Skip NA values using "FUN=first" - r

Question

there's probably really an simple explaination as to what I'm doing wrong, but I've been working on this for quite some time today and I still can not get this to work. I thought this would be a walk in the park, however, my code isn't quite working as expected.
So for this example, let's say I have a data frame as followed.
df
Row# user columnB
1 1 NA
2 1 NA
3 1 NA
4 1 31
5 2 NA
6 2 NA
7 2 15
8 3 18
9 3 16
10 3 NA
Basically, I would like to create a new column that uses the first (as well as last) function (within the TTR library package) to obtain the first non-NA value for each user. So my desired data frame would be this.
df
Row# user columnB firstValue
1 1 NA 31
2 1 NA 31
3 1 NA 31
4 1 31 31
5 2 NA 15
6 2 NA 15
7 2 15 15
8 3 18 18
9 3 16 18
10 3 NA 18
I've looked around mainly using google, but I couldn't really find my exact answer.
Here's some of my code that I've tried, but I didn't get the results that I wanted (note, I'm bringing this from memory, so there are quite a few more variations of these, but these are the general forms that I've been trying).
df$firstValue<-ave(df$columnB,df$user,FUN=first,na.rm=True)
df$firstValue<-ave(df$columnB,df$user,FUN=function(x){x,first,na.rm=True})
df$firstValue<-ave(df$columnB,df$user,FUN=function(x){first(x,na.rm=True)})
df$firstValue<-by(df,df$user,FUN=function(x){x,first,na.rm=True})
Failed, these just give the first value of each group, which would be NA.
Again, these are just a few examples from the top of my head, I played around with na.rm, using na.exclude, na.omit, na.action(na.omit), etc...
Any help would be greatly appreciated. Thanks.

Answer 1

A data.table solution
require(data.table)
DT <- data.table(df, key="user")
DT[, firstValue := na.omit(columnB)[1], by=user]

Answer 2

Here is a solution with plyr :
ddply(df, .(user), transform, firstValue=na.omit(columnB)[1])
Which gives :
Row user columnB firstValue
1 1 1 NA 31
2 2 1 NA 31
3 3 1 NA 31
4 4 1 31 31
5 5 2 NA 15
6 6 2 NA 15
7 7 2 15 15
8 8 3 18 18
9 9 3 16 18
If you want to capture the last value, you can do :
ddply(df, .(user), transform, firstValue=tail(na.omit(columnB),1))

Answer 3

Using data.table
library (data.table)
DT <- data.table(df, key="user")
DT <- setnames(DT[unique(DT[!is.na(columnB), list(columnB), by="user"])], "columnB.1", "first")

Answer 4

Using a very small helper function
finite <- function(x) x[is.finite(x)]
here is an one-liner using only standard R functions:
df <- cbind(df, firstValue = unlist(sapply(unique(df[,1]), function(user) rep(finite(df[df[,1] == user,2])[1], sum(df[,1] == user))))
For a better overview, here is the one-liner unfolded into a "multi-liner":
# for each user, find the first finite (in this case non-NA) value of the second column and replicate it as many times as the user has rows
# then, the results of all users are joined into one vector (unlist) and appended to the data frame as column
df <- cbind(
df,
firstValue = unlist(
sapply(
unique(df[,1]),
function(user) {
rep(
finite(df[df[,1] == user,2])[1],
sum(df[,1] == user)
)
}
)
)
)