Creating lag variables for matched factors - r

Question

I have a question about creating lag variables depending on a time factor.
Basically I am working with a baseball dataset where there are lots of names for each player between 2002-2012. Obviously I only want lag variables for the same person to try and create a career arc to predict the current stat. Like for example I want to use lag 1 Average (2003) , lag 2 Average (2004) to try and predict the current average in 2005. So I tried to write a loop that goes through every row (the data frame is already sorted by name and then year, so the previous year is n-1 row), check if the name is the same, and if so then grab the value from the previous row.
Here is my loop:
i=2 #as 1 errors out with 1-0 row
for(i in 2:6264){
if(TS$name[i]==TS$name[i-1]){
TS$runvalueL1[i]=TS$Run_Value[i-1]
}else{
TS$runvalueL1 <- NA
}
i=i+1
}
Because each row is dependent on the name I cannot use most of the lag functions. If you have a better idea I am all ears!
Sample Data won't help a bunch but here is some:
edit: Sample data wasn't producing useable results so I just attached the first 10 people of my dataset. Thanks!
TS[(6:10),c('name','Season','Run_Value')]
name Season ARuns
321 Abad Andy 2003 -1.05
3158 Abercrombie Reggie 2006 27.42
1312 Abercrombie Reggie 2007 7.65
1069 Abercrombie Reggie 2008 5.34
4614 Abernathy Brent 2002 46.71
707 Abernathy Brent 2003 -2.29
1297 Abernathy Brent 2005 5.59
6024 Abreu Bobby 2002 102.89
6087 Abreu Bobby 2003 113.23
6177 Abreu Bobby 2004 128.60
Thank you!

Answer 1

Smth along these lines should do it:
names = c("Adams","Adams","Adams","Adams","Bobby","Bobby", "Charlie")
years = c(2002,2003,2004,2005,2004,2005,2010)
Run_value = c(10,15,15,20,10,5,5)
library(data.table)
dt = data.table(names, years, Run_value)
dt[, lag1 := c(NA, Run_value), by = names]
# names years Run_value lag1
#1: Adams 2002 10 NA
#2: Adams 2003 15 10
#3: Adams 2004 15 15
#4: Adams 2005 20 15
#5: Bobby 2004 10 NA
#6: Bobby 2005 5 10
#7: Charlie 2010 5 NA

Answer 2

An alternative would be to split the data by name, use lapply with the lag function of your choice and then combine the splitted data again:
TS$runvalueL1 <- do.call("rbind", lapply(split(TS, list(TS$name)), your_lag_function))
or
TS$runvalueL1 <- do.call("c", lapply(split(TS, list(TS$name)), your_lag_function))
But I guess there is also a nice possibility with plyr, but as you did not provide a reproducible example, that is all for the beginning.
Better:
TS$runvalueL1 <- unlist(lapply(split(TS, list(TS$name)), your_lag_function))

Answer 3

This is obviously not a problem where you want to create a matrix with cbind, so this is a better data structure:
full=data.frame(names, years, Run_value)
The ave function is quite useful for constructing new columns within categories of other columns:
full$Lag1 <- ave(full$Run_value, full$names,
FUN= function(x) c(NA, x[-length(x)] ) )
full
names years Run_value Lag1
1 Adams 2002 10 NA
2 Adams 2003 15 10
3 Adams 2004 15 15
4 Adams 2005 20 15
5 Bobby 2004 10 NA
6 Bobby 2005 5 10
7 Charlie 2010 5 NA
I thinks it's safer to cionstruct with NA, since that will help prevent errors in logic that using 0 for prior years in year 1 would not alert you to.