similar to: Calculating difference between values in data frame based on separate column

Hello, I am not entirely sure the subject line captures what I am trying to do, but hopefully this description of the problem will help folks to see my challenge and hopefully offer constructive assistance. I have an experimental setup where I measure the decrease in oxygen in small vials as an organism, such as an oyster, consumes the oxygen. Each vial is calibrated before the experiment and

Hi all, I have a data set that looks a bit like this. feed1 RFU Site Vial Time lnRFU 1 44448 1 1 10 10.702075 2 47521 1 1 20 10.768927 3 42905 1 1 30 10.66674 4 46867 1 1 40 10.755069 5 42995 1 1 50 10.668839 6 43074 1 1 60 10.670675 7 41195 1 1 70 10.626072 8 47090 1 2 10 10.759816 9 48100 1

Hi all, first of all thanks for the answer to my previous question on lattice some time ago. In particular to Patrick Connolly for advices on netiquette (I hope this time I'm doing right....) and to Deepayan Sarkar fro the help on lattice. Now, my nowaday problem. Please consider the following mydf<-cbind.data.frame( RESPONSE = c(rnorm(9,rep(2:4,each=3),10),

Hi all, I'm trying to work out the following problem, but I can't imagine how. I have the following (much reduced & oversimplified) dataset My.df <- cbind.data.frame(PPM=c(15.78, 15.81, 15.87, 15.83, 15.81, 15.84, 15.91, 15.90, 15.83, 15.81, 15.93, 15.83, 15.70, 15.92, 15.76, 15.81, 15.91, 15.75, 15.84, 15.86, 15.82, 15.79,

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit

##I am trying to test for fixed factor main effects in an unbalanced mixed effects model but when I fit the reduced model for "mic" factor effects, the extra degrees of freedom are being allocated to a nested term rather than the residuals. The model has inc, mic and spp are independent variables and vial nested within spp. inc and spp are already coded as factors since they were

I seem to be running into the same problem reported in https://stat.ethz.ch/pipermail/r-help/2010-November/258265.html I cannot seem to transform a dataset from long to wide using reshape2. Clearly I am missing something very simple but a look at the manual and the reshape paper in JSS does not suggest anything. Any advice would be welcome ===========================load

This is my reproducible example: example<-structure(list(SENSOR = structure(1:6, .Label = c("A", "B", "C", "D", "E", "F"), class = "factor"), VALUE = c(270, 292.5, 0, 45, 247.5, 315), DATE = structure(1:6, .Label = c(" 01/01/2010 1", " 01/01/2010 2", " 01/01/2010 3", " 01/01/2010

Seems like this should be easy but I'm struggling a bit. How do I rearrange a data frame to go from the first one to the second shown below ? State Date lbs TX 200701 400 TX 200702 650 TX 200703 950 TX 200704 1000 FL 200701 200 FL 200702 300 FL 200703 500 FL 200704 333 NJ 200701 409 NJ 200702 308 NJ 200703 300 NJ 200704 800 Date TX FL NJ 200701 400 200 409 200702 650

Hi, I've tried to look through all the previous related Threads/posts but can't find a solution to what's probably a simple question. ? I have a data frame comprised of three columns e.g.: ? ID1?ID2?Value a?b?1 b?d?1 c?a?2 c?e?1 d?a?1 e?d?2 ? I'd like to convert the data to a matrix i.e.: ? ?a b c d e a n/a 1 2 1 n/a b 1 n/a n/a 1 n/a? c 2 n/a n/a n/a 1 d 1 1 n/a n/a 2 e n/a n/a 1

Dear help list: I am trying to reshape a data frame from long to wide format and with a reduced variable list using reshape2. The original data frame format is: Site Obs_no LengthSite 1 Obs 1 10Site 1 Obs 2 13Site 1 Obs 3 14.........Site 2 Obs 1 5Site 2 Obs 2 7Site 2 Obs 3 9 Site and Obs_no are factors and Length is a numeric variable. There are 15

Hi Dennis, Thanks for your quick response and sorry for not being clear. That helped, but I need an actual matrix of e.g., 12 x 12 and those functions give me a matrix with only the "filled" locations. I need a 12 by 12 matrix with sums (0 if there's not data and the actual sum where there is) as follows: 1 2 3 4 5 6 7 8 9 10 11 12 1 0 0 0 . . . . . . 0 0 0 2 0 0 0 . 3 0

Hello, I'd like to transpose data to create an analysis-friendly dataframe. See below for an example, I was unable to use t(x) and I couldn't find a function with options like PROC TRANSPOSE in SAS. The ideal solution handles variable quantities of SITE - but beggars can't be choosers. :-) Thank you in advance, Mike ## INPUT DATA USER_ID<-c(1,1,1,2,2,2,3,3,4) SITE

> str(data) 'data.frame': 250 obs. of 3 variables: $ student: chr "A" "B" "C" "D" ... $ data : num 20.2 20.4 22.5 22.1 23.3 ... $ param : Factor w/ 4 levels "AGE","SCHOOL",..: 1 1 1 1 1 1 1 1 1 1 Hi , i would like to split the dataframe so that each level of param is a column At the end it should look like

This is my first excursion into using reshape2 and I want to ensure that the melt() function call is syntactically correct. The unmodifed data frame is organized this way: head(tds.anal) site sampdate param quant 1 UDS-O 2006-12-06 TDS 10800 4 STC-FS 1996-06-14 Cond 280 7 UDS-O 2007-10-04 Mg 1620 9 UDS-O 2007-10-04 SO4 7580 19 JCM-10B 2007-06-21 Ca 79 20

Hello, I have a dataset with student performance on a math test. There are multiple cases for each student (identified by id) and the concept as a variable. > rtest id test subject grade concept correct tested per_corr year 1 1 83 Mathema 8 8.2.D 1 1 100 2011 2 1 83 Mathema 8 8.3.A 1 2

Hi Hadley et all, I am struggling with reshape2 and melt works and melt_check (filtered151) seems fine My cast command was acast (filtered151, Time ~ Species ~ Number) > melt_check (filtered151) Using time, Species as id variables $id [1] "time" "Species" $measure [1] "Number" When I execute cast the data matrix is in the correct order but the data

I get the following message when using the reshape2 package line > tDat.m<- melt(Dataset) Using Item, Subject as id variables > tDatCast<- acast(tDat.m,Subject~Item) Aggregation function missing: defaulting to length Note Problem Statement- convert dataframe Subject Item Score 1 Subject 1 Item 1 1 2 Subject 1 Item 2 0 3 Subject 1 Item 3 1 4 Subject 2 Item 1 1 5

Hello, This is my first time trying to use plyr, and I'm getting nowhere. I have teacher ratings data (1:4), on 10 components, by external observers and internal observers, in schools in areas. I want to calculate the percentage of each rating given on each component, by each type of observer, within each school, within each area. The data look like this: unit area ext.obs rating comp 11

Hi all, I have played a bit with the "reshape" package and function along with "melt" and "cast", but I feel I still don't have a good handle on how to use them efficiently. Below I have included a application of "reshape" that is rather clunky and I'm hoping someone can offer advice on how to use reshape (or melt/cast) more efficiently. #For this