I still get the impression from your mixing of information types that you are
thinking like this is Excel.
Perhaps something like
drug_study$admin_period <- ave( "Y" == drug_study$drug_admin, drug_study$ID,
FUN=cumsum )
library(dplyr)
result0 <- ( drug_study
%>% filter( 0 != admin_period )
%>% group_by( ID, admin_period )
%>% summarise( start = min( date ) )
%>% mutate( admin_period1 = admin_period -1 )
)
result <- ( result0
%>% select( -admin_period )
%>% inner_join( result0 %>% select( ID, admin_period1, end=start )
, by = c( ID="ID", admin_period ="admin_period1" )
)
%>% mutate( ddays = end - start )
)
--
Sent from my phone. Please excuse my brevity.
On July 3, 2016 10:24:51 AM PDT, Kevin Wamae <kwa...@kemri-wellcome.org> wrote:
>HI Jeff, it’s been an uphill task working with the dataset and I am not
>the first to complain. Nonetheless, data-cleaning is ongoing and since
>I cannot wait for that to get done, I decided to make the most of what
>the dataset looks like at this time. It appears the process may take a
>while.
>
>Thanks for the script. From the output, I noticed that “result”
>contains the first and last date for each of the individuals and not
>taking into account the variable “drug-admin”.
>
>ID start end
>J1/3 1/5/09 12/25/10
>R1/3 1/4/07 12/15/08
>R10/1 1/4/07 3/5/12
>
>My aim is to pick the date, for example in 2007, where drug-admin ==
>“Y” as my start and the date in the subsequent year (2008 in this case)
>where drug-admin == “Y” as my end. Then, I should populate the variable
>“study_id” with “start” up to the entry just above the one whose date
>matches “end”, as the output below shows (I hope its structure is
>maintained as I have copied it from R-Studio). The goal for now is to
>then get difference in days between “date” and “study_id” and still get
>to keep that column for “study_id” as I might use it later.
>
>From the output, it can be seen that for this individual, the dates run
>from 2007 to 2008. However, for some individuals, the dates run from
>2008-2009, 2009-2010 and so on. Therefore, I need to make the script
>deal with all the years as the dates range from 2001-2016
>
>ID date drug_admin year month study_id
>R1/3 5/11/07 Y 2007 5 5/11/07
>R1/3 5/16/07 2007 5 5/11/07
>R1/3 5/22/07 2007 5 5/11/07
>R1/3 5/28/07 2007 5 5/11/07
>R1/3 6/5/07 2007 6 5/11/07
>R1/3 6/11/07 2007 6 5/11/07
>R1/3 6/18/07 2007 6 5/11/07
>R1/3 6/25/07 2007 6 5/11/07
>R1/3 7/2/07 2007 7 5/11/07
>R1/3 7/16/07 2007 7 5/11/07
>R1/3 7/29/07 2007 7 5/11/07
>R1/3 8/2/07 2007 8 5/11/07
>R1/3 8/7/07 2007 8 5/11/07
>R1/3 8/13/07 2007 8 5/11/07
>R1/3 9/18/07 2007 9 5/11/07
>R1/3 9/24/07 2007 9 5/11/07
>R1/3 10/6/07 2007 10 5/11/07
>R1/3 10/8/07 2007 10 5/11/07
>R1/3 10/15/07 2007 10 5/11/07
>R1/3 10/22/07 2007 10 5/11/07
>R1/3 10/29/07 2007 10 5/11/07
>R1/3 11/8/07 2007 11 5/11/07
>R1/3 11/12/07 2007 11 5/11/07
>R1/3 11/19/07 2007 11 5/11/07
>R1/3 11/29/07 2007 11 5/11/07
>R1/3 12/6/07 2007 12 5/11/07
>R1/3 12/10/07 2007 12 5/11/07
>R1/3 12/21/07 2007 12 5/11/07
>R1/3 1/7/08 2008 1 5/11/07
>R1/3 1/14/08 2008 1 5/11/07
>R1/3 1/21/08 2008 1 5/11/07
>R1/3 1/28/08 2008 1 5/11/07
>R1/3 2/4/08 Y 2008 2
>
>
>Regards
>-------------------------------------------------------------------------------
>Kevin Wame
>
>###############################################################
>
>###############################################################
>
>
>
>On 7/3/16, 7:05 PM, "Jeff Newmiller" <jdnew...@dcn.davis.ca.us> wrote:
>
>result <- setNames( data.frame( aggregate( date~ID, data=drug_study,
>FUN=min ), aggregate( date~ID, data=drug_study, FUN=max )[2] ), c(
>"ID", "start", "end" ) )
>
>
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