如何使用世界地图和颜色指示我的数据中的标签进行绘图
How to make plot with world map and colours indicating labels from my data
我想做这样的情节，
但我希望根据我的数据对区域进行着色，而不是将区域设为灰色，如下所示：
例如，第 1 组我想涂成红色，第 2 组我想涂成绿色。
有谁知道这样的剧情是怎么做出来的？
干杯！
dput() 的输出
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"))
好问题。
我可以向您展示一种方法。但由于您没有提供任何可复制的信息（我的数据），结果将与上面显示的不同。幸运的是，上面显示的地图很容易识别，所以我假设你显示的地图是 spData 包中名为 world 的地图。
另请注意，下面创建的组将与上面显示的“集群”（我的数据？）不匹配，该集群未包含在该问题中。
获取数据/合并数据：
wm <- spData::world
n <- c(1:3)
set.seed(0803)
gp <- sample(n, 177, replace = T)
wm.gp <- cbind(gp, wm)
创建群组：
gp1 <- wm.gp[gp == 1, ] # create groups
gp2 <- wm.gp[gp == 2, ]
gp3 <- wm.gp[gp == 3, ]
绘图数据：
plot(gp1$geom, col = "blue") # plot group
plot(gp2$geom, col = "red", add = TRUE)
plot(gp3$geom, col = "green", add = TRUE)
剧情可在下方link查看
[![World in Color]]
附加信息（第二次）：
使用上面提供的信息，我成功创建了一个数据框并为其分配了一个名为 my_data 的变量。 my_data 的尺寸为 153 x 2。
在大图中，my_data 数据框将与世界数据集中的一些数据合并。然后这个新的数据框将成为一个将被绘制的 sf 对象。国家/地区将根据来自集群的那些值按颜色绘制。
获取数据。更改 my_data 中的列名称：
wm <- spData::world
names(my_data)[1] <- "name_long"
创建新变量。创建 sp 对象：
my_map <- left_join(my_data, wm, by = "name_long")
world_cluster <- st_as_sf(my_map)
绘制地图 - 基于聚类值的颜色：
plot(world_cluster["cluster"])
下面link查看剧情。
Map
我想做这样的情节，
但我希望根据我的数据对区域进行着色，而不是将区域设为灰色，如下所示：
例如，第 1 组我想涂成红色，第 2 组我想涂成绿色。
有谁知道这样的剧情是怎么做出来的？ 干杯！
dput() 的输出
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"))
好问题。
我可以向您展示一种方法。但由于您没有提供任何可复制的信息（我的数据），结果将与上面显示的不同。幸运的是，上面显示的地图很容易识别，所以我假设你显示的地图是 spData 包中名为 world 的地图。
另请注意，下面创建的组将与上面显示的“集群”（我的数据？）不匹配，该集群未包含在该问题中。
获取数据/合并数据：
wm <- spData::world
n <- c(1:3)
set.seed(0803)
gp <- sample(n, 177, replace = T)
wm.gp <- cbind(gp, wm)
创建群组：
gp1 <- wm.gp[gp == 1, ] # create groups
gp2 <- wm.gp[gp == 2, ]
gp3 <- wm.gp[gp == 3, ]
绘图数据：
plot(gp1$geom, col = "blue") # plot group
plot(gp2$geom, col = "red", add = TRUE)
plot(gp3$geom, col = "green", add = TRUE)
剧情可在下方link查看
[![World in Color]]
附加信息（第二次）：
使用上面提供的信息，我成功创建了一个数据框并为其分配了一个名为 my_data 的变量。 my_data 的尺寸为 153 x 2。
在大图中，my_data 数据框将与世界数据集中的一些数据合并。然后这个新的数据框将成为一个将被绘制的 sf 对象。国家/地区将根据来自集群的那些值按颜色绘制。
获取数据。更改 my_data 中的列名称：
wm <- spData::world
names(my_data)[1] <- "name_long"
创建新变量。创建 sp 对象：
my_map <- left_join(my_data, wm, by = "name_long")
world_cluster <- st_as_sf(my_map)
绘制地图 - 基于聚类值的颜色：
plot(world_cluster["cluster"])
下面link查看剧情。
Map