similar to: importing ascii grids (for gstat)

Hello, Could someone please tell if there is a way to append header info to the top of an exported dataframe (exported with write.table). I want to append the following, which are the defininitions for an asciigrid: ncols 532 nrows 999 xllcorner 510465 yllcorner 4766375 cellsize 30 NODATA_value -9999 Thanks very much, femke [[alternative HTML version deleted]]

Dear All, I am a research student in environment. I have only little programming knowledge. I am currently doing the last project about rainfall impact on ground water quality in an area. It happens that I have to use R to read rainfall data (3 dimension) from ASC file (*.asc), and then write them into one NCDF file (*.nc). I have been working very hard on study R, but I

Dear R-help list, I'm trying to overlay a number of data objects in a plot. Following an earlier example on the list I've created an empty plot as follows > xlim <- range(as.numeric(c("0","10000"))) > ylim <- range(as.numeric(c("0","25")) ) > plot(NA, xlim=xlim, ylim=ylim, xlab="distance", ylab="semivariance")

Hello, I'm an R newbie and was wondering whether there are R commands for iterating over files in a directory. Basically what I want to do is to iterate over many files and apply some R functions to each file seperately. e.g. for (each file in a directory) do { some R calculation with the info in that file } Could someone please give me a pointer to how (or if) this might be done with R

Greetings List, I'm having some trouble with the use of the names function in a formula. Below is an example of something that works (i.e first line), and the second line is the same formula which doesn't. I want to be able to reference the column of the dataC table so I can run the variogram iteratively with a loop. > v<-variogram(A1~1,loc=~x+y, dataC) >

Dear useRs, I have the following plot: pos <- c(27/44, 11/32, 8/40, 4/42, 3/40, 4/40, 2/40) tmin <- c(15.8, 12.6, 10.5, 2.4, 5.2, 8.5, 7.9) plot(tmin, pos) I would like to fit an exponential curve to it. How could I be able to do this? Thanks in advance, Rafael. ____________________________________________________________________________________ [[elided Yahoo spam]]

I have a set of time-series climate data with missing entries. I need to add rows for these missing entries to this data set. The only way I know to do this is unsing a foreloop, but this won't work on a list. I've tried to convert the list to a data frame, but that won't happen, either. I want to fill rows in this table: > newtest[10:15,] yrmos yearmo snow.sum snow.mean

Hello everyone, I am working for a few days already on a basic algorithm, very common in applied agronomy, that aims to determine the degree-days necessary for a given individual to reach a given growth stade. The algorithm (and context) is explained here: http://www.oardc.ohio-state.edu/gdd/glossary.htm , and so I implemented my function in R as follows: DD <- function(Tmin, Tmax, Tseuil,

I am trying to run the following code in R on a Linux cluster. I would like to use the full processing power (specifying cores/nodes/memory). The code essentially runs predictions based on a GAM regression and saves the results as a CSV file for multiple sets of data (here I only show two). Is it possible to run this code using HPC packages such as Rmpi/snow/doParallel? Thank you!

Estimados erreros, Estoy intentando entender como calcula el paquete dismo ( https://cran.r-project.org/web/packages/dismo/index.html) un coeficiente de variación. Os pongo un ejemplo: tmin <- c(10,12,14,16,18,20,22,21,19,17,15,12) # temperatura mínima media mensual de un año tmax <- tmin + 5 # temperatura máxima media mensual de un año prec <- c(0,2,10,30,80,160,80,20,40,60,20,0)

Dear all, I have weekly data by city (variable citycode). I would like to take the average of the previous two, three, four weeks (without the current week) of the variable called value. This is what I have tried to compute the average of the two previous weeks; df = df %>% mutate(value.lag1 = lag(value, n = 1)) %>% mutate(value .2.previous = rollapply(data = value.lag1,

Hola, en la misma definici?n de la funci?n: # P15. Precipitation Seasonality(Coefficient of Variation) # the "1 +" is to avoid strange CVs for areas where mean rainfaill is < 1) p[,15] <- apply(prec+1, 1, cv) Un saludo, Jorge On Martes, 19 de Junio de 2018 13:07:27 Marcelino de la Cruz Rot escribi?: > Hola Jaume: > > Si miras el c?digo de biovars() ver?s que la

Hi, I'm pretty new to R and have run into a task which although I'm certain is within R's capabilities, falls outside of mine. :-) Consider the following data set: 2012-07-22 12:12:00, 21 2012-07-22 12:15:00, 22 2012-07-22 12:18:00, 24 2012-07-22 12:39:00, 21 2012-07-22 12:45:00, 25 2012-07-22 12:49:00, 26 2012-07-22 12:53:00, 20 2012-07-22 13:00:00, 18 2012-07-22 13:06:00, 22 My

Hi, I am using mgcv:gam and have developed a model with 5 smoothed predictors and one factor. gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + s( Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts") +factor(site),data=dat3) The total deviance explained = 70.4%. I would like to extract the variance explained

I have one data frame with a column of dates and I want to fill another data frame with one column of dates, one of years, one of months, one of a unique combination of year and month, and one of days, but R seems to have some problems with this. My initial data frame looks like this (ignore the NAs in the other fields): > mans[1:10,] date loc snow.new prcp tmin snow.dep tmax 1

Hi R bayesians, I need an advise how to resolve the two different estimates applying a traditional glm (TG) and a bayes glm (BG), and different results depending on the data formats of response data and the prior specs using bayesglm in R. I'm not familiar with bayes estimate and my colleague asked me to look into this because the EPA from France reported a quite different estimates for

Dear fellow R-users I am trying to do some imputation using K-NN with the yaImpute library. All seems to be going well until I try to use AsciiGridImpute. All my data are correctly formatted and I am able to run and view the results of yai. Below is my code: **************************************************************************************************** library("yaImpute") data

Is there a package in R that can do a variability plot? A variability plot is a kind of categorized dot plot. (If there is a lot of data in each category, box plots are used rather than dot plots.) Usually, the categories are factor level combinations. All the dot plots appear in the same window; below the x-axis a hierarchy of factors shows which dot plot corresponds to which factor-level

Message: 63 Date: Wed, 26 Jan 2005 04:28:51 +0000 (UTC) From: Gabor Grothendieck <ggrothendieck at myway.com> Subject: Re: [R] chron: parsing dates into a data frame using a forloop To: r-help at stat.math.ethz.ch Message-ID: <loom.20050126T052153-333 at post.gmane.org> Content-Type: text/plain; charset=us-ascii Benjamin M. Osborne <Benjamin.Osborne <at> uvm.edu>

I have a series of nertCDF files containing global data for a particular variable, e.g. tmin/tmax/precipiation/windspeed/relative humuidity/radiation etc. I get the following information when using *nc_open* function in R: datafile: https://www.dropbox.com/s/xpo7zklcmtm3g5r/gfdl_preci.nc?dl=0 File gfdl_preci.nc (NC_FORMAT_NETCDF4_CLASSIC): 1 variables (excluding dimension variables):