similar to: install.packages

Dear All, This question sounds very simple but I don't know where I am wrong. I just want to pad leading zeros in some string, for example, "123" becomes "00123". What is wrong if I do following? > sprintf("%05s", "123") [1] " 123" It didn't return "00123", instead it padded with 'blank'. Thank you for your help

Hi All, I have two Rs, one has been installed in Windows system and another one has been installed under UNIX system. Is there any environmental variable or function to tell me which R I am using? The reason that I need to know it is under different system, the data path could be different. I want to do something like if it is R under Windows path =

Hi All, I have a data frame like a = data.frame(date = c(20081201, 20081202, 20081201), product = c("a b c d e", "a b c g h t", "d e h a c e h g"), sales = c(1, 2, 3)) Now I want to aggregate the sales by part of the a$product. 'Product' is the product name, a string separated by a space. The key in my aggregate function is

Hi All, I have a data frame, whose colnames like "A.x, B.x, C.x, A.y, B.y, C.y". There could be many columns like this pattern. I want to compare data in columns A.x with A.y, B.x with B.y and C.x with C.y etc. Suppose my data frame is d, names(d) = c("A.x", "B.x", "C.x", "A.y", "B.y", "C.y"); If I want to D1 =

Hi All, I have a self-cooked package and save it to a zip file after running make, say named xxx.zip. After installing it to R by running "Install packages from local zip files" under Packages menu in R (Windows), I realized I needed to change some source codes and re-make it. My question is that if I need re-install that package or R would automatically update the R

Hi all, I have a dataset. Each time I want to sample N(i) elements from it and I want to repeated sampling M times. N(i) is varied from time to time. For example, dataset = 1:50; a = list(); M = 1000; I want to do something like for(i in 1:M) { a[[i]] = sample(dataset,

Hi All, I know in R there is function named 'step', which does the stepwise regression and choose the model by AIC. However, if I want to choose a model per this logic: 1. Run a full model (linear regression, f = lm(y ~., data = ZZZ), for example) 2. Pick up the variable with biggest p value, delete it from the module and get a new regression model. 3. Repeat step 2

Hi All, I have a matrix like m <- matrix( letters[1:10], ncol=5) How to conver it to 10 * 3 matrix, which the first col is row index of m, second col is colum index of m and third column is the value of matrix, say 1 1 1 "a" 2 1 2 "c" 1 3 "e" etc... Thanks. [[alternative HTML version deleted]]

Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) Thanks. [[alternative HTML version deleted]]

I have a data.frame named "df". The dput of df is at the bottom of this e-mail. What I'd like to do is replace the "n/a " values with NA. On Mac OSX, it works to do this: df[df == "n/a"] <- NA However, it does not work on Ubuntu. See below. Thanks in advance, Garrett > x <- df[27, 4] # complete data.frame dput is below > dput(x) "n/a?"

Hi All, I have a question about package installation in R. We have developed a package, say 'ABC'. We have installed it in two machines, A and B by running 'Install Package(s) from local zip file'. Everything was fine. Right now, suppose that package got damaged in machine A and our zipped file is gone, My question is that may I directly copy ../library/ABC from machine B to

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )

This is my first post to R-help and I look forward receiving some advice for a novice like me... I?ve got a simple repeated (4 periods so far) 10-question survey data that is very easy to work on Excel. However, I?d like to move the compilation to R but I?m having some trouble operating on count list data in a neat way. The data C > str(C) 'data.frame': 551 obs. of 13

Hi All, Suppose I have the following codes: x = data.frame(A = rnorm(20), B = rnorm(20), C = rnorm(20)) a = list() a[["1.1"]] = x a[["1.2"]] = x b = list() b[["1.1"]] = c("A", "B") b[["1.2"]] = c("B", "C") Now I want to apply b to a like this, for each element of 'a', only select the corresponding columns

The following dummy data frame has factor Q (with 2 levels) nesting factor P (with levels p1 and p2 nested under q1, and p3 and p4 nested under q2), but both crossing the random variate s, which has 8 levels. The dependent measure is dv. > # The data frame: > testnest dv s P Q 1 1 s1 p1 q1 2 2 s2 p1 q1 3 1 s3 p1 q1 4 2 s4 p1 q1 5 1 s5 p1 q1 6 3 s6 p1 q1 7 3 s7

Dear R-help, I wonder if you could give me some suggestions in how to do a union join of two data frames as follow: -> union join the common column, and insert a 0 if one is missing. I made a function to perform the following, and I know it may not that quite welly written, but it works. Any suggestions are welcome, many thanks. Anthony > q1 =

sample report data that i want to forecast quarter quarter_index Revenue 2007 Q1 1 $3,856,799 2007 Q2 2 $4,243,328 2007 Q3 3 $4,930,369 2007 Q4 4 $5,443,579 2008 Q1 5 $5,164,830 2008 Q2 6 $5,104,413 2008 Q3 7

sample report data that i want to forecast quarter quarter_index Revenue 2007 Q1 1 $3,856,799 2007 Q2 2 $4,243,328 2007 Q3 3 $4,930,369 2007 Q4 4 $5,443,579 2008 Q1 5 $5,164,830 2008 Q2 6 $5,104,413 2008 Q3 7

Dear list members, Any help on this efficiency issue would be greatly appreciated. I would like to find the most efficient way to run a non-vectorized function (here: fisher exact test p-value) iteratively using 4 matrices with identical dimensions. And as a result I aim for an array with identical dimensions containing the corresponding p-values. Please consider some code using a trivial

I am new to R and have tried for a good while to figure out how to code this in R. The dataset below: FTIStandKey State County FTITract CoverType Ver_CT V_Origin V_SpGrp NAH6005-001 Texas Jasper NAH6005 PPLB-2000-U PPLB-2000-U P P NAH6005-002 Texas Jasper NAH6005 NHHX-1950-O NHHX-1950-I N H NAH6253-001 Texas Tyler NAH6253 PPLB-2001-U PPLB-2001-U P P