I have a large dataset (~1 million rows) of three variables: ID
(patient's name), DATE (of appointment) and DIAGNOSIS (given on that
date).
Patients may have been assigned more than one diagnosis at any one
appointment - leading to two rows, same ID and DATE but different
DIAGNOSIS.
The diagnoses may change between appointments.
I want to subset the data in two ways:
- define groups of patients by the first diagnosis given
- define groups of patients by the last diagnosis given.
The problem:
Unfortunately, a small number of patients have been given more than
one diagnosis at their first (or last) appointment. These individuals
I need to identify and remove, as it's not possible to say uniquely
what their first (or last) diagnosis was. So I need to identify and
remove these individuals which have pairs of rows with the same ID
and (lowest or highest) DATE. The size of the dataset precludes the
option of doing this by eye.
I suspect there is a very elegant way of doing this in R.
This is what I've come up with:
- Sort by DATE then ID
- Make a ragged array of DATE by ID
- Remove IDs that only occur once.
- Subtract the first and second DATEs. Remove IDs for which
this = zero, as this will only be true for IDs for which the
appointment is recorded twice (because there were two diagnoses
recorded on this date).
- (Then do the same to get the 'last appointment'
duplicates, by reversing the initial sort by DATE.)
I am stuck at the 'Subtract dates' step: I would like to get the data
out of the ragged array by columns (so e.g. I end up with a matrix of
ID, 1st DATE, 2nd DATE). But I can't get the dates out by column from
the ragged array.
I hope someone can help. My ugly code is below, with some data for
testing.
Stuart
Dr Stuart John Leask DM FRCPsych MB BChir MA
Clinical Senior Lecturer and Honorary Consultant Pychiatrist
Institute of Mental Health, Innovation Park
Triumph Road, Nottingham, Notts. NG7 2TU. UK
Tel. +44 115 82 30419
stuart.leask at nottingham.ac.uk<mailto:stuart.leask at nottingham.ac.uk>
Google 'Dr Stuart Leask'
ID <- c(58,58,58,58,167,167,323,323,323,323,323,323,323
,547,794,814,814,814,814,814,814,841,841,841,841,841
,841,841,841,841,910,910,910,910,910,910,999,1019,1019
,1019)
DATE <- c(20060821,20061207,20080102,20090904,20040205,20040323,20051111
,20060111,20071119,20080107,20080407,20080521,20080711,20041005
,20070905,20020814,20021125,20040429,20040429,20071205,20080227
,20050421,20060130,20060428,20060602,20060816,20061025,20061129
,20070112,20070514,20091105,20091117,20091119,20091120,20091210
,20091224,20050503,19870508,19880223,19880330)
id.d <- cbind (ID,DATE )
rag.a <- split ( id.d [ ,2 ], id.d [ ,1]) # create
ragged array, 1-n DATES for every NAME
# Inelegant attempt to remove IDs that only have one entry:
rag.s <-tapply (id.d [ ,2], id.d [ ,1], sum) #add up the dates per row
# Since DATE is in 'year mo da', if there's only one date, sum will
be less than 2100000:
rag.t <- rag.s [ rag.s > 21000000 ]
multi.dates <- rownames ( rag.t ) # all the
IDs with >1 date
rag.am <- rag.a [ multi.dates ] # rag.am
only has IDs with > 1 Date
# But now I'm stuck.
# Each row of the array is rag.am$ID.
# So I can't pick columns of DATEs from the ragged array.
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