similar to: Finding Highest value in groups

Since the aggregate S3 method for class formula already has got na.action = na.omit, ## S3 method for class 'formula' aggregate(formula, data, FUN, ..., subset, na.action = na.omit) I think that to deal with NA's, it is enough: aggregate(Value~ID, dta, max) Moreover, passing na.rm = FALSE/TRUE is "don't care": aggregate(Value~ID, dta, max, na.rm=FALSE)

Assuming your dataframe is in a variable x: > require(dplyr) > x %>% group_by(ID) %>% summarise(maxVal = max(Value,na.rm=TRUE)) On Fri, 2016-04-22 at 13:51 +0000, Saba Sehrish via R-help wrote: > Hi > > > I have two columns in data frame. First column is based on "ID" assigned to each group of my data (similar ID depicts one group). From second column, I

Hi I have two columns in data frame. First column is based on "ID" assigned to each group of my data (similar ID depicts one group). From second column, I want to identify highest value among each group and want to assign the same ID to that highest value. Right now the data looks like: ID Value 1 0.69 1 0.31 2 0.01 2 0.99 3 1.00 4 NA 4

$ R --version R 1.6.1 (2002-11-01). So I would like to perform principal components analysis on a 16X16 correlation matrix, [princomp(cov.mat=x) where x is correlation matrix], the problem is princomp complains that it is not non-negative definite. I called eigen() on the correlation matrix and found that one of the eigenvectors is close to zero & negative (-0.001832311). Is there any way

Dear list, I have observed a weird behaviour from R --- apologies if I am missing something obvious! df3f826f28 df3f826f28 Say you type in R: >c.preec <- 10074 >c.gd <- 2200 >p1 <- .2 >c.neo <- p1*9451 + (1-p1)*3883 >n.preec <- 3710 >n.gd <- 2650 >n.neo <- 2120 >n.pcos <- 53000 >unit.met <- 94 >cost.met <- 94*n.pcos >effect <-

Hi, LLVM 2.1-pre1 test results: Linux (SUSE) on x86 (P4) Release mode, but with assertions enabled LLVM srcdir == objdir # of expected passes 2250 # of expected failures 5 I ran the llvm-test suite on my desktop while I was also working on that PC, so don't put too much trust in the timing info. Especially during the "spiff" test the machine was swapping

Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release

Dear All, I am working on a large matrix of dimension 20000x700 say 'mat'. I have calculated pearson correlation for the rows of the matrix and their p-values using rcorr function in library Hmisc. Now I wish to filter out those pairs who's PCC value is above 0.8 cut off and p-value is less than 0.05. >library(Hmisc) >mat_cor=rcorr(t(mat),type="pearson")

Below are the results for the Polyhedron 2005 benchmarks compiled with the llvm/compiler-rt/dragonegg 3.3 release and 3.4svn against FSF gcc 4.8.2. The *-stock-de runs omit the -fplugin-arg-dragonegg-enable-gcc-optzns flag and the *-de-optnz runs include it. There seems to be little improvement between llvm 3.3 and 3.4 in the stock case which relies entirely on llvm for vectorization. Duncan, has

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.

Hello, Darik Horn sent a pull request adding support for LZ4. LZ4 is supposed to be a very fast compression algorithm, especially when it comes to decompression. I did a quick benchmark with zlib, LZO and LZ4. Now, there are many benchmarks you can find online, but most of them deal with compressing large files. Tinc on the other hand has to compress small packets individually. So I did the

On Oct 20, 2009, at 6:02 AM, Duncan Sands wrote: > Hi Tanya, > >> 1) Compile llvm from source and untar the llvm-test in the projects >> directory (name it llvm-test or test-suite). Choose to use a pre- >> compiled llvm-gcc or re-compile it yourself. > > I compiled llvm and llvm-gcc with separate objects directories. > Platform is x86_64-linux-gnu. > Ok.

Hi I have a sorted array ( in ascending order) and I want to find the subscript of a number in the array which is the the next highest number to a given number. For example,if I have 67 as a given number and if I have a vector x=c(23,36,45,62,79,103,109), then how do I get the subscript 5 from x (to get 79 which is the next highest to 67) without using a for loop? Thx -- 'Raghu'

I am using the package hdrcde to get the highest density region. I have the data from an unknown distribution. And I used the subroutine hdr from the package to get the highest density region. But I always got a error message. I do not know why. Who can help?Thanks! The codes look like this: j is the data set, > hdr(j) Error in bw.SJ(x) : no solution in the specified range of bandwidths

I am looking for simple introduction to cluster analysis using R, that would be understandable to a novice in statistics. Or, could someone perhaps help me understand how to proceed in my analysis? I am very new to both statistics and R, but am trying hard to avoid having to use SPSS as everyone around me... I have dataset on people presenting their opinions on different religious

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Greetings.? My wife is teaching an introductory stat class at UC Davis.? The class emphasizes the use of simulations, rather than mathematics, to get insight into statistics, and R is the mandated tool.?? A student in the class recently inquired about different approaches to sampling from a binomial distribution.? I've appended some code that exhibits the idea, the gist of which is that using

Full_Name: Michael Chajewski Version: 2.9.0 OS: Windows XP Submission from: (NULL) (150.108.71.185) I was programming a routine which kept reducing the array from which a random sample was taken, resulting in a single number. I discovered that when R attempts to sample from an object with only one number it does not reproduce/report the number but instead chooses a random number between 1 and

I have a more complicated function I am trying to write, but I run in to a problem when I want to add something to the plot from more than one data set while simultaneously controlling the appearance of the additional layer. # Toy data: foo <- data.frame ( x = 1:4, y = 4:1 , membership = c( "A", "A", "B", "B" ) ) bar <- data.frame ( x = 1:4 + 1 , y

I have a problem with R. I try to compute the confidence interval for my df. When I want to create the plot I have this problem: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ. I try this code: library(dplR) df.rwi <- detrend(rwl = df, method = "Spline",nyrs=NULL) write.table(df.rwi,file="rwi.txt",quote=FALSE,row.names=TRUE)