similar to: lattice: overlap histogram

Dear R users, Suppose I have the following data.frame: myID myType myNum1 myNum2 myNum3 a Single 10 11 12 b Single 15 25 35 c Double 22 33 44 d Double 4 6 8 and I want to have new records: myID myType myNum1 myNum2 myNum3 e Single 12.5 18

Hi, I am trying to aggregate the sum of my test data.frame as follow: testDF <- data.frame(v1 = c("a", "a", "a", "a", "a", "b", "b", "b", "b", "b", "c", "c", "c", "c", "c", "d", "d", "d", "d",

Hi, I have newbie question. Suppose I have the following data: temp <- data.frame(type1 = c("male", "female", "male", "female", "female"), type2 = c("low", "med", "high", "low", "med"), a = c(1,2,4, NA, 3), b = .... [TRUNCATED] temp type1 type2 a b c 1 male low 1 5 0 2 female

hello, i am trying to extract text using regex as follows: "* < <* this is my text > > " into: "this is my text" below what I did: varReg <- "* < <* this is my text > > " ## either this pattern patReg <- "(^[ <*]+)" ## or below patten patReg <- "([ > ]+$)" sub(patReg, '', varReg) depending

Dear R-Users, Suppose I have data in the following format: CODE_NAME ZIP_CODE John 12345 John 23456 John 34567 Jane 13242 Jane 22123 I want to transpose / convert it into: CODE_NAME ZIP_CODE John 12345,23456,34567 Jane 13242,22123 Any idea/pointer is appreciated.

Any thoughts on the following I'd be most grateful - I'm sure there is an easy and quick way to do this but I'm having a mental block this evening. Essentially, I'm trying to replace missing data in my dataset with reference values based on age and sex. So an example dataset is set.seed(1) X = data.frame(age=rnorm(10, 50, 10), sex=rbinom(10, 1, 0.5), A=rnorm(10), B=rnorm(10))

Hi, I have the following type of data: myData <- data.frame(x = 1:5, y = letters[1:5], xDate = seq(as.Date("2001/2/1"), as.Date("2005/2/1"), by="year") ) > myData x y xDate 1 a 2001-02-01 2 b 2002-02-01 3 c 2003-02-01 4 d 2004-02-01 5 e 2005-02-01 What I need is a new column, say xDate2, that for each xDate (or for each unique combination of

Hi, I am trying to display / print certain columns in my data frame that share certain condition (for example, part of the column name). I am using for loop, as follow: # below is the sample data structure all.data <- data.frame( NUM_A = 1:5, NAME_A = c("Andy", "Andrew", "Angus", "Alex", "Argo"), NUM_B = 1:5, NAME_B =

With R 1.8.1 running in Fedora Core 1 Linux, I am having some trouble recoding and ploting some factor variables. First, can I give you some example data? Here is a column of names for age groups: agegroups <- c( "15-19", "20-24", "25-29","30-34", "35-39",

Dear R users, How to change lattice panel label/text from the automatically generated label (based on the conditioning) to our own set of label? for example: someStuff <- data.frame(area = rep(c("SOUTH", "NORTH", "EAST", "WEST"), each = 25), group = rep(c("A","B","C","D"), each = 5),

Hey y''all, Are any folks planning on taking the ferry from Seattle to Vancouver (via Victoria)? I''ve never done it before so I have no idea how much of an adventure it is. Flights are a $100+ less to Seattle than Vancouver so the price works out the same or a bit less. My plan is to fly to Seattle (from Savannah, GA) on Tuesday to catch the early ferry on Wednesday

Hi I would like to superimpose group-specific densityplots on top of an overall histogram using panel.histogram and panel.densityplot. Furthermore, I would like to automatically adjust the range of the y-axis to take into account the ranges of both histogram and densityplot. This last part is where I have a problem. I believe using the prepanel argument of histogram is typically the way to go,

Hello! I am trying to run a GLMM using LME4, and keep getting the warning message: "In mer_finalize(ans) : false convergence (8)" I am quite new to R, and in looking into this thus far, it appears that there are a variety of reasons why this might occur, such as needing to standardize some parameters or if all subjects in one combination of parameters all have the same outcome. I also

Hello everyone, I use Bob Muenchen's approach for reading in "in-stream" (to use SAS parlance) delimited data within a script. This works great: mystring <- "id,workshop,gender,q1,q2,q3,q4 1,1,f,1,1,5,1 2,2,f,2,1,4,1 3,1,f,2,2,4,3 4,2, ,3,1, ,3 5,1,m,4,5,2,4 6,2,m,5,4,5,5 7,1,m,5,3,4,4 8,2,m,4,5,5,5" mydata <- read.table( textConnection(mystring),

Is there any function like par(new=T) for lattice. I want to plot a histogram in percentages on the right hand side and also superimpose the densityplot with its density scale on the lhs. so far I am only able to do this histogram( temp[,2]~ temp[,1],nint=100,type="desnity", xlab = "Population Size", panel = function(x, ...) {

Hi, I have what I believe is a repeated-measures dataset that I'm trying to analyze using lme(). This is *not* homework, but an exercise in my trying to self-teach myself repeated-measure ANOVA for other *real* datasets that I have and that are extremely similar to the following design. I'm fairly sure the dataset described below would work with lme() -- but it'd be great if anybody

Dear Members, Greetings! I have come across a discrepancy shown by R and SAS results on same data for logistics regression.. When I processed the above csv file(1000.csv) for predicting the Action (i/c) by Age Group(1-7,Na) and Gender(M,F,Na) with GLM of R I get: R result Call: glm(formula = Action ~ Gender + AgeGroup, family = binomial, data = mydata1, na.action = na.pass) Deviance

Dear R gurus... How do I control "smoothing" of a density plot in panel.densityplot when using histogram? Thanks much, Santosh [[alternative HTML version deleted]]

Hi, Is there a package I can use to write to multiple sheets on xls file, other than using the paid version of xlsReadWrite package (xlsReadWritePro)? Thank you, Ferry

Hello. I am attempting to duplicate a negative binomial regression in R. SAS uses generalized estimating equations for model fitting in the GENMOD procedure. proc genmod data=mydata (where=(gender='F')); by agegroup; class id gender type; model count = var1 var2 var3 /dist=NB link=log offset=lregtm; repeated subject=id /type=exch; run; Since my dataset has several observations for