similar to: Why are these different?

I'm having a problem reading data to set control totals for a dataframe. I want to adjust a dataframe based on a 2-d table of values, which I get by using : > CurrentX1Sums <- as.matrix(xtabs(~tripid_nu+lineon, data=SurveyData)) > CurrentX2Sums <- apply(CurrentX1Sums, 1, sum) I've created a .csv file with new (target) sums that looks like this: tripid_nu Warner

I want to calculate "expansion factors" for elements in my dataframe based on a 2-d cross classification. Since I'll have "missing values" (many combinations will have no record) I'll need a second "expansion factor" for each "row". I've included my "work to date" below, but I'm not very close to getting this right. My

I have a dataframe with many hundreds of survey records containing: tripid_nu, lineon, MuchOtherData, X1, X2, X3, X4 tripid_nu = identifier for each "Trip" {E.g. for EB MTA-901 leaving the first stop at 07:24} lineon = identifier for each of the stop locations Where X1 is the ratio (for each trip/stop combination) of some target number to the number of records.

I'm trying to learn how to calibrate/postStratify/rake survey data in preparation for a large survey effort we're about to embark upon. As a working example, I have results from a small survey of ~650 respondents, ~90 response fields each. I'm trying to learn how to (properly?) apply the aforementioned functions. My data are from a bus on board survey. The expansion in the

I am trying to combine two data sets, one with daily values and one with weekly values.  SurveyData conatins environmental data collected on a daily basis.  sat.data contains satellite sea surface temperature that is an average of satellite measurements over a six day period.  I would like to combine the two files so that my output file has the daily dates from SurveyData and the weekly average

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...

I have two problems with svyhist. I hope this is easy, and it may be that "simple R syntax" will do what I need. 1) I'm able to get a single plot out, but I need to put two distributions in the same graphic to visually compare them. 2) I get uniform breaks at intervals of 10. I can plot the unweighted data using MyBreaks with hist as shown in the second code snippet

I can't figure out the syntax I need to get subset to work. I'm trying to split my dataframe into two parts. I'm sure this is a simple issue, but I'm stumped. I either get all or none of the original "rows". > XTTable <- xtabs( ~ direction_ , SurveyData) > XTTable direction_ EASTBOUND

I'm unable to open an SPSS file over my network. If I copy it to my local C:/ drive I can read it. I saved the command (in a "crib sheet" text file) in order to avoid all the typing, so I'm pretty sure I've done it before. I verified that the file I'm trying to read is OK. This is what happens: > SurveyData <-

I'd like to take a different approach to the old question of dataset variable labels. Support for their use in output is low. For example, among packages that provide tables of model estimates only 'stargazer' provides support for these labels (apologies if I missed support from aprstable, memisc, texreg, xtable). Part of the lack of support could be that variable labels are not

hello, I am trying to do exploratory factor analysis with BRFSS dataset ( http://www.cdc.gov/brfss/technical_infodata/surveydata/2008.htm) for a couple of days, but I was not able to do that and got frustrated. Can anybody help me with step by step guide? BRFSS dataset provides ASCII or SAS format. Thank you. chloe [[alternative HTML version deleted]]

Basic question: when I use names() to extract the name of a dataframe element, why does it have "." instead of " " between words? Context: I'm importing a CSV file of survey results for analysis. I read them like this: df <- read.csv("surveydata.csv",nrows=40,header=TRUE,

Changing the vapply() behavior makes sense in principle. I analyzed the CRAN code base using the R parser and found 143 instances of calling vapply with USE.NAMES=FALSE. These would need to be inspected to understand the consequences of the change. For reference: /AzureML/R/datasets.R:226 /BBmisc/R/toRangeStr.R:33 /DBI/R/DBDriver.R:205 /Kmisc/R/str_rev.R:37 /Matrix/R/diagMatrix.R:98