similar to: SAS PROC SORT nodupkey

Hi, I am trying to simultaneously remove duplicate variables from two or more variables in a small R data.frame. I am trying to reproduce the SAS statements from a Proc Sort with Nodupkey for those familiar with SAS. Here's my example data : test <- read.csv("test.csv", sep=",", as.is=TRUE) > test date var1 var2 num1 num2 1 28/01/11 a 1 213 71 2

Hi everyone,   I have got the following problem:   x1 <- rnorm(10,5,1) x2 <- runif(10,0,1) nam1 <- paste("A",1:4,sep=".") nam2 <- paste("A",6:9,sep=".") nam <- c(nam1,"A.4",nam2,"A.9") mydata <- data.frame(x1,x2) rownames(mydata) <- nam Error in `row.names<-.data.frame`(`*tmp*`, value = c("A.1",

Let's say I have a data.frame like A B C TS other columns 1 1 1 12345 1 1 1 56789 1 2 1 23456 1 2 2 23457 2 4 7 23458 2 4 7 34567 2 4 7 45678 and I want the last row for each unique combination of A/B/C, where by "last" I mean greatest TS. A B C TS other columns 1 1 1 56789 1 2 1 23456 1 2 2 23457 2 4 7 45678 I did this simply in SAS: proc sort data=DF; by A B C descending TS

I have a dataset w/ 184K obs & 16 variables. In SAS I proc sort nodupkey it in seconds by 11 variables. I tried to do the same thing in R using both the unique & then the !duplicated functions but it just hangs there & I get no output. Does anyone know how to solve this? This is how I tried to do it in R: detail3 <-

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Dear R users, I have a very simple question. What is the easiest way to recode data in one of the columns of a data frame and put it into a new column? For example, here is a simple data frame: var1 1 1 2 2 3 3 How do I add a new column to data - 'var2' in which I group values in 'var1', for example: var1 var2 1 1 4 2 2 4 3 3 5 It's got to

Hello, I wanted to create a histogram, but somehow I got stuck... The interval limits are: x = 1, 2, 3, 3.5, 4.5, 5, 5.5 The interval widths are therefore: 1, 1, 0.5, 1, 0.5, 0.5 Nothing I tried worked... Can anyone help me please? Thanks Tobias -- View this message in context: http://www.nabble.com/Histogram-tf4315900.html#a12288850 Sent from the R help mailing list archive at Nabble.com.

Hi Mark, I don't know wether you recived a sufficient reply or not, so here are my comments to your problem. Supressing the constant term in a regression model will probably lead to a violation of the classical assumptions for this model. From the OLS normal equations (in matrix notation) (1) (X'X)b=X'y and the definition of the OLS residuals (2) e = y-Xb you get - by