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 <j...@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
[...]
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