similar to: Getting only label column of a data frame

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

Hi. I searched the list and didn't found nothing similar to this. I simplified my example like below: #I need calculate correlation (for example) between 2 columns classified by a third one at a data.frame, like below: #number of rows nr = 10 #the third column is to enforce that I need correlation on two variables only dataf =

Dear all, In order to ease the transition from SPSS to R for some of my colleagues, I am trying to create a function which would show the variables and their labels (if those exist), using function "label" in package Hmisc. A toy example would be this: my.data <- data.frame(age=c(24,35,28), gender=c("Male", "Female", "Male")) require(Hmisc)

Hello, I have two problems in automating multiple glm(s) operations. The data file is tab delimited file with headers and two columns. like "ABC" "EFG" 1 2 2 3 3 4 dat <- read.table("FILENAME", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dataf <- read.table("FILENAME", header=FALSE,

Hi, I have a global data-frame in my R script. At some point in my script, I want to update certain columns of this data-frame by calling in an update function. The function looks like this: # get events data. This populates a global event data frame in the R-script events <- getEvents(con, eventsFilePath) # events has columns eventid, timeStamp, isSynchronized, timeDiff; with millions of

Hello All, I am trying to write a function which would operate on columns of a dataframe specified in parameters passed to that function. f = function(dataf, col1 = "column1", col2 = "column2") { dataf$col1 = dataf$col2 # just as an example } The above, of course, does not work as intended. In some languages one can force evaluation of a variable, and then

Hello; I am having a problems with the interpretation of models using ordered or unordered predictors. I am running models in lmer but I will try to give a simplified example data set using lm. Both in the example and in my real data set I use a predictor variable referring to 3 consecutive days of an experiment. It is a factor, and I thought it would be more correct to consider it ordered. Below

Hi, 1.) The "median" function does not work well. Please refer to the data below (same data is attached as txt-delimited). This is what I try to do in R: median ( dataf [2:9] ) I get warning: "needs numeric data" 2.) BUT if apply the median to a single vector: median ( dataf [,2]] ) then it works: 3.) How come the "median"

In R, I'm imported a data frame of 2,321,123 by 4 called "dataF". I converted the data frame "dataF" to a matrix dataM <- as.matrix(dataF) Does R have an efficient routine to treat the special elements that contain "inf" in them. For example, can you separate the rows that have "inf" elements from the matrix into a separate matrix without

Hi, I like the formatting and the appearance of lattice plots. But I have not succeeded in gettting the right format in my plots with the lattice package in one of my applications. In?the code shown below, I start by constructing a general data frame and show my attempts with the lattice package commands. After that, I use the graphics package and show the kind of plot that I want to get. I would

Hi, I am writing a function that writes to a csv file for every call. However, for the subsequent calls, I want to append the data to the existing csv file without appending the column names again. I tried searching in the previous posts, but I am stuck with different errors. Here is what I am doing (dataF is a data-frame):- outputFilePath <- paste(getwd(), "/",

Dear All I am reposting because I my problem is real issue and I have been working on this. I know this might be simple to those who know it ! Anyway I need help ! Let me clear my point. I have huge number of datapoints plotted using either base plot function or xyplot in lattice (I have preference to use lattice). name xvar p 1 M1 1 0.107983837 2 M2 11

Dear R users, Here is my problem: # example data name <- c(paste ("M", 1:1000, sep = "")) xvar <- seq(1, 10000, 10) set.seed(134) p <- rnorm(1000, 0.15,0.05) dataf <- data.frame(name,xvar, p) plot (dataf$xvar,p) abline(h=0.05) # I can know which observation number is less than 0.05 which (dataf$p < 0.05) [1] 12 20 80 269 272 338 366 368 397 403 432 453

Dear R HELP, ABOUT glmmPQL and the anova command. Here is an example of a repeated-measures ANOVA focussing on the way starling masses vary according to (i) roost situation and (ii) time (two time points only). library(nlme);library(MASS)

Dear useRs, What is an efficient way to randomly sample from clustered data such that I get equal representation from each cluster? For example, let's say I want to randomly sample two cases from each cluster created by the "id" variable in the following data frame: > id<-c(rep("100", 4),rep("101", 3), rep("102", 6), rep("103",

Hi listers, I want to make lattice plots xyplots with the indication of legends inside each panel with only the points and the lines actually ploted inside each given panel according to the group(ing) factor. The code below shows what I have achieved so far and I hope will make clear what I want to have. It seems to me that my solution is a very "dirty hack" and there certainly is

Dear R-Experts I am sure this might look simple question for experts, at least is problem for me. I have a large data frame with over 1000 variables and each have different distribution( i.e. have different quantile). I want to create a new grouped data frame, where the new variables where the value falling in first (<25%), second (25% to <50%), third (50% to <75%) and fourth quantiles

Dear list I need your help: Execuse me for my limited R knowledge. #example data set set.seed (134) lm=c(1:4) block = c(rep(lm,6)) gen <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4),rep(5, 4),rep(6, 4)) X1 = c( rnorm (4, 10, 4), rnorm (4, 12, 6), rnorm (4, 10, 7),rnorm (4, 5, 2), rnorm (4, 8, 4), rnorm (4,7, 2)) X2 = X1 + rnorm(length(X1), 0,3) yvar <- c(X1, X2) X <- c(rep( 1,

R 3.1.0 OS X Colleagues, I recently updated to 3.1.0 and I have encountered Warning messages: ... Name partially matched in data frame when I do something like: DATAFRAME$colname where colname is actually something longer than that (but unambiguous). I have much appreciated the partial matching capabilities because it fits with my workflow. I often receive updated data months after the

Hello, On Mac OS X, certain Aqua/Quartz UI functionality requires an application to be launched from within an app bundle, or (alternatively) requires a Carbon application with a resource fork. Playing with the wxWidgets distribution, I discovered that it is quite easy and transparent to make such a Carbon app from (I guess) any command line application. When applied to the R executable called