similar to: Sum a list of tables

Dear all, I want to get a series of boxplots (grouped by two factors) and I want to overlay the original observations and the following code does almost what I want: library(ggplot) ddf <- data.frame(x=factor(rep(LETTERS[1:4], each=30)), y = runif(120,0,10), grp = factor(rep(rep(1:3, 10), 4))) ggplot(ddf, aes(x, y, colour=grp)) + geom_boxplot() + geom_point() Yet the position of the points

Dear all, I want to use aggregate.formula to conveniently summarize a data.frame. I have quiet some variables in the data.frame and thus I don't want to write all these names by hand, but instead create them on the fly. This approach has the advantage that if there will be even more columns in the data.frame I don't have to change the code. I've hence tried to construct a formula

Dear all, In a function I paste a string and convert it to a formula which I pass to lm[e]. The idea is to write a function which takes the name of the response variable and the explanatory variable and the data frame as an argument and calculates an lm[e]. (see example below) This works fine, but if I want to make a prediction on this model, R complains that the object holding the formula

Hi everybody, Is there a particular reason, why this code does not work as intended: z <- factor(LETTERS[1:3], ordered = TRUE) u <- 4:6 min(z[u > 4]) Error in Summary.factor(2:3, na.rm = FALSE) : min not meaningful for factors I agree that min is indeed not meaningful for not ordered factors, but it makes sense for ordered factors. Especially since z[3] <

Dear all, Assume that I have the following data structure: d <- expand.grid(subj=1:5, time=1:3, treatment=LETTERS[1:3]) d$value <- 10 ^ (as.numeric(d$treatment) + 1) + 10 * d$subj + d$time d$value2 <- 100000 + d$value where d$treatment == "C" stands for my control group. What I want to achieve now is to subtract the values corresponding to d$treatment == "C" from

Dear all, I produced the following graph with ggplot which is almost fine, yet I don't like that the legend for "Means" and "Observations" includes a line, though no line is used in the plot for those two (the line for "Overall Mean" on the other hand is wanted): library(ggplot2) ddf <- data.frame(x = factor(rep(LETTERS[1:2], 5)), y = rnorm(10)) p <-

Hi everybody, I want an x-axis which has xlim=c(max, min) rather than xlim=c(min, max) in order to reflect the type of the process (cooling): library(lattice) myprepanel <- function(x,y,...) list(xlim=rev(range(x))) x <- rep(1:10, 100) z <- factor(sample(10, 1000, T)) y <- rnorm(1000, x, as.numeric(z)) xyplot(y~x|z, scales=list(x="free"), prepanel=myprepanel) This works

Dear all, Today I figured out that there is a neat function called droplevels, which, well, drops unused levels in a data frame. I tried the function with some of my data sets and it turned out that not only the unused levels were dropped but also the contrasts I set via "C". I had a look into the code, and this behaviour arises from the fact that droplevels uses simply factor to drop

Hi everybody, how is it possible to refer to an argument passed to a function in the function call? What I like to do, is something like f <- function(x,y) x+y f(2, x) # should give 4 The problem is of course that x is only known inside the function. Of course I could specify something like f(z<-2,z) but I'm just curious whether it is possible to use a fancy combination of eval,

If I have a class Foo, then i can write an S3 method for sum for it: >setClass("Foo",representation(a="integer"));aFoo=new("Foo",a=c(1:3,NA)) >sum.Foo <- function(x,na.rm){print(x);print(na.rm);sum(x at a,na.rm=na.rm)} >sum(aFoo) But how do I write an S4 method for this? All my attempts to do so have foundered. For example

Dear all, I am developing a package, which bundles my most frequently used functions. One of those is a modified version of droplevels from the base package (basically, it preserves any contrast function which was used to create the factor, contrast matrices are not kept, for they could be wrong if a level is dropped). In my NAMESPACE file I've the following directives, which should export

Hi everybody, I'd like to have a list with two elements, where both elements have the same value: z <- list(a=1, b=1) If it happens, that I've to change the value, I've to assure that I change both. This is error prone. Hence, a better way to achieve this is to define: tmp <- 1 z <- list(a=tmp, b=tmp) Now, I'm wondering if it is possible to make this more compact: z

Dear list, yet another question on S4 method behaviour or rather its side effects: Consider the following example (see below) where a new S4 method 'predict' is defined which by doing so makes 'predict' a S4 standardGeneric. It works and simple test examples run but I'm cautious whether this is a 'silly' thing to do. Is this a dangerous thing to do, already known to

High everybody, currently I'm writinig a package that, for a given family of variance functions depending on a parameter theta, say, computes the extended quasi likelihood (eql) function for different values of theta. The computation involves a couple of calls of the 'glm' routine. What I'm doing now is to call 'lapply' for a list of theta values and a function, that

Dear all, Is there a reason, why there is no +-operator for aes (i.e. uneval) objects (as there is for themes and gg objects)? I had a couple of cases where such an operator would be useful, for instance to combine the result of aes and aes_string in functions. Any flaws with the following proposition: `+.uneval` <- function(e1, e2) { dup <- names(e1) %in% names(e2) if (any(dup)) {

I'd like to have a class A that computes a likelihood, and a subclass B that computes the same likelihood by sometimes throws in an additional term (B includes measurement error). So B's likelihood needs to call A's, and then (sometimes) multiply by an additional term. It sounds as if, in the S3 scheme, NextMethod is supposed to do this: like.A <- function(stuff) compute value

My simple functions were to compare the result with the gfortran compiler sum() function. I thought that the Fortran sum could not be less precise than R. I was wrong. I am impressed. The R sum does in fact match the result if we use the Kahan algorithm. P. I am glad to see that R sum() is more accurate than the gfortran compiler sum. On 16/03/18 11:37 AM, luke-tierney at uiowa.edu wrote:

Dear R People: In the function "decompose", the object has the class of "decomposed.ts". (from package stats) I would like to see the class definition and the method for the plotting. However, when I use isClass("decomposed.ts") I get "FALSE". When I check getMethods("plot") there is no method for plot on decomposed.ts Any suggestions,

What a delicious example! (I'm taking the liberty of sharing it with r-devel, since it raises some good issues.) You have two questions, presumably: 1 - how could the order of the setMethod calls make a difference in the results? 2 - what's causing the infinite loop & how could it be avoided, reliably? Second question first. The danger sign is the "vector" method:

Hello everybody, Currently I'm developing a library, which uses some functions from another package (namely plotrix). Consequently, I listed this dependency in the DESCRIPTION file. When I try to run "R CMD check mypackage", the check fails, for the library plotrix cannot be found on the system. As far as I understood "R CMD check" uses --vanilla implicitly, thus, no