similar to: Rcmdr numSummary: means of multiple variables without grouping

I know that my question is like a very newbie question, but at the moment I stacked with it and I need a quick solution. I need to make an overall statistical overview of various datasets, the summary() and numSummary() functions are fully sufficient. My question is, how can I export results to a spreadsheet-like file, as a .csv. For the summary() with an "x" dataset I can use this way:

Dear R users, I have issues regarding latex() from Hmisc and numSummary() from Rcmdr. Here's an example: > library(Rcmdr) > data(Angell, package="car") > numSummary(Angell[,"hetero"], statistics=c("mean", "sd", "quantiles"), quantiles=c( 0,.25,.5,.75,1 )) > .numSummary <- popOutput() > latex(as.table(.numSummary$table),

Hi Every one, I have a dataframe "class" with "name", "sex", "age", "height", "Weight". if i caluclate summary statistics with the below code numSummary(class[,c("Height", "Weight")], groups=class$Name, statistics=c("mean", "sd", "quantiles"), quantiles=c(0, .25,.5,.75,1)) iam getting

Hello everyone, I have several problems with exporting to LaTeX the output of numSummary() from the abind package. > numSummary(finance[,"Cash_flow"], statistics=c("mean", "sd", "quantiles")) mean sd 0% 25% 50% 75% 100% n NA 188070.9 414771.9 -426804 26743 53866 150975.5 1871500 54 4 >

I issued the following command to obtain the std dev for each month. psd<-numSummary(Sal, groups=month, statistics=c("sd")) which resulted in > psd sd n NA 1 6.930340 9367 2319 2 7.847003 10827 1008 3 5.962308 12988 404 4

Hello everyone, The thread title speaks for itself. Here's the code that worked for me: > numSummary(finance[,"Employees"], statistics=c("mean", "sd", "quantiles")) mean sd 0% 25% 50% 75% 100% n NA 11492.92 29373.14 1777 3040 4267 6553 179774 53 5 > str(numSummary(finance[,"Employees"],

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46

HI Greg, Sorry, I misunderstand your question. I am not sure whether it works with numSummary() from library(Rcmdr). You could use other ways, such as: test<-read.table(text=" ?id year incidents ?100??? 1??????? 0 ?101??? 1??????? 1 ?102??? 1??????? 21 ?100??? 1??????? 27 ?101??? 1??????? 3 ?102??? 1??????? 12 ?100??? 2??????? 5 ?101??? 2??????? 5 ?102??? 2??????? 19 ?100??? 2??????? 10

Hi For first question, maybe I am completely wrong but cor(swiss[,-1], swiss[,1]) should give you what you want in one step. Second question Without an example it is hard to say but maybe aggregate is the way forward. > aggregate(iris[,1:4], list(iris$Species), function (x) c(mean=mean(x), sd=sd(x))) Group.1 Sepal.Length.mean Sepal.Length.sd Sepal.Width.mean Sepal.Width.sd 1

Hola a tod en s, me gustaría usar solo un(os) comando(s) específico(s) de una librería pero sin cargarla entera a la manera: library(laquesea) ¿se puede hacer de alguna manera? Y saludos y gracias jm~ _______________________________ J. Miguel Marin http://www.est.uc3m.es/jmmarin Dep. of Statistics University Carlos III of Madrid Spain (E.U.)

Dear list, I have the following how-to-do it in R, questions. Suppose I have ten independent variables, and one dependent variable. I want to find the Pearson correlation of all the IVs with the DV, but not the correlation between the IVs. What I know so far, about R, that I have to type the cor () function ten times, each time requesting for a correlation between one IV and the DV. I was

i just found the following list, i wondered if anybody could add to this as i have to characterize a large data set and am new to R...the list below was so helpful....can you add to this??? Just to forestall confusion amongst those who would like to use one of the functions called "describe"... Hmisc package - describe numeric name count of observations count of missing

Admittedly I am using an old version 1.7.1, but can anyone tell if this is or was a problem. I can only get nlm (nonlinear minimization) to adjust the first three components of function variable. No gradient or hessian is supplied. E.G.; fnoise function(y) { y[5]/(y[4]*sp2) * exp(-((x[,3]-y[1]-y[2]*x[,1]-y[3] *x[,2])/y[4])^2/2) + (1-y[5])/(y[9]*sp2) * exp(-((x[,3]-y[6]-y[7]*x[,1]-y[8]

Dear all, I'm trying to gsub() "%" with "\%" with no obvious success. > temp1 <- c("mean", "sd", "0%", "25%", "50%", "75%", "100%") > temp1 [1] "mean" "sd" "0%" "25%" "50%" "75%" "100%" > gsub("%",

I am trying to `cut' a continuous variable into contiguous classes containing approximately an equal number of observations. I thought quantile() was the appropriate function to use in order to find the breakpoints, but I end up with classes of different sizes - see example below. Does anybody have an explanation for that? And what is the `recommended' way of computing what I am looking

When I try to connect to * using a Cisco ATA 188 configured with a MGPC firmware (v3.1.1), I just keep getting this message every 30 seconds or so : May 10 10:08:21 NOTICE[7913]: chan_mgcp.c:1509 find_subchannel: Gateway '192.168.1.27' (and thus its endpoint '*') does not exist Using tcpdump, I have checked that the ATA188 (with IP 192.168.1.27 and port 2427) sends UDP packets to

Readers, Is it possible to create a plot command based upon the indices of missing values in a data set? dataset1<-read.table(text=' 10 2 20 NA 30 5 40 7 50 NA 60 NA 70 2 80 6 90 NA 100 9 ') dataset2<-read.table(text=' 0.2 0.4 0.1 0.9 0.2 0.3 1.1 0.7 0.9 0.6 0.4 ') The 'approx' function is used to obtain the interpolated values for 'NA' in dataset1.

Hoping someone out there can help me...this seems like an easy task but I can't figure it out... I want to replace variables in one dataset (Dataset1) with a variable from another dataset (Dataset2). All the values for variables x1 and x2 in Dataset1 have a unique match to the variable uniquenum in Dataset2. For example, in Dataset1 grpnum A has a value of 343 for variable x1. In Dataset2,

Hi everyone, I've got two data sets as below. My question now is: how can I use Dataset2 as a filter for Dataset1? My goal is just to keep the rows of Dataset1 where the first column (Date) matches the Dates in Dataset2. I would appreciate any solutions to this issue. Many thanks! S.B. Dataset1: Date A B C D 1

When I make a sample in sp, for example a random samping of 5 points in dataset1: sample.dataset1=spsample(dataset1, n = 5, "random") I have a sp object for the selected sampling... My question is : How can I extract from dataset2 the points & data for the coordinates(sample.dataset1) ? The dataset2 would be an object with the same area and coordinate system, but I would like to