Hi Bill,
Modifying `f2` seems to solve the problem.
f2 <- function (data)
{
library(dplyr)
data%>%
group_by(id, value) %>%
mutate(date=as.Date(date))%>%
arrange(date) %>%
filter(indx =any(c(abs(diff(date)),NA) >31)& date==min(date)) %>%
filter(row_number()==1)
}
f2(dat)
Source: local data frame [3 x 3]
Groups: id, value
id date value
1 a 2000-01-01 x
2 c 2000-09-10 y
3 c 2000-10-11 z
f2(dat1)
Source: local data frame [1 x 3]
Groups: id, value
id value date
1 A x 2000-10-02
A.K.
On Wednesday, July 16, 2014 4:25 PM, William Dunlap <wdun...@tibco.com> wrote:
> filter(any(c(abs(diff(as.Date(date))),NA)>31)& date == min(date))
Note that the 'date == min(date)' will cause superfluous output rows
when there are several readings on initial date for a given id/value
pair. E.g.,
> dat1 <- data.frame(stringsAsFactors=FALSE, id=rep("A", 4), value=rep("x", 4),
> date=as.Date("2000-10-1")+c(1,1,50,50))
> f2(dat1) # want 1 output row: A, x, 2000-10-2
Source: local data frame [2 x 3]
Groups: id, value
id value date
1 A x 2000-10-02
2 A x 2000-10-02
where f2 is your code wrapped up in a function (to make testing and use easier)
f2 <- function (data)
{
library(dplyr)
data %>% group_by(id, value) %>% arrange(date = as.Date(date)) %>%
filter(any(c(abs(diff(as.Date(date))), NA) > 31) & date == min(date))
}
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Wed, Jul 16, 2014 at 7:49 AM, arun <smartpink...@yahoo.com> wrote:
> Hi,
> If `dat` is the dataset
>
> library(dplyr)
> dat%>%
> group_by(id,value)%>%
>
> arrange(date=as.Date(date))%>%
> filter(any(c(abs(diff(as.Date(date))),NA)>31)& date == min(date))
> #Source: local data frame [3 x 3]
> #Groups: id, value
> #
> # id date value
> #1 a 2000-01-01 x
> #2 c 2000-09-10 y
> #3 c 2000-10-11 z
> A.K.
>
>
>
>
> On Wednesday, July 16, 2014 9:10 AM, Williams Scott
> <scott.willi...@petermac.org> wrote:
> Hi R experts,
>
> I have a dataset as sampled below. Values are only regarded as Œconfirmed¹
> in an individual (Œid¹) if they occur
> more than once at least 30 days apart.
>
>
> id date value
> a 2000-01-01 x
> a 2000-03-01 x
> b 2000-11-11 w
> c 2000-11-11 y
> c 2000-10-01 y
> c 2000-09-10 y
> c 2000-12-12 z
> c 2000-10-11 z
> d 2000-11-11 w
> d 2000-11-10 w
>
>
> I wish to subset the data to retain rows where the value for the
> individual is confirmed more than 30 days apart. So, after deleting all
> rows with just one occurrence of id and value, the rest would be the
> earliest occurrence of each value in each case id, provided 31 or more
> days exist between the dates. If >1 value is present per id, each value
> level needs to be assessed independently. This example would then reduce
> to:
>
>
> id date value
> a 2000-01-01 x
> c 2000-09-10 y
> c 2000-10-11 z
>
>
>
> I can do this via some crude loops and subsetting, but I am looking for as
> much efficiency as possible
> as the dataset has around 50 million rows to assess. Any suggestions
> welcomed.
>
> Thanks in advance
>
> Scott Williams MD
> Melbourne, Australia
>
>
>
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