Question

I have a dataset akin to this

User    Date        Value
A       2012-01-01  4
A       2012-01-02  5   
A       2012-01-03  6
A       2012-01-04  7
B       2012-01-01  2
B       2012-01-02  3   
B       2012-01-03  4
B       2012-01-04  5

I want to create a lag of Value, respecting User.

User    Date        Value   Value.lag
A       2012-01-01  4       NA
A       2012-01-02  5       4
A       2012-01-03  6       5
A       2012-01-04  7       6
B       2012-01-01  2       NA
B       2012-01-02  3       2   
B       2012-01-03  4       3
B       2012-01-04  5       4

I’ve done it very inefficiently in a loop

df$value.lag1<-NA
levs<-levels(as.factor(df$User))
levs
  for (i in 1:length(levs)) {
    temper<- subset(df,User==as.numeric(levs[i]))
    temper<- rbind(NA,temper[-nrow(temper),])  
df$value.lag1[df$User==as.numeric(as.character(levs[i]))]<- temper
      }

But this is very slow. I’ve looked at using by and tapply, but not figured out how to make them work.

I don’t think XTS or TS will work because of the User element.

Any suggestions?

Answer 1

You can use ddply: it cuts a data.frame into pieces and transforms each piece.

d <- data.frame( 
  User = rep( LETTERS[1:3], each=10 ),
  Date = seq.Date( Sys.Date(), length=30, by="day" ),
  Value = rep(1:10, 3)
)
library(plyr)
d <- ddply( 
  d, .(User), transform,
  # This assumes that the data is sorted
  Value = c( NA, Value[-length(Value)] ) 
)