Hello again,
Sometimes it's better to create indices for each condition and then
assemble them with logical operations as needed.
i <- t1$sex_chromosome_aneuploidy_f22019_0_0 == "Yes"
j <- is.na(t1$sex_chromosome_aneuploidy_f22019_0_0)
t1[!i & j, ]
j means is.na(.)
!i means (.) != "Yes"
Hope this helps,
Rui Barradas
Às 21:21 de 03/10/19, Rui Barradas escreveu:
Hello,
Then it's easier, is.na alone will do it.
j <- is.na(t1$sex_chromosome_aneuploidy_f22019_0_0)
t1[j, ]
Hope this helps,
Rui Barradas
Às 20:29 de 03/10/19, Ana Marija escreveu:
Hi Rui,
sorry for confusion, I would only need to extract from my t1 dataframe
rows which have NA in sex_chromosome_aneuploidy_f22019_0_0
in other words to REMOVE rows with "Yes" and to keep rows with NA. How
to do that?
On Thu, Oct 3, 2019 at 2:26 PM Rui Barradas <ruipbarra...@sapo.pt> wrote:
Hello,
You have to use is.na to get the NA values.
t1 <- data.frame(sex_chromosome_aneuploidy_f22019_0_0 = c(NA, "Yes"),
other = 1:2)
i <- t1$sex_chromosome_aneuploidy_f22019_0_0 == "Yes" &
!is.na(t1$sex_chromosome_aneuploidy_f22019_0_0)
i
t1[i, ]
Hope this helps,
Rui Barradas
Às 19:58 de 03/10/19, Ana Marija escreveu:
Hello,
I have a dataframe (t1) with many columns, but the one I care about
it this:
unique(t1$sex_chromosome_aneuploidy_f22019_0_0)
[1] NA "Yes"
it has these two values.
I would like to remove from my dataframe t1 all rows which have "Yes"
in t1$sex_chromosome_aneuploidy_f22019_0_0
I tried selecting those rows with "Yes" via:
t11=t1[t1$sex_chromosome_aneuploidy_f22019_0_0=="Yes",]
but I got t11 which has the exact same number of rows as t1.
If I do:
table(t1$sex_chromosome_aneuploidy_f22019_0_0)
Yes
620
So there is for sure 620 rows which have "Yes". How to remove those
from my t1 data frame?
Thanks
Ana
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