Adding a second column as a function of first with data.table's (`:=`) [duplicate] - r

Question

I want to create a new data.table or maybe just add some columns to a data.table. It is easy to specify multiple new columns but what happens if I want a third column to calculate a value based on one of the columns I am creating. I think plyr package can do something such as that. Can we perform such iterative (sequential) column creation in data.table?
I want to do as follows
dt <- data.table(shop = 1:10, income = 10:19*70)
dt[ , list(hope = income * 1.05, hopemore = income * 1.20, hopemorerealistic = hopemore - 100)]
or maybe
dt[ , `:=`(hope = income*1.05, hopemore = income*1.20, hopemorerealistic = hopemore-100)]

Answer 1

You can also use <- within the call to list eg
DT <- data.table(a=1:5)
DT[, c('b','d') := list(b1 <- a*2, b1*3)]
DT
a b d
1: 1 2 6
2: 2 4 12
3: 3 6 18
4: 4 8 24
5: 5 10 30
Or
DT[, `:=`(hope = hope <- a+1, z = hope-1)]
DT
a b d hope z
1: 1 2 6 2 1
2: 2 4 12 3 2
3: 3 6 18 4 3
4: 4 8 24 5 4
5: 5 10 30 6 5

Answer 2

It is possible by using curly braces and semicolons in j
There are multiple ways to go about it, here are two examples:
# If you simply want to output:
dt[ ,
{hope=income*1.05;
hopemore=income*1.20;
list(hope=hope, hopemore=hopemore, hopemorerealistic=hopemore-100)}
]
# if you want to save the values
dt[ , c("hope", "hopemore", "hopemorerealistic") :=
{hope=income*1.05;
hopemore=income*1.20;
list(hope, hopemore, hopemore-100)}
]
dt
# shop income hope hopemore hopemorerealistic
# 1: 1 700 735.0 840 740
# 2: 2 770 808.5 924 824
# 3: 3 840 882.0 1008 908
# 4: 4 910 955.5 1092 992
# 5: 5 980 1029.0 1176 1076
# 6: 6 1050 1102.5 1260 1160
# 7: 7 1120 1176.0 1344 1244
# 8: 8 1190 1249.5 1428 1328
# 9: 9 1260 1323.0 1512 1412
# 10: 10 1330 1396.5 1596 1496