similar to: ragged data.frame? using plyr

I am working with a heat map, as in the REPREX below. The code works fine as long as "bigger numbers imply greener and smaller numbers imply redder". These are time series where bigger numbers are "better", like total employment for example. But I also have cases within the heat map where "bigger numbers imply redder and smaller numbers imply greener". These are

Peter Grayson wrote: > Can you elaborate on what kind of CPU vagaries come into play besides > endianess? One issue I found was the cpu behaviour when converting from float/double to int32 when the source float is outside the range of values that can be represented by the int32. For instance: float int32_ppc in32_x86 2147483649.0 2147483647 -2147483648

> One issue I found was the cpu behaviour when converting from > float/double to int32 when the source float is outside the range > of values that can be represented by the int32. > > For instance: > > float int32_ppc in32_x86 > 2147483649.0 2147483647 -2147483648 > 2147483648.0 2147483647 -2147483648 > 2147483647.0

How can I add an extra column containing the rank to a ragged array indexed by more than one grouping factors? E.g. with the barley dataset: How can I to add an additional column ``rank'' containing the rank of the ``yield'' of the different varieties in relation to the indices ``year'' and ``site'' to the barley dataframe? I achieved to calculate the ranks with:

Can anyone please suggest how I can print: a <- matrix(c( "Heading 1", "This is some info\nabout heading 1", "Heading 2", "This is some info\nabout heading 2", ), byrow=T, nrow=2) to look like: Heading 1 This is some info about heading 1 Heading 2 This is some info about heading 2 (if you're not using a fixed width

I wonder what's the right way in R to do the following -- placing objects of the same kind together in subarrays of varying length. Here's what I mean: > word <- c("a","b","c","d","e","f","g","h","i","j") > kind <- c(1,1,1,2,3,4,5,5,7,7) > d <-

Hi All, I'm wanting to convert a ragged list of values into a structured matrix for further analysis later on, i have a solution to this problem (below) but i'm dealing with datasets upto 1GB in size, (i have 24GB of memory so can load it) but it takes a LONG time to run the code on a large dataset. I was wondering if anyone had any tips or tricks that may make this run faster? Below is

On 5/3/07, Erik de Castro Lopo <mle+la@mega-nerd.com> wrote: > Peter Grayson wrote: > > Personally I think universal binaries are a bad idea. They have obviously served some purpose, but I tend to agree that the concept does not seem to scale well once outside the Apple microcosm. > For one of my projects, libsndfile, endian issues is not the only > think that breaks in

Dear R users, I am currently writing a MCMC algorithm for spatial Poisson model. One problem which I need to solve is as follows: I have a vector X[1:J] which describes characteristics of cells of a rectangular grid. Further, for each cell I have a list of adjascent cells adj[] and the vector listing numbed of adjascent cells num[]. Thus if I want to list cells adjascent to cell i I write

Hello, I have a big problem which I’m just not able to solve. I created the following mean value from the following dataset structure: Id |value 1 | 2 1 | 3 1 | 4 2 | 2 2 | 1 3 | 5 4 | 3 etc.|etc. with the command: mean_rating <- tapply(ratok$value, ratok$project_id , mean,simplify = FALSE) this gives me a ragged array: > mean_rating [1] $`14`

Hi and thanks in advance, I am fairly new with R so I hope this problem isn't too amateur. I have a vector of count data which correspond to vectors of date (%m/%d/%Y) and time of day (%H:%M:%S). I am trying to compute various statistics (e.g. daily max) by lumping the data together by day. I have been able to utilize tapply() and group the counts together, but with the method I use I end

Hello, I'm running an algorithm for graph structural cohesion that requires a depth-first search of subgraphs of a rather large network. The algorithm will necessarily be redundant in the subgraphs it recurses to, so to speed up the process I implemented a check at each subgraph to see if it's been searched already. This algorithm is very slow, and takes days to complete on a

Hi, I have a list made up of character strings with each item a different length (each item is between 1and 6 strings long). Is there a way to convert a "ragged" list to a matrix such that each item is its own row? Here is a simple example: a=list(); a[[1]] = c("a", "b", "c"); a[[2]] = c("d", "e"); a[[3]] = c("f",

I have a large dataset (~1 million rows) of three variables: ID (patient's name), DATE (of appointment) and DIAGNOSIS (given on that date). Patients may have been assigned more than one diagnosis at any one appointment - leading to two rows, same ID and DATE but different DIAGNOSIS. The diagnoses may change between appointments. I want to subset the data in two ways: - define groups