similar to: suggestions/improvements for recoding strategy

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,

Full_Name: Steven McKinney Version: 2.9.0 OS: Mac OS X 10.5.6 Submission from: (NULL) (142.103.207.10) A corrupt data frame can be constructed as follows: foo <- matrix(1:12, nrow = 3) bar <- data.frame(foo) bar$NewCol <- foo[foo[, 1] == 4, 4] bar lapply(bar, length) > foo <- matrix(1:12, nrow = 3) > bar <- data.frame(foo) > bar$NewCol <- foo[foo[, 1] == 4, 4]

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 <-

Using this data as an example dat <- read.table(textConnection("Id myvar 12 1 12 2 12 6 34 9 34 4 34 8 65 15 65 23"), header = TRUE) closeAllConnections() how can I create another data set that does not have duplicate entries for 'Id', but the included values are randomly selected from the available ones. Thanks! Juliet

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

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

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

Hola, Por si lo quieres con colores rellenando cada punto: #---------------- library(data.table) library(lattice) dat <- read.table("pba.csv", header=TRUE, dec=",", as.is=TRUE) row.names(dat) <- NULL dat <- as.data.table(dat) dat$mycol <- ifelse(dat$sol =="ControlAE", "red", dat$sol) dat$mycol <- ifelse(dat$mycol

I would really like some help with understanding the panel function, in zoo. Thank you. R 15.1 and zoo 1.7-7. library(zoo) x = seq(0,3*pi,length.out=100) y = sin(x) zobj = zoo(y, x) ########################################################### ## EXAMPLE 1 - GLOBAL ARGUMENT ## This panel function works ## But, it relies on mycol, which is a global variable

Hi useRs, A recent coding infelicity along these lines yielded a corrupt data frame. foo <- matrix(1:12, nrow = 3) bar <- data.frame(foo) bar$NewCol <- foo[foo[, 1] == 4, 4] bar lapply(bar, length) > foo <- matrix(1:12, nrow = 3) > bar <- data.frame(foo) > bar$NewCol <- foo[foo[, 1] == 4, 4] > bar X1 X2 X3 X4 NewCol 1 1 4 7 10 <NA> 2 2 5 8 11

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

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 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

This is probably easy for experienced users but I could not find a solution. I have several R scripts that process several columns of a dataframe (several dataframes and columns actually, but simplified for my question). References such as: myDF$myCol are all over. I like to automate this for other dataframes and columns by defining a reference only once in the beginning of the script. One

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 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

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

> On Apr 15, 2018, at 4:08 AM, Allaisone 1 <Allaisone1 at hotmail.com> wrote: > > > Hi all .., > > > I have a list of 7000 dataframes with similar column headers and I wanted to add a new column to each dataframe based on a certain condition which is the same for all dataframes. > > > When I extract one dataframe and apply my code it works very well as

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

Hi All, I'm having a problem with barplot: mydata [1,] 2 108 0 0 0 1 3 0 0 0 0 0 7 18 3 4 8 20 26 20 19 7 1 1 mycol = c(rep('yellow', 2), rep('white', 3), rep('orange',2), rep('white', 5), rep('orange',3), rep('red',9)) barplot(mydata, col = mycol) gives me an uniformly yellow barplot. How do I solve this? bw Federico