Categorizing Hospital Admittance Data in Excel or R - r

Question

I have patient data that looks like this:
ID DATE DUR
82 29/08/2014 10.32
82 29/08/2014 0.32
82 12/09/2014 13.35
82 12/09/2014 0.16
82 12/09/2014 0.24
82 12/09/2014 0.31
82 22/12/2014 100.39
82 22/12/2014 0.1
219 31/11/2012 -300.32
219 31/11/2012 0.23
219 12/01/2013 80.20
219 12/01/2013 0.20
In the first column is a patient ID, In the second there is a date and time (time is visually missing but is in there) and the third is just the duration difference (which I have been using to determine different admittance of patients). Each different row is a check up on the patient but they may have come here at a later date (not with in the same time frame).
Basically what I want to do is to be able to categorize each patients number so that when they admit a second time there id becomes "82a" and third time "82b" and so on. It wouldn't have to be alphabetic it could be any such indicator. Sometimes there can be patients with as many as 50 different admissions (separate occasion admissions). So after this I want to have it look something like:
ID DATE DUR
82 29/08/2014 10.32
82 29/08/2014 0.32
82a 12/09/2014 13.35
82a 12/09/2014 0.16
82a 12/09/2014 0.24
82a 12/09/2014 0.31
82b 22/12/2014 100.39
82b 22/12/2014 0.1
219 31/11/2012 -300.32
219 31/11/2012 0.23
219a 12/01/2013 80.20
219a 12/01/2013 0.20
I have been working in Excel for the time being and at first had used
=IF(AND(ABS(C3)>1,A3=A2),1,0)
Just to allow to indicate when an ID is repeated on a new admission date, then I did this again to indicate the 3rd admission and began drawing out columns for 4th,5th,6th and planned on merging them. This is simply not an efficient solution, especially with a large data set. I am familiar with R and think that might be a better way for manipulation but I am just stuck with how to do this for the entire data set and to continually add a new "indicator" every time the same patient is admitted again. I am not even sure exactly how to tell the computer what to do with pseudo. Perhaps something like this
Pseudo-Code
-> Run through ID Column
-> IF Dur is > 1 (it will always be > 1 for a new admission)
ANDIF ID already exists above with DUR > 1 = a, or if DUR > 1 TWICE for
same ID = b, or if DUR > THREE TIMES = c, and so on....
Any help would be great

Answer 1

In R, you have a lot of options. Your data has issues, however; since November only has 30 days, converting the DATE column to an actual date format will introduce NAs. (You could, of course, just leave it as character, but date formats are easier to work with.)
With dplyr:
library(dplyr)
df %>% mutate(DATE = as.Date(DATE, '%d/%m/%Y')) %>% # parse date data
group_by(ID) %>% # group data by ID
mutate(visit = as.integer(factor(DATE))) # make an integer factor of DATE
# Source: local data frame [12 x 4]
# Groups: ID [2]
#
# ID DATE DUR visit
# (int) (date) (dbl) (int)
# 1 82 2014-08-29 10.32 1
# 2 82 2014-08-29 0.32 1
# 3 82 2014-09-12 13.35 2
# 4 82 2014-09-12 0.16 2
# 5 82 2014-09-12 0.24 2
# 6 82 2014-09-12 0.31 2
# 7 82 2014-12-22 100.39 3
# 8 82 2014-12-22 0.10 3
# 9 219 <NA> -300.32 NA
# 10 219 <NA> 0.23 NA
# 11 219 2013-01-12 80.20 1
# 12 219 2013-01-12 0.20 1
Base R has a lot of options, including ave and tapply, but to keep it simple so you can see what happens step-by-step in a split-apply-combine model, split by grouping variable, lapply across the list, and use do.call(rbind to reassemble:
df$DATE <- as.Date(df$DATE, '%d/%m/%Y')
df <- do.call(rbind, lapply(split(df, df$ID),
function(x){data.frame(x,
visit = as.integer(factor(x$DATE)))}))
rownames(df) <- NULL # delete useless rownames
df
# ID DATE DUR visit
# 1 82 2014-08-29 10.32 1
# 2 82 2014-08-29 0.32 1
# 3 82 2014-09-12 13.35 2
# 4 82 2014-09-12 0.16 2
# 5 82 2014-09-12 0.24 2
# 6 82 2014-09-12 0.31 2
# 7 82 2014-12-22 100.39 3
# 8 82 2014-12-22 0.10 3
# 9 219 <NA> -300.32 NA
# 10 219 <NA> 0.23 NA
# 11 219 2013-01-12 80.20 1
# 12 219 2013-01-12 0.20 1