similar to: select a subset from a sample

Hello, I didn't give enough information when I sent an query before, so I'm trying again with a more detailed explanation: In this data set, each patient has a different number of measured variables (they represent tumors, so some people had 2 tumors, some had 5, etc). The problem I have is that often in later cycles for a patient, tumors that were originally measured are now missing (or

I have a text file similar to this (separated by spaces): x <- "DF12 This is an example 1 This DF12 This is an 1232 This is DF14 This is 12334 This is an DF15 This 23 This is an example " and I know the field lengths of each variable (there is 5 variables in this data set), which are: varlength <- c(2, 2, 18, 5, 18) How can I import this kind of data into R, using the varlength

Hi R-help, I am using the sem package to run confirmatory factor analysis (cfa) on some questionnaire data collected from 307 participants. I have been running R-2.15.3 in Windows in conjunction with R studio. The model I am using was developed from exploratory factor analysis of a separate dataset (n=439); it includes 18 items that load onto 3 factors. I have used the sem package documentation

Hello, I'm a new R user. I have two different dummy tables with the variable name tb1 and tb2. tb1< v1 v2 v3 v4 "chr1" 22 23 3 "chr1" 36 37 1 "chr1" 54 55 0 "chr1" 77 78 1 "chr2" 80 81 4 "chr2" 85 86 0 "chr2" 99 100 1

Hi, I am newbie to R and working on result presentation? My Input table is in following format A B C D X T C K Z U Z M E V Z R Z U Z M E V P R I need to present my result in the

I've been looking for how to change a certain percentage of values in a data frame, but I've been struggling to find information in R. For example: #################example data############## > data V1 V2 V3 V4 V5 V6 V7 1 chr1 500 500 CHH 0 0.5 + 2 chr1 550 550 CHH 0 0.0 + 3 chr2 700 700 CHH 0 0.0 + 4 chr2 1000 1000 CHH 0 0.0 + 5 chr3

I don't fully understand the logic you are trying to implement, but something along the lines of foo <- cut(trialData$date, breaks = as.Date(c("2007-01-01", "2008-05-01", "2009-04-01", "2010-05-01",

#I would like to use the below data to make a scatter plot matrix with #code similar to that below the data #conceptually this is the right approach I think #thanks in advance melt.gg <- structure(list(stream = c("Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower",

R community, I would like to know how to extract rows from a data frame (DF) such that each row in the new data frame (D.F) represents the first instance of a unique variable pairing in the original dataframe (ordered first by variable V1 then by variable V2). The unique function does not seem to be able to accomplish this. I imagine there must be a simple solution for this, but I can't

d <- structure(list(Site = structure(c(8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L, 8L, 12L, 7L, 6L, 11L, 5L, 10L, 4L, 3L, 2L, 1L, 9L,

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Hello, Hope that someone could help me plotting longitudinal data below: 7213 3333330001 0.8300 13.05.09 1 1 3333330001 0.8700 09.02.05 NULL 4797 3333330001 0.7700 21.03.07 NULL 2399 3333330001 0.7800 12.04.06 NULL 2400 3333330002 NULL 27.03.06 NULL 7230 3333330002 0.8200 14.05.09 0 2 3333330002 0.8400 09.02.05 NULL 4798 3333330002 0.8700 20.03.07 0 4799 3333330003 0.9000 20.03.07 13 2401

Hi, I am having trouble trying to figure out why if_else is behaving the way it is, it may be my code or the way the data is structured. Below is a snapshot of a database am working on and it represents a longitudinal survey of study participants in a trial with weekly follow up. The variable "survey_start" represents the start of the study-defined one year follow up (which we called

What is the way to add an arbitrary fit from a model to a lattice conditioning plot ? For example xyplot(v1 ~v2 | v3,data=mydata, panel=function(...){ panel.xyplot(...) panel.loess(...,col.line="red") } ) Will add a loess smoother. Instead, I want to put a fit from lm (but not a simple straight line) and the fit has to be done for each panel

Here is a small reproducible example: data <- structure(list(V1 = structure(1:3, .Label = c("Name1", "Name2", "Name3"), class = "factor"), V2 = structure(c(1L, 3L, 2L), .Label = c("nam1", "name-1", "name_12"), class = "factor"), V3 = structure(1:3, .Label = c("nam2", "nam_34",

Dear all, let say I have following data: dat <- structure(list(V1 = structure(c(1L, 4L, 5L, 3L, 3L, 5L, 6L, 6L, 4L, 3L, 5L, 6L, 5L, 5L, 4L, 4L, 6L, 2L, 3L, 4L, 3L, 3L, 2L, 5L, 3L, 6L, 3L, 3L, 6L, 3L, 6L, 1L, 6L, 5L, 2L, 2L), .Label = c("C", "G", "I", "O", "R", "T"), class = "factor"), V2 = c(0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L,

I have tData as below. I need to set the names with the headers from the first row in sHeaders Sorry .. forgot how to set the names from row in another data frame .. pls advise. names(tData) = sHeaders[1,] does not work correctly Also, why doesn't drop.levels(sHeaders) not work? dput(tData) structure(list(V1 = structure(c(3L, 1L, 1L, 2L), .Label = c("P H Ravi Kumar", "Rahul

Dear R helpers, I have following data.frame giving asset class (i.e. bank, corporate, sovereign etc. as there could be number of classes) and rating-wise default frequency. I need to plot graphs for each of these classes i.e. bank, corporate and sovereign where I will be plotting ratings (AAA, AA, etc) on the x-axis and their respective default probabilities on Y-axis. I have tried to write a

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)