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Hello all, I am doing an aggregation where the aggregating function returns not a single numeric value but a vector of two elements using return(c(val1, val2)). I don't know how to access the individual columns of that vector in the resulting dataframe though. How is this done correctly? Thanks, robert > agg <- aggregate(formula=df$value ~ df$quarter + df$tool, + FUN=cp.cpk,

Hello all, given two vectors X and Y I'd like to receive a vector Z which contains, for each element of X, the index of the corresponding element in Y (or NA if that element isn't in Y). Example: x <- c(4,5,6) y <- c(10,1,5,12,4,13,14) z <- findIndexIn(x, y) z [1] 5 3 NA 1st element of z is 5, because the 1st element of x is at the 5th position in y 2nd element of z is 3,

Hello, is it possible to replace the text of tick marks in a plot? Specifically, I'd like to have a ppnorm plot in which the theoretical quantiles are not expressed in terms of standard deviations, but in actual percentages. Anybody who's seen a probability plot in MINITAB knows what I'm talking about. I have somewhat listlessly looked at mtext(), thinking that I could maybe first

Hello, me again. I have a data frame that looks like this (actual dput output at bottom): > head(tencor) date lot wf.id s1 s2 s3 s4 s5 1 08.05.2012 W0X3H0 9 1238 1263 1244 1200 1183 2 08.05.2012 W0X3H0 10 1367 1396 1371 1325 1311 3 08.05.2012 W0X3H0 11 1383 1417 1393 1346 1328 I'd like to add a column to this that gives, for each row, the averages of the

Hi all, I'm just getting started in R. My problem is the following: I have a data frame (v1) with lots of production data measurements. Each row contains a single measurement ('ARI_MIT') with a timestamp. I want to "lump" the data by months with their mean and standard deviation. I have already successfully managed to do the lumping by adding another column to my data

Hello all, I have a dataframe that looks like this: head(df) date y 1 2010-09-27 1356 2 2010-10-04 1968 3 2010-10-11 2602 4 2010-10-17 3116 5 2010-10-24 3496 6 2010-10-31 3958 I need a function that, given any date, returns the y value corresponding to the given date or the last day before the given date. Example: Input: as.Date("2010-10-06"). Output: 1968 (because the

Hello all, here's a real-world example: I'm measuring a quantity (d) at five sites (site1 thru site5) on a silicon wafer. There is a clear site-dependence of the measured value. To find out if this is a measurement artifact I measured the wafer four times: twice in the normal position (posN), and twice rotated by 180 degrees (posR). My data looks like this (full, self-contained code at

Hello all, I have a bunch of aggregated measurement data. The data describe two different physical properties that correlate, and I want to estimate the coefficients (slope and intercept) from the dataset. This is of course easy, I've done it, and I got the expected result. But here's the thing: Each data point in X and Y is actually a mean of N individual (automated) measurements taken

Hello all, please look at my code below. The problems start where it says # PROBLEMS START HERE. Some sample data is at the very bottom. This is the disgnostic output from the script: > source('load.R') ts.null 1 NA 2 NA 3 NA 4 NA 5 NA 6 NA [1] "Adding data" "VS1A" ts.null VS1A.ts.null VS1A.tts 1 NA NA NA 2

Hello all, I'm very satisfied to say that my grip on both R and statistics is showing the first hints of firmness, on a very greenhorn level. I'm faced with a problem that I intend to analyze using ANOVA, and to test my understanding of a primitive, one-way ANOVA I've written the self-contained practice script below. It works as expected. But here's my question: How can I not