Adjust R Data Frame with Element Wise Function - sql

Question

I am using RODBC to pull down server data into a data frame using this statement:
df <- data.frame(
sqlQuery(
channel = ODBC_channel_string,
query = SQLquery_string
)
)
The resultant data set has the following grouping attributes of interest:
Scenario
Other Group By 1
Other Group By 2
...
Other Group By K
With key variables:
[Time] = Future Year(i)
[Spot] = Projected Effective Discount Rate For Year(i-1) to Year(i)
Abbreviated Table Snip
What I would like to do is transform the [Spot] column into a discount factor that is dependent on consistent preceding values:
Scenario
Other Group By 1
Other Group By 2
...
Other Group By K
With key variables:
[Time] = Future Year(i)
[Spot] = Projected Effective Discount Rate For Year(i-1) to Year(i)
[Disc_0] = prod([Value]), for [All Grouping] = [This Grouping] and [Time] <= [This Time]
Excel Version of Abbreviated Goal Snip
I could code the solution using a for loop, but I suspect that will be very inefficient in R if there are significant row counts in the original data frame.
What I am hoping is to use some creative implementation of dplyr's mutate:
df %>% mutate(Disc_0 = objective_function{?})
I think that R should be able to do this kind of data wrangle quickly, but I am not sure if that is the case. I am more familiar with SQL and may attempt to produce the necessary variable there.