similar to: Reshape or Stack? (To produce output as columns)

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

Dear all, I have a grid of 720 columns by 360 rows of global population density values, and hope to convert this to column format using the 'melt' command in the 'reshape' package. I'm not receiving any errors as such, but when the code has finished running, my output looks like this: > head(PopDens.long) Latitude Longitude PopDensity 1 -84.75 V1 0 2

Dear R Users, I am using the reshape package to reformat gridded data into column format using the code shown below. However, when I display the resulting object, a single column is fomed (instead of three) and all the latitude values (which should be in either column one or two) are collected at the bottom. Also, the NA values aren't removed, despite this being requested in the code. Code:

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 am attempting to add row and column names to a series of tables (120 in total) which have 2 variable parts to their name. These tables are created as follows: # Create table indexes index <- expand.grid(year = sprintf("%04d", seq(1986, 1995)), month = sprintf("%02d", 1:12)) # Read in and assign file names to individual objects with variable name components

Dear all, I am attempting to perform what should be a relatively simple calculation on a number of data frame columns. I am hoping to find the average on a per-row basis for each of the 50 columns. If on a particular row a 'NA' value is encountered, then this should be ignored and the mean formed on the basis of the other rows. However, I'm finding that the end result for each row

Dear all, I have a data frame of dimension 720 columns by 360 rows, to which I am trying to add numerical row and column labels to, using the 'sequence' command. The original data, which I read in using 'read.table', had no such labels at all. I've got as far as successfully using the sequence command and getting the labels to display. However, I'm finding that for the

Dear all, I have two files, both of similar formats. In column 1 are Latitude values (real numbers, e.g. -179.25), column 2 has Longitude values (also real numbers) and in one of the files, column 3 has Population Density values (integers); there is no column 3 in the other file. However, the main difference between these two files is that one has fewer rows than the other. So what I'm

Hej, i need the which() funktion to find the positions of an entry in a matrix. the entries i'm looking for are : seq(begin,end,0.01) and there are no empty spaces i'm searching in the right range. so i was looking for the results R can find and i recieved this answer. for (l in

Dear sir,    How to do bilinear time series in R?Is there any functions or packages?  thank you! -----Sincerely yours Kuangnan Fang 方匡南 敬上 department of statistics ,Economics school,Xia men University. Fujian Province (361005) China Mobile Phone：15860721915 SKYPE: ruiqwy MSN Messenger: ruiqwy@hotmail.com QQ:39863401 --- 09年3月31日，周二, r-help-request@r-project.org

Hello, I'm a new R user and I'm having a little trouble getting started. I'm hoping someone can help me out. I read numbers (integer) from a SQL database. Some calculations as mean()are possible but hist() give an error. Here are the commands: library(RMySQL) Loading required package: DBI > con <- dbConnect(dbDriver("MySQL"), dbname = "test") >

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the

Hi Everyone, I have a dataset like this mean sd 0% 25% 50% 75% 100% n BODY TEMPERATURE 36.41099 0.4015699 35.1 36.22222 36.5 36.66667 37.1 89 DIASTOLIC BLOOD PRESSURE 73.60079 9.4656186 50.0 67.00000 73.0 80.00000 95.0 253 HEIGHT 171.94000 9.2011670 153.5 166.50000 173.0 176.25000 190.0

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

Hi, Is the http://xenbits.xensource.com/linux-2.6-xen.hg tree still being updated? if not, what''s the preferred Linux tree to track that has all of the Xen bits? Thanks, Muli _______________________________________________ Xen-devel mailing list Xen-devel@lists.xensource.com http://lists.xensource.com/xen-devel