Plot pre-1990 world map in R - r

Question

I am working with some global data from before 1991, so before the USSR, Yugoslavia and Czechoslovakia split up. I would like to plot the data using rworldmap or maps, but the package appears to only have the modern world map easily accessible. All the pre-1991 countries show up blank and with the boundaries dividing their post-1991 counterparts.
This code produces the historical map:
if (requireNamespace("mapdata", quietly=TRUE) && packageVersion("mapdata") >= "2.3")
{map("mapdata::worldLores", fill = TRUE, col = 1:10)}
EDIT: also, as per the helpful comment below, a historical map shapefile is easily obtained from:
library(cshapes)
cshp.data<-cshp(as.Date("1990-01-01"))
plot(cshp.data)
But I cannot figure out if it is possible to combine this with the rworldmap functions ... or if I will have to figure out how to use the maps package, which seems to work differently. (Or maybe there is a ggplot solution?)
The rworldmap code I use currently (to get the modern map) is:
#make example data including Soviet Union
country <- as.vector(c("Afghanistan","Australia","Iceland","Soviet Union",
"Zimbabwe"))
value <- as.vector(c(5,10,100,10,50))
df<-data.frame(country,value)
#make map
map1 <- joinCountryData2Map(df, joinCode = "NAME", nameJoinColumn =
"country")
mapCountryData( map1, addLegend=F, catMethod="fixedWidth",
nameColumnToPlot="value" )
#...Soviet Union is blank

Answer 1

Ahah, there is a ggplot solution using the old map from the mapdata package:
library(ggplot2)
library(dplyr)
library(mapdata)
df<-data.frame(country=c("Afghanistan","Australia","Iceland","USSR","Zimbabwe"),
value=c(5,10,100,10,50),stringsAsFactors=FALSE)
WorldData <- map_data('worldLores') #use the old map
WorldData <- fortify(WorldData)
mapped <- ggplot() +
geom_map(data=WorldData, map=WorldData,
aes(x=long, y=lat, group=group, map_id=region),
fill="white", colour="#7f7f7f", size=0.5) +
geom_map(data=df, map=WorldData,
aes(fill=value, map_id=country),
colour="#7f7f7f", size=0.5)
mapped
(mapping code borrowed from this post, cheers #hrbrmstr)