search for: dapli

Dear list, I get some strange results with daply from the plyr package. In the example below, the average age per municipality for employed en unemployed is calculated. If I do this using tapply (see code below) I get the following result: no yes A NA 36.94931 B 51.22505 34.24887 C 48.05759 51.00198 If I do this using daply: municipality no yes

Dear all Is it possible to set globally the option .progress = "text" to all the apply functions in 'plyr'. For example, current default is daply(..., .progress = "none"). I would like to set it to daply(..., .progress = "text"), so as to avoid writing the argument every time I call such a function. I looked into ?daply and ?create_progress_bar without much

I?m wondering which is the most efficient (time, than memory usage) way to obtain a multivariate time series object from a data frame (the easiest data structure to get data from a database trough RODBC). I have a starting point using timeSeries or xts library (these libraries can handle time zones), below you can find code to test. Merging parallelization (cbind) is something I?m thinking at

Hi all, I have read posts of heat map creation but I am one step prior -- Here is what I am trying to do and wonder if you have any tips? We are trying to map sequence reads from tumors to viral genomes. Example input file : 111 abc 111 sdf 111 xyz 1079 abc 1079 xyz 1079 xyz 5576 abc 5576 sdf 5576 sdf How may xyz's are there for 1079 and 111? How many abc's, etc?

I retrieve for a few hundred times a group of time series (10-15 ts with 10000 values each), on every group I do some calculation, graphs etc. I wonder if there is a faster method than what presented below to get an appropriate timeseries object. Making a query with RODBC for every group I get a data frame like this: > X ID DATE VALUE 14 3 2000-01-01 00:00:03 0.5726334

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,

Thanks to all. I summarized (in order to thank the list) the solutions to help future workers searching subjects like this at R help.   # Number of rows nr = 10 # Data set dataf = as.data.frame(matrix(c(rnorm(nr),rnorm(nr)*2,runif(nr),sort(c(1,1,2,2,3,3,sample(1:3,nr-6,replace=TRUE)))),ncol=4)) names(dataf)[4] = "class" #----------------------------------------------------- #Solution 1:

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 is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

plyr is a set of tools for a common set of problems: you need to break down a big data structure into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to: * fit the same model to subsets of a data frame * quickly calculate summary statistics for each group * perform group-wise transformations like scaling or standardising *

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 transformations

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 transformations

Hi, I am an R novice and I am trying to do something that it seems should be fairly simple, but I can't quite figure it out and I must not be using the right words when I search for answers. I have a dataset with a number of individuals and observations for each day (7 possible codes plus missing data) So it looks something like this Individual A, B, C, D Day1 1,1,1,1 Day 2 1,3,4,2 Day3

Hello again, Ley say I have 1 matrix and 1 data frame: > mat <- matrix(1:15, 5) > match_df <- data.frame(Seq = 1:5, criteria = sample(letters[1:5], 5, replace = T)) > mat [,1] [,2] [,3] [1,] 1 6 11 [2,] 2 7 12 [3,] 3 8 13 [4,] 4 9 14 [5,] 5 10 15 > match_df Seq criteria 1 1 c 2 2 e 3 3 c 4 4 c 5

Dear R-experts Execuse me for an easy question, but I need help, sorry for that. >From days I have been working with a large dataset, where operations are needed within a component of dataset. Here is my question: I have big dataset where x1:.....x1000 or so. What I need to do is to work on 4 consequite variables to calculate a statistics and output. So far so good. There are more vector

Dear R gurus, I regularly come across a situation where I would like to apply a function to a subset of data in a dataframe, but I have not found an R function to facilitate exactly what I need. More specifically, I'd like my function to have a context of where the data it's analyzing came from. Here is an example: ### BEGIN ### func<-function(x){ m<-median(x$x) if(m > 2 &

Hello- A basic question which has nonetheless floored me entirely. I have a dataset which looks like this: Type ID Date Value A 1 16/09/2020 8 A 1 23/09/2010 9 B 3 18/8/2010 7 B 1 13/5/2010 6 There are two Types, which correspond to different individuals in different conditions, and loads of ID labels (1:50)

As Jim has indicated, memory usage problems can require very specific diagnostics and code changes, so generic help is tough to give. However, in most cases I have found the dplyr package to be more memory efficient than plyr, so you could consider that. Also, you can be explicit about only saving the minimum results you want to keep rather than making a list of complete results and extracting

Hi. I searched the list and didn't found nothing similar to this. I simplified my example like below: #I need calculate correlation (for example) between 2 columns classified by a third one at a data.frame, like below: #number of rows nr = 10 #the third column is to enforce that I need correlation on two variables only dataf =