similar to: access (exactly/only) one dimension of a multidimensional table

Hello, Say I have 2 columns, bmi and gender, the first being all the values and the second being male or female. How would I subset this into males only and females only? I have searched these fora and read endlessly about select[] and split() functions but to no avail. Also the table is not ordered. bmi gender -> bmi gender + bmi gender 1 24.78 male

Hi, I have a data set similar to the following State Gender Quantity TX Male 1 NY Female 2 TX Male 3 NY Female 4 I need to calculate cumulative sum of the quantity by State and Gender. The expected output is State Gender Quantity CumQuantity TX Male 1 1 TX Male 3 4 NY Female 2 2 NY Female 4 6 I highly appreciate if someone can give me some hints on solving that in R. Hao -- View this

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh

I might have factored the gender. I'm not sure it would in any way be quicker. But might be to some extent easier to develop variations of. And is sort of what factors should be doing... # make dummy data gender <- c("Male", "Female", "Male", "Female") WC <- c(70,60,75,65) TG <- c(0.9, 1.1, 1.2, 1.0) myDf <- data.frame( gender, WC, TG ) #

Dear R users, I have a problem, which I can not find a solution. Probably someone could help me? I have a result from my classification, like this > credit.toy [[1]] age married ownhouse income gender class 1 20-30 no no low male good 2 40-50 no yes medium female good [[2]] age married ownhouse income gender class 1 20-30 yes yes high male

Hello, R friends, I'm trying to crack this nut: Example Data. pet    gender dog    male dog    female dog    male cat    female cat    female cat    male Plot Task. Horizontal 100% bars where y axis shows gender factor (male vs. female) and x axis shows percentage of kind of pets (dog vs. cat) so that % dogs + % cats are stacked in 1 bar and sum up to 100% (for each gender group 1

ScopedProxy uses with_scope and proxy objects to make it easy to find and count different types of records. Example: class Person < ActiveRecord::Base scoped_proxy :minor, :conditions => ''age <= 17'' scoped_proxy :adult, :conditions => ''age >= 18'' scoped_proxy :old, :conditions => ''age >= 70'' scoped_proxy :male,

Hi, Im having trouble creating the following graph. Here is my code: library(plotrix) library(prettyR) female_improvement <-read.table("C://project/graphs/gender/breakdown/gender-improvement/female-improvement.csv", sep=",", header=TRUE) barp(rbind(rep(length(female_improvement$gender),2),freq(female_improvement$reason)[[1]]), ylab="22 Males participated in the

Hey, Is it possible that R can calculate each options under each column and return a summary table? Suppose I have a table like this: Gender Age Rate Female 0-10 Good Male 0-10 Good Female 11-20 Bad Male 11-20 Bad Male >20 N/A I want to have a summary table including the information that how many answers in each category, sth like this: X

Hi I think/hope there will be a simple solution to this but google-ing has provided no answers (probably not using the right words) I have a long data frame of >2 000 000 rows, and 6 columns. Across this there are 24 000 combinations of gene in a column (n=12000) and gender in a column (n=2... obviously). I want to create 2 new columns in the data frame that on each row gives, in one column

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

I currently have a data set in which gender is inputed as "Male" and "Female" , and I'm trying to convert this into "1" and "0". I found a website which reccomended using two commands: data$scode[data$sex=="M"] <- "1" data$scode[data$sex=="F"] <- "2" to convert to numbers, and: data$scode <-

There are many techniques Callum and yours is an interesting twist I had not considered. Yes, you can specify what integer a factor uses to represent things but not what I meant. Of course your trick does not work for some other forms of data like real numbers in double format. There is a cost to converting a column to a factor that is recouped best if it speeds things up multiple times. The

I'm looking for an R method to produce latex versions of tables for table/array objects of 3 or more dimensions, which, of necessity is flattened to a 2D display, for example with ftable(), or vcd::structable, as shown below. I'd be happy to settle for a flexible solution for the 3D case. > UCB <- aperm(UCBAdmissions, c(2, 1, 3)) > ftable(UCB) Dept A B

Just a minor point in the suggested solution: df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) since WC and TG are not conditional, would this be a slight improvement? df$LAP <- with(df, TG*(WC - ifelse(G=='male', 65, 58))) -----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Jorgen Harmse via R-help Sent: Friday,

Hi, i am new to this forum and not sure how it works, I am trying to do deskriptive descripe my data in terms of gender: head(scltotal) pbnr dat dep dys sop ago mis age female messpunkt2 messpunkt1 tage eintrittsjahr 1 10023 1994-02-21 0.75 1.00 0.50 0.50 0.75 35 1 8817 8817 0 1994 2 10023 1994-05-25 0.75 1.00 0.50 0.50 0.75 35 1 8910 8817

I am trying to automate a report that my company does every couple of years for the state of Maine. In the past we have used SPSS to run the data and then used complicated Excel template to make the tables/graphics which we then imported into Word. Since there are 256 tables/graphics for this report, this work flow is a little painful. I would like to automate the process and I think I can do

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <-

Dear useRs, Am trying to use xtable on the following data.frame and I don't get what I expect: example.table <- data.frame(rbind( c("Gender"," "," "," "), cbind(rep(" ",2),c("Male","Female"),c(3.0,4.0),c(3/7,4/7)) )) colnames(example.table) <- c(" "," ","number of

This dataset below is one sample answer the questioner from our customer. > testbank <- read.table("testbank.txt", header=T) > testbank age married income gender ownhouse class 1 20-30 no high female yes 1st 2 30-40 no high female yes 1st 3 40-50 no low female yes 1st 4 50-60 no high female yes 1st 5 60-70