create a new data frame with existing ones - r

Question

Suppose I have the following data frames
treatmet1<-data.frame(id=c(1,2,7))
treatment2<-data.frame(id=c(3,7,10))
control<-data.frame(id=c(4,5,8,9))
I want to create a new data frame that is the union of those 3 and have an indicator column that takes the value 1 for each one.
experiment<-data.frame(id=c(1:10),treatment1=0, treatment2=0, control=0)
where experiment$treatment1[1]=1 etc etc
What is the best way of doing this in R?
Thanks!

Answer 1

Updated as per # Flodel:
kk<-rbind(treatment1,treatment2,control)
var1<-c("treatment1","treatment2","control")
kk$df<-rep(var1,c(dim(treatment1)[1],dim(treatment2)[1],dim(control)[1]))
kk
id df
1 1 treatment1
2 2 treatment1
3 7 treatment1
4 3 treatment2
5 7 treatment2
6 10 treatment2
7 4 control
8 5 control
9 8 control
10 9 control
If you want in the form of 1 and 0 , you can use table
ll<-table(kk)
ll
df
id control treatment1 treatment2
1 0 1 0
2 0 1 0
3 0 0 1
4 1 0 0
5 1 0 0
7 0 1 1
8 1 0 0
9 1 0 0
10 0 0 1
If you want it as a data.frame, then you can use reshape:
kk2<-reshape(data.frame(ll),timevar = "df",idvar = "id",direction = "wide")
names(kk2)[-1]<-sort(var1)
> kk2
kk2
id control treatment1 treatment2
1 1 0 1 0
2 2 0 1 0
3 3 0 0 1
4 4 1 0 0
5 5 1 0 0
6 7 0 1 1
7 8 1 0 0
8 9 1 0 0
9 10 0 0 1

Answer 2

df.bind <- function(...) {
df.names <- all.names(substitute(list(...)))[-1L]
ids.list <- setNames(lapply(list(...), `[[`, "id"), df.names)
num.ids <- max(unlist(ids.list))
tabs <- lapply(ids.list, tabulate, num.ids)
data.frame(id = seq(num.ids), tabs)
}
df.bind(treatment1, treatment2, control)
# id treatment1 treatment2 control
# 1 1 1 0 0
# 2 2 1 0 0
# 3 3 0 1 0
# 4 4 0 0 1
# 5 5 0 0 1
# 6 6 0 0 0
# 7 7 1 1 0
# 8 8 0 0 1
# 9 9 0 0 1
# 10 10 0 1 0
(Notice how it does include a row for id == 6.)

Answer 3

Taking
treatment1<-data.frame(id=c(1,2,7))
treatment2<-data.frame(id=c(3,7,10))
control<-data.frame(id=c(4,5,8,9))
You can use this:
x <- c("treatment1", "treatment2", "control")
f <- function(s) within(get(s), assign(s, 1))
r <- Reduce(function(x,y) merge(x,y,all=TRUE), lapply(x, f))
r[is.na(r)] <- 0
Result:
> r
id treatment1 treatment2 control
1 1 1 0 0
2 2 1 0 0
3 3 0 1 0
4 4 0 0 1
5 5 0 0 1
6 7 1 1 0
7 8 0 0 1
8 9 0 0 1
9 10 0 1 0

Answer 4

This illustrates what I was imagining to be the rbind strategy:
alldf <- rbind(treatmet1,treatment2,control)
alldf$grps <- model.matrix( ~ factor( c( rep(1,nrow(treatmet1)),
rep(2,nrow(treatment2)),
rep(3,nrow(control) ) ))-1)
dimnames( alldf[[2]])[2]<- list(c("trt1","trt2","ctrl"))
alldf
#-------------------
id grps.trt1 grps.trt2 grps.ctrl
1 1 1 0 0
2 2 1 0 0
3 7 1 0 0
4 3 0 1 0
5 7 0 1 0
6 10 0 1 0
7 4 0 0 1
8 5 0 0 1
9 8 0 0 1
10 9 0 0 1