Hello,
I don't have an answer on the reason why this happens but it seems like
a bug. Where?
In which of `[<-.data.frame` or `[<-.default`?
A solution is to subset and assign the vector:
set.seed(2020)
df2 <- data.frame(unit = 1:10)
df2$treated <- FALSE
df2$treated[sample(nrow(df2), 3)] <- TRUE
df2
# unit treated
#1 1 FALSE
#2 2 FALSE
#3 3 FALSE
#4 4 FALSE
#5 5 FALSE
#6 6 TRUE
#7 7 TRUE
#8 8 TRUE
#9 9 FALSE
#10 10 FALSE
Or
set.seed(2020)
df3 <- data.frame(unit = 1:10)
df3$treated <- FALSE
df3[sample(nrow(df3), 3), "treated"] <- TRUE
df3
# result as expected
Hope this helps,
Rui Barradas
Às 13:49 de 19/06/2020, Sébastien Lahaie escreveu:
I ran into some strange behavior in R when trying to assign a treatment to
rows in a data frame. I'm wondering whether any R experts can explain
what's going on.
First, let's assign a treatment to 3 out of 10 rows as follows.
df <- data.frame(unit = 1:10)
df$treated <- FALSE
s <- sample(nrow(df), 3)
df[s,]$treated <- TRUE
df
unit treated
1 1 FALSE
2 2 TRUE
3 3 FALSE
4 4 FALSE
5 5 TRUE
6 6 FALSE
7 7 TRUE
8 8 FALSE
9 9 FALSE
10 10 FALSE
This is as expected. Now we'll just skip the intermediate step of saving
the sampled indices, and apply the treatment directly as follows.
df <- data.frame(unit = 1:10)
df$treated <- FALSE
df[sample(nrow(df), 3),]$treated <- TRUE
df
unit treated
1 6 TRUE
2 2 FALSE
3 3 FALSE
4 9 TRUE
5 5 FALSE
6 6 FALSE
7 7 FALSE
8 5 TRUE
9 9 FALSE
10 10 FALSE
Now the data frame still has 10 rows with 3 assigned to the treatment. But
the units are garbled. Units 1 and 4 have disappeared, for instance, and
there are duplicates for 6 and 9, one assigned to treatment and the other
to control. Why would this happen?
Thanks,
Sebastien
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