[R] R - Populate Another Variable Based on Multiple Conditions | For a Large Dataset

Kevin Wamae KWamae at kemri-wellcome.org
Sun Jul 3 22:08:44 CEST 2016


Hi Jeff, It works on well on a dataset with 100000 rows and I figure it will work well with the “real” dataset. You’ve been of great help and I am starting to make headway. 

It creates a new dataframe (result), as shown below that doesn’t quite have the result as I would want it.

ID	admin_period	start	end	ddays
J1/3	1	5/11/07	8/13/07	94
J1/3	2	8/13/07	11/12/07	91
J1/3	3	11/12/07	2/4/08	           84
J1/3	4	2/4/08	            5/5/08	            91
J1/3	5	5/5/08	             5/4/09            364
J1/3	6	5/4/09	             5/17/10	378
J1/3	7	5/17/10	5/16/11	364
J10/1	1	5/11/07	8/13/07	94
J10/1	2	8/13/07	11/12/07	91
J10/1	3	11/12/07	2/4/08	            84
J10/1	4	2/4/08	              5/5/08	91
J10/1	5	5/5/08	              5/8/09	368
J10/1	6	5/8/09	             5/17/10	374
J10/1	7	5/17/10	5/16/11	364
J102/1	1	5/15/07	8/15/07	92
J102/1	2	8/15/07	11/13/07	90
J102/1	3	11/13/07	2/5/08	           84
J102/1	4	2/5/08	              5/6/08	91
J102/1	5	5/6/08	              5/5/09	364
J102/1	6	5/5/09	              5/19/10	379

My supervisor doesn’t want me to create a new dataset, she’s afraid I might lose some data…I cannot fight that.

Like you mentioned earlier, I might be mixing up things which I think is what you alluded to earlier.

After consultation with my supervisor, this is what we’ve agreed. For every individual, given the start and end date, create a new column (say, diff_days) and for every row that falls within the range of start and end_date, get the difference between the date in that row and start date and add it to the diff_days column. Below is an example of the result. As it can be seen 5/11/2007 is the start while 2/4/2008 is the end. The diff_days has been populated excluding the end date and that is because that is the start of the study in 2008 that will continue into 2009 and thus from 2/4/2008, I should compute diff_days till 2009 and so no (I hope this makes sense).

ID	date	drug_admin	year	month	diff_days
R1/3	5/11/2007	Y	2007	5	0
R1/3	5/16/2007		2007	5	6
R1/3	5/22/2007		2007	5	11
R1/3	5/28/2007		2007	5	17
R1/3	1/14/2008		2008	1	248
R1/3	1/21/2008		2008	1	255
R1/3	1/28/2008		2008	1	263
R1/3	2/4/2008	Y	2008	2	


Regards
-------------------------------------------------------------------------------
Kevin Wame 
 

On 7/3/16, 10:09 PM, "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> wrote:

Typo on the second line

result <- (   result0 
          %>% select( -admin_period1 )
          %>% 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 11:55:14 AM PDT, Kevin Wamae <KWamae at kemri-wellcome.org> wrote:
>Hi Jeff, “likes its Excel”, I don’t follow. Pardon me for any mix up.
>
>Thanks for the code.  After running it, this is the error I get.
>
>Error: cannot join on columns 'admin_period' x 'admin_period1': index
>out of bounds
>
>Regards
>-------------------------------------------------------------------------------
>Kevin Wame | Ph.D. Student (IDeAL)
>KEMRI-Wellcome Trust Collaborative Research Programme
>Centre for Geographic Medicine Research
>P.O. Box 230-80108, Kilifi, Kenya
> 
>
>On 7/3/16, 9:34 PM, "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> wrote:
>
>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
><KWamae at 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" <jdnewmil at 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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