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Trouble reshaping data to my satisfaction

2 messages · Jennifer Sabatier, Jim Lemon

Original
Jennifer Sabatier
# 
#I am having a lot of trouble reshaping this data.
#This is just an examination of sample size on the margin of error that I
did for a colleague.
#Nothing complicated.
#But restructuring the data...another story

#Here's code to produce the dataset:


n <- seq(10000, 100000, by=10000)

P <- seq(0.1, 0.5, by=0.1)

Meff <- seq(3, 8, by=1)


# 4 age groups by 2 sex categories - these are population distribution
sizes - order is males 18-29, females 18-29, males 30-44,females 30-44,
males 45-59, females 45-59, males 60+, females 60+

Age1 <- data.frame(matrix(c(0.196598808, 0.224390935, 0.149289474,
0.151446387,0.08750253, 0.076399683, 0.060799283, 0.053572898), nrow=4,
ncol=2, byrow=T, dimnames=list(seq(1,4, by=1), c("Males", "Females"))))

MOE <- NULL

for (i in n){
   for (p in P){
      for (m in Meff){
         for (a in Age1[,1]){

         ni <- i * a
         M <- sqrt((1.92^2)*p*(1-p)*m/ni)
  dta <- data.frame(a, i, ni, p, m, M)

         colnames(dta) <- c("Proportion", "Total_n", "Stratum_n", "P",
"Meff", "MOE")

         dta$Sex <- "Males"
MOE <- rbind(MOE, dta)

}

         for (a in Age1[,2]){

         ni <- i * a
         M <- sqrt((1.92^2)*p*(1-p)*m/ni)
  dta <- data.frame(a, i, ni, p, m, M)

         colnames(dta) <- c("Proportion", "Total_n", "Stratum_n", "P",
"Meff", "MOE")

         dta$Sex <- "Females"
MOE <- rbind(MOE, dta)

}
}
}
}

# What I want is data that looks like:
# Meff  Proportion  Sample Size  Males_0.1 Females_0.1 Males_0.2
Females_0.2 ..... Males_0.5 Females_0.5

#I'm stumped on how to reshape this.

#I tried this but it gave me the attached output (yuck):


library(reshape2)

library(plyr)


tmp <- ddply(MOE, .(Meff), mutate, id=paste(Meff, Sex, seq_along(MOE)))

wd <- dcast(tmp, id + Meff ~ P + Sex, value.var="MOE")
Jim Lemon
# 
Hi Jennifer,
This is very hacky, but I think it does most of what you want. I can't
really work out what "Sample Size" is supposed to be:

MOERS<-data.frame(MOE[MOE$SEX_P=="Males_0.1",c("Meff","Proportion")],
 Males_0.1=MOE[MOE$SEX_P=="Males_0.1","MOE"],
 Females_0.1=MOE[MOE$SEX_P=="Females_0.1","MOE"],
 Males_0.2=MOE[MOE$SEX_P=="Males_0.2","MOE"],
 Females_0.2=MOE[MOE$SEX_P=="Females_0.2","MOE"],
 Males_0.3=MOE[MOE$SEX_P=="Males_0.3","MOE"],
 Females_0.3=MOE[MOE$SEX_P=="Females_0.3","MOE"],
 Males_0.4=MOE[MOE$SEX_P=="Males_0.4","MOE"],
 Females_0.4=MOE[MOE$SEX_P=="Females_0.4","MOE"],
 Males_0.5=MOE[MOE$SEX_P=="Males_0.5","MOE"],
 Females_0.5=MOE[MOE$SEX_P=="Females_0.5","MOE"])

Jim


On Fri, Mar 25, 2016 at 8:14 AM, Jennifer Sabatier
<plessthanpointohfive at gmail.com> wrote: