How to make plot with world map and colours indicating labels from my data

Question

I want to make a plot like this,

but instead of regions being grey, I want them to be colored according to my data, which looks like this:

So cluster 1 I want to be colored red for example, cluster 2 i want it to be colored green.

Does anyone know how I could make this kind of plot? Cheers!

Output from dput()

structure(list(countries = structure(c(7L, 1L, 5L, 2L, 4L, 147L, 
6L, 8L, 9L, 10L, 25L, 15L, 24L, 12L, 23L, 11L, 19L, 14L, 16L, 
18L, 21L, 13L, 22L, 17L, 20L, 30L, 28L, 137L, 32L, 33L, 37L, 
27L, 42L, 35L, 34L, 29L, 36L, 39L, 40L, 41L, 56L, 44L, 43L, 45L, 
3L, 46L, 48L, 132L, 49L, 51L, 52L, 53L, 54L, 148L, 55L, 57L, 
61L, 58L, 60L, 59L, 62L, 65L, 64L, 38L, 63L, 66L, 69L, 68L, 72L, 
70L, 71L, 67L, 73L, 74L, 75L, 76L, 77L, 78L, 81L, 26L, 130L, 
80L, 83L, 84L, 85L, 133L, 86L, 82L, 87L, 97L, 95L, 88L, 94L, 
91L, 99L, 96L, 98L, 92L, 93L, 89L, 90L, 104L, 105L, 103L, 101L, 
106L, 100L, 102L, 107L, 108L, 110L, 112L, 113L, 114L, 116L, 115L, 
111L, 109L, 117L, 79L, 118L, 119L, 120L, 122L, 134L, 123L, 126L, 
125L, 47L, 121L, 129L, 124L, 131L, 135L, 127L, 128L, 136L, 50L, 
138L, 31L, 142L, 141L, 139L, 143L, 144L, 145L, 140L, 146L, 150L, 
149L, 151L, 152L, 153L), .Label = c("Afghanistan", "Albania", 
"Algeria", "Andorra", "Angola", "Argentina", "Aruba", "Australia", 
"Austria", "Azerbaijan", "Bahrain", "Bangladesh", "Barbados", 
"Belarus", "Belgium", "Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina", 
"Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burundi", 
"Cambodia", "Cameroon", "Canada", "Cape Verde", "Central African Republic", 
"Chad", "Chile", "China", "Colombia", "Congo", "Costa Rica", 
"Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", 
"Democratic Republic of Congo", "Denmark", "Djibouti", "Dominican Republic", 
"Egypt", "El Salvador", "Eritrea", "Estonia", "Eswatini", "Ethiopia", 
"Finland", "France", "Gabon", "Georgia", "Germany", "Ghana", 
"Greece", "Guam", "Guatemala", "Guinea", "Guyana", "Haiti", "Honduras", 
"Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran", 
"Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Kazakhstan", 
"Kenya", "Kosovo", "Kuwait", "Kyrgyz Republic", "Latvia", "Lebanon", 
"Liberia", "Libya", "Luxembourg", "Macao", "Madagascar", "Malawi", 
"Malaysia", "Mali", "Mauritania", "Mauritius", "Mexico", "Moldova", 
"Mongolia", "Morocco", "Mozambique", "Myanmar", "Nepal", "Netherlands", 
"New Zealand", "Nicaragua", "Niger", "Nigeria", "Norway", "Oman", 
"Pakistan", "Palestine", "Panama", "Paraguay", "Peru", "Philippines", 
"Poland", "Portugal", "Puerto Rico", "Qatar", "Romania", "Russia", 
"Rwanda", "San Marino", "Saudi Arabia", "Senegal", "Serbia", 
"Sierra Leone", "Singapore", "Slovak Republic", "Slovenia", "Somalia", 
"South Korea", "South Sudan", "Spain", "Sri Lanka", "Sudan", 
"Suriname", "Sweden", "Switzerland", "Syria", "Tajikistan", "Tanzania", 
"Thailand", "Togo", "Trinidad and Tobago", "Tunisia", "Turkey", 
"Ukraine", "United Arab Emirates", "United Kingdom", "United States", 
"Uruguay", "Uzbekistan", "Venezuela", "Vietnam"), class = "factor"), 
cluster = c(4, 1, 3, 3, 4, 1, 3, 5, 5, 3, 1, 2, 4, 1, 3, 
1, 5, 1, 4, 1, 1, 4, 4, 4, 1, 1, 2, 2, 1, 4, 1, 1, 1, 3, 
1, 1, 3, 2, 4, 5, 2, 2, 2, 1, 1, 1, 3, 2, 4, 3, 2, 2, 1, 
2, 5, 1, 5, 5, 3, 4, 3, 5, 3, 5, 1, 2, 1, 1, 2, 1, 3, 4, 
5, 2, 5, 1, 1, 3, 3, 4, 4, 1, 3, 3, 3, 1, 4, 4, 4, 5, 1, 
3, 1, 2, 4, 5, 1, 2, 4, 1, 2, 4, 1, 2, 2, 2, 1, 4, 1, 1, 
1, 1, 1, 1, 3, 5, 3, 5, 1, 5, 5, 1, 3, 1, 2, 1, 1, 2, 1, 
2, 2, 5, 2, 3, 5, 4, 1, 3, 3, 2, 3, 2, 2, 4, 4, 5, 4, 1, 
4, 1, 3, 3, 4)), class = "data.frame", row.names = c("Aruba", 
"Afghanistan", "Angola", "Albania", "Andorra", "United Arab Emirates", 
"Argentina", "Australia", "Austria", "Azerbaijan", "Burundi", 
"Belgium", "Burkina Faso", "Bangladesh", "Bulgaria", "Bahrain", 
"Bosnia and Herzegovina", "Belarus", "Bermuda", "Bolivia", "Brazil", 
"Barbados", "Brunei", "Bhutan", "Botswana", "Central African Republic", 
"Canada", "Switzerland", "Chile", "China", "Cote d'Ivoire", "Cameroon", 
"Democratic Republic of Congo", "Congo", "Colombia", "Cape Verde", 
"Costa Rica", "Cuba", "Cyprus", "Czech Republic", "Germany", 
"Djibouti", "Denmark", "Dominican Republic", "Algeria", "Egypt", 
"Eritrea", "Spain", "Estonia", "Ethiopia", "Finland", "France", 
"Gabon", "United Kingdom", "Georgia", "Ghana", "Guinea", "Greece", 
"Guatemala", "Guam", "Guyana", "Hong Kong", "Honduras", "Croatia", 
"Haiti", "Hungary", "Indonesia", "India", "Ireland", "Iran", 
"Iraq", "Iceland", "Israel", "Italy", "Jamaica", "Japan", "Kazakhstan", 
"Kenya", "Kyrgyz Republic", "Cambodia", "South Korea", "Kuwait", 
"Lebanon", "Liberia", "Libya", "Sri Lanka", "Luxembourg", "Latvia", 
"Macao", "Morocco", "Moldova", "Madagascar", "Mexico", "Mali", 
"Myanmar", "Mongolia", "Mozambique", "Mauritania", "Mauritius", 
"Malawi", "Malaysia", "Niger", "Nigeria", "Nicaragua", "Netherlands", 
"Norway", "Nepal", "New Zealand", "Oman", "Pakistan", "Panama", 
"Peru", "Philippines", "Poland", "Puerto Rico", "Portugal", "Paraguay", 
"Palestine", "Qatar", "Kosovo", "Romania", "Russia", "Rwanda", 
"Saudi Arabia", "Sudan", "Senegal", "Singapore", "Sierra Leone", 
"El Salvador", "San Marino", "Somalia", "Serbia", "South Sudan", 
"Suriname", "Slovak Republic", "Slovenia", "Sweden", "Eswatini", 
"Syria", "Chad", "Togo", "Thailand", "Tajikistan", "Trinidad and Tobago", 
"Tunisia", "Turkey", "Tanzania", "Ukraine", "Uruguay", "United States", 
"Uzbekistan", "Venezuela", "Vietnam"))

Answer 1

Good question.

I can show you one way this can be done. But since you did not provide any reproducible information (my data), the result will differ from what is displayed above. Fortunately, the map displayed above is easily recognizable, so I've made the assumption that the map you showed is the map named, world, from the spData package.

Also note, the groups created below will not match the "clusters" displayed above (my data ?) that was not included with this question.

get data / combine data:

wm <- spData::world            
n <- c(1:3)                     
set.seed(0803)
gp <- sample(n, 177, replace = T)  
wm.gp <- cbind(gp, wm)             

Create groups:

gp1 <- wm.gp[gp == 1, ]        # create groups
gp2 <- wm.gp[gp == 2, ]
gp3 <- wm.gp[gp == 3, ]

Plot data:

plot(gp1$geom, col = "blue")           # plot group
plot(gp2$geom, col = "red", add = TRUE)
plot(gp3$geom, col = "green", add = TRUE)

The plot can be viewed by from the link below.

[![World in Color]]

Additional Information (second time around):

Using the information provided above, I succeeded in creating a data frame and assigned it a variable named, my_data. The dimensions of my_data are 153 x 2.

In the big picture, the my_data dataframe will be joined with some data from the world dataset. Then this new dataframe will become a sf object that will be plotted. Countries will be plotted by colors based on those values that came from cluster.

Get data. Change column name in my_data:

wm <- spData::world 
names(my_data)[1] <- "name_long" 

Create new variable. Create sp object:

my_map <- left_join(my_data, wm, by = "name_long")
world_cluster <- st_as_sf(my_map)

Plot map - colors based on cluster values:

plot(world_cluster["cluster"])

The link below to view the plot.

Map