There are many opportunities to find maps which are good for R, also You can easily find country level maps.

TIP1: GADM database + basic plot function

For example GADM is an awesome site, You can freely download any country map in a format of SpatialPolygonsDataFrame. Most of the countries has multiple levels.

Here is a simple example: (I will use map of Hungary through the whole example)

#load the data
download.file("http://biogeo.ucdavis.edu/data/gadm2.8/rds/HUN_adm1.rds",
              "HUN_adm1.rds", mode = "wb")
countries = readRDS("HUN_adm1.rds")
 
#check the structure of the data
countries@data
 
#simply plot it with random color
plot(countries, col = colorRampPalette(c("white", "red"))(nrow(countries@data)))

TIP2: googleVis

Also there is a cool interactive web-based solution thanks to Google (and of course the creators of googleVis package) which also supoorts most of the countries:

library(googleVis)
library(sp)
 
#create random database with county codes 
countyName = c("HU-BU",
                "HU-BK",
                "HU-BA",
                "HU-BE",
                "HU-BZ",
                "HU-CS",
                "HU-FE",
                "HU-GS",
                "HU-HB",
                "HU-HE",
                "HU-JN",
                "HU-KE",
                "HU-NO",
                "HU-PE",
                "HU-SO",
                "HU-SZ",
                "HU-TO",
                "HU-VA",
                "HU-VE",
                "HU-ZA")
randomData = runif(length(countyName),0,100)
exampleData <- data.frame(countyName, randomData)
 
GeoMaps <- gvisGeoChart(exampleData, "countyName", "randomData",
                          options=list(region="HU", 
                                       displayMode="regions", 
                                       resolution="provinces",
                                       width=600, height=400))
plot(GeoMaps)

TIP3: Eurostat geodata + basic plot function

But it was really hard to find a NUTS2 level country maps for me, but finally I came across the geodata of Eurostat. I recommend to use the 1:1 Million scale if You want to plot countries.

library(rgdal)
 
#download the file
temp <- tempfile(fileext = ".zip")
download.file("http://ec.europa.eu/eurostat/cache/GISCO/geodatafiles/NUTS_2013_01M_SH.zip", temp)
unzip(temp)
 
#load the data and filter it to Hungary and NUTS2 level
EU_NUTS = readOGR(dsn = "./NUTS_2013_01M_SH/data", layer = "NUTS_RG_01M_2013")
map_nuts2 <- subset(EU_NUTS, STAT_LEVL_ == 2) # set NUTS level
country <- substring(as.character(map_nuts2$NUTS_ID), 1, 2)
map <- c("HU") # limit it to Hungary
map_nuts2a <- map_nuts2[country %in% map,]
 
#plot it
plot(map_nuts2a, col = colorRampPalette(c("white", "red"))(nrow(map_nuts2a@data)))

Bonus!

When I used the geodata for my project I also used the cartography package which is an easy-to-use map creator.
Here is a small example how You can use it:

library(cartography)
 
plot(map_nuts2a)
 
cols <-	 carto.pal(pal1 = "green.pal", n1 = nrow(map_nuts2a@data)+1)
nuts2_id = map_nuts2a@data[,"NUTS_ID"]
value = runif(nrow(map_nuts2a@data),0,50)
hun_nuts2_df = data.frame(nuts2_id, value)
 
choroLayer(spdf = map_nuts2a, # SpatialPolygonsDataFrame of the regions
           df = hun_nuts2_df, # target data frame 
           var = "value", # target value
           breaks = c(0,5,10,15,20,25,30,35,100), # list of breaks
           col = cols, # colors 
           border = "white", # color of the polygons borders
           lwd = 2, # width of the borders
           legend.pos = "right", # position of the legend
           legend.title.txt = "",
           legend.values.rnd = 2, # number of decimal in the legend values
           add = TRUE) # add the layer to the current plot
 
labelLayer(spdf = map_nuts2a, # SpatialPolygonsDataFrame used to plot he labels
           df = hun_nuts2_df, # data frame containing the lables
           txt = "nuts2_id", # label field in df
           col = "black", # color of the labels
           cex = 0.9, # size of the labels
           font = 2)  # label font

Please write it down if You have an other source of NUTS2 level geodata which is also compatible with R especially if there is a (interactive) JavaScript based solution.