similar to: Multiply List by a Numeric

Sorry, I'm sure I'm not using the appropriate vocab here, which is undoubtedly why I can't seem to find a fix to this (hopefully very easy) problem. Suppose you have a factor abc <- factor(c(2,2,3,4,7,7)) And you want to know what the number in the nth spot in that would be abc[1] [1] 2 Levels: 2 3 4 7 shows the correct label of the first element - but if I want to pull out the

This is hopefully a quick question on decimal accuracy. Is any decimal accuracy lost when casting a numeric vector as a matrix? And then again casting the result back to a numeric? I'm finding that my calculation values are different when I run for loops that manually calculate matrix multiplication as compared to when I cast the vectors as matrices and multiply them using "%*%".

I was surprised this morning, that it seems as though the min() function does not work as *I* anticipated when given vector arguments. For example: a <- 1:10 b <- c(rep(1, times=5), rep(10, times=5)) Result: > min(a,b) 1 What I actually wanted was a term by term minimum, i.e.: ifelse(a<=b, a, b) 1 1 1 1 1 6 7 8 9 10 Am I losing much in terms of computation power if I use

I'm not trying to start a Windows vs. Linux debate, but I've been using R on a Windows machine for a while, and was recently wondering if R's performance would be faster on a Linux machine. And similarly, if any incremental increase in processing speed would be worth the time it would take me to migrate my entire system to Linux (including a database that I access via an R package.)

Hopefully this is an easy problem... I'm trying to add a partitioned rank column to a data frame where the rank is calculated separately across a partition by categories, the way you could easily do in SQL. I found this solution in the archives that looked like it might work: http://tolstoy.newcastle.edu.au/R/e11/help/10/09/8675.html The example has a data frame with several car companies,

Is there a way to start multiple instances of R in an automated manner? Since I'm not sure that question makes tons of sense, here's my scenario: I have a number of data updates that need to be completed on an ongoing basis with the data pulled from and then stored to another location. The updates are manually triggered, so can build up in terms of the volume that needs to be updated.

Hi, I'm trying to run an R script using Rcmd Batch from the command line on a Windows Vista machine. I am using R version 2.8.1. I installed the batch files 4-3 found at http://cran.r-project.org/contrib/extra/batchfiles/ and added them to my path. I also had to install the latest version of perl (it's Strawberry perl if that makes a difference) and have added this to my path. Now when

Hi All, Most of the work I am doing with R uses data which I am pulling from various SQL queries. To streamline the process even more, I was wondering if it was possible to embed SQL queries in R - that way avoiding the need to first get the data, then move to R to process it. I haven't found anything out there on this yet, so if you know of a good resource that includes this topic, I would

Hi All, I am iterating through dated materials, with variable start and end dates, and would like to skip procedures everytime I encounter a weekend or holiday. To do this, I thought the easiest way would be to create a TRUE/FALSE vector corresponding to each day where it is TRUE if a workday, and FALSE if a weekend or holiday. So far I have been able to do this for weekdays: startDate <-

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very

My apologies if this is a bit of a 'newbie' question. I am using R v 2.8.0 in Windows and am a bit confused about the memory size/allocation. A script I wrote faulted out with the error: "Error: cannot allocate vector of size 5.6 Mb" After this error, I still have: > memory.size() [1] 669.3517 > memory.limit() [1] 1535.875 Since the memory size is well under 5.6Mb less

Hello, I am writing out to a file and have two quick questions that I can't seem to track down the correct answers for. Luckily, I *think* they are both simple enough that someone might be able to point me in the right direction on them without too much trouble. Both questions relate to the process below where CompleteFrame is a data frame containing what I want printed to a file. filename

When executing the command: print(cat(paste("Input criteria does not meet specifications. Check input against the following requirements: a >= 0 b <= 0 c >= 0 ", sep=""), "")) I get: Input criteria does not meet specifications. Check input against the following requirements: a >= 0 b <=

I'm working on a problem where I'm introducing random error and have been using the built in function runif to provide that random error. However, I realized that I seem to be getting some unexpected behavior out of the function and was hoping someone could share some insight. I don't know the runif algorithm at all, but from the behavior I'm seeing, it seems that whenever I open

Hi All, I was wondering if there was a function in R that would output the total run time for various scripts. For now I have the following workaround: begTime <- Sys.time() ... the rest of the R script... runTime <- Sys.time()-begTime Is there another function that I don't know about that would return this information in a more elegant manner? Thanks! [[alternative HTML

Hi all, I am doing some basic regression analysis, and am getting a bit confused on how to enter non-polynomial formulas to be used. For example, consider that I want to find A and r such that the formula y = A*exp(r*x) provides the the best fit to the line y=x on the interval [0,50]. I can set: xpts <- seq(0, 50, by=0.1) ypts <- seq(0, 50, by=0.1) I know I can find a fitted polynomial

Hi All, I have an script in R which accepts user inputs for certain parameters, particularly dates, which the user inputs as character strings. eg: > date1 <- "2009-01-21" The script later parses the input via the as.Date function: > as.Date(date1) However, as.Date encounters an error when the string does not represent an actual date. eg: > date1 <-

Hoping for a little insight into how to make sure I have R running as efficiently as possible. Suppose I have a data frame, A, with n rows and m columns, where col1 is a date time stamp. Also suppose that when this data is imported (from a csv or SQL), that the data is already sorted such that the time stamp in col1 is in ascending (or descending) order. If I then wanted to select only the rows

Hi, I am trying to parse XML files and read them into R as a data frame, but have been unable to find examples which I could apply successfully. I'm afraid I don't know much about XML, which makes this all the more difficult. If someone could point me in the right direction to a resource (preferably with an example or two), it would be greatly appreciated. Here is a snippet from one of

I am using R as a data manipulation tool for a SQL database. So in some of my R scripts I use the RODBC package to retreive data, then run analysis, and use the sqlSave function in the RODBC package to store the results in a database. There are two problems I want to avoid, and they are highly related: (1) having R rerun analysis which has already been done and saved into output database table,