similar to: which data structure for a set of time series ?

Hello all, I'm trying to use the apply function on a data frame, by applying a function that takes a one row data.frame as argument . Here's the example : myfun = function(x) paste(x$f1 , x$f2) df = data.frame(f1 = c(1,4,10),f2 = "hello") apply(df,1,myfun) ==> Does not work (I get "character(0)" ) Though : myfun(df[1,]) works, and myfun(df) works as well. So if

Dear R help I was hoping you might be able to show me how to write a loop function take would ccomplish this task. library(prob) { a <- sample ( 1:20, 100, replace=T) b<-sample(5:24,100,replace=T) } dd <- data.frame(a,b) dd # code piece I am looking for if(subset(dd,c(1,23,ordered=F))is found))( print subset) else( continue evaluating subsets) subset(dd,isin(dd,c(1,23), ordered =

I'm doing these manipulations on the data frame and wondering why does R have to remember historical data on my operation and not just keep the needed info. Probably a basic fundamentals of the way R handles data .. Pls point me to the manual if possible .. I have this Index data: > head(NIFTY_INDX) Constituents.list.of.S.P.CNX.Nifty X X.1 X.2 X.3

Hi, I am an R novice working with financial data. I am developing a portfolio strategy evaluation technique to back-test the performance of our screens; checking how the screened stock would've performed over the period in question. I am using quantmod in R to download the historical data from yahoo and then analyzing it using PerformanceAnalytics. My problem is that, as our screens are done

I am trying to use if...else loop and have included a code snippet which I might like to expand. Maybe you could steer me in the right direction. library(stats) library(prob) { a <- sample ( 1:4,100, replace=T,prob=c(0.1,0.2,0.5,0.3)) b<-sample(3:6,100,replace=T,prob=c(0.2,0.2,0.2,0.4)) } dd <- data.frame(a,b) if (subset finds a vector) ( print that vector) (else

Hi R, Can I convert ISIN numbers into Bloomberg tickers in the RBloomberg package? BR, Shubha [[alternative HTML version deleted]]

Hi, I''m learning puppet as that is what they use at my current work, though that could change... Question 1: Last place of work, we wrote our own perl based system which was extremely simple and concise to drive - eg to distribute a file, we would put it in: <nfsdir>/noarch/dist/etc/syslog-ng/syslog-ng.conf/ # which means create a file /etc/syslog-ng/syslog-ng.conf on the

All, I'm relatively new to using R, having used it thus far for some simple statistics and plotting. However, I'm not new to programming by any measure. I've been looking at the various modules available for clustering, factor analysis, etc. and find that I need advice on which modules I should be focusing on and their application. I have a data set comprised of columns of both

Does anyone know of a test for stationarity of a time series, or like all ordination techniques it is a qualitative assessment of a quantitative result. Books, papers, etc. suggestions welcome. thanks Stephen -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

I've found RColorBrewer useful for its qualitative palettes, but wished that it could generate more than 12 qualitative palettes (e.g. with Set3). Any suggestions for alternative color palette generators that can handle e.g. 18 distinctive colors? (I'm aware of using rainbow(), but this doesn't generate enough distinct colors when the number of palettes is large). Thanks, Andrew

Hello, My name is Dontrece Smith. I am creating figures for my GAMs. I change my qualitative variables to 1 or 2 in my dataset, so I would be able to run my GAMs. However, R will only display plots for my quantitative variables and not my qualitative variables. Is there any way to fix this issue? I listed some of my code below: > library(mgcv) This is mgcv 1.7-13. For overview type

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

I am using the read.table function to load an Excel data set into R. It has a few variables with very long qualitative (free response typically in sentences) response that I would like to keep, but would like to limit the "length" of the response that R shows. Is there some sort of string or column width command I can include in the read.table function to limit the length of words used

Hi David, I tried the following and get the below error messages.... con = blpConnect(show.days="trading",na.action="previous.days",periodicity="da ily")# connecting Bloomberg > dat <- blpGetData(con,"US4009703799 Equity","PX_LAST",start=as.chron(as.Date("01/01/2005",

Hi! Apologies if this is not the correct place to ask. I am attempting a MFA analysis of a dataset based on wine chemical and sensory analysis, based on the STHDA tutorial [1]. (I am using this dataset here too, as an example dataset to work on without posting my actual data. I've tried this with both my data and the example data, with the exact same results.) The only issue I am having is

I'm a bit confused about the anova/aov functions. Both seem to rely on data models, where the relationship between the dependent variables and the independent variables can be expressed as a formula. In what I am trying to do, all of my independent variables are qualitative, not quantitative. For example, for each of two options, "option A" and "option B" I have

Hello. I have a data base with 50 qualitative variables and a lot of individuals. I try to estimate the links between one of these variables (landcover) and the 49 others (geomorphology, hydrography...). I want to use a "discriminant analysis on qualitative variables" (as DISQUAL in SPAD) or a " log-linear model ". Which R-Package(s) or other methods can you advise me.

When I load a table from a data source and run summary() on it, the summary gives me basic summary statistics I'm looking for, and it also discriminates between quantitative and qualitative data and summarizes them accordingly. For example, if I do this: mydata <- read.table("data.txt") summary(mydata) I would get output like this: > summary(mydata) County