Thank you all. I now have the right solution for this (perhaps of interest to
some):
check_pre <- function(idx, k) { pre_vec <- sapply(1:length(idx), function(x)
+any(idx[x:(pmin(x + k, length(idx)))] %in% 1)); pre_vec[idx == 1] <- 0;
return(pre_vec) }
df %>%
group_by(country) %>%
mutate(
idx = +( (lag(X1) == 0 & X1 == 1) | row_number() == 1 & X1 == 1),
X1_pre4 = check_pre(idx, 4),
X1_pre5 = check_pre(idx, 5),
idx = NULL
)
> On 27 Jul 2019, at 10:45, Faradj Koliev <farad...@gmail.com> wrote:
>
> 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
>>
>>
>>
>>
>>
>>
>>
>>
>>
>
[[alternative HTML version deleted]]
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