Aggregation of variables by date in R - r

Question

I am more used to working with STATA and have been trying to switch to R, and having trouble getting this aggregation using dplyr/summarise to work.
I have a dataframe with admission/discharge variables, and series of columns with binary (0,1) results indicating drug received on 'DrugDate'.
# ID AdmitDate DCdate DrugDate DrugA DrugB .. DrugZ
# 1 03/01/2017 03/04/2017 03/01/2017 1 0 0
# 1 03/01/2017 03/04/2017 03/02/2017 1 0 0
# 1 03/01/2017 03/04/2017 03/02/2017 0 1 0
# 1 03/01/2017 03/04/2017 03/03/2017 1 0 0
# 1 03/01/2017 03/04/2017 03/04/2017 1 0 0
Where each row is essentially an series of indicators of what drugs a patient received that day.
STEP 1.
I would like to first consolidate the dataset like so:
# ID AdmitDate DCdate DrugDate DrugA DrugB .. DrugZ
# 1 03/01/2017 03/04/2017 03/01/2017 1 0 0
# 1 03/01/2017 03/04/2017 03/02/2017 1 1 0
# 1 03/01/2017 03/04/2017 03/03/2017 1 0 0
# 1 03/01/2017 03/04/2017 03/04/2017 1 0 0
So that there is now one row per day (whereas before duplicate DrugDates existed when more than one drug given on a certain day).
STEP 2
I would then like to create a new dataset that counts "drug days" i.e.
# ID AdmitDate DCdate TotDays DrugDaysA DrugDaysB .. DrugZ
# 1 03/01/2017 03/04/2017 4 4 1 0
Step 2 I figured out how to do, but I thought maybe the community would have opinions about the fastest way to compute as the dataset is quite large. My understanding is dplyr is usually computationally efficient.
I would prefer not to simply do something like:
DF %>% group_by(id, drugdate) %>% summarise(NewVar = max(DrugA))
Because there are many variables.
It would be ideal for me to define a list of varnames, then use apply/for-loop to automate the process.

Answer 1

You can reshape or melt the different drugs into one column using a package like reshape2 or a tidyverse package.
Then the call to dplyr doesn't matter how many variables (drugs) you have. I provided a simple example bellow that should illustrate the point. You can extend as needed.
library(dplyr)
library(reshape2)
# set up for data
set.seed(5)
n <- 9
#create data frame
df <- data.frame(id = as.factor(rep(1:3, n/3)),
date = as.character(sample(size=n, 1:10)),
drugA = sample(size=n, 1:2, replace=TRUE),
drugB = sample(size=n, 1:2, replace=TRUE))
#melt data
dfm <- melt(df, id.vars=c("id", "date"))
#call to dplyr
dfms <- dfm %>% group_by(id, date, variable) %>% summarise(max = max(value))
> head(dfms)
Source: local data frame [6 x 4]
Groups: id, date [3]
id date variable max
<fctr> <fctr> <fctr> <int>
1 1 6 drugA 1
2 1 6 drugB 2
3 1 7 drugA 2
4 1 7 drugB 2
5 1 9 drugA 2
6 1 9 drugB 1
To get back into wide format you can use the cast functions.
> head(dcast(dfms, id + date ~ variable, value.var = "max"))
id date drugA drugB
1 1 6 1 2
2 1 7 2 2
3 1 9 2 1
4 2 10 1 2
5 2 2 2 1
6 2 8 1 1