similar to: randomly select duplicated entries

Hi List, Here is some example data. myDat <- read.table(textConnection("group id 1 101 1 201 1 301 2 401 2 501 2 601 3 701 3 801 3 901"),header=TRUE) closeAllConnections() corr_mat <-read.table(textConnection("1 1 .5 0 0 0 0 0 0 0 2 .5 1 0 0 0 0 0 0 0 3 0 0 1.0 0 0 0 0 0 0 4 0 0 0 1 .5 .5 0 0 0 5 0 0 0 .5 1

Here is some sample data: mydata <- read.table(textConnection("Est Group Tri 0 0 4.639644 1 0 4.579189 2 0 4.590714 0 1 4.443696 1 1 4.588243 2 1 4.650505 0 2 4.296608 1 2 4.826036 2 2 4.765386"),header=TRUE); closeAllConnections(); I can form two plots,

I am recoding some data. Many values that should be 1.5 are recorded as 1-2. Some example data and my solution is below. I am curious about better approaches or any other suggestions. Thanks! # example input data myData <- read.table(textConnection("id, v1, v2, v3 a,1,2,3 b,1-2,,3-4 c,,3,4"),header=TRUE,sep=",") closeAllConnections() # the first column is IDs so remove

Hi Group, I posted this question on the ggplot list and was advised to try here also. The code below produces a plot as a png and pdf. The pdf looks great, and I cannot make the png look this way. I've tried various combinations of height, width, and dpi, but it has not worked out so far. Any suggestions to make the png look like the pdf? I received a response that the problem does not occur

I have a data set that has 4 columns and 53860858 rows. I was able to read this into R with: cc <- rep("character",4) myData <- read.table("myData.csv",header=FALSE,skip=1,colClasses=cc,nrow=53860858,sep=",") I need to reshape this data from long to wide. On a small data set the following lines work. But on the real data set, it didn't finish even when I

I am trying to improve the look of an HTML table for a report (that needs to be pasted into Word). Here is an example. table2 <- structure(c(26L, 0L, 40L, 0L, 10L, 0L, 0L, 188L, 0L, 281L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L), .Dim = c(6L, 3L), .Dimnames = structure(list( myvar = c("Don't know", "Somewhat likely", "Somewhat unlikely", "Very

Is there an easy way to make graphs for the following data. I have pretest and posttest scores for men and women. I would like to form a 'titlted segment' plot for the data. That is, make segments joining the scores, with different types of segments for men and women. Example data: menpre <- c(43,42,26,39,60,60,46) menpost <- c(40,41,36,42,54,58,43) womenpre <-

I wish to rearrange the matrix, df, such that all there are not repeated x values. Particularly, for each value of x that is reated, the corresponded y value should fall under the appropriate column. For example, the x value 3 appears 4 times under the different columns of y, i.e. y1,y2,y3,y4. The output should be such that for the lone value of 3 selected for x, the corresponding row entries

Hi All, I had posted a question on a similar topic, but I think it was not focused. I am posting a modification that I think better accomplishes this. I hope this is ok, and I apologize if it is not. :) I am looping through variables and running several regressions. I have reason to believe that the data is being duplicated because I have been monitoring the memory use on unix. How can I avoid

Hi, I have a line such as: myline <- " 0.100000 1.5000 0.6000 538 0.369404" and I would like to put the numbers into a vector. Some combination of tabs and spaces occur between the numbers. I tried: try1 <- strsplit(myline,"[[:blank:]]+") > try1 [[1]] [1] "" "0.100000" "1.5000"

Hi, Here are three plots: library(ggplot2) data(diamonds) randind <- sample(nrow(diamonds),1000,replace=FALSE) dsmall <- diamonds[randind,] qplot(carat, data=dsmall, geom="histogram",binwidth=1) qplot(carat, data=dsmall, geom="histogram",binwidth=.1) qplot(carat, data=dsmall, geom="histogram",binwidth=.01) What are ways to put these three plots on a single

All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake. Thanks for your help. Juliet Hannah

Hi Group, Here is some data. p <- runif(1000) # sample data groups <- rep(c(1,2),each=500) #conditioning variable mydata <- cbind(p,groups) n <- length(p) u <- (1:n)/(n + 1) # uniform distribution reference for qqplot logp <- -log(p,base=10) logu <- -log(u,base=10) qqplot(logp,logu) How can I make the above qqplot in lattice and/or ggplot2. The sample is uniform, and I take

Hi Group, I have a matrix, and I would like to replace numbers less than 0 by the smallest minimum number. Below is an small matrix, and the loop I used. I would like to get suggestions on the "R way" to do this. Thanks, Juliet # example data set mymat <- structure(c(-0.503183609420937, 0.179063475173256, 0.130473004669938, -1.80825226960127, -0.794910626384209, 1.03857280868547,

Dear list, I have a data frame with one column (group) and want to add a second column (sub) with a serial number that says to which subgroup a cell belongs. A subgroup contains the consecutive rows of the same group. The number of a subgroup is based on its position in the table. The first subgroup of A's and B's should have nr A1 and B1, the second nr A2 and B2, etc.. I hope the

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

Hi, This is not an R question, but I've seen opinions given on non R topics, so I wanted to give it a try. :) How would one treat a variable that was measured once, but is known to fluctuate a lot? For example, I want to include a hormone in my regression as an explanatory variable. However, this hormone varies in its levels throughout a day. Nevertheless, its levels differ substantially

Hi, I've been working with R for a couple of years, and I've been able to get most of the things done that I needed (sometimes in a roundabout way). A few experienced statisticians told me that R is best for interactive data analysis, but for large-scale computations, one needs something else. I understand that this all depends on what you are trying to accomplish, and R offers many ways

Hi Group, Consider a data frame like this: mylabel1 <- rep(c("A","B","C"),each=3) mylabel2 <- rep(c("A","B","C"),3) corrs <- c(1,.8,.7,.8,1,.7,.7,.7,1) myData <- data.frame(mylabel1,mylabel2,corrs) myData mylabel1 mylabel2 corrs 1 A A 1.0 2 A B 0.8 3 A C 0.7 4 B

Here is an example barplot that needs some tweaking: library(gplots) ratios <- data.frame(c(0.05,0.10,0.9),c(0.06,0.15,0.76)) rownames(ratios) <- c("T1","T2","T3") colnames(ratios) <- c("A1","A2") ratios <- as.matrix(ratios) myplot <- barplot2(ratios, beside = TRUE,col = c("blue",