Dear R-users,
I need an urgent help with the following: I have a country-year data covering
the period 1982 - 2013. I want to assess how the variable X (a certain policy)
affects the Y variable. The X variable is =1 when a country introduces that
policy in a specific year, otherwise =0.
What
Dear Faradj
I am afraid your post is unreadable since this is a plain text list and
you sent in HTML.
Michael
On 03/10/2019 12:17, Faradj Koliev wrote:
Dear R-users,
I need an urgent help with the following: I have a country-year data covering
the period 1982 - 2013. I want to assess how t
Dear Michael Dewey,
Thanks for reaching out about this. I trying again, now with plain text, and
hope it works.
Best,
Faradj
Dear R-users,
I need an urgent help with the following: I have a country-year data covering
the period 1982 - 2013. I want to assess how the variable X (a certai
Hi Faradj,
Suppose your data frame is labeled 'a'. Then the following seems to do what
you want.
v <- rep(NA_integer_,max(a$country_code))
v[ a$country_code[a$x==1] ] <- a$year[a$x==1]
a$treatment <- sapply( 1:nrow(a), function(i) { a$year[i] -
v[a$country_code[i]]})
HTH,
Eric
On Thu, Oct 3, 20
Dear Eric,
Thank you very much for this - it worked perfectly!
A small thing: I wonder whether it’s possible to include those cases where the
x is =0 for the whole study period. I have countries with x=0 for the whole
period and the treatment variable is=NA for these observations.
Best,
Fa
Hi Faradj,
What should the treatment variable be in those cases? If you want to set it
to a constant y (such as y=0), you can add something like
y <- 0
a$treatment[ is.na(a$treatment) ] <- y
HTH,
Eric
On Thu, Oct 3, 2019 at 4:54 PM Faradj Koliev wrote:
> Dear Eric,
>
> Thank you very much for
Hi,
I was thinking that it could simply show the negative counts. For ex: if a
country hasn’t introduced the policy X, and it's in the dataset from 1982 to
2014, then the treatment variable would take a value -33 in 1982 and -1 in
2014.
Best,
Faradj
> 3 okt. 2019 kl. 16:11 skrev Eric Ber
You can replace the last line in my first suggestion by the following two
lines
d <- 2014 # the default (set by the user)
a$treatment <- sapply( 1:nrow(a), function(i) { b <- v[a$country_code[i]];
a$year[i] - ifelse(is.na(b),d,b)})
Best,
Eric
On Thu, Oct 3, 2019 at 5:19 PM Faradj Koliev wr
Thank you very much for your help!
All the best,
Faradj
> 3 okt. 2019 kl. 16:37 skrev Eric Berger :
>
> You can replace the last line in my first suggestion by the following two
> lines
>
> d <- 2014 # the default (set by the user)
> a$treatment <- sapply( 1:nrow(a), function(i) { b <- v[a$
In one case they are exactly 0 and in the other they are almost zero. This
is the reason for different results.
Of course, they should be exactly the same, but this is due to some integer
values not being exactly represented as real values on binary computers.
Best,
Aleš Žiberna
On Fri, Sep 27,
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
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 Bar
Hello,
I expected the code you posted to work just as you presumed it would,
but without a reproducible example--I can only speculate as to why it
didn't.
In the t1 dataframe, if indeed you only want to remove rows of the
t1$sex_chromosome_aneuploidy_f22019_0_0 column which are undefined,
you cou
Hello,
Then it's easier, is.na alone will do it.
j <- is.na(t1$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_ch
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"
Hi,
On 10/3/19 11:58, Ana Marija wrote:
> Hello,
>
> I have a dataframe (t1) with many columns, but the one I care about it this:
>> unique(t1$sex_chromosome_aneuploidy_f22019_0_0)
> [1] NA"Yes"
>
> it has these two values.
>
> I would like to remove from my dataframe t1 all rows which have
Can we do this very simply?
My understanding is that you have a column where all the elements are
zero except for perhaps a single one.
Consider an example 0 0 1 0 0 where you want -2 -1 0 1 2. This is 1 2
3 4 5 - 3.
> v <- c(0,0,1,0,0)
> w <- which(v == 1)
> a <- seq(along=v) - if (length(w) == 0
I think the problem may lie in your understanding of what "==" does with NA
and/or what "[]" does with NA.
> x <- c(NA, "Yes")
> x == "Yes"
[1] NA TRUE
Since you say you DON'T want the rows with "Yes", you just want
x[is.na(x)]
or in your case
t11 <- t1[is.na(t1$sex_chromosome_aneuploidy_f22019_0
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