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

Answer 1

 # example dataframe
id <- c(rep(1:4,25))
date <- c(rep("21/01/14",30),rep("21/01/14",30),rep("22/01/14",30),rep("23/01/14",10))
bp <- c(rnorm(100,100))
df <- data.frame(id,date,bp)

# reorder the dataframe
library(dplyr)
df2 <- group_by(df,id) # group by id
df2 <- arrange(df2, date) # order each group by date
df3 <- mutate(df2,   # add a colum with ascending number per for each group
              c = 1:length(date))

# use dcast
library(reshape2)
dcast(df3[,c(1,4,3)],id~c)

Answer 2

With the data.table package (which has an improved implementation of the melt and dcast functions of reshape2) you could do this as follows:

newdf <- dcast(setDT(df)[, idx := 1:.N, by = id], id ~ paste0("bp",idx), value.var = "bp")

Or utilizing the new rowid function:

newdf <- dcast(setDT(df), id ~ rowid(prefix="bp",id), value.var = "bp")

both options give the same result:

> newdf
   id bp1 bp2 bp3
1:  1 120 134 129
2:  2 110 124 119

---

But as @SamDickson said, when you want to calculate the mean of (for example) the first two blood pressure measurements, then you can also add a new variable to your existing dataframe df with:

# using base R
df$first2mn <- ave(df$bp, df$id, FUN = function(x) mean(x[1:2])) 
# using data.table
setDT(df)[, first2mn := mean(bp[1:2]), id] 

which both give:

> df
   id    date  bp first2mn
1:  1 21/1/14 120      127
2:  1 19/3/14 134      127
3:  1  3/5/14 129      127
4:  2 21/1/14 110      117
5:  2 19/3/14 124      117
6:  2  3/5/14 119      117

Or just calculate the mean with:

# using base R
aggregate(bp ~ id, df, function(x) mean(x[1:2])) 
# using data.table
setDT(df)[, .(bp = mean(bp[1:2])), id] 

which both give:

  id  bp
1  1 127
2  2 117

---

Used data:

df <- read.table(text="id  date  bp
1 21/1/14             120
1 19/3/14             134
1  3/5/14             129
2 21/1/14             110
2 19/3/14             124
2  3/5/14             119", header=TRUE)

Answer 3

The other answers have provided a number of methods for calculating the mean across groups. A related post provides a number of methods for calculating group level maxima. You would need to replace max with mean in these answers.

Here is an additional method for reshaping wide using the base R function reshape.

Use the data.frame provided by @jaap, add a variable for counting the observations by ID:

df$times <- ave(df$bp, df$id, FUN=seq_along)

Now, perform the reshape, dropping the unwanted date variable:

reshape(df, direction="wide", drop="date", timevar="times")
  id bp.1 bp.2 bp.3
1  1  120  134  129
4  2  110  124  119

Answer 4

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