Creating a starting value variable with longitudinal data (conditional) - r

Question

I am trying to create a new variable that is basically the starting value of another variable in my dataframe. Example data:
id <- rep(c(1, 2), each = 8)
outcome <- rep(1:5, length.out = 16)
time <- rep(c(0, 1, 3, 4),4)
Attitude <- rep(c('A1', 'A2', 'A1', 'A2'), each = 4)
df <- data.frame(id, Attitude, outcome, time)
What I'd like to get is a new column named new_var (or whatever) that is equal to the value of outcome at time == 0 for id = id and also depends on Attitude. Thus what I'd like to extend the dataframe to is:
df$new_var <- c(1,1,1,1,5,5,5,5,4,4,4,4,3,3,3,3)
Only then with some decent coding. In SAS I know I can do this with the lag function. I would really appreciate a solution that isn't a 'work around' so it is like SAS, but rather the proper r solution. In the end I want to get stronger in r too.
Related: Retain and lag function in R as SAS
However I prefer some solution that is based on indices or the 'usual' r way. And here it's also not dependent on other conditions.
So, important here is that the coding works for the different ids, attitude levels / variables (A1, A2, ...) and that the outcome value at time == 0 is basically copied to new_var.
I hope I am clear in conveying my message. If not I think the small piece of example code and how I'd like to extend it should be clear enough. Looking forward to suggestions.
EDIT Another example code for #jogo answer.
ID <- rep(1, 36)
Attitude <- rep(c('A1', 'A2','A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9'),
length.out =36)
Answer_a <- rep(1:5, length.out = 36)
time <- as.character(rep(c(0, 1, 3, 4), each = 9))
df <- data.frame(ID, Attitude, Answer_a, time)
df$time <- as.character(df$time)

Answer 1

I think this is what you mean - assuming the data is always in the correct order?
EDIT Added an arrange step to ensure the data is always correctly ordered.
library(tidyverse)
df %>% group_by(id, Attitude) %>%
arrange(time) %>%
mutate(new_var2 = first(outcome[!is.na(outcome)])
# A tibble: 16 x 6
# Groups: id, Attitude [4]
id Attitude outcome time new_var new_var2
<dbl> <fct> <int> <dbl> <dbl> <int>
1 1.00 A1 1 0 1.00 1
2 1.00 A1 2 1.00 1.00 1
3 1.00 A1 3 3.00 1.00 1
4 1.00 A1 4 4.00 1.00 1
5 1.00 A2 5 0 5.00 5
6 1.00 A2 1 1.00 5.00 5
7 1.00 A2 2 3.00 5.00 5
8 1.00 A2 3 4.00 5.00 5
9 2.00 A1 4 0 4.00 4
10 2.00 A1 5 1.00 4.00 4
11 2.00 A1 1 3.00 4.00 4
12 2.00 A1 2 4.00 4.00 4
13 2.00 A2 3 0 3.00 3
14 2.00 A2 4 1.00 3.00 3
15 2.00 A2 5 3.00 3.00 3
16 2.00 A2 1 4.00 3.00 3

Answer 2

Here is a solution with data.table:
library("data.table")
setDT(df)
df[, new_var:=outcome[1], rleid(Attitude)][] # or
# df[, new_var:=outcome[time==0], rleid(Attitude)][]
For testing I named the new column new_var2:
id <- rep(c(1, 2), each = 8)
outcome <- rep(1:5, length.out = 16)
time <- rep(c(0, 1, 3, 4),4)
Attitude <- rep(c('A1', 'A2', 'A1', 'A2'), each = 4)
df <- data.frame(id, Attitude, outcome, time)
df$new_var <- c(1,1,1,1,5,5,5,5,4,4,4,4,3,3,3,3)
library("data.table")
setDT(df)
df[, new_var2:=outcome[1], rleid(Attitude)][]
# > df[, new_var2:=outcome[1], rleid(Attitude)][]
# id Attitude outcome time new_var new_var2
# 1: 1 A1 1 0 1 1
# 2: 1 A1 2 1 1 1
# 3: 1 A1 3 3 1 1
# 4: 1 A1 4 4 1 1
# 5: 1 A2 5 0 5 5
# 6: 1 A2 1 1 5 5
# 7: 1 A2 2 3 5 5
# 8: 1 A2 3 4 5 5
# 9: 2 A1 4 0 4 4
# 10: 2 A1 5 1 4 4
# 11: 2 A1 1 3 4 4
# 12: 2 A1 2 4 4 4
# 13: 2 A2 3 0 3 3
# 14: 2 A2 4 1 3 3
# 15: 2 A2 5 3 3 3
# 16: 2 A2 1 4 3 3
Your second example shows that you have to reorder the rows of the data. Usinf data.table this can be done by setkey():
ID <- rep(1, 36)
Attitude <- rep(c('A1', 'A2','A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9'),
length.out =36)
Answer_a <- rep(1:5, length.out = 36)
time <- as.character(rep(c(0, 1, 3, 4), each = 9))
df <- data.frame(ID, Attitude, Answer_a, time)
df$time <- as.character(df$time)
library("data.table")
setDT(df)
setkey(df, ID, Attitude, time)
df[, new_var:=Answer_a[1], rleid(Attitude)]
df