similar to: turning data with start and end date into daily data

Dear everybody, I have the following challenge. I have a data set with 2 subgroups, dates (days), and corresponding values (see example code below). Within each subgroup: I need to aggregate (sum) the values by week - for weeks that start on a Monday (for example, 2008-12-29 was a Monday). I find it difficult because I have missing dates in my data - so that sometimes I don't even have the

Hello! I have my data frame "mydata" (below) and data frame "reference" - that contains all the dates I would like to be present in the final data frame. I am trying to merge them so that the the result data frame contains all 8 dates in both subgroups (i.e., Group1 should have 8 rows and Group2 too). But when I merge it it's not coming out this way. Any hint would be

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

Hello! my data set has many variables. Unfortuantely, many of those variables contain spaces in their names. I need advice on: how to refer to variable names in the formula for "aggregate". See example below: ### Generating example data set: mydate = rep(seq(as.Date("2008-12-01"), length = 3, by = "month"),4) value1=c(1,10,100,2,20,200,3,30,300,4,40,400)

Hello! I have a data frame with dates. I need to create a new "month" that starts on the 20th of each month - because I'll need to aggregate my data later by that "shifted" month. I wrote the code below and it works. However, I was wondering if there is some ready-made function in some package - that makes it easier/more elegant? Thanks a lot! # Example data:

Hi, all Here I created a data frame like mydates<- seq(as.Date("2010-05-29"), length = 43, by = "day") myvalues<-runif(43,0,1) myframe<-data.frame(dates=mydates, day=weekdays(dates), value=myvalues) dates day value 1 2010-05-29 Saturday 0.14576143 2 2010-05-30 Sunday 0.37669604 3 2010-05-31 Monday 0.74813943 4 2010-06-01 Tuesday

(Sorry for reposting. Please delete previous msgs. Thanks!) Hi, all Here I created a data frame like mydates<- seq(as.Date("2010-05-29"), length = 43, by = "day") myvalues<-runif(43,0,1) myframe<-data.frame(dates=mydates, day=weekdays(dates), value=myvalues) dates day value 1 2010-05-29 Saturday 0.14576143 2 2010-05-30 Sunday 0.37669604

Hello, everyone! I have a very simple task - I have a data frame (see MyData below) and I need to stack the data (see result below). I wrote the syntax below - it's very basic and it does what I need. But I am sure what I am trying to do is a very typical task and there must be a much shorter/more elegant way of doing it. Any advice? Thank you very much!

Dear R'ers, I am fighting with a problem that is driving me crazy. I use "lm" in my user-defined function, but it seems to be looking for weights outside of my function's environment: ### Generating example data: x<-data.frame(y=rnorm(100,0,1),a=rnorm(100,1,1),b=rnorm(100,2,1)) myweights<-runif(100) data.for.regression<-x[1:3] ### Creating function

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

Hello! I am trying to create a formula object using as.formula. But it's not working: examplephraze<-"for.my.example" myformula<-as.formula(paste(examplephraze,"~group, sum, data=mydata",sep="")) What's the problem? Thanks a lot! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

I have a data set with about 6 million rows and 50 columns. It is a mixture of dates, factors, and numerics. What I am trying to accomplish can be seen with the following simplified data, which is given as dput output below. > head(myData) mydate gender mygroup id 1 2012-03-25 F A 1 2 2005-05-23 F B 2 3 2005-09-08 F B 2 4 2005-12-07 F B 2

Dear list, I have a problem with defining a function (see below) to read my testfile (see testfile). My function only returns mydata I wish to work with attr(mydata, 'fc') as well (for labelling a plot). Principally it works if I do not insist on this function but it would be much easer if it is possible to return mydata AND attr(mydata, 'fc') by using a function. 1) testfile:

# I have a simple function problem. I thought that I could write a function to modify a couple of vectors but I am doing something wrong #I have a standard cost vector called "fuel" and some adjustments to the #costs called "adjusts". The changes are completely dependend on the length #of the dataframe newdata I then need to take the modifed vectors and use # them later. I

I've run the function randomForest with importance=T. All my variables (predictors and the dependent variable) are numeric. rf<-randomForest(formula, data=mydata, importance=T, etc.) my results object "rf" contains predictor importances: rf$importance I am seeing two columns: %IncMSE IncNodePurity V1 -0.01683558 58.10910 V2 0.04000299 71.27579 V3 0.01974636

Dear R experts, I got help to build a loop but there is a bug inside it that causes one part of the mechanism to fail. It should grow once, but if keep growing on rows where $ti_all is not NA. Here is a wall of code that very crudely demonstrates the problem, there is a couple of dim() outputs at the end where you can see how it the second time around keeps adds (2) rows, but this does not

Hello all, I have generated a principal components regression model using the pcr() function from the PLS package (R version 2.12.0). I am getting a "non-conformable arguments" error when I try to use the predict() function on new data, but only when I try to read in the new data from a separate file. More specifically, when my data looks like this #########training data

Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm): logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y))

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

Dear All,I encountered a problem when I use RWeka for prediction. Specifically, I use the following: res=J48(X1~.,data=mydata); predict(res), #it worked fine but when I tried to use a different data set, i.e. predict(res,newdata=mynewdata); all the predictions I get is 0, which apparently is problematic. What is weird is, if I use the old data, but use the newdata option, i.e.