similar to: Multiple Channel Variables with AMI Originate

Dear R users, A co-worker and I are writing a function to facilitate graph plotting in R. The function makes use of a lot of lists in its defaults. However, we discovered that R does not necessarily preserve the defaults if we were to input them in the form of list() when initializing the function. For example, if you feed the function codes below into R: myfunction=function( list1=list

Hello, I'm using SPSS at work but really would like to switch to R. Right now I'm trying to learn R in reproducing calculations I did with SPSS but am stuck with something that is quite simple and comprehensible in SPSS-Syntax: IF (variable1.fac = 0 AND variable2.num = 0) variable3=1. IF (variable1.fac = 0 AND variable2.num >= 1) variable3=2. IF (variable1.fac = 1 AND variable2.num =

Hi there, I have a huge dataframe containing 70,000 observations. I have filtered this dataframe (let it's name be "transformed_dataframe") as I wanted to select only those observations which are greater than or equal to 60,001 regarding the very first identity column. So I have a transformed dataframe now including 10,000 obeservations (from 60,001 - to 70,000) and if you send

Hello, How do you print a variable name in a for loop? I'm trying to construct a csv file that looks like this: Hello, variable1, value_of_variable1, World, Hello, variable2, value_of_variable2, World, Hello, variable3, value_of_variable3, World, Using this: for (variable in list(variable1, variable2, variable3)){ cat("Hello,", ???variable???, variable, ",

I have a text file that has semi-colon separated values. The table is nearly 10,000 by 585. The files looks as follows: ******************************************* First line: Skip this line Second line: skip this line Third line: skip this line variable1 Variable2 Variable3 Variable4 Unit1 Unit2 Unit3 10 0.1 0.01 0.001 20

Listers, I have a question regarding correlation matrices. It is fairly straight forward to build a correlation matrix of an entire data frame. I simply use the command cor(MyDataFrame). However, what I would like to do is construct a smaller correlation matrix using just three of the variable out of my data set. When I run this: cor(MyDataFrame$variable1,

Dear list, I have a set of 100+ variables. I would like to have one by one crosstables for each variable. I started with table(variable1, variable2) table(variable1, variable3) table(variable1, variable4) ... table(variable2, variable3) table(variable2, variable4) ... It seems rather tedious. Any better ideas around? Thanks for any help! Gerit -- NUR NOCH BIS 31.01.! GMX FreeDSL -

Hi everyone, I’m looking for a clever bit of code to replace NA’s with a specific score depending on an indicator variable. I can see how to do it using lots of if statements but I’m sure there most be a neater, better way of doing it. Any ideas at all will be much appreciated, I’m dreading coding up all those if statements!!!!! My problem is as follows: I have a data set with

Hello I wrote a bootstrap program in C language that is called and run by R. When I tried it, it is slow and I'm trying to write and run the whole thing in C. But I cannot use handy functions in R and need to figure out how to write those functions by myself. Is there any way that I can get the actual codes that implement functions in R so that I can translate them into other languages? For

Hello R-Helpers, I am not sure if it is a very simple question but I would like to use two (...) in a function, for example, this is a script where I would like to input the variable names (in one of the (...)) and the variances associated to those variables which are not calculated in the script because there is a specific software to calculate it (the other (...)) data <- function

Hello, I'd like to ask you something again. I have a database and it has a column which looks like this one here: small_factor <- factor(c("d_variable1","d_variable2","d_variable3")) small_factor Now the thing is that I would like to convert each element of this factorized column. Basically I want to rewrite the words in it without the first two characters:

Hello, I have about 2000 data files which I want to analyse. The file names are all very similar => p"variable1"_t"variable2"_c"variable3".txt There aren?t so much different variables (about 70) , just different combinations of them. To allow an easy way of handling those data files I was wondering whether R is able to do variable-substitution

Buenas tardes: Hasta ahora he utilizado savePlot() en ejecuciones de R en Windows, donde lo que hago es grabar la ventana de gráficos mediante dev.cur() en formato pdf. Concretamente éste es el código: > savePlot(filename = ruta_pdf, type = "pdf", device = dev.cur(), restoreConsole = TRUE) Este comando está dentro de un bucle de "n" ejecuciones, por lo que guardo un pdf por

Hi all, I have already some important variables in the current R session; and I wanted to load some rData file which was saved during the last R session using "save.image"... But how do I avoid the newly loaded data/variables messing with my existing data/variables? In Matlab, I can do: mynewdata=load("imagefromlastRsession.rData") and then I can access the variables

Hi guys, Am having a problem. I have this def in my index controller that works like this: def do_something $a = variable1 $b = variable2 $c = variable3 $d = ""<script language=\"Javascript\"> \n new Insertion.Bottom(''#{$c}'', ''#{$a} - #{$b}''); \n </script>" $trigger = "1" render(:partial =>

Hi, I have a question about running multiple in regressions in R and then storing the coefficients. I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 select variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state, ie run a regression of

Good morning, I am analyzing a dataset composed by 364 subjects and 13 binary variables (0,1 = absence,presence). I am testing possible association (co-presence) of my variables. To do this, I was trying with cluster analysis. My main interest is to check for the significance of the obtained clusters. First, I tried with the pvclust() function, by using method.hclust="ward" and

Dear R experts, I would like to please ask for your help with repeating steps in an apply statement. I have a dataframe that lists multiple variables for a given id and visit, as well as drug treatment. > head(exp) id visit variable1 variable2 variable3 variable4 drug 1 3 1 13 10 7 11 0 2 3 5 10 15 9 9 0 3 3 12

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

Dear list, I am trying to use lme to build the analogue of the following SAS MIXED random specification: random int+Variable1+Variable2 /subject = Subject group=Condition type=vc; which gives a Condition-blocked heterogeneity in the random effects variance-covariance matrix. Needless to say, I have a hard time in specifying Condition-specific heterogeneities in the variance-covariance