Say I have (fake) patient data from their visits:
## Create a fake dataframe
foo <- data.frame(PatientNumber=c(11,11,11,22,22,33,33,33,44,55,55),
VisitDate=c("11/03/07","11/03/07","11/20/07","12/20/08",
"12/30/09","09/20/12","09/20/12","10/25/07","05/09/08","06/09/13","06/09/13"),
ICD9=c(10,15,10,30,30,25,60,25,14,40,13))
Which gives:
PatientNumber VisitDate ICD9
1 11 11/03/07 10
2 11 11/03/07 15
3 11 11/20/07 10
4 22 12/20/08 30
5 22 12/30/09 30
6 33 09/20/12 25
7 33 09/20/12 60
8 33 10/25/07 25
9 44 05/09/08 14
10 55 06/09/13 40
11 55 06/09/13 13
I would like to have a unique row for each patient at a given visit date. If the patient has multiple codes for a date, I would like a new column for all ICD code given at that visit. This is what it would look like:
WhatIWant <- data.frame(PatientNumber=c(11,11,22,22,33,33,44,55),
VisitDate=c("11/03/07", "11/20/07", "12/20/08", "12/30/09", "09/20/12","10/25/07","05/09/08","06/09/13"),
ICD9_1=c(10,10,30,30,25,25,14,40),
ICD9_2= c(15,NA,NA,NA,60,NA,NA,13))
> WhatIWant
PatientNumber VisitDate ICD9_1 ICD9_2
1 11 11/03/07 10 15
2 11 11/20/07 10 NA
3 22 12/20/08 30 NA
4 22 12/30/09 30 NA
5 33 09/20/12 25 60
6 33 10/25/07 25 NA
7 44 05/09/08 14 NA
8 55 06/09/13 40 13
I've tried reshape, but it seems to add all the ICD9 codes in a column and add the value in a column if they have a value or not (as shown below).I will end up with something like 200 columns, I would only like 3 (the max # of codes per patient per visit in the data set I have, ie ICD9_1, ICD9_2, ICD9_3).
test <- reshape(foo, idvar = c("VisitDate"), timevar = c("PatientNumber"), direction = "wide")
> test
VisitDate ICD9.11 ICD9.22 ICD9.33 ICD9.44 ICD9.55
1 0007-11-03 10 NA NA NA NA
3 0007-11-20 10 NA NA NA NA
4 0008-12-20 NA 30 NA NA NA
5 0009-12-30 NA 30 NA NA NA
6 0012-09-20 NA NA 25 NA NA
8 0007-10-25 NA NA 25 NA NA
9 0008-05-09 NA NA NA 14 NA
10 0013-06-09 NA NA NA NA 40
Sorry if the title isn't as specific as it could be, I'm not really sure how to exactly title what I am looking for.
Thanks in advance for your help!
Also,
library(dplyr)
library(tidyr) # See below on how to get tidyr
foo %>%
group_by(PatientNumber, VisitDate) %>%
mutate(n=paste("ICD9",row_number(), sep="_")) %>%
spread(n, ICD9)
#Source: local data frame [8 x 4]
# PatientNumber VisitDate ICD9_1 ICD9_2
#1 11 11/03/07 10 15
#2 11 11/20/07 10 NA
#3 22 12/20/08 30 NA
#4 22 12/30/09 30 NA
#5 33 09/20/12 25 60
#6 33 10/25/07 25 NA
#7 44 05/09/08 14 NA
#8 55 06/09/13 40 13
Package tidyr
is not available on CRAN yet. Install it like this (see tidyr
git):
# install.packages("devtools")
devtools::install_github("hadley/tidyr")
The basic problem for reshape
in this case is that it doesn't have a real "time" variable. That's easy to create with ave
:
foo$time <- with(foo, ave(rep(1, nrow(foo)),
PatientNumber, VisitDate,
FUN = seq_along))
Then, you can use reshape
as follows:
reshape(foo, direction = "wide",
idvar=c("PatientNumber", "VisitDate"),
timevar="time")
# PatientNumber VisitDate ICD9.1 ICD9.2
# 1 11 11/03/07 10 15
# 3 11 11/20/07 10 NA
# 4 22 12/20/08 30 NA
# 5 22 12/30/09 30 NA
# 6 33 09/20/12 25 60
# 8 33 10/25/07 25 NA
# 9 44 05/09/08 14 NA
# 10 55 06/09/13 40 13
Of course, once you have that "time" variable, you can also use dcast
from "reshape2".
library(reshape2)
dcast(foo, PatientNumber + VisitDate ~ time, value.var="ICD9")
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