Hi Eberhard,
Here is one possibility using dplyr.
library(dplyr)
set.seed(3)
## set up some fake data
dtV <- as.Date("2020-08-01") + 0:4
x <- sample(dtV,20,repl=TRUE)
provider <- sample(LETTERS[1:3],20,repl=TRUE)
lDf <- data.frame(Provider=provider,CollectionDate=x,stringsAsFactors=FALSE)
## get min/max date for each provider
a <- lDf %>% dplyr::group_by( Provider ) %>%
dplyr::mutate( minDt=min(CollectionDate), maxDt=max(CollectionDate)) %>%
dplyr::summarize( u = min(minDt), v = max(maxDt) )
## get the common interval
c(max(a$u), min(a$v))
# [1] "2020-08-02" "2020-08-04"
HTH,
Eric
On Fri, Aug 21, 2020 at 12:34 PM Rasmus Liland <jral at posteo.no> wrote:
> On 2020-08-21 09:03 +0200, Dr Eberhard Lisse wrote:
> > Hi,
> >
> > I have a small test sample with lab
> > reports (PAP smears) from a number of
> > different providers. These have
> > Collection Dates and the relevant
> > columns glimpse() something like
> > this:
> >
> > $ Provider <chr> "Dr C", "Dr D",
"Dr C", "Dr D"
> > $ CollectionDate <chr> "2016-11-03",
"2016-11-02", "2016-11-03",
> "2016-11-03"
> >
> > I am looking to find (filter) the
> > reports which were collected in the
> > time period common to all providers?
> >
> > Something like
> >
> > the largest First Common CollectionDate
> > and
> > the smallest Last Common CollectionDate
> >
> > How would I do that?
> >
> > I can of course do this "manually", ie
> > collect all Providers and their first
> > and last Collection dates and then
> > find the Common First and Last one,
> > but wonder if there is an elegant way
> > of doing this :-)-O
>
> Dear Eberhard,
>
> Is each report in a csv file with those
> two columns, and you want to unify them
> into a dataframe with CollectionDate
> along the rows, and other details for
> each provider along the columns? This
> can be done with various apply calls and
> reshape. Can you please subset some
> more example data here using dput. It
> makes it so much easier.
>
> /Rasmus
> ______________________________________________
> R-help at 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.
>
[[alternative HTML version deleted]]
Eric, Rasmus, thank you very much, ALLPAP %>% group_by(Provider) %>% mutate( minDt=min(CollectionDate), maxDt=max(CollectionDate)) %>% summarize( minDt = min(minDt), maxDt = max(maxDt), .groups="keep" ) %>% ungroup() %>% mutate(MAX_MIN_DATE = max(minDt), MIN_MAX_DATE = min(maxDt)) %>% distinct(MAX_MIN_DATE, MIN_MAX_DATE) gives me # A tibble: 1 x 2 MAX_MIN_DATE MIN_MAX_DATE <chr> <chr> 1 2010-02-05 2019-08-30 which is correct, and what I wanted. This is so cool :-)-O el On 21/08/2020 11:41, Eric Berger wrote:> Hi Eberhard, > Here is one possibility using dplyr.[...]> > HTH, > Eric > > > On Fri, Aug 21, 2020 at 12:34 PM Rasmus Liland <jral at posteo.no> wrote: >> On 2020-08-21 09:03 +0200, Dr Eberhard Lisse wrote:[...]>> >> Dear Eberhard, >> >> Is each report in a csv file with those two columns, and you want to >> unify them into a dataframe with CollectionDate along the rows, and >> other details for each provider along the columns? This can be done >> with various apply calls and reshape. Can you please subset some >> more example data here using dput. It makes it so much easier. >> >> /Rasmus[...] -- Dr. Eberhard W. Lisse \ / Obstetrician & Gynaecologist el at lisse.NA / * | Telephone: +264 81 124 6733 (cell) PO Box 8421 Bachbrecht \ / If this email is signed with GPG/PGP 10007, Namibia ;____/ Sect 20 of Act No. 4 of 2019 may apply -- If you want to email me, replace nospam with el
Using mutate followed by summarise in this case is completely unnecessary.
a <- ( lDf
%>% dplyr::group_by( Provider )
%>% dplyr::summarise( u = min( CollectionDate )
,, v = max( CollectionDate )
)
)
On August 21, 2020 2:41:26 AM PDT, Eric Berger <ericjberger at gmail.com>
wrote:>Hi Eberhard,
>Here is one possibility using dplyr.
>
>library(dplyr)
>set.seed(3)
>
>## set up some fake data
>dtV <- as.Date("2020-08-01") + 0:4
>x <- sample(dtV,20,repl=TRUE)
>provider <- sample(LETTERS[1:3],20,repl=TRUE)
>lDf <-
>data.frame(Provider=provider,CollectionDate=x,stringsAsFactors=FALSE)
>
>## get min/max date for each provider
>a <- lDf %>% dplyr::group_by( Provider ) %>%
>dplyr::mutate( minDt=min(CollectionDate), maxDt=max(CollectionDate))
>%>%
> dplyr::summarize( u = min(minDt), v = max(maxDt) )
>
>## get the common interval
>c(max(a$u), min(a$v))
>
># [1] "2020-08-02" "2020-08-04"
>
>HTH,
>Eric
>
>
>On Fri, Aug 21, 2020 at 12:34 PM Rasmus Liland <jral at posteo.no>
wrote:
>
>> On 2020-08-21 09:03 +0200, Dr Eberhard Lisse wrote:
>> > Hi,
>> >
>> > I have a small test sample with lab
>> > reports (PAP smears) from a number of
>> > different providers. These have
>> > Collection Dates and the relevant
>> > columns glimpse() something like
>> > this:
>> >
>> > $ Provider <chr> "Dr C", "Dr D",
"Dr C", "Dr D"
>> > $ CollectionDate <chr> "2016-11-03",
"2016-11-02", "2016-11-03",
>> "2016-11-03"
>> >
>> > I am looking to find (filter) the
>> > reports which were collected in the
>> > time period common to all providers?
>> >
>> > Something like
>> >
>> > the largest First Common CollectionDate
>> > and
>> > the smallest Last Common CollectionDate
>> >
>> > How would I do that?
>> >
>> > I can of course do this "manually", ie
>> > collect all Providers and their first
>> > and last Collection dates and then
>> > find the Common First and Last one,
>> > but wonder if there is an elegant way
>> > of doing this :-)-O
>>
>> Dear Eberhard,
>>
>> Is each report in a csv file with those
>> two columns, and you want to unify them
>> into a dataframe with CollectionDate
>> along the rows, and other details for
>> each provider along the columns? This
>> can be done with various apply calls and
>> reshape. Can you please subset some
>> more example data here using dput. It
>> makes it so much easier.
>>
>> /Rasmus
>> ______________________________________________
>> R-help at 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.
>>
>
> [[alternative HTML version deleted]]
>
>______________________________________________
>R-help at 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.
--
Sent from my phone. Please excuse my brevity.
On 2020-08-21 13:45 +0200, Dr Eberhard Lisse wrote:
|
| Eric, Rasmus,
|
| thank you very much,
|
| ALLPAP %>%
| group_by(Provider) %>%
| mutate( minDt=min(CollectionDate),
| maxDt=max(CollectionDate)) %>%
| summarize( minDt = min(minDt),
| maxDt = max(maxDt), .groups="keep" ) %>%
| ungroup() %>%
| mutate(MAX_MIN_DATE = max(minDt),
| MIN_MAX_DATE = min(maxDt)) %>%
| distinct(MAX_MIN_DATE, MIN_MAX_DATE)
|
| gives me
|
| # A tibble: 1 x 2
| MAX_MIN_DATE MIN_MAX_DATE
| <chr> <chr>
| 1 2010-02-05 2019-08-30
|
| which is correct, and what I wanted.
|
| This is so cool :-)-O
Dear Eberhard,
handling Dates is a bit tricky in normal
R, but as long as they are characters,
like in your example there, everything
is fine. So I made this example based
on Eric's example:
set.seed(3)
size <- 20
x <- as.Date("2016-11-03") +
sample(
0:30,
size,
repl=TRUE)
provider <- paste("Dr",
sample(
LETTERS[1:3],
size,
repl=TRUE))
lDf <- data.frame(
Provider=provider,
CollectionDate=x,
stringsAsFactors=FALSE)
Provider <- sort(unique(lDf$Provider))
a <- t(sapply(Provider, function(provider, lDf) {
cd <- lDf[
lDf$Provider==provider,
"CollectionDate"]
c("Provider"=provider,
as.character(c(
"u"=min(cd),
"v"=max(cd))))
}, lDf=lDf))
a
which yields
Provider u v
Dr A "Dr A" "2016-11-06" "2016-12-01"
Dr B "Dr B" "2016-11-07" "2016-12-03"
Dr C "Dr C" "2016-11-04" "2016-11-12"
Before I did that, I thought about doing
something with reshape2, but I could not
come up with something good.
If you want to work with tibbles in that
tidyverse thing, which probably can more
easily work with Dates, rbinding tibbles
together apparently works:
a <- lapply(Provider, function(provider, lDf) {
cd <- lDf[
lDf$Provider==provider,
"CollectionDate"]
dplyr::tibble(
"Provider"=provider,
"u"=min(cd),
"v"=max(cd))
}, lDf=lDf)
a <- do.call(rbind, a)
a
which yields
# A tibble: 3 x 3
Provider u v
<chr> <date> <date>
1 Dr A 2016-11-06 2016-12-01
2 Dr B 2016-11-07 2016-12-03
3 Dr C 2016-11-04 2016-11-12
Best,
Rasmus
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