Creating cohort-style data frame from set of observations [duplicate]

Question

I'm new to R and have a simple question, as I'm still learning the style of R data manipulation/management.

I have a dataset of observations of basic clinical features (blood pressure, cholesterol, etc) over a period of time. Each observation has a patient ID and date, but are entered as separate line items. Something like this:

Patient ID    Date  Blood Pressure
         1 21/1/14             120
         1 19/3/14             134
         1  3/5/14             127

I want to transform the data such that for a given variable (e.g. blood pressure), I have a data frame with one line per patient and all of the blood pressure values observed throughout time in chronological order. Something like this:

Patient ID BP1 BP2 BP3 
         1 120 134 127

I want to do this because I want to be able to write code to select the mean of the first three observed blood pressures, for example.

Any advice or reading recommendations would greatly be appreciated.

Answer 1

You can achieve the desired formatting by reshaping your data with a number of methods including using the reshape() function in Base R or dcast() in the reshape2 package, but it might be easier to just be able to get to your answer directly using a form of aggregation. Here's one method using ddply() from the plyr package:

library(plyr)

df <- read.table(text="id  date  bp
1 21/1/14             120
1 19/3/14             134
1  3/5/14             127",header=TRUE)

df1 <- ddply(df, .(id), summarize, mean.bp = mean(bp[1:3]))

df1
#   id mean.bp
# 1  1     127

Of course, if you really just want to do what you asked about, you can do the following:

library(reshape2)

df$bp.id <- ave(df$id,df$id,FUN=function(x) paste0("BP",seq(along=x)))
df2 <- dcast(df[df$bp.id %in% paste0("BP",1:3)], id~bp.id, value.var="bp")    

df2
#   id BP1 BP2 BP3
# 1  1 120 134 127