search for: tdat

Hi all, I have a data frame tdat <- read.table(textConnection("A B C Y A12 B03 C04 0.70 A23 B05 C06 0.05 A14 B06 C07 1.20 A25 A23 A12 3.51 A16 A25 A14 2,16"),header = TRUE) I want match tdat$B with tdat$A and populate the column values of tdat$A ( col A and Col B) in the newly created columns (col D and col E)....

...output from your script A B C Y D E 1 A12 B03 C04 0.70 0 0 2 A23 B05 C06 0.05 0 0 3 A14 B06 C07 1.20 0 0 4 A25 A23 A12 3.51 1 1 5 A16 A25 A14 2,16 4 4 On Wed, Dec 13, 2017 at 4:36 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote: > Hello, > > Here is one way. > > tdat$D <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0) > tdat$E <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$C], 0) > > > Hope this helps, > > Rui Barradas > > > On 12/13/2017 9:36 PM, Val wrote: > >> Hi all, >> >> I have a data frame >> tdat &lt...

Hi Bill, I put stringsAsFactors = FALSE still did not work. tdat <- read.table(textConnection("A B C Y A12 B03 C04 0.70 A23 B05 C06 0.05 A14 B06 C07 1.20 A25 A23 A12 3.51 A16 A25 A14 2,16"),header = TRUE ,stringsAsFactors = FALSE) tdat$D <- 0 tdat$E <- 0 tdat$D <- (ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0)) tdat$E <- (ifelse(tdat$B...

Hello, Here is one way. tdat$D <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0) tdat$E <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$C], 0) Hope this helps, Rui Barradas On 12/13/2017 9:36 PM, Val wrote: > Hi all, > > I have a data frame > tdat <- read.table(textConnection("A B C Y > A12 B03 C04 0...

...0 0 > 2 A23 B05 C06 0.05 0 0 > 3 A14 B06 C07 1.20 0 0 > 4 A25 A23 A12 3.51 1 1 > 5 A16 A25 A14 2,16 4 4 > > > On Wed, Dec 13, 2017 at 4:36 PM, Rui Barradas <ruipbarradas at sapo.pt> > wrote: > > > Hello, > > > > Here is one way. > > > > tdat$D <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$B], 0) > > tdat$E <- ifelse(tdat$B %in% tdat$A, tdat$A[tdat$C], 0) > > > > > > Hope this helps, > > > > Rui Barradas > > > > > > On 12/13/2017 9:36 PM, Val wrote: > > > >> Hi all,...

...ey Folks, Could somebody help me rewrite the following code? I am looping through all records across 5 fields to calculate the cumulative percentage of each record (relative to each individual field). Is there a way to rewrite it so I don't have to loop through each individual record? ##### tdat is my data frame ##### j is my field index ##### k is my record index ##### tsum is the sum of all values in field j ##### tmp is a vector containing the values in field j ##### tdat[k,paste("cpct,j,sep="")] creates new fields "cpct1",...,"cpct5" for(j in 1:5)...

Hi All, I have a sample of data set show as below. tdat <- read.table(textConnection("stat year Y AL 2003 25 AL 2003 13 AL 2004 21 AL 2006 20 AL 2007 12 AL 2009 16 AL 2010 15 FL 2006 63 FL 2007 14 FL 2007 25 FL 2009 64 FL 2009 47 FL 2010 48 NY 2003 50 NY 2004 51 NY 2006 57 NY 2007 62 NY 2007...

... and similar to Jim's suggestion but perhaps slightly simpler (or not!): > cross <- xtabs( Y ~ stat + year, data = tdat) > keep <- apply(cross, 1, all) > keep <- names(keep)[keep] > cross[keep,] year stat 2003 2004 2006 2007 2009 2010 AL 38 21 20 12 16 15 NY 50 51 57 98 183 230 > ## for counts just do: > xtabs( ~ stat + year, data = tdat[tdat$stat %in% keep, ])...

...AL 2 1 1 NY 1 1 2 Thank you again. On Thu, May 17, 2018 at 8:48 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > ... and similar to Jim's suggestion but perhaps slightly simpler (or not!): > > > cross <- xtabs( Y ~ stat + year, data = tdat) > > keep <- apply(cross, 1, all) > > keep <- names(keep)[keep] > > cross[keep,] > year > stat 2003 2004 2006 2007 2009 2010 > AL 38 21 20 12 16 15 > NY 50 51 57 98 183 230 > > > > > ## for counts just do: > > xt...

I get the following message when using the reshape2 package line > tDat.m<- melt(Dataset) Using Item, Subject as id variables > tDatCast<- acast(tDat.m,Subject~Item) Aggregation function missing: defaulting to length Note Problem Statement- convert dataframe Subject Item Score 1 Subject 1 Item 1 1 2 Subject 1 Item 2 0 3 Subject 1 Item 3 1 4...

...but It doesn't seem to work for this particular function. It overlays the old plot rather than plotting as an inset. Here is a simple version of what I'm trying. Note that if I change the inset plot to be an ordinary scatter, for instance, it works as expected. library(ade4) library(vcd) tdat <- data.frame(x=runif(20), y=rlnorm(20), z=rlnorm(20)) insetPlot <- function(data){ ternaryplot(data) } ternaryplot(tdat) add.scatter(insetPlot(tdat), posi="topleft", ratio=.2) Thanks very much, Jennifer Young Fisheries and Oceans Canada Jennifer.Young@dfo-mpo.gc.ca [[alterna...

...concerning the npudens function in the np package. I am trying to find a kernel density function of a multivariate dataset and the density evaluated at each of the 176 points. I have 2 continuous and 3 ordered discrete variables. My sample size is 176. So edata is a 176x(2+3) data frame, while tdat is a 1x(2+3) vector. bw_cx[i,] is a 1x (2+3) vector representing the bandwidths h for each variable, which were calculated using the npcdensbw function. (no this is not a mistake, I deliberately use the conditional one) For this I use the below function in R. kerz <- npudens(bws=(bw_cx[i,])...

...g the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1) Variance StdDev (Intercept) 0.1829931 0.427777 Residual 1.3907732 1.179310 > 0.18299/(0.18299+1.39077) [1] 0.1162757 Using the bootstrap library, I can set up theta to do the ICC as follows: >theta<-function(x,DATA){tmod<-lm...

...ode came from a solution given by Deepayan Sarkar. ------------------- library(lattice) set.seed(12345) x <- 0:20 y.male.obs <- - 1.2 * x + 22 + rnorm(length(x), sd = 3) y.male.prd <- - 1.2 * x + 22 y.fema.obs <- - 2.2 * x + 30 + rnorm(length(x), sd = 2) y.fema.prd <- - 2.2 * x + 30 tdat <- data.frame(x = rep(x, 8), y = rep(c(y.male.obs, y.male.prd, y.fema.obs, y.fema.prd), 2), sex = rep(rep(c("m", "f"), each = 2*length(x)), 2), cohort = rep(c("1970", "1980"), each = 4*length(x)), source = rep(rep(c(&quot...

Hi I would like to transform a data frame like Subject Item Score Subject 1 Item 1 1 Subject 1 Item 2 0 Subject 1 Item 3 1 Subject 2 Item 1 1 Subject 2 Item 2 1 Subject 2 Item 3 0 .... *to * Subject Item1 Item2 Item3 .....Item N Subject1 1 0 1 Subject2 1 1 0 ........ SubjectP.. Apologize for the simple nature of my query but I am stuck.

I am using the following script to make .jpg files. jpeg('plotx.jpg') ddat <-read.table("file",header=T) attach(ddat) tdat<-read.table("file1") plot(xx1,yy1,type='p',pch=1,col="blue",cex=0.2,xlim=c(0,3.5),ylim=c(-75,75)) points(tdat,col="green",pch=1,cex=0.2) dev.off() The problem is that I want the points to be very small; however, if I make cex any smaller than 0.2 the plot...

I'm trying to create a function which will allow me to subset a data set based on values of various specified variables. I also want to then apply some other function(s) (e.g., summary). This is what I've tried so far.... > test.fx <- function(dta, expvar, expval) { + newdta <- subset(dta, eval(expvar)>expval) + summary(newdta$eval(expvar)) + } > >

...splines grid stats graphics grDevices utils datasets [8] methods base other attached packages: [1] ggplot2_0.7 MASS_7.2-44 RColorBrewer_1.0-2 proto_0.3-8 [5] reshape_0.8.1 plyr_0.1 Here's the context in which I encountered the problem: library(ggplot2) tdat <- data.frame(date=seq.Date(as.Date("2008-07-07"), as.Date("2008-10-10"),by="day"), x=1:96) q2 = qplot(date,x,data=tdat,geom="smooth") print(q2+geom_point()) I can track the problem down to a call like floor_date...

...g the same results when I compare the point of interest to samples of which it is not a part (density estimates that are either extremely small, which is acceptable, or much greater than one, which doesn't seem right to me). Any thoughts would be greatly appreciated, Chris > fitted(npudens(tdat=training_df[training_cols_select][training_df$cat == i,])) [1] 7.762187e+18 9.385532e+18 6.514318e+18 7.583486e+18 6.283017e+18 [6] 6.167344e+18 9.820551e+18 7.952821e+18 7.882741e+18 1.744266e+19 [11] 6.653258e+18 8.704722e+18 8.631365e+18 1.876052e+19 1.995445e+19 [16] 2.323802e+19 1.203780e+19...

...svn rev 43063 language R version.string R version 2.6.0 (2007-10-03) start=c(-1.5, 9.5, 0.09, 10.25) names(start)<-c("A","B","xmid","scal") gnls(response~SSllogis(conc,A,B,xmid,scal),tdat,start=start,weights=varPower(),verbose=TRUE) **Iteration 1 GLS step: Objective: NULLvarStruct parameters: power 0.3373199 NLS step: RSS = 0 model parameters:-0.799941 8.99983 -0.522623 212.314 iterations: 2 Convergence: params varStruct 1.172208 1.000000 ### After about 1...