[R] Splitting data frame into columns with dplyr

I have a data frame with a structure similar to the following. The variable z is a grouping variable; x and y are measurement variables. library(dplyr) df <- data.frame(z = rep(c("A", "B")), x = 1:6, y = 7:12) %>% arrange(z) z x y 1 A 1 7 2 A 3 9 3 A 5 11 4 B 2 8 5 B 4 10 6 B 6 12 I nee

[R] Omitting NA's using dcast (reshape2 package)

I'm using the "dcast" function from Hadley's "reshape2" package to do some tabulations. I can't get it to exclude NA's in the variables being tabulated. Here's a simple example. v1 <- c(rep("A", 5), rep("B", 5), NA) v2 <- c("X", "Y", "Y", "Z", "Z", "X", "Y", "Y", "Z", NA, "Z") v3 <- c(rep("a", 4)

[R] Flat tables for confidence intervals with "survey" package

I'm preparing some reports for substate regions from BRFSS survey data. I can get estimates easily enough, but am having problems putting the results in convenient form. Here's some code using the New Hampshire portion of the public BRFSS "SMART" data: library(foreign) library(survey) # download

[R] Using "survey" package with ACS PUMS

I'm trying to reproduce some results from the American Community Survey PUMS data using the "survey" package. I'm using the one-year 2012 estimates for New Hampshire (http://www2.census.gov/acs2012_1yr/pums/csv_pnh.zip) and comparing to the estimates for user verification from http://www.census.go

Re: [R] Using "survey" package with ACS PUMS

Setting the options did the trick. The code using "Fay" came from another post, but this works: options( "survey.replicates.mse" = TRUE) pums_p.rep <- svrepdesign(repweights = pums_p[207:286], weights = ~PWGTP, combined.weights = TRUE,

[R] Using calibrate for raking (survey package)

I'm studying the calibration function in the survey package in preparation for raking some survey data. Results from the rake function below agree with other sources. When I run calibrate, I get a warning message and the M and F weights seem to be reversed. Even allowing for that, the deviation bet

Re: [R] Excluding all missing values with dcast ("reshape2" package)

Thanks for the response. It turns out this will work if the data include no other variables with missings; unfortunately, that was the case. I was successful using xtabs with the extra step of converting the returned table to a data frame. Thanks again! -M.L. Hi, ?na.omit() dat1<-read.table(text=

Re: [R] Excluding all missing values with dcast ("reshape2" package)

Thanks for the response. The drop option of dcast was the first thing I tried. Sorry for the lack of reproducibility, but my data set is large, I couldn't find anything in the package examples, and I had hoped the modification would be straightforward. Using dcast was my first choice because it ret

[R] Excluding all missing values with dcast ("reshape2" package)

Hello--I'm doing a simple crosstab using dcast: rawfreq <- dcast(nh11brfs, race3~CHCCOPD, length) with the results race3 Yes No NA 1 White non-Hispanic 446 5473 21 2 Other non-Hispanic 29 211 0 3 Hispanic 6 81 1 4 10 83 1 How would I modify

[R] Using extract function for dates in sqldf

I'm trying to use sqldf to query for the earliest date of a blood test when patients have had multiple tests in a given year. My query looks like this: test11 <- sqldf("select CHILD_ID, min(SAMP_DATE) from lab group by CHILD_ID having extract (ye

[R] Applying function to only numeric variable (plyr package?)

My data frame consists of character variables, factors, and proportions, something like c1 <- c("A", "B", "C", "C") c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N")) x <- c(0.5234, 0.6919, 0.2307, 0.1160) y <- c(0.9251, 0.7616, 0.3624, 0.4462) df <- data.frame(c1, c2, x, y) pct <- function(x) roun

[R] Model fit using mcexact from exactLoglinTest

I'm using the mcexact function from the exactLoglinTest package on data comparing performance of rapid and laboratory tests for detection of H1N1 flu. My setup is as follows: ridt.res <- c("A-B-", "A+B-", "A-B+") pcr.res <- c("Negative", "AH3", "B") xtab <- expand.grid(ridt = ridt.res, pcr = pcr.

[R] Packages for quasi-symmetry and quasi-independence

Are there any packages with functions that can fit quasi-symmetry and quasi-indepedence models to square contingency tables? M. Laviolette __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide

[R] Overlaying maps

off() only the last map appears, the previous ones having been cleared. Can someone clarify? Thanks, Michael Laviolette PhD MPH New Hampshire Department of Health and Human Services __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/

[R] Risk differences with survey package

I'm trying to use the survey package to calculate a risk difference with confidence interval for binge drinking between sexes. Variables are X_RFBING2 (Yes, No) and SEX. Both are factors. I can get the group prevalences easily enough with result <- svyby(~X_RFBING2, ~SEX, la04.svy, svymean, na.rm