search for: tract

I am using the code below to extract census tract information. save.tract$state, save.tract$county and save.tract$tract are returned as factors. In the last three statements, I need to save the actual value of the factor, but, instead, the code is yielding the position of the factor. How do I instead return the value of the factor? B...

I have a multilevel dataframe (df): ID Date Segment Slice Tract Lesion 1 CSPP005 12/4/2007 1 1 LCST 0 2 CSPP005 12/4/2007 1 1 LPC 2 3 CSPP005 12/4/2007 1 1 RPC 3 4 CSPP005 12/4/2007 1 1 RCST 1 5 CSPP005 12/4/2007 1 1 LGM 0 6 CSPP005 12/4/200...

I'm trying to generate 50+ graphs using the UScensus2000tract data. I need to access the data for just about all of the states, so I was hoping to create a simple loop that will take the relevant state from my data and load the associated census data from the UScensus2000tract package. Below is a sample of what I'm trying to do. Any suggestions are mu...

...ALSE 1 Alberta Alberta.1 132 -110.36250 60.00006 2 FALSE 1 Alberta Alberta.1 132 -110.35103 60.00006 3 FALSE 1 Alberta Alberta.1 132 and the data set "tract" is the map data without the "Number" variable. I looked into working this out from by researching the geom_map function here: http://ggplot2.tidyverse.org/reference/geom_map.html my code looks like this: # get the raw map data can_map <- getData('GADM', country = "CA...

Hello, I have a bunch of street addresses that I want to obtain the census tract data for. Is there any way I can do this in R? Thank you, -- Michael Leitson Michael.Leitson at gmail.com

...more efficient way to do it with cumsum(), though. Cheers, Bert On Sat, Oct 14, 2023 at 12:53?AM Jason Stout, M.D. <jason.stout at duke.edu> wrote: > > This seems like it should be simple but I can't get it to work properly. I'm starting with a data frame like this: > > Tract Pct Totpop > 1 0.05 4000 > 2 0.03 3500 > 3 0.01 4500 > 4 0.12 4100 > 5 0.21 3900 > 6 0.04 4250 > 7 0.07 5100 > 8...

Does anyone know the appropriate R code to obtain census tracts (tract and block level) from addresses by using Zack Almquist's USCensus2000 package? Thank you, -- Michael Leitson Michael.Leitson at gmail.com

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of three. eg. Call: glm(formula = inj ~ lp + rdm, family = poisson(), data = ww, offset = log(pop)) Deviance Residuals: Min 1Q...

This seems like it should be simple but I can't get it to work properly. I'm starting with a data frame like this: Tract Pct Totpop 1 0.05 4000 2 0.03 3500 3 0.01 4500 4 0.12 4100 5 0.21 3900 6 0.04 4250 7 0.07 5100 8 0.09 4700 9 0.0...

Can anyone point me at a good doc on farting about with the registry? Or do you feel like giving me the fool's guide? I have large tracts of registry entries to stuff in, and adding them via Edit/new in regedit is an absolute PITA and unworkably slow. I have files from exported from a win98 registry (via windoze regedit) with the correct info. I need to add because programs are not finding installed packages. TIA, -- Declan Mo...

...e ppp files from csv data and map shape files, but I am getting some errors which I have been unable to fix by searching them online: library(spatstat) library(maps) library(maptools) NYC2<-readShapePoly("nybb.shp") # this is a map of the NYC boroughs without waterways and no census tract divisions (but it does include lines separating the 5 boroughs) plot(NYC2) NYBorough<-readShapePoly("NYBoroughsShapeMerge.shp") # this is a map of the NYC boroughs with census tract divisions plot(NYBorough) myfile<-read.csv("myfile.csv") # my csv file contains 4 columns...

...lection and BIC criterions, to the Boston housing > data. >  The Boston housing data contains 506 observations, and is publicly available in the R package mlbench (dataset “BostonHousing”).  The response variable Y is the median value of owner-occupied homes (MEDV) in each of the 506 census tracts in the Boston Standard Metropolitan Statistical Areas, and there are thirteen predictor variables.  We are interested in the relationship between MEDV and the other predictor variables. >  Setup your parametric model and use rank-based regression methodology to > select and estimate the p...

...status after encoding? It would be nice if we could leave all the raw data processing within Speex, all we're missing is a way to determine if a packet contained enough energy to warrant transmission. Finally - has anyone looked at or be able to point me to papers on integration of vocal tract modeling into CELP systems? I'm very interested in reproducing some of the intra-CODEC features we had at OnLive that allowed primitive mouth placement from the audio stream. Its on our long term target list, and I was curious if anyone had any suggestions... Thanks! <p>--- >8 -...

...nWzpCfBz6aP0YhCGemy-bP6xEtJEwobdDQ$ > On 2023-10-13 4:13 p.m., Jason Stout, M.D. wrote: > > Caution: External email. > > > > > > This seems like it should be simple but I can't get it to work properly. I'm starting with a data frame like this: > > > > Tract Pct Totpop > > 1 0.05 4000 > > 2 0.03 3500 > > 3 0.01 4500 > > 4 0.12 4100 > > 5 0.21 3900 > > 6 0.04 4250 > > 7...

...zohDtpWBBey7cb53uSXIPTTqs5fgaz-BKlNnWzpCfBz6aP0YhCGemy-bP6xEtJEwobdDQ$ On 2023-10-13 4:13 p.m., Jason Stout, M.D. wrote: > Caution: External email. > > > This seems like it should be simple but I can't get it to work properly. I'm starting with a data frame like this: > > Tract Pct Totpop > 1 0.05 4000 > 2 0.03 3500 > 3 0.01 4500 > 4 0.12 4100 > 5 0.21 3900 > 6 0.04 4250 > 7 0.07 5100 > 8...

...h it is typical to see one peak resonance for the fundamental frequency (typically 50-440Hz) and one additional peak per 1000Hz. From this we can see that the best order for an LPC speech model is based upon the bandwidth of the sampled audio. In speex linear prediction is used to model the vocal tract of the speaker. For narrow-band mode we low-pass filter the speech to 4000Hz. Using our knowledge of speech we choose a suitable model of 5 poles (1 pole for the fundamental frequency and 4 poles for the vocal tract). Since each pole requires 2 coefficients we arrive at 10 LPC coefficients. For...

...2 + V3 + V4 + (1 + V1 + V2|GROUP)) Can I do something like this, though correctly without getting the error ("Error in max(kindex) : object "kindex" not found")? Mod2=coxme(Surv(YDAYS, Y) ~ V1 + V2 + V3 + V4, data=ds1, random=~1 + V1 + V2|GROUP The GROUP variable is a census tract, while V1 & V2 are individual-level characteristics of people. Thanks in advance. Mark Powers mark.powers at illinois.gov

...Steve, I'll post your question also to the R-help mailing list since I think this can help to other RArcInfo users. As I mentioned in the last release of the package, I have a tutorial nearly finished. I hope to release it in the next days. > I have downloaded an .e00 file of 2000 Census Tracts for the southern US > State of Georgia, and I'm wondering which steps I need to go through to use > RArcInfo on the data. If I can get to a plot today I'll be happy. I've looked for the data you mentioned and I have downlowaded from http://www.census.gov . It's a nice set...

Dear Jason, The code could look something like: dummyData = data.frame(Tract=seq(1, 10, by=1), ?? ?Pct = c(0.05,0.03,0.01,0.12,0.21,0.04,0.07,0.09,0.06,0.03), ?? ?Totpop = c(4000,3500,4500,4100,3900,4250,5100,4700,4950,4800)) # Define the cutoffs # - allow for duplicate entries; by = 0.03; # by = 0.01; cutoffs <- seq(0, 0.20, by = by) # Create a new column with cutof...

...is O(n^2). Jeff's approach using findInterval may be faster. Of course implementation details matter. -- Bert On Mon, Oct 16, 2023 at 4:41?AM Leonard Mada <leo.mada at syonic.eu> wrote: > > Dear Jason, > > The code could look something like: > > dummyData = data.frame(Tract=seq(1, 10, by=1), > Pct = c(0.05,0.03,0.01,0.12,0.21,0.04,0.07,0.09,0.06,0.03), > Totpop = c(4000,3500,4500,4100,3900,4250,5100,4700,4950,4800)) > > # Define the cutoffs > # - allow for duplicate entries; > by = 0.03; # by = 0.01; > cutoffs <- seq(0, 0.20, by = by)...