How to prepare my data fo a factorial repeated measures analysis? - r

Question

Currently, my dataframe is in wide-format and I want to do a factorial repeated measures analysis with two between subject factors (sex & org) and a within subject factor (tasktype). Below I've illustrated how my data looks with a sample (the actual dataset has a lot more variables). The variable starting with '1_' and '2_' belong to measurements during task 1 and task 2 respectively. this means that 1_FD_H_org and 2_FD_H_org are the same measurements but for tasks 1 and 2 respectively.
id sex org task1 task2 1_FD_H_org 1_FD_H_text 2_FD_H_org 2_FD_H_text 1_apv 2_apv
2 F T Correct 2 69.97 68.9 116.12 296.02 10 27
6 M T Correct 2 53.08 107.91 73.73 333.15 16 21
7 M T Correct 2 13.82 30.9 31.8 78.07 4 9
8 M T Correct 2 42.96 50.01 88.81 302.07 4 24
9 F H Correct 3 60.35 102.9 39.81 96.6 15 10
10 F T Incorrect 3 78.61 80.42 55.16 117.57 20 17
I want to analyze whether there is a difference between the two tasks on e.g. FD_H_org for the different groups/conditions (sex & org).
How do I reshape my data so I can analyze it with a model like this?
ezANOVA(data=df, dv=.(FD_H_org), wid=.(id), between=.(sex, org), within=.(task))
I think that the correct format of my data should like this:
id sex org task outcome FD_H_org FD_H_text apv
2 F T 1 Correct 69.97 68.9 10
2 F T 2 2 116.12 296.02 27
6 M T 1 Correct 53.08 107.91 16
6 M T 2 2 73.73 333.15 21
But I'm not sure. I tryed to achieve this wih the reshape2 package but couldn't figure out how to do it. Anybody who can help?

Answer 1

I think probably you need to rebuild it by binding the 2 subsets of columns together with rbind(). The only issue here was that your outcomes implied difference data types, so forced them both to text:
require(plyr)
dt<-read.table(file="dt.txt",header=TRUE,sep=" ") # this was to bring in your data
newtab=rbind(
ddply(dt,.(id,sex,org),summarize, task=1, outcome=as.character(task1), FD_H_org=X1_FD_H_org, FD_H_text=X1_FD_H_text, apv=X1_apv),
ddply(dt,.(id,sex,org),summarize, task=2, outcome=as.character(task2), FD_H_org=X2_FD_H_org, FD_H_text=X2_FD_H_text, apv=X2_apv)
)
newtab[order(newtab$id),]
id sex org task outcome FD_H_org FD_H_text apv
1 2 F T 1 Correct 69.97 68.90 10
7 2 F T 2 2 116.12 296.02 27
2 6 M T 1 Correct 53.08 107.91 16
8 6 M T 2 2 73.73 333.15 21
3 7 M T 1 Correct 13.82 30.90 4
9 7 M T 2 2 31.80 78.07 9
4 8 M T 1 Correct 42.96 50.01 4
10 8 M T 2 2 88.81 302.07 24
5 9 F H 1 Correct 60.35 102.90 15
11 9 F H 2 3 39.81 96.60 10
6 10 F T 1 Incorrect 78.61 80.42 20
12 10 F T 2 3 55.16 117.57 17
EDIT - obviously you don't need plyr for this (and it may slow it down) unless you're doing further transformations. This is the code with no non-standard dependencies:
newcolnames<-c("id","sex","org","task","outcome","FD_H_org","FD_H_text","apv")
t1<-dt[,c(1,2,3,3,4,6,8,10)]
t1$org.1<-1
colnames(t1)<-newcolnames
t2<-dt[,c(1,2,3,3,5,7,9,11)]
t2$org.1<-2
t2$task2<-as.character(t2$task2)
colnames(t2)<-newcolnames
newt<-rbind(t1,t2)
newt[order(newt$id),]