Peter Dalgaard,
Thanks for this.
I’ll try to think of ways to apply this logic. At the moment, I’m trying to do
this with “mutate” using dplyr package. But it’s not easy..
> On 27 Jul 2019, at 10:33, peter dalgaard <pda...@gmail.com> wrote:
>
> Some pointers (not tested, may contain blunders...)
>
> (a) you likely need some sort of split-operate-unsplit construct, by country.
> E.g.,
>
> myfun <- function(d) {....operate on data frame with only one country....}
> ll <- split(data, data$country)
> ll.new <- lapply(ll, myfun)
> data.new <- unsplit(ll.new, data$country)
>
> (There might be a tidyverse idiom for this too)
>
> (b) your X1_pre5count looks like it is the same as cumsum(1-X1)*X1 (within
> country)
>
> (c) if you count in the opposite direction, tt <- rev(cumsum(rev(1-X1))) you
> get number of years until agreement. Then X1_pre4 should be as.integer(tt <=4
> & tt > 0)
>
> -pd
>
>> On 27 Jul 2019, at 09:13 , Faradj Koliev <farad...@gmail.com> wrote:
>>
>> Re-post, now in *plain text*.
>>
>>
>>
>> Dear R-users,
>>
>> I’ve a rather complicated task to do and need all the help I can get.
>>
>> I have data indicating whether a country has signed an agreement or not
>> (1=yes and 0=otherwise). I want to simply create variable that would capture
>> the years before the agreement is signed. The aim is to see whether pre or
>> post agreement period has any impact on my dependent variables.
>>
>> More preciesly, I want to create the following variables:
>> (i) a variable that is =1 in the 4 years pre/before the agreement, 0
>> otherwise;
>> (ii) a variable that is =1 5 years pre the agreement and
>> (iii) a variable that would count the 4 and 5 years pre the agreement
>> (1,2,3,4..).
>>
>> Please see the sample data below. I have manually added the variables I
>> would like to generate in R, labelled as “X1_pre4” ( 4 years before the
>> agreement X1), “X2_pre4”, “X1_pret5” ( 5 years before the agreement X5), and
>> “X1pre5_count” (which basically count the years, 1,2,3, etc). The X1 and X2
>> is the agreement that countries have either signed (1) or not (0). Note
>> though that I want the variable to capture all the years up to 4 and 5. If
>> it’s only 2 years, it should still be ==1 (please see the example below).
>>
>> To illustrate the logic: the country A has signed the agreement X1 in 1972
>> in the sample data, then, the (i) and (ii) variables as above should be =1
>> for the years 1970, 1971, and =0 from 1972 until the end of the study
>> period.
>>
>> The country A has signed the agreement X2 in 1975, then, the (i) variable
>> should be =1 from 1971 to 1974 (post 4 years) and (ii) should be =1 for the
>> 1970-1974 period (post 5 years before the agreement is signed).
>>
>> Later, I would also like to create post_4 and post_5 variables, but I think
>> I’ll be able to figure it out once I know how to generate the pre/before
>> variables.
>>
>> All suggestions are much appreciated!
>>
>>
>>
>> data<-structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
>> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>> 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
>> 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A", "B", "C"), class = "factor"),
>> year = c(1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L,
>> 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L,
>> 1986L, 1987L, 1988L, 1970L, 1971L, 1972L, 1973L, 1974L, 1975L,
>> 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L, 1984L,
>> 1985L, 1986L, 1987L, 1988L, 1970L, 1971L, 1972L, 1973L, 1974L,
>> 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L,
>> 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L),
>> X1 = c(0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L), X2 = c(0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
>> 1L, 1L, 1L, 1L), X1_pre4 = c(1L, 1L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), X2_pre4 = c(0L, 1L, 1L,
>> 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
>> 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), X1_pre5 = c(1L,
>> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
>> X1_pre5_count = c(1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 3L,
>> 4L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 3L, 4L, 5L, 0L, 0L, 0L,
>> 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
>> -60L))
>>
>>> On 26 Jul 2019, at 21:58, Bert Gunter <bgunter.4...@gmail.com> wrote:
>>>
>>> Because you posted in HTML, your example got mangled and resulted in an
>>> error. Re-post in *plain text* please (making sure that you cut and paste
>>> correctly)
>>>
>>> Bert Gunter
>>>
>>> "The trouble with having an open mind is that people keep coming along and
>>> sticking things into it."
>>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>>
>>>
>>> On Fri, Jul 26, 2019 at 12:25 PM Faradj Koliev <farad...@gmail.com> wrote:
>>> Dear R-users,
>>>
>>> I’ve a rather complicated task to do and need all the help I can get.
>>>
>>> I have data indicating whether a country has signed an agreement or not
>>> (1=yes and 0=otherwise). I want to simply create variable that would
>>> capture the years before the agreement is signed. The aim is to see whether
>>> pre or post agreement period has any impact on my dependent variables.
>>>
>>> More preciesly, I want to create the following variables:
>>> (i) a variable that is =1 in the 4 years pre/before the agreement, 0
>>> otherwise;
>>> (ii) a variable that is =1 5 years pre the agreement and
>>> (iii) a variable that would count the 4 and 5 years pre the agreement
>>> (1,2,3,4..).
>>>
>>> Please see the sample data below. I have manually added the variables I
>>> would like to generate in R, labelled as “X1_pre4” ( 4 years before the
>>> agreement X1), “X2_pre4”, “X1_pret5” ( 5 years before the agreement X5),
>>> and “X1pre5_count” (which basically count the years, 1,2,3, etc). The X1
>>> and X2 is the agreement that countries have either signed (1) or not (0).
>>> Note though that I want the variable to capture all the years up to 4 and
>>> 5. If it’s only 2 years, it should still be ==1 (please see the example
>>> below).
>>>
>>> To illustrate the logic: the country A has signed the agreement X1 in 1972
>>> in the sample data, then, the (i) and (ii) variables as above should be =1
>>> for the years 1970, 1971, and =0 from 1972 until the end of the study
>>> period.
>>>
>>> The country A has signed the agreement X2 in 1975, then, the (i) variable
>>> should be =1 from 1971 to 1974 (post 4 years) and (ii) should be =1 for the
>>> 1970-1974 period (post 5 years before the agreement is signed).
>>>
>>> Later, I would also like to create post_4 and post_5 variables, but I think
>>> I’ll be able to figure it out once I know how to generate the pre/before
>>> variables.
>>>
>>> All suggestions are much appreciated!
>>>
>>>
>>>
>>> data<–structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
>>> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>>> 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
>>> 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A", "B", "C"), class = "factor"),
>>> year = c(1970L, 1971L, 1972L, 1973L, 1974L, 1975L, 1976L,
>>> 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L,
>>> 1986L, 1987L, 1988L, 1970L, 1971L, 1972L, 1973L, 1974L, 1975L,
>>> 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L, 1984L,
>>> 1985L, 1986L, 1987L, 1988L, 1970L, 1971L, 1972L, 1973L, 1974L,
>>> 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, 1983L,
>>> 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L),
>>> X1 = c(0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L), X2 = c(0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L), X1_pre4 = c(1L, 1L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), X2_pre4 = c(0L, 1L, 1L,
>>> 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
>>> 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), X1_pre5 = c(1L,
>>> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
>>> X1_pre5_count = c(1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 3L,
>>> 4L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 3L, 4L, 5L, 0L, 0L, 0L,
>>> 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
>>> -60L))
>>>
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>
>> ______________________________________________
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Office: A 4.23
> Email: pd....@cbs.dk Priv: pda...@gmail.com
>
>
>
>
>
>
>
>
>
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