when performing a cran type check on the package. Even with a very simple test package, it still fails int he same place. Example: In R: rm(list=ls()) foo <- function(x){print(x)} package.skeleton(name="foo") Then, at the command line: R CMD build foo R CMD check --as-cran

Hello, I need to calculate a moving average and an exponentially weighted moving average over a fairly large data set (500K rows). Doing this in a for loop works nicely, but is slow. ewma <- data$col[1] N <- dim(data)[1] for(i in 2:N){ data$ewma <- alpha * data$ewma[i-1] + (1-alpha) * data$value[i] } Since the moving average "accumulates" as we move through the data,

Hello, I was just assigned to perform a cost effectiveness study in healthcare. We are studying the cost effectiveness of a proposed diagnostic vs. current screening procedures. One of the team members suggest a commercial software package called "TreeAge Pro". Looking at the description, it appears to be a nice GUI to some very simple models that could be easily constructed in R.

I have a bunch of data sets that were created for the libsvm tool. They are in "colon separated sparse format". i.e. 1 5:1 27:3 345:10 Is a row with the label of "1" and only has values in columns 5, 27, and 345. I want to read these into a data.frame in R. Is there a simple way to do this? -- Noah Silverman, M.S. UCLA Department of Statistics 8117 Math Sciences

Hello, I need to design a fairly simple front-end for someone to use an R script system that I've built. My thought was to just use the text based menus available in the base R package, perhaps in some kind of loop. How have other people done this? Any "best practices" that you can recommend? Thanks! -- Noah Silverman UCLA Department of Statistics 8117 Math Sciences Building

Hello, I have a time series object (xts) that I iterate over in a loop. Works fine. My challenge is that I want to be able to reference other entries in the series by math. i.e. For today's observation, what were the last 5 observations? If indexed numerically, it is trivial, but I can figure out how to do this with dates. This is slightly more difficult as there may not be an

Hello, I noticed something odd when working with data frames and xts objects. If I read in a CSV file, R creates a nice data.frame. This works well. If I then convert to an XTS object, I see that all the values in the data are now quoted. My data is a mix of numeric and character. This is usually seen when converting a data.frame to a matrix, as R will treat all the data as the same class.

Hi, Looking at a large data set with many factors. I would like to expand each factor variable into multiple new variables for each level. (0,1) coding. My first though was just to code a big nasty loop, to take each level and cbind a column onto my data set. But, that seems painful. There must be a better way. Is there an "easy" way to do this in R? (Note, I don't want to

Unfortunately, some data I need to work with was delivered in a rather messy Excel file. I want to import into R and clean up some things so that I can do my analysis. Pulling in a CSV from Excel is the easy part. My current challenge is dealing with some text mixed in the values. i.e. 118 5.7 <2.0 3.7 Since this column in Excel has a "<2.0" value, then R reads the

Hi, I have a very large data set stored as an xts object. xts is very nice about showing row labels as "human readable" dates and times. I want the actual epoch values that are stored internally. The only way I can find to access them is one-at-a-time using the internal function: xcoredata() Calling this in an entire column, the "R" way doesn't work. It will only

Hi, I have several data structures (xts structures). I then have a list of the names of those objects. I'd like to access the object by name. For example: foo1 <- as.xts(....) foo2 <- as.xts(...) foo3 <- as.xts(...) structs <- c("foo1", "foo2", "foo3") for (thisOne in structs){ print(thisOne$colA) } The above fails. Clearly I'm missing a

Hi, When using the glmnet() function of the package glmnet, A series of coefficients is returned for a list of descending lambda values. I am unable to locate anything in the documentation that explains HOW this choice of lambda series is made. (There is documentation about how to choose my own, but I want to understand how the authors are doing it) Any ideas? -- Noah Silverman UCLA

Hi, I'm interested in doing some sound analysis with R. Does anyone have any experience/methods for reading in a wav file? -- Noah Silverman UCLA Department of Statistics 8208 Math Sciences Building Los Angeles, CA 90095 [[alternative HTML version deleted]]

Hi, i have some data in a matrix. It has zero values scattered throughout, at random. I'd like to create a line plot, with a line for each row, that *excludes* the zero or NA values. The data looks like this (toy example) 10 12 21 0 23 0 43 0 NA 41 0 0 0 34 35 0 35 0 44 0 NA NA NA 3 2 5 0 3 2 etc... Suggestions on an easy way to do this? Thanks! -- Noah Silverman UCLA Department of

Hi, I have a rather large data frame (500 x 5000) that I want to convert to a proper xts object. I am able to properly generate an xts object with the correct time index. However, all of my numerical values are now strings. b <- as.xts(a[,2:dim(a)[2]], order.by=as.POSIXct(strptime(paste(a$Date), '%m/%d/%Y'))) My guess is that somewhere in the large data frame there are a few

Hi, I have some data, that when plotted looks very close to a log-normal distribution. My goal is to build a regression model to test how this variable responds to several independent variables. To do this, I want to use the fitdistr tool from the MASS package to see how well my data fits the actual distribution, and also build a generalized linear model using the glm command. The summary

Hello, I have a fairly complex hierarchical model that I using rjags to fit. Short test runs verify that it works and everything appears to be setup correctly. Now that I want to collect a larger sample from the posterior (5,000 or more). This looks like it will take several days to run on my hardware (Intel core i7, 16GB RAM) I read in the rjags documentation that there is an option for

Hi, I'm having difficulty coming up with a good way to subest some data to generate statistics. My data frame has multiple observations by group. Here is an overly-simplified toy example of the data ========================== code v1 v2 G1 1.2 2.3 G1 0 2.4 G1 1.4 3.4 G2 2.9 2.3 G2 4.3 4.4 etc.. ========================= I want to normalize the data *by group* for certain variable.

Hi, I have function that I want to optimize. Am playing with the optim() function in R Two issues: 1) I can't seem to get it to work with a function that takes multiple inputs. Dummy Example: myFunc <- function(A,B,D,D){ # Do stuff return E } > myFunc(1,2,3,4) [1] 12 # works fine from command line > optim( par=c(1,2,3,4), fn=myFunc) Error in A+B : 'B' is missing