search for: adpli

Dear friends - I have a data.frame with "," instead of "." and found the discussion http://r.789695.n4.nabble.com/How-to-replace-all-commas-with-semicolon-in-a-string-tt4721187.html#a4721192 so copying the code of Ulrik(I hope:-)) I tried (making some data) AX <-

Hello R-helpers, A fundamental question ...I'm trying to understand the differences between loop and vectorization ... I understand that it should be a natural choice to use apply / adply when it is needed to perform the same function across all rows of a data frame. Any pointers on why this is so? Unable to find the right reading place on the WWW which explains the concept. Thanks for your

And as follow up, fff<-function(x) gsub(",", ".", x) BX <- apply(apply(AX, 2, fff), 2, as.numeric) this seems to be easier. Cheers Petr S pozdravem | Best Regards RNDr. Petr PIKAL Vedouc? V?zkumu a v?voje | Research Manager PRECHEZA a.s. n?b?. Dr. Edvarda Bene?e 1170/24 | 750 02 P?erov | Czech Republic Tel: +420 581 252 256 | GSM: +420 724 008 364 petr.pikal at

Hi list, In a function I am writing, I need to extract the dimension names of an array. I know this can be acheived easily using dimnames() but my problem is that I want my function to be robust when the number of dimensions varies. Consider the following case: foo <- array(data = rnorm(32), dim = c(4,4,2), dimnames=list(letters[1:4], LETTERS[1:4], letters[5:6])) # What I want is to extract

Hi All! I have some experience with R, but less experience writing scripts using R and have run into a challenge that I hope someone can help me with. I have multiple .csv files of data each with the same 3 columns of data, but potentially of varying lengths (some data files are from short measurements, others from longer ones). One file for example might look like this... Time, O2_conc,

Greetings all, I am curious to know if either of these two sets of code is more efficient? Example1: ## t-test ## colA <- temp [ , j ] colB <- temp [ , k ] ttr <- t.test ( colA, colB, var.equal=TRUE) tt_pvalue [ i ] <- ttr$p.value or Example2: tt_pvalue [ i ] <- t.test ( temp[ , j ], temp[ , k ], var.equal=TRUE) ------------- I have three loops, i, j, k. One to test the all of

Hi. I have 2 tables, with same dimensions (8000 x 5). Something like: tab1: V1 V2 V3 V4 V5 14.23 1.71 2.43 15.6 127 13.20 1.78 2.14 11.2 100 13.16 2.36 2.67 18.6 101 14.37 1.95 2.50 16.8 113 13.24 2.59 2.87 21.0 118 tab2: V1 V2 V3 V4 V5 1.23 1.1 2.3 1.6 17 1.20 1.8 2.4 1.2 10 1.16 2.6 2.7 1.6 11 1.37 1.5 2.0 1.8 13 1.24 2.9 2.7 2.0 18 I need generate a table of averages, the

plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together. It's already possible to do this with split and the apply functions, but plyr just makes it all a bit easier with: * consistent names, arguments and outputs * input from and output to data.frames,

plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each pieces and then put all the pieces back together. It's already possible to do this with split and the apply functions, but plyr just makes it all a bit easier with: * consistent names, arguments and outputs * input from and output to data.frames,

Dear all, I have a question regarding the possibility of parallel computation in plyr version 1.7. The help files of the following functions mention the argument '.parallel': ddply, aaply, llply, daply, adply, dlply, alply, ldply, laply However, the help files of the following functions do not mention this argument: ?d_ply, ?aply, ?lply Is it because parallel computation is not

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

# plyr plyr is a set of tools for a common set of problems: you need to __split__ up a big data structure into homogeneous pieces, __apply__ a function to each piece and then __combine__ all the results back together. For example, you might want to: * fit the same model each patient subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise

Hi, I have a three dimensional array, e.g., my.array = array(0, dim=c(2,3,4), dimnames=list( d1=c("A1","A2"), d2=c("B1","B2","B3"), d3=c("C1","C2","C3","C4")) ) what I would like to get is then a dataframe: d1 d2 d3 value A1 B1 C1 0 A2 B1 C1 0 . . . A2 B3 C4 0 I'm sure there is one function to do

Dear helpers, I'm using plyr to process a large matrix for the first time. My code is set up to work with matrixes, since I learned the hard way that dataframes are considerably slower to process. I started using aaply(), but the data was rearranged from a flat matrix to a [, , 4] array for larger input matrixes. I'm sure something clever is happening that I'm just not seeing -

Hello All, Would like to be able to convert a matrix to a dataframe without losing an assigned dimname. Here is an example that should illustrate what I'm talking about. tableData <- state.x77[c(7, 38, 20, 46), c(7, 1, 8)] names(dimnames(tableData)) <- c("State", "") tableData State Frost Population Area Connecticut 139 3100 4862

Dear Group, I have a following dataset: > a A B C D 1 22 3 31 40 2 26 31 36 32 3 3 7 49 16 4 24 40 27 26 5 20 45 47 0 6 34 43 11 18 7 48 48 24 2 8 3 16 39 48 9 20 49 7 21 10 17 36 47 10 > dput(a) structure(list(A = c(22L, 26L, 3L, 24L, 20L, 34L, 48L, 3L, 20L, 17L), B = c(3L, 31L, 7L, 40L, 45L, 43L, 48L, 16L, 49L, 36L), C = c(31L, 36L, 49L, 27L, 47L, 11L, 24L,