Grouping by column and finding preceeding value of another column - r

Question

I have a very long sales data, below an exemplary excerpt:
| Date | CountryA | CountryB | PriceA | PriceB | |
+------------+----------+----------+--------+--------+--+
| 05/09/2019 | US | Japan | 20 | 55 | |
| 28/09/2019 | Japan | Germany | 30 | 28 | |
| 16/10/2019 | Canada | US | 25 | 78 | |
| 28/10/2019 | Germany | Japan | 60 | 17 | |
+------------+----------+----------+--------+--------+--+
I would like to group on column "CountryB" and then generate a new column which displays the preceding value of PriceA of that respective country, i.e. when that specific country was present in column "CountryA" the last time based on date order. In this exemplary table, I want to get the following results:
| Date | CountryA | CountryB | PriceA | PriceB | PriceA_lag1 | |
+------------+----------+----------+--------+--------+-------------+--+
| 05/09/2019 | US | Japan | 20 | 55 | | |
| 28/09/2019 | Japan | Germany | 30 | 28 | | |
| 16/10/2019 | Canada | US | 25 | 78 | 20 | |
| 28/10/2019 | Germany | Japan | 60 | 17 | 30 | |
+------------+----------+----------+--------+--------+-------------+--+
I have tried the following with dplyr:
data=data%>%group_by(CountryB)%>%mutate_at(list(lag1=~dplyr::lag(.,1,order_by=Date)),.vars=vars(PriceA))
However this does not give me the preceding value when the respective country is in column "CountryA", but rather when the respective country is in "CountryB".
Can someone please help me out on this one?
Thanks.

Answer 1

Quite possibly some of the ugliest code I've written, but...
# install.packages('dplyr', 'magrittr')
library(dplyr)
library(magrittr)
d <- data.frame(
stringsAsFactors = FALSE,
Date = c("05/09/2019", "28/09/2019", "16/10/2019", "28/10/2019"),
CountryA = c("US", "Japan", "Canada", "Germany"),
CountryB = c("Japan", "Germany", "US", "Japan"),
PriceA = c(20L, 30L, 25L, 60L),
PriceB = c(55L, 28L, 78L, 17L)
) %>%
mutate(Date = as.Date(Date, format = '%d/%m/%Y'))
priceA_lag <- c()
for(row in 1:nrow(d)){
country <- slice(d, row) %$% CountryB
date <- slice(d, row) %$% Date
thePrice <- d %>%
filter(CountryA == country,
date > Date) %>%
filter(Date == max(Date)) %$%
PriceA
thePrice <- ifelse(length(thePrice) > 0, thePrice, NA)
priceA_lag <- priceA_lag %>%
append(thePrice)
}
d$priceA_lag <- priceA_lag
> d
Date CountryA CountryB PriceA PriceB priceA_lag
1 2019-09-05 US Japan 20 55 NA
2 2019-09-28 Japan Germany 30 28 NA
3 2019-10-16 Canada US 25 78 20
4 2019-10-28 Germany Japan 60 17 30