similar to: R code for overlapping variables -- count

If they are binary (0/1 dummies), can't you just "&" them as in table(Female & USA & MidIncome) (or sum() if you don't care about the number of 0s) -pd > On 2 Jun 2024, at 00:31 , Shadee Ashtari <shadee.ashtari at gmail.com> wrote: > > Hi! > > I am trying to find the code for how to get counts for intersectional > variables. For example,

Dear Shadee, If you have a data.frame with the following columns: n = 100; # population size x = data.frame( ??????Sex = sample(c("M","F"), n, T), ??????Country = sample(c("AA", "BB", "US"), n, T), ??????Income = as.factor(sample(1:3, n, T)) ) # Dummy variable ONE = rep(1, nrow(x)) r = aggregate(ONE ~ Sex + Income + Country, length, data = x)

?s 18:34 de 02/06/2024, Leo Mada via R-help escreveu: > Dear Shadee, > > If you have a data.frame with the following columns: > > n = 100; # population size > x = data.frame( > ??????Sex = sample(c("M","F"), n, T), > ??????Country = sample(c("AA", "BB", "US"), n, T), > ??????Income = as.factor(sample(1:3, n, T)) > )

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

I'm making some illustrations and it would be convenient to automatically shade the overlapping portions of circles. These illustrations are for Social Choice theory, a field in political science and economics. I've wrestled together some examples so you can see what I mean, but have not mastered the "color overlapping sections" problem (as you will see):

Dear Listers, I am drawing a plot of two density curves, for male and female incomes. I would like to shade/hatch/color (whatever) the areas under the curves which are distinctive for each gender. This is the code I have tried so far: m <- density(topmal.d$y, bw = "sj") f <- density(topfem.d$y, bw = "sj") par(mfrow = c(1,1)) plot(x = c(0,400), y = c(0,0.02), type =

I have a case where the easiest way to draw a particular symbol would be to draw something a little bigger, and then use polygon(... , col=0) to erase the extra stuff. Just how to do this best when par('bg') = 'transparent' is, however, eluding me. I've looked through the archives and the book R Graphics without quite seeing the light. Help or pointers to help would be

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

It being friday, I would like to call your attention to an innovative data analysis by Leslie Lamport available from: http://research.microsoft.com/users/lamport/pubs/hair.pdf I particularly liked the graphics. url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois

I'm trying to remove the elements "No" from the character vector

Dear list, I have following data set, which I want to plot the "Scale" variable on the x-axis and "Mean"´on the y-axis for each Ageclass and for each sex. The Mean value of each Ageclass for each sex would be connected by a line. Totally, there should be 6 lines, from which three present the Mean values of each Ageclass for respective sex. Are there any easy ways to do

Hi All, I have the data like this : >sample <- read.csv(file="sample.csv",sep=",",header=TRUE) > sample stdate Domain sex age Login 1 01/11/09 xxx FeMale 25 2 2 01/11/09 xxx FeMale 35 4 3 01/11/09 xxx Male 18 30 4 01/11/09 xxx Male 31 3 5 02/11/09 xxx Male 32 11 6 02/11/09 xxx Male 31 1 7 02/11/09

Hello R-users/experts, I am new to R- I have a simple question: Let say I have a data set as follows temp:[file attached] the data structure is a follows: sex age female 28 female 53 female 53 female 36 male 42 male 29 male 43 male 36 male 41 Here we are grouping all male value into male and all female value in to female

Hi all, I have data like this: >sample <- read.csv(file="sample.csv",sep=",",header=TRUE) > sample stdate Domain sex age Login 1 01/11/09 xxx FeMale 25 2 2 01/11/09 xxx FeMale 35 4 3 01/11/09 xxx Male 18 30 4 01/11/09 xxx Male 31 3 5 02/11/09 xxx Male 32 11 6 02/11/09 xxx Male 31 1 7 02/11/09

Hi, I have newbie question. Suppose I have the following data: temp <- data.frame(type1 = c("male", "female", "male", "female", "female"), type2 = c("low", "med", "high", "low", "med"), a = c(1,2,4, NA, 3), b = .... [TRUNCATED] temp type1 type2 a b c 1 male low 1 5 0 2 female

##Q1. confint.glm(...) fails for an example of HSAUR data("womensrole", package = "HSAUR"); ## summary(womensrole); womensrole_glm_2 <- glm(fm2, data = womensrole,family = binomial()) ## summary(womensrole_glm_2); confint(womensrole_glm_2); ## -------Fail--------- # Waiting for profiling to be done... # Error in if (any(y < 0 | y > 1)) stop("y values must be 0

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

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

I want to write a function to standardize regression predictors, which will require me to do some character-string manipulation to parse the variables in a call to lm() or glm(). For example, consider the call lm (y ~ female + I(age^2) + female:black + (age + education)*female). I want to be able to parse this to pick out the input variables ("female", "age",

I am trying to add confidence (error) bars to lattice barcharts (and dotplots, and xyplots). I found this helpful message from Deepayan Sarkar and based teh code below on it: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/50299.html However, I can't get it to work with groups, as illustrated. I am sure I am missing something elementary, but I am unsure what. Using R 2.1.1 on various