similar to: Create new data frame with conditional sums

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 cutoffs dummyData$Cutoff

If one makes the reasonable assumption that Pct is much larger than Cutoff, sorting Cutoff is the expensive part e.g O(nlog2(n) for Quicksort (n = length Cutoff). I believe looping 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

Well, here's one way to do it: (dat is your example data frame) Cutoff <- seq(0, .15, .01) Pop <- with(dat, sapply(Cutoff, \(p)sum(Totpop[Pct >= p]))) I think there must be a 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

Under the hood, sapply() is also a loop (at the interpreted level). As is lapply(), etc. -- Bert On Sun, Oct 15, 2023 at 2:34?AM Jason Stout, M.D. <jason.stout at duke.edu> wrote: > > That's very helpful and instructive, thank you! > > Jason Stout, MD, MHS > Box 102359-DUMC > Durham, NC 27710 > FAX 919-681-7494 > ________________________________ > From: John

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

That's very helpful and instructive, thank you! Jason Stout, MD, MHS Box 102359-DUMC Durham, NC 27710 FAX 919-681-7494 ________________________________ From: John Fox <jfox at mcmaster.ca> Sent: Saturday, October 14, 2023 10:13 AM To: Jason Stout, M.D. <jason.stout at duke.edu> Cc: r-help at r-project.org <r-help at r-project.org> Subject: Re: [R] Create new data frame with

Hi, I am looking for an elegant way to transform a vector into percentages of values that meet certain criteria. store<-c(1,1.4,3,1.1,0.3,0.6,4,5) # now I want to get the precentages of values # that fall into the categories <=M , >M & <=N , >N # let M <-.8 N <- 1.2 # In my real example I have many more of these cutoff-points # What I did is: out <- matrix(NA,1,3)

Guys.. I just noticed that my grandstream handytone 286 ata are having problems with voice cutoffs... We can listen to the person on the zap channel (x100p cards) without problems but they sometimes listen to us with cutoffs.. like "He ...lo..... ow...r.. you" and it comes and goes.. this doesnt happen all the time but often enough. Any ideas what might be happening or what do I need

Después del "gather()" puedes hacer un "arrange()" que es una ordenación. Y dentro de "arrange()" le indicas la variable por la que ordenas (no hacen falta comillas)... Lo ordenará alfabéticamente. Saludos, Carlos Ortega www.qualityexcellence.es El mar., 19 feb. 2019 a las 13:47, Antonio Rodriguez Andres (< antoniorodriguezandres70 en gmail.com>) escribió:

can someone email me a list of special spots in quality settings, or point me to a website that tells these settings? i know i may be confusing, i'm talking about like the jump between -q4.99 and -q5 because of the whole lossy/lossless channel coupling, and filesize jump...i also know somewhere above there there is different high and low frequency cutoffs, and a point where there IS NO

hello, I am using the ROC package to evaluate predictive models I have successfully plot the ROC curve, however ?is there anyway to obtain the value of operating point=optimal threshold value (i.e. the nearest point of the curve to the top-left corner of the axes)? thank you very much, jose daniel anadon area de ecologia universidad miguel hernandez espa?a

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

Dear R-users, I am trying to add some text in a textbox to all panels in the following example file. Using the panel-function, I can add a white rectangle with panel.rect but then I have to fit in the text into the box by hand and it will not automatically be centered. Does anyone know how to add the text centered with a white box around it automatically? Is there something like panel.textbox for

> gather(pobla, key = year, value = totpop, year60:year63) Country year totpop 1 Afghanistan year60 8996351 2 Albania year60 1608800 3 Algeria year60 11124888 4 Andorra year60 13411 Gracias Carlos Antonio On Tue, 19 Feb 2019 at 12:54, Carlos Ortega <cof en qualityexcellence.es> wrote: > Sí, tienes varias formas. > > Mira la función

I have population data (?totpopE?) at the census tract level (?GEOID?), which are nested within Precincts (?Precinct?). Please see below my data structure. I used the code to sum population data per precinct: inters <- inters %>% group_by(Precinct) %>% mutate(TotalPop = sum(totpopE) ) However, said code produced too large sums because each census tract (?GEOID?) has multiple

Hello, I am just a beginner of R 1.9.0. I try to construct a predictive score for the development of liver cancer in cirrhotic patients. So dependant variable is binanry (cancer yes or no). Independant variables are biological data. The aim is to find out a cut-off value which differentiate (theoratically) from normal to pathological state for each biological data. How can I step in procedue to

Hi, I am trying to use a CGI service (Pubchem PUG) via RCurl and am running into a problem where the data must be supplied via POST - but I don't know the keyword for the argument. The data to be sent is an XML fragment. I can do this via the command line using curl: I save the XML string to a file called query.xml and then do curl -d @query.xml

I needed to compute a complicated cross tabulation to show weighted means and standard deviations and the only method I could get that worked uses a series of nested for next loops. I know that there must be a better way to do so, but could use some assistance pointing the way. Here is my working, but inefficient script: library(Hmisc) rm(list=ls()) load('NHTS.Rdata') day.wt <-

I'm using the {lattice} "levelplot" function to make a (more or less) 2-d histogram, and for the most part it's working fine with my data. However, I can't get the color key to do what I need. I can give it labels and custom cutoffs, but my cutoff lines (and hence my labels) aren't evenly spaced, instead they're more-or-less logarithmic, starting at

Hi Carsten, On 02/11/2018 at 07:46 PM Carsten Bock wrote: > Hi, > > Lost percent (%).... Are you sure? I'm seeing here: ...........Receive......... .........Transmit.......... Count Lost Pct Jitter Count Lost Pct Jitter RTT.... 188K 0 0 0.000 188K 16641K 8809 0.000 0.026 => This doesn't sound reliable to me: there are 188K packets and 16641K