similar to: aggregate

Dear R users, I want to aggregate all *d *data from all combination of n *plots* taken by k. Thank very much! My data is like that: plot d 1 14 1 13 1 12 1 14 1 18 1 20 1 21 1 43 1 108 1 43 2 41 2 61 2 83 2 61 2 84 2 45 2 21 2 12 2 11 ... 100 10 100 12 -- --- Catalin-Constantin ROIBU Forestry engineer, PhD Forestry Faculty of Suceava

Dear R-Users, I have a big data set "mydata" with repeated observation and some missing values. It looks like the format below: userid sex item score1 score2 1 0 1 1 1 1 0 2 0 1 1 0 3 NA 1 1 0 4 1 0 2 1 1 0 1 2 1 2 NA 1 2 1 3 1

I have 2 dataframes. "mydata" contains numerical data. "mybys" contains information on the "group" each row of the data is in. I wish to aggregate each column in mydata using the corresponding column in mybys. Please see the example below. What is a more elegant or "better" way to accomplish this task? Thanks! mydata =

Hi everyone. I have these data : myData = data.frame(Name = c('a', 'a', 'b', 'b'), length = c(1,2,3,4), type = c('x','x','y','z')) which gives me: Name length type 1 a 1 x 2 a 2 x 3 b 3 y 4 b 4 z I would group (mean) this DF using 'Name' as grouping factor. However, I have a

I am calculating the mean of each column grouped by the variable 'id'. I do this using aggregate, data.table, and plyr. My aggregate results do not match the other two, and I am trying to figure out what is incorrect with my syntax. Any suggestions? Thanks. Here is the data. myData <- structure(list(var1 = c(31.59, 32.21, 31.78, 31.34, 31.61, 31.61, 30.59, 30.84, 30.98, 30.79, 30.79,

Hi, I am trying to find the min of day for each student in each year. Here is the dataset: date studentid year 1/1/05 6:07 AM 236 20082009 3/27/09 9:45 AM 236 20082009 4/29/09 8:44 AM 236 20082009 3/27/09 11:36 AM 310 20082009 4/1/09 10:43 AM 310 20082009 10/15/09 8:54 AM 310

Hello R users, Problem.......I do not understand how to use "aggregate","by", or the appropriate "apply" to perform a function on data with more than one factor on unbalanced data... I have a data frame in the long format that does not contain balanced data. The ID is a unique identifier corresponding to the experimental unit that will later be examined by ANOVA,

Hello, I'm cc'ing R-Help. Sorry but your question was asked 3.5 years ago, I really don't remember it. Can you please post a question to R-Help, with a reproducible example that describes your problem? Rui Barradas ? Citando catalin roibu <catalinroibu at gmail.com>: > Dear Rui, > ? > I helped me some time ago with a code..... regarding aggregated data >

Hello! I am aggregating using a formula in aggregate - of the type: aggregate(cbind(var1,var2,var3)~factor1+factor2,sum,data=mydata) However, I actually have an object (vector of my variables to be aggregated): myvars<-c("var1","var2","var3") I'd like my aggregate formula (its "cbind" part) to be able to use my "myvars" object. Is it

I have a data set(Bill) of with 1 variable (var1), with 100 obs that are in ascending order. I want to sample every 10 observations and save them in 10 different groups such as Group1 is obs 1-10 Group 2 is obs-11-20 and so on. First step is to subset them into the 10 groups, then calculate the mean of var1 for each of the 10 groups. Any help would be appreciated. Thanks.

Hello, I have some code that currently works fine and I am endeavoring to convert the major pieces of it into functions. This involves taking "hard coded" names of variables that are used in various places and figuring out how to abstract them out into functions where the arguments (i.e. a list of variables)?can be passed to the parent function and used within that function for various

I have a data set of many rows and many columns in which both the rows and the columns have associated grouping factors. Is there a way to do what 'aggregate' does but in the other dimension? The way I have been doing this is to use 'aggregate' on the data in the usual way and then rotate the result and apply 'aggregate' again. This works but is a little messy and I was

Hi, How are you? I have a quick question.... I have code that works perfectly with R desktop, but does not work with RWeb. Could you please tell me how to modify the code below so it will work with RWeb? #Read in txt file happyguys<-read.table("c:/test8.txt", header=TRUE, row.names=1) #Subset the txt file to only include certain values test<-subset(happyguys, GRADE == 7

Dear R users, A have a dataframe (matrix) with two collumns (plot, and diameter (d)). I want all diameters values for different combination of plots. For example I want all d values for all posible combination, 100C2 (all d values for plot 1 with all d values in the plot 2.......with all d values from plot 1 with all d values from plot 100, ...... with all d values from plot 99 with all d values

Hi everyone, I'm struggling with a little problem for a while, and I'm wondering if anyone could help... I have a dataset (from retailing industry) that indicates which brands are present in a panel of 500 stores, store , brand 1 , B1 1 , B2 1 , B3 2 , B1 2 , B3 3 , B2 3 , B3 3 , B4 I would like to know how many brands are present in each store, I tried: result <-

Dear R community Recently, dear Henrique Dallazuanna literally saved me solving one problem on data transformation which follows: (n_, _n, j_, k_ signify numbers) SOURCE DATA: id cycle1 cycle2 cycle3 ? cycle_n 1 c c c c 1 m m m m 1 f f f f 2 m m m NA 2

Hi All, I'm using the Hmisc summarize function and used list instead of llist to provide the by variables. It generated an error message. Is this a bug, or do I misunderstand how Hmisc works with lists? The program below demonstrates the error message. Thanks, Bob x<-1:8 group <- c(1,1,1,1,2,2,2,2) gender<- c(1,2,1,2,1,2,1,2) mydata<-data.frame(x,group,gender)

hello, can someone tell me how to generate the means for a data frame that looks like this? My data frame has many more variables, but I won't bother you with those; these are the one's that I'm interested in. Needless to say, z is the variable in which I'm interested. I'd like to find out the mean score of z for NDP managers, Conservative managers and Liberal managers

Dear all I'm trying to run a cluster analysis with R Here are the commands: mydata <- na.omit(matrix) # listwise deletion of missing mydata <- scale(matrix) # standardize variables fit <- kmeans(mydata, 8) # 8 cluster solution # get cluster means aggregate(mydata,by=list(fit$cluster),FUN=mean) # append cluster assignment mydata <- data.frame(mydata, fit$cluster)

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),