英文:
State Map by Intervals - Choropleth map R
问题
我有以下的数据和代码，需要使用它们创建两个准确的美国区域地图，根据代码中设置的区间。目前，这个代码正在填充各个州的地图，但根据数据集中每个州应分配的十六进制颜色代码，地图上的颜色显示不正确。例如，根据数据集，亚利桑那州应该是#FDAE61，但在地图上显示为#fee090。我还不知道如何在地图上包括阿拉斯加和夏威夷，但它们是我要绘制的数据集的一部分。
这是我目前的代码：
# 将数据按年份分割
yearly_data <- split(avg_inpatient_beds, f = avg_inpatient_beds$year)
# 创建各年份的数据框
avg_2020 <- (yearly_data$"2020")
avg_2021 <- (yearly_data$"2021")
avg_2022 <- (yearly_data$"2022")
# 保留百分比和州列，并重命名年份列
avg_2021 <- as.data.frame(avg_2021[, c(1, 5)])
names(avg_2021)[2] <- "2021"
avg_2022 <- as.data.frame(avg_2022[, c(1, 5])
names(avg_2022)[2] <- "2022"
# 合并两年的数据框
avg_beds <- merge(avg_2021, avg_2022, by = "state")
# 删除不在典型州地图中的州
avg_beds <- subset(avg_beds, !(row.names(avg_beds) %in% c(4, 9, 41, 49)))
avg_beds
# 更新州列以匹配地图数据的州名
full_state <- state.name[match(avg_beds$state, state.abb)]
# 替换州缩写列为全名列
avg_beds <- cbind(full_state, avg_beds)
avg_beds <- avg_beds[, c(1, 3, 4)]
names(avg_beds)[1] <- "state"
# 获取用于确定区间的范围
range(avg_beds$"2021")
range(avg_beds$"2022")
# 创建用于分割数据为区间的断点
breaks <- c(40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90)
# 定义颜色
beds_colors <- brewer.pal(10, "RdYlBu")
beds_colors <- beds_colors[10:1]
# 分配颜色
twenty_one_cuts <- cut(avg_beds$"2021", breaks)
twenty_two_cuts <- cut(avg_beds$"2022", breaks)
twenty_one_colors <- beds_colors[twenty_one_cuts]
twenty_two_colors <- beds_colors[twenty_two_cuts]
twenty_one <- cbind(avg_beds[, c(1, 2)], twenty_one_colors)
twenty_two <- cbind(avg_beds[, c(1, 3)], twenty_two_colors)
# 按字母顺序排列数据框，以便与地图匹配
twenty_one <- twenty_one[order(twenty_one$state), ]
twenty_one$state <- tolower(twenty_one$state)
twenty_two <- twenty_two[order(twenty_two$state), ]
twenty_two$state <- tolower(twenty_two$state)
colored_map <- map("state", fill = TRUE, col = twenty_one$twenty_one_colors)
我目前得到了这个结果，但与数据集比较时颜色不正确。
[![enter image description here][1]][1]
这是数据集：
```R
> dput(avg_inpatient_beds)
structure(list(state = c("AK", "AK", "AK", "AK", "AL", "AL",
"AL", "AL", "AR", "AR", "AR", "AR", "AS", "AS", "AS", "AZ", "AZ",
"AZ", "AZ", "CA", "CA", "CA", "CA", "CO", "CO", "CO", "CO", "CT",
"CT", "CT", "CT", "DC", "DC", "DC", "DC", "DE", "DE", "DE", "DE",
"FL", "FL", "FL", "FL", "GA", "GA", "GA", "GA", "HI", "HI", "HI",
<details>
<summary>英文:</summary>
I have the following data and code that I need to use to create 2 accurate choropleth maps of the US based on intervals set in the code. Currently, this is filling in the map of the states, but according to the hex codes each state should be assigned, it's showing up differently in the scale on the 10 class "RdYlBu" palette. For example, Arizona should be #FDAE61 (according to the dataset), but it is showing up as #fee090 on the map. I also don't know how to include Alaska and Hawaii in the map, but it is part of the data set I'm trying to map.
This is the code I have currently:
Split the dataframe by year
yearly_data <- split(avg_inpatient_beds, f = avg_inpatient_beds$year)
Create individual year dataframes
avg_2020 <- (yearly_data$"2020")
avg_2021 <- (yearly_data$"2021")
avg_2022 <- (yearly_data$"2022")
Retain only percentage and state columns and rename column for year
avg_2021 <- as.data.frame(avg_2021[, c(1, 5)])
names(avg_2021)[2] <- "2021"
avg_2022 <- as.data.frame(avg_2022[, c(1, 5)])
names(avg_2022)[2] <- "2022"
Merge two years to one data frame
avg_beds <- merge(avg_2021, avg_2022, by = "state")
Remove states that are not in the typical state map
avg_beds <- subset(avg_beds, !(row.names(avg_beds) %in% c(4, 9, 41, 49)))
avg_beds
Update state column to state names to match map data
full_state <- state.name[match(avg_beds$state, state.abb)]
Replace state abb column with full name column
avg_beds <- cbind(full_state, avg_beds)
avg_beds <- avg_beds[, c(1, 3, 4)]
names(avg_beds)[1] <- "state"
Get ranges for figuring intervals
range(avg_beds$"2021")
range(avg_beds$"2022")
Create breaks to split data into intervals
breaks <- c(40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90)
Define colors
beds_colors <- brewer.pal(10, "RdYlBu")
beds_colors <- beds_colors[10:1]
Assign colors
twenty_one_cuts <- cut(avg_beds$"2021", breaks)
twenty_two_cuts <- cut(avg_beds$"2022", breaks)
twenty_one_colors <- beds_colors[twenty_one_cuts]
twenty_two_colors <- beds_colors[twenty_two_cuts]
twenty_one <- cbind(avg_beds[, c(1, 2)], twenty_one_colors)
twenty_two <- cbind(avg_beds[, c(1, 3)], twenty_two_colors)
Order dataframes alphabetically to allow it to match map
twenty_one <- twenty_one[order(twenty_one$state), ]
twenty_one$state <- tolower(twenty_one$state)
twenty_two <- twenty_two[order(twenty_two$state), ]
twenty_two$state <- tolower(twenty_two$state)
colored_map <- map("state", fill = TRUE, col = twenty_one$twenty_one_colors)
I am getting this currently, but the colors are incorrect when compared with the dataset.
[![enter image description here][1]][1]
This is the dataset:
> dput(avg_inpatient_beds)
structure(list(state = c("AK", "AK", "AK", "AK", "AL", "AL",
"AL", "AL", "AR", "AR", "AR", "AR", "AS", "AS", "AS", "AZ", "AZ",
"AZ", "AZ", "CA", "CA", "CA", "CA", "CO", "CO", "CO", "CO", "CT",
"CT", "CT", "CT", "DC", "DC", "DC", "DC", "DE", "DE", "DE", "DE",
"FL", "FL", "FL", "FL", "GA", "GA", "GA", "GA", "HI", "HI", "HI",
"HI", "IA", "IA", "IA", "IA", "ID", "ID", "ID", "ID", "IL", "IL",
"IL", "IL", "IN", "IN", "IN", "IN", "KS", "KS", "KS", "KS", "KY",
"KY", "KY", "KY", "LA", "LA", "LA", "LA", "MA", "MA", "MA", "MA",
"MD", "MD", "MD", "MD", "ME", "ME", "ME", "ME", "MI", "MI", "MI",
"MI", "MN", "MN", "MN", "MN", "MO", "MO", "MO", "MO", "MS", "MS",
"MS", "MS", "MT", "MT", "MT", "MT", "NC", "NC", "NC", "NC", "ND",
"ND", "ND", "ND", "NE", "NE", "NE", "NE", "NH", "NH", "NH", "NH",
"NJ", "NJ", "NJ", "NJ", "NM", "NM", "NM", "NM", "NV", "NV", "NV",
"NV", "NY", "NY", "NY", "NY", "OH", "OH", "OH", "OH", "OK", "OK",
"OK", "OK", "OR", "OR", "OR", "OR", "PA", "PA", "PA", "PA", "PR",
"PR", "PR", "PR", "RI", "RI", "RI", "RI", "SC", "SC", "SC", "SC",
"SD", "SD", "SD", "SD", "TN", "TN", "TN", "TN", "TX", "TX", "TX",
"TX", "UT", "UT", "UT", "UT", "VA", "VA", "VA", "VA", "VI", "VI",
"VI", "VI", "VT", "VT", "VT", "VT", "WA", "WA", "WA", "WA", "WI",
"WI", "WI", "WI", "WV", "WV", "WV", "WV", "WY", "WY", "WY", "WY"
), year = c("2020", "2021", "2022", "2023", "2020", "2021", "2022",
"2023", "2020", "2021", "2022", "2023", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023",
"2020", "2021", "2022", "2023", "2020", "2021", "2022", "2023"
), avg_beds = c(1482.3485915493, 1579.34794520548, 1722.99726027397,
1611.08235294118, 10263.0081967213, 15258.8849315068, 14412.6712328767,
12341.9176470588, 7620.39202657807, 8970.35890410959, 8286.20273972603,
7564.30588235294, 150, 150, 150, 10922.8459016393, 16757.9150684932,
15837.5452054795, 14664.8117647059, 54697.1607717042, 63854.0520547945,
63370.4602739726, 62733.7411764706, 8349.11945392492, 11614.4575342466,
11095.8246575342, 10311.3058823529, 7811.21799307958, 8253.53150684931,
7801.68493150685, 7520.22352941176, 3078.63440860215, 3499.66301369863,
3369.04931506849, 3248.97647058824, 2720.75, 3236.13424657534,
2904.96712328767, 2442.03529411765, 53886.7796610169, 57841.8657534247,
57473.0410958904, 56006.7647058823, 16365.931372549, 22393.0630136986,
21705.4109589041, 21049.6, 1620.81147540984, 2591.40273972603,
2555.15068493151, 2523.18823529412, 7090.79738562091, 7865.93150684932,
7215.30410958904, 6983.58823529412, 3524.44897959184, 3591.14794520548,
3039.05753424658, 2908.62352941176, 24285, 30953.3534246575,
29198.5205479452, 28424.6352941176, 11896.4371584699, 18502.7671232877,
16081.8712328767, 14564.6352941176, 6259.70447761194, 8649.8602739726,
8453.32602739726, 7940.51764705882, 11450.9869281046, 13096.9260273973,
11895.8219178082, 12042.8, NA, 13463.8164383562, 12650.2493150685,
12495.9058823529, 16865.7857142857, 18369.9835616438, 17447.1808219178,
15897.7058823529, 8396.2091503268, 11356.002739726, 11094.7753424658,
10408.2705882353, 2748.74836601307, 3250.99726027397, 3042.84383561644,
2847.78823529412, 19097.3888888889, 22814.0520547945, 21593.3068493151,
20813.5882352941, 8464.00819672131, 9984.75342465753, 9349.83287671233,
9347.81176470588, 16171.2268370607, 16679.5643835616, 15829.2794520548,
14754.9647058824, 6234.09463722398, 8559.61369863014, 8566.53150684931,
7959.34117647059, 2061.41256830601, 3053.95890410959, 2786.01095890411,
2573.76470588235, 16280.7404371585, 23422.2054794521, 22315.9424657534,
20619.4941176471, 2118.23202614379, 2268.7698630137, 2148.16164383562,
1938.69411764706, 4507.30718954248, 4347.65205479452, 4500.17534246575,
4699.50588235294, 2875.26785714286, 3029.40273972603, 2756.00547945205,
2427.25882352941, 14989.2418300654, 23662.2493150685, 23451.0684931507,
23140.9647058824, 2818.71672354949, 4313.10410958904, 4073.8,
3784.44705882353, 5789.30459770115, 8680.15616438356, 8016.82739726027,
7301.35294117647, 38705.7508532423, 50604.397260274, 46850.701369863,
45106.5764705882, 23244.1176470588, 31288.3424657534, 27543.5561643836,
26225.0235294118, 9241.88888888889, 10415.0575342466, 9769.58082191781,
9260.01176470588, 4907.88782051282, 7027.76712328767, 6829.62739726027,
6773.44705882353, 22642.7058823529, 31180.8657534247, 31712.3232876712,
31315.4588235294, 6714.30546623794, 9353.34794520548, 8594.23561643836,
6564.63529411765, 1875.22368421053, 2436.26575342466, 2194.10684931507,
2214.61176470588, 9285.72786885246, 10837.9698630137, 10910.8821917808,
9961.62352941177, 2100.54609929078, 2759.58904109589, 2305.18630136986,
2077.10588235294, 17183.6313993174, 20020.0246575342, 19495.0794520548,
18164.3764705882, 45096.1639344262, 67333.6602739726, 61701.1589041096,
57172.7176470588, 5026.24738675958, 6042.06849315068, 5884.36438356164,
5822.6, 13327.8196721311, 18029.2657534247, 17448.8520547945,
16969.0941176471, 143.731343283582, 168.112328767123, 170.112328767123,
208.847058823529, 1025.37931034483, 1271.89589041096, 1241.14246575342,
1178.96470588235, 10205.9772727273, 13070.1095890411, 11642.2630136986,
10954.0470588235, 10528.6699346405, 13154.5945205479, 11998.5397260274,
11552.4823529412, 5347.03164556962, 6112.14794520548, 5390.77260273973,
4918.96470588235, 1308.81699346405, 1683.56712328767, 1629.08767123288,
1327.91764705882), avg_beds_used = c(859.514084507042, 1031.90410958904,
1336.44109589041, 1271.81176470588, NA, 11420.0246575342, 10671.6493150685,
9339.91764705882, 4833.80066445183, 6361.31232876712, 5899.79178082192,
5409.70588235294, 69.4538461538462, 67.2301369863014, 65.1566265060241,
8254.48196721311, 12088.1260273973, 11970.9095890411, 11742.8588235294,
36386.2958199357, 49016.8739726027, 49290.0054794521, 50077.8588235294,
5384.49146757679, 7960.87671232877, 7574.07397260274, 6974.57647058824,
5247.73356401384, 6507.43835616438, 6055.90684931507, 5929.91764705882,
2298.45161290323, 2698.0602739726, 2695.86575342466, 2686.05882352941,
1906.80714285714, 2407.89315068493, 2346.27397260274, 2094.89411764706,
34351.5288135593, 45450.8164383562, 44357.4739726027, 44287.9529411765,
11806.7581699346, 17503.1287671233, 17315.3424657534, 16434.8,
1460.71038251366, 1867.11506849315, 1972.61643835616, 1986.32941176471,
3877.73529411765, 5074.71506849315, 4826.48493150685, 4695.37647058823,
1715.47959183673, 2258.34520547945, 2141.17260273973, 2066.25882352941,
NA, 21714.6904109589, 20805.8931506849, 19975.5529411765, 7218.73770491803,
12180.8356164384, 11711.8246575342, 10382.8352941176, NA, 5154.47397260274,
4924.76712328767, 4708.48235294118, 7118.85620915033, 8618.89863013699,
8964.02191780822, 9267.81176470588, NA, 9377.41369863014, 8943.85479452055,
8615.22352941177, 12337.7142857143, 15264.095890411, 14973.8,
13675.1294117647, 6609.22222222222, 9216.59178082192, 9080.94520547945,
8546.72941176471, 1721.41176470588, 2292.81369863014, 2263.33698630137,
2195.10588235294, 13118.908496732, 17985.8136986301, 17276.2136986301,
16595.0588235294, NA, 7725.37534246575, 7821.78356164384, 7765.78823529412,
NA, 13279.1095890411, 12996.7452054795, 12252.8, 4077.80126182965,
5538.44383561644, 5367.3698630137, 5270.2, 1271.74316939891,
2035.58082191781, 1840.67397260274, 1673.21176470588, NA, 17873.7616438356,
17379.5205479452, 16728.2705882353, 1337.61764705882, 1634.50684931507,
1626.37260273973, 1392.67058823529, 2732.67320261438, 3004.75616438356,
3266.20821917808, 3400.17647058824, 1882.575, 2233.58356164384,
2222.84109589041, 2003.36470588235, NA, 16389.4136986301, 16091.5780821918,
15967.8588235294, NA, 3082.44657534247, 3000.12876712329, 2742.45882352941,
4106.17528735632, 6579.17808219178, 6228.57534246575, 5693.83529411765,
27533.1467576792, 38432.4931506849, 36969.3150684932, 36087.2941176471,
15721.1764705882, 22884.9315068493, 21579.4438356164, 20819.0235294118,
5369.90522875817, 7306.44109589041, 6892.15068493151, 6729.51764705882,
3297.5641025641, 5256.22191780822, 5386.10136986301, 5348.8,
16351.3104575163, 25367.7205479452, 25152.4657534247, 24211.8705882353,
3788.38585209003, 5606.55616438356, 5794.81369863014, 4504.89411764706,
1598.75657894737, 2183.77534246575, 1941.18356164384, 1950.54117647059,
6813.28196721311, 8441.33150684932, 8314.68219178082, 7780.95294117647,
1195.37234042553, 1616.48219178082, 1475.36164383562, 1347.98823529412,
NA, 13369.3698630137, 13911.4410958904, 13302.0117647059, 30255.9508196721,
49958.2328767123, 47551.9698630137, 45613.7529411765, NA, 3654.07671232877,
3883.23561643836, 3643.21176470588, NA, 13147.2712328767, 12598.5863013699,
11930.6352941176, NA, 95.358904109589, 104.172602739726, 126.882352941176,
NA, 868.32602739726, 913.249315068493, 866.705882352941, 7538.48051948052,
9722.10410958904, 9771.67123287671, 9187.48235294118, NA, 8704.84109589041,
8572.38082191781, 8377.32941176471, 3578.38291139241, 4651.43835616438,
4363.65205479452, 3982.89411764706, 553.062091503268, 783.830136986301,
727.032876712329, 594.988235294118), avg_pct_used = c(58.0341335678192,
65.3918285451338, 77.486689789602, 78.7069859419761, NA, 74.8323725749902,
74.0321411255795, 75.6673005341654, 60.7230210185479, 70.8459629988653,
71.114302653934, 71.4884791485398, 46.3025641025641, 44.8200913242009,
43.4377510040161, 76.4187396426551, 72.0622470157661, 75.5299532951362,
80.064864649838, 63.6566790011334, 76.7010909079842, 77.7577244390066,
79.8272641974943, 63.8469922075423, 68.5281388333889, 68.2810792760171,
67.6394107258107, 67.145180309496, 78.8209944195422, 77.6208246032181,
78.8539611914541, 73.6668959214497, 77.0737279466314, 80.0078235586996,
82.6650165945598, 70.4450989480634, 74.3348186010574, 80.8703111122651,
85.7984589728335, 61.5900367839076, 78.5692691611553, 77.1576751070881,
79.0648222425613, 70.5613743599451, 78.148310972946, 79.7712065488659,
78.0682633956077, NaN, 72.0543489077551, 77.2172137028582, 78.7181154144497,
52.6737074029103, 64.6168898952453, 66.9346718717226, 67.2220044908927,
47.2141877609525, 63.0377100873095, 70.5391653388271, 70.993450834333,
NA, 70.1477645800593, 71.2467634826167, 70.2740078757236, 54.3301380159047,
65.8514105025912, 72.8863091365527, 71.2854674936872, NA, 59.6045663483043,
58.2535536447001, 59.2894209267201, 59.5841607101206, 66.5339868749684,
75.3609285976828, 76.9394742584137, NA, 69.8108153059624, 70.6825562358614,
68.9190642728273, 72.6039025535447, 83.0839704811626, 85.7944028777476,
86.0226922376057, 77.3792722022137, 81.1486231839679, 81.8115259991818,
82.1175691360236, 62.8762311540265, 70.5136920428069, 74.453161232248,
77.0807731157178, 65.0460381738816, 78.8431039948139, 79.9936513152379,
79.7317724832292, NA, 77.3170219033029, 83.6292752542574, 83.0602240942635,
NA, 79.5422311805706, 82.088985443897, 83.015381442142, 61.7698860682214,
64.6670682078301, 62.6186207779669, 66.1985866915137, 56.6876936563504,
66.7195368457316, 65.9968194906312, 65.0161981058136, NA, 76.3154613571695,
77.9093413466763, 81.1345886796999, 59.8677172344674, 72.1077397115384,
75.6406873882103, 71.8103617830332, 56.9990151519118, 69.151870898337,
72.5638645928381, 72.3531071802958, 64.6950144458858, 73.7638511183982,
80.6893174931963, 82.4492586491483, NA, 69.2391241671435, 68.5926909559758,
68.978171777324, NA, 71.4690590158144, 73.6567942166183, 72.4545832176507,
61.5439639959734, 75.839423218283, 77.6720889964072, 77.9847437399422,
71.2578092100634, 76.0387684922576, 78.9065607260507, 79.9989677871531,
64.9955230470055, 73.1589740461094, 78.341179701438, 79.3747049853637,
55.2038005519259, 70.1570005816478, 70.5013626193212, 72.5909485205156,
68.2320333974356, 74.7679794346103, 78.8818625465407, 78.9732884146987,
65.9361384618222, 81.388519390194, 79.3451781172181, 77.3487329364629,
58.9938404930082, 59.945262227112, 67.7187242430109, 68.5841922329257,
83.7378026402648, 89.634872807669, 88.4890957351854, 88.069196916687,
72.4583212802935, 77.8491530227364, 76.2724879883023, 78.1044771908371,
53.7613268371485, 58.5535864783643, 64.0525119957538, 64.8919251656738,
NA, 66.8044646550824, 71.3742115780498, 73.2277578779517, 59.2918761516133,
74.2471431901894, 77.0988716989269, 79.7628244596268, NA, 60.3836722252458,
65.8969040316745, 62.5580000888279, NA, 72.9107734587356, 72.1817163421604,
70.3024802885588, NA, 56.7388220028929, 61.2388159417545, 60.8105742296919,
NA, 68.3657287871175, 73.575127499954, 73.5177797687125, 72.8386838356664,
74.5968026257042, 83.9757756275687, 83.8693574396094, NA, 66.2691862765157,
71.4280050528217, 72.5039877649086, 63.7259250722131, 76.1371272643305,
80.9239305142574, 80.9557245485105, 43.6256840298479, 46.4657999422649,
44.5542417729596, 44.7866269949582)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -215L), groups = structure(list(
state = c("AK", "AL", "AR", "AS", "AZ", "CA", "CO", "CT",
"DC", "DE", "FL", "GA", "HI", "IA", "ID", "IL", "IN", "KS",
"KY", "LA", "MA", "MD", "ME", "MI", "MN", "MO", "MS", "MT",
"NC", "ND", "NE", "NH", "NJ", "NM", "NV", "NY", "OH", "OK",
"OR", "PA", "PR", "RI", "SC", "SD", "TN", "TX", "UT", "VA",
"VI", "VT", "WA", "WI", "WV", "WY"), .rows = structure(list(
1:4, 5:8, 9:12, 13:15, 16:19, 20:23, 24:27, 28:31, 32:35,
36:39, 40:43, 44:47, 48:51, 52:55, 56:59, 60:63, 64:67,
68:71, 72:75, 76:79, 80:83, 84:87, 88:91, 92:95, 96:99,
100:103, 104:107, 108:111, 112:115, 116:119, 120:123,
124:127, 128:131, 132:135, 136:139, 140:143, 144:147,
148:151, 152:155, 156:159, 160:163, 164:167, 168:171,
172:175, 176:179, 180:183, 184:187, 188:191, 192:195,
196:199, 200:203, 204:207, 208:211, 212:215), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -54L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
[1]: https://i.stack.imgur.com/WilTk.png
</details>
# 答案1
**得分**: 1
您需要使用`regions`参数将颜色映射到州，但由于此代码仍然不按您的期望工作，因为它使用了R内置的图形功能。
``` r
library(maps)
library(RColorBrewer)
# <移除您的代码，因为响应内容不适合，必须在每个reprex上执行此操作>
colored_map <- map("state", regions = twenty_one$state, fill = TRUE, col = twenty_one$twenty_one_colors)
我通过更改一些颜色十六进制值为基本图形颜色来测试了这个理论。
twenty_one <- twenty_one |>
dplyr::mutate(twenty_one_colors = dplyr::if_else(twenty_one_colors == "#FDAE61", "orange1", twenty_one_colors)) |>
dplyr::mutate(twenty_one_colors = dplyr::if_else(twenty_one_colors == "#F46D43", "orange4", twenty_one_colors))
colored_map <- map("state", regions = twenty_one$state, fill = TRUE, col = twenty_one$twenty_one_colors)
我认为您可以根据这个帖子来解决这个问题，但SDMTools不再在CRAN上 - 您可以在GitHub上获取它。
或者，您可以使用一些较新且更受支持的工具，如sf和ggplot2。
library(sf)
library(tidyverse)
library(RColorBrewer)
library(maps)
usa <- st_as_sf(maps::map("state", fill=TRUE, plot =FALSE))
twenty_one <- dplyr::left_join(twenty_one, usa, by = c("state" = "ID"))
ggplot(twenty_one) +
geom_sf(aes(fill = twenty_one_colors, geometry = geom)) +
scale_fill_identity() +
coord_sf(crs = st_crs("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"), datum = NA) +
ggthemes::theme_map()
关于您的另一个问题，如何将阿拉斯加和夏威夷映射到同一地图，有多种方法 - 参考这个帖子。如果可能的话，我真的很喜欢使用像fiftystater这样的工具，因为它更简单。
library(sf)
library(tidyverse)
library(RColorBrewer)
library(maps)
library(fiftystater)
usa <- st_as_sf(maps::map("state", fill=TRUE, plot =FALSE))
twenty_one <- dplyr::left_join(twenty_one, usa, by = c("state" = "ID"))
ggplot(twenty_one) +
geom_map(aes(map_id = state, fill = twenty_one_colors), map = fifty_states) +
geom_sf(aes(fill = twenty_one_colors, geometry = geom)) +
scale_fill_identity() +
coord_sf(crs = st_crs("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"), datum = NA) +
ggthemes::theme_map()
英文:
You need to use the regions param to map colors to states, but this code still doesn't work as you're expecting because maps using R's built in graphics.
library(maps)
library(RColorBrewer)
<Removing your code because response won't fit, have to do this on every reprex>
colored_map <- map("state", regions = twenty_one$state, fill = TRUE, col = twenty_one$twenty_one_colors)
<!-- -->
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
I tested this theory by changing a few of the color hexes to base graphics colors.
twenty_one <- twenty_one |>
dplyr::mutate(twenty_one_colors = dplyr::if_else(twenty_one_colors == "#FDAE61", "orange1", twenty_one_colors)) |>
dplyr::mutate(twenty_one_colors = dplyr::if_else(twenty_one_colors == "#F46D43", "orange4", twenty_one_colors))
colored_map <- map("state", regions = twenty_one$state, fill = TRUE, col = twenty_one$twenty_one_colors)
<!-- -->
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
I think you can work around this based on this post, but SDMTools isn't on CRAN anymore - you can get it on Github.
Alternatively, you can use tools that are a bit newer and more supported with sf & ggplot2.
library(sf)
library(tidyverse)
library(RColorBrewer)
library(maps)
usa <- st_as_sf(maps::map("state", fill=TRUE, plot =FALSE))
twenty_one <- dplyr::left_join(twenty_one, usa, by = c("state" = "ID"))
ggplot(twenty_one) +
geom_sf(aes(fill = twenty_one_colors, geometry = geom)) +
scale_fill_identity() +
coord_sf(crs = st_crs("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"), datum = NA) +
ggthemes::theme_map()
<!-- -->
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
Regarding your separate question of mapping Alaska & Hawaii,there are a number of ways to include them on the same map - refer to this post. I really like using something like fiftystater if I can because it's a lot simpler.
library(sf)
library(tidyverse)
library(RColorBrewer)
library(maps)
library(fiftystater)
usa <- st_as_sf(maps::map("state", fill=TRUE, plot =FALSE))
twenty_one <- dplyr::left_join(twenty_one, usa, by = c("state" = "ID"))
ggplot(twenty_one) +
geom_map(aes(map_id = state, fill = twenty_one_colors), map = fifty_states) +
geom_sf(aes(fill = twenty_one_colors, geometry = geom)) +
scale_fill_identity() +
coord_sf(crs = st_crs("+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"), datum = NA) +
ggthemes::theme_map()
<!-- -->
<sup>Created on 2023-04-03 with reprex v2.0.2</sup>
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