similar to: Filling a grid based on existing data

Hi all, I want to make all possible combination from dataset below: V1 <- c(0,1,2) V2 <- c(0,1) V3 <- c(0,1) V4 <- c(0,1) V5 <- c(0,1) V6 <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20) V7 <- c(0,1,2,3,4,5,6) V8 <- c(0,1) V9 <- c(0,1) V10 <- c(0,1) V11 <- c(0,1) V12 <- c(0,1) V13 <- c(0,1) V14

Hi everybody, I'm a new R french user. Sorry if my english is not perfect. Hope you'll understand my problem ;) I have to work on temperature data (35000 lines in one file) containing some missing data (N/A). Sometimes I have only 2 or 3 N/A following each other, but I have also sometimes 100 or 200 N/A following each other. Here's an example of my data, when I have only small gaps

Hi everybody, I have a little problem about filling some gaps of NAs in my data. These gaps are between nearly constant data (temperature under snow). Here's a fake example to illustrate how it looks like approximately: DF <- data.frame(data=c(-0.51,-0.51,-0.48,-0.6,-0.54,-0.38,-0.6,-0.42,NA,NA,NA,NA,NA,NA,NA, -0.25,-0.41,-0.5,-0.5,-0.35,-0.7,-1,-0.87)) I would like to replace my NAs

Dear all, I have a data frame of 18556 rows and 19 columns and wish to create a new grid from these data of dimensions 360 rows and 720 columns. The existing data frame is set up so that every 38 rows makes up one row of the new data frame, with 2 NA values at the end of each 'block' that should be removed before being inserted into the new grid. So to be clear, row 1 of the new data

Dear all, I have a grid (data frame) dataset at 0.5 x 0.5 degrees spatial resolution (720 columns x 360 rows; regular spacing) and wish to coarsen this to a resolution of 2.5 x 2.5 degrees. A simple calculation which takes the mean of a block of points to form the regridded values would do the trick. Values which should be excluded from the calculation are -9999 (unless all points within a block

Sadly the latest optimization broke completely everything. The asm code isn't gas compliant. the libFLAC linker script has a typo, disabling the asm optimization and/or altivec won't let a correct build anyway. Instant fixes for the asm stuff: sed -i -e"s:;:\#:" on the lpc_asm.s to load address instead of addis+ori you could use lis and la and PLEASE use the @l(register)

I'm trying to apply a function defined in the VW R docs, that attemps to convert a data.table object to Vowpal Wabbit format. In the process i'm getting the error in printf mentioned in the subject. The original function is here: https://github.com/JohnLangford/vowpal_wabbit/blob/master/R/dt2vw.R Below there is a small example that reproduces the error. The function works great with

Hi, I'm wondering if I can do this in R. I have a data set with questions (x-value) and respondents (y-value). If the respondent answered the question they get a 1 and if the respondent didn't answer the question they have a 0. Is there anyway where I can graph something similar to a grid where if a person answered a questions (value of 1) a cell gets shaded and if a person didn't

Hi all, I have some trouble reading data from a csv file. I used command "read.delim("clipboard") to read in the data. > aalpha.data <- read.delim("clipboard") > class(aalpha.data) [1] "data.frame" > dim(aalpha.data) [1] 8 25 > colnames(aalpha.data) [1] "X" "V1" "V2" "V3" "V4"

Hi Everyone, I have a some data from a sports gps device like the following: time latitude longitude altitude distance heartrate 1 1277648884 0.304048 -0.793819 260 0.000000 94 2 1277648885 0.304056 -0.793772 262 4.307615 95 3 1277648888 0.304060 -0.793696 263 11.262347 97 4 1277648894 0.304075 -0.793544 263 25.237911 103 5 1277648898

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Hi. I have a simple (I think) question My dataset have these variables: names(data) [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"

Hi, I'm having a problem using sample() within a function. Basically I get an error reading: Error in sample(v, 1, prob = h) : non-positive probability Can anyone advise me as to the possible origin of this error? Here is my code #Discretised Gillespie algorithm function (From SMfSB, D.J. Wilkinson) gillespied=function (N, T=100, dt=1, ...) { tt=0 n=T%/%dt x=N$M

Hi, Why does the upper left panel (in the plot below) not have a gray background? Cheers, Marius require(grid) require(gridBase) pdf(file="Rplot.pdf", width=8, height=8, onefile=FALSE) ## set up the grid layout gl <- grid.layout(5, 5, widths=unit(c(1.8, 8, 0.8, 8, 0.8), "cm"), heights=unit(c(0.8, 8, 0.8, 8, 1.5), "cm")) if(FALSE) grid.show.layout(gl)

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

Dear R Users, I have a data frame of 360 rows by 720 columns (259200 values). For each value in this grid I am hoping to apply an equation to, to generate a new grid. One of the parts of the equation (called 'p') relies on reading from a separate reference table. This is Table 4 at: http://www.fao.org/docrep/s2022e/s2022e07.htm#3.1.3%20blaney%20criddle%20method (scroll down a little).

Dear all, I'm having trouble interpolating a number of gridded datasets that I have. I'm quite new to R so any help/advice that can be offered would be much appreciated! Firstly I'll describe my dataset. The data is a grid of the planet at 1 degree spatial resolution, with each grid cell containing a value describing a particular variable (e.g. population density) for coordinate

Wouldn't it make sense to have this in the man page? The 8192-byte limitation for 'fmt' is mentioned but not this one. Thanks, H. On 08/25/2015 02:08 AM, Prof Brian Ripley wrote: > From the sources: > > #define MAXNARGS 100 > /* ^^^ not entirely arbitrary, but strongly linked to > allowing %$1 to %$99 !*/ > > > > On 22/08/2015 04:21, Martin

Dear All I want to chose just spacial columns in R. (read table) data1<- read.table("/home/Documents/data.txt",header=F,sep = "\t", as.is =F) data.2<-data1[sub.data[,1],] The dimension of data.2 is correct but my data are transformed NA head(data.2) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 NA <NA> NA NA NA

Dear all, I have a file which I've converted from NetCDF (.nc) to text (.txt) using ncdump in Unix (as I had problems using the ncdf package to do this). The first few rows (as copied and pasted from the Unix console) of the file appear as follows: _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _,