如何分别对每个facet_wrap进行排序

Question

我正在尝试绘制某些农作物的产量，并按每种农作物的前十大生产国排序。我使用facet_wrap，第一个面板排序，但其余面板不排序。

我尝试按照tidytuesday屏幕截图中的示例进行操作，但是由于某种原因，我的图形无法正确排序。

以下是截屏视频的链接和时间 https://youtu.be/rxJZT0duwfU?t=1841

以下是crop_dataset的数据：

structure(list(country = structure(c(1L, 1L, 2L, 2L, 3L, 4L, 
5L, 5L, 5L, 5L, 6L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 10L, 10L, 11L, 
11L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 15L, 16L, 17L, 17L, 17L, 
18L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 22L, 
23L, 23L, 23L, 23L, 24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 
27L, 27L, 27L, 27L, 28L, 28L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 
30L, 31L, 31L, 32L, 32L, 32L, 33L, 33L, 34L, 35L, 35L, 35L, 35L, 
36L, 36L, 36L, 37L, 37L, 37L, 37L, 38L, 39L, 39L, 39L, 40L, 40L, 
40L, 41L, 41L, 42L, 42L, 43L, 43L, 44L, 44L, 45L, 45L, 45L, 45L, 
46L, 47L, 47L, 48L, 48L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 51L, 
51L, 51L, 52L, 52L, 52L, 53L, 54L, 55L, 55L, 56L, 56L, 56L, 57L, 
57L, 58L, 58L, 59L, 59L, 60L, 61L, 61L, 61L, 61L, 62L, 62L, 63L, 
63L, 64L, 64L, 65L, 65L, 65L, 66L, 66L, 67L, 67L, 68L, 69L, 70L, 
70L, 70L, 70L, 71L, 71L, 71L, 72L, 72L, 72L, 73L, 73L, 74L, 74L, 
75L, 75L, 75L, 75L, 76L, 76L, 77L, 77L, 77L, 78L, 78L, 78L, 78L, 
79L, 79L, 79L, 80L, 80L, 80L, 81L, 82L, 82L, 83L, 83L, 84L, 84L, 
84L, 85L, 85L, 86L, 86L, 87L, 87L, 87L, 88L, 89L, 89L, 90L, 90L, 
90L, 91L, 91L, 92L, 93L, 93L, 93L, 94L, 95L, 96L, 97L, 97L, 97L, 
97L, 98L, 98L, 98L, 99L, 99L, 99L, 100L, 101L, 101L, 101L, 102L, 
103L, 104L, 104L, 105L, 105L, 105L, 105L, 106L, 107L, 108L, 109L, 
109L, 109L, 110L, 110L, 111L, 111L, 111L, 112L, 112L, 112L, 113L, 
114L, 115L, 116L, 116L, 116L, 116L, 117L, 117L, 118L, 118L, 118L, 
118L, 119L, 119L, 120L, 120L, 120L, 121L, 121L, 121L, 121L, 122L, 
122L, 122L, 123L, 123L, 123L, 123L, 124L, 124L, 124L, 124L, 125L, 
125L, 125L, 125L, 126L, 126L, 127L, 127L, 128L, 129L, 130L, 130L, 
131L, 131L, 132L, 132L, 133L, 133L, 134L, 134L, 135L, 135L, 135L, 
136L, 136L, 137L, 138L, 138L, 139L, 140L, 140L, 140L, 141L, 141L, 
142L, 142L, 142L, 143L, 143L, 144L, 144L, 145L, 146L, 146L, 147L, 
147L, 147L, 148L, 149L, 149L, 149L, 150L, 150L, 150L, 151L, 151L, 
152L, 152L, 152L, 152L, 153L, 153L, 154L, 154L, 154L, 155L, 155L, 
156L, 156L, 156L, 156L, 157L, 157L, 158L, 158L, 159L, 159L, 159L, 
160L, 161L, 161L, 162L, 163L, 163L, 163L, 164L, 164L, 165L, 166L, 
166L, 166L, 166L, 167L, 167L, 167L, 168L, 168L, 168L, 169L, 170L, 
171L, 171L, 171L, 171L, 172L, 172L, 172L, 173L, 174L, 174L, 175L, 
175L, 175L, 176L, 176L, 176L), .Label = c("Afghanistan", "Albania", 
"Algeria", "American Samoa", "Angola", "Antigua and Barbuda", 
"Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", 
"Bahamas", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", 
"Benin", "Bhutan", "Bolivia (Plurinational State of)", "Bosnia and Herzegovina", 
"Botswana", "Brazil", "Bulgaria", "Burkina Faso", "Burundi", 
"C\xf4te d'Ivoire", "Cabo Verde", "Cambodia", "Cameroon", "Canada", 
"Central African Republic", "Chad", "Chile", "China, mainland", 
"China, Taiwan Province of", "Colombia", "Comoros", "Congo", 
"Costa Rica", "Croatia", "Cuba", "Czechia", "Democratic People's Republic of Korea", 
"Democratic Republic of the Congo", "Denmark", "Djibouti", "Dominica", 
"Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", 
"Eritrea", "Eswatini", "Ethiopia", "Fiji", "France", "French Guiana", 
"French Polynesia", "Gabon", "Gambia", "Georgia", "Germany", 
"Ghana", "Greece", "Grenada", "Guadeloupe", "Guam", "Guatemala", 
"Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Honduras", "Hungary", 
"India", "Indonesia", "Iran (Islamic Republic of)", "Iraq", "Israel", 
"Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", 
"Kuwait", "Kyrgyzstan", "Lao People's Democratic Republic", "Lebanon", 
"Lesotho", "Liberia", "Libya", "Lithuania", "Luxembourg", "Madagascar", 
"Malawi", "Malaysia", "Maldives", "Mali", "Martinique", "Mauritania", 
"Mauritius", "Mexico", "Micronesia (Federated States of)", "Montenegro", 
"Montserrat", "Morocco", "Mozambique", "Myanmar", "Nepal", "Netherlands", 
"New Caledonia", "New Zealand", "Nicaragua", "Niger", "Nigeria", 
"Oman", "Pakistan", "Panama", "Papua New Guinea", "Paraguay", 
"Peru", "Philippines", "Poland", "Portugal", "Puerto Rico", "Qatar", 
"R\xe9union", "Republic of Korea", "Republic of Moldova", "Romania", 
"Russian Federation", "Rwanda", "Saint Vincent and the Grenadines", 
"Samoa", "Sao Tome and Principe", "Saudi Arabia", "Senegal", 
"Serbia", "Sierra Leone", "Slovakia", "Slovenia", "Solomon Islands", 
"Somalia", "South Africa", "South Sudan", "Spain", "Sri Lanka", 
"Sudan", "Suriname", "Switzerland", "Syrian Arab Republic", "Tajikistan", 
"Thailand", "The former Yugoslav Republic of Macedonia", "Timor-Leste", 
"Togo", "Trinidad and Tobago", "Turkey", "Turkmenistan", "Uganda", 
"Ukraine", "United Arab Emirates", "United Republic of Tanzania", 
"United States of America", "Uruguay", "Uzbekistan", "Vanuatu", 
"Venezuela (Bolivarian Republic of)", "Viet Nam", "Wallis and Futuna Islands", 
"Yemen", "Zambia", "Zimbabwe"), class = "factor"), item = structure(c(1L, 
4L, 1L, 3L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 1L, 3L, 4L, 1L, 1L, 3L, 
4L, 1L, 3L, 1L, 3L, 1L, 4L, 1L, 3L, 4L, 1L, 4L, 1L, 1L, 1L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 3L, 4L, 1L, 3L, 1L, 1L, 2L, 
3L, 4L, 1L, 3L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 1L, 2L, 4L, 1L, 4L, 
1L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 1L, 2L, 4L, 
1L, 2L, 4L, 1L, 3L, 1L, 4L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 4L, 1L, 
1L, 4L, 1L, 4L, 1L, 2L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 3L, 
4L, 2L, 1L, 1L, 4L, 1L, 3L, 4L, 1L, 4L, 1L, 3L, 1L, 4L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 4L, 1L, 3L, 1L, 4L, 
4L, 1L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 4L, 1L, 4L, 1L, 4L, 
1L, 2L, 3L, 4L, 1L, 3L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 
1L, 3L, 4L, 1L, 1L, 3L, 1L, 4L, 1L, 3L, 4L, 1L, 3L, 1L, 3L, 1L, 
3L, 4L, 1L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 1L, 
1L, 1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 2L, 4L, 1L, 1L, 3L, 4L, 4L, 
1L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 1L, 1L, 1L, 3L, 4L, 1L, 4L, 1L, 
3L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 1L, 4L, 1L, 2L, 
3L, 4L, 1L, 4L, 1L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 3L, 1L, 4L, 1L, 1L, 
1L, 4L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 4L, 
1L, 2L, 1L, 1L, 2L, 4L, 1L, 3L, 1L, 2L, 4L, 1L, 3L, 1L, 3L, 2L, 
1L, 4L, 1L, 3L, 4L, 1L, 1L, 3L, 4L, 1L, 3L, 4L, 1L, 4L, 1L, 2L, 
3L, 4L, 1L, 3L, 1L, 3L, 4L, 1L, 3L, 1L, 2L, 3L, 4L, 1L, 3L, 1L, 
3L, 1L, 2L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 4L, 1L, 3L, 1L, 1L, 2L, 
3L, 4L, 1L, 3L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 2L, 3L, 4L, 1L, 3L, 
4L, 4L, 1L, 4L, 1L, 3L, 4L, 1L, 3L, 4L), .Label = c("Maize", 
"Oil palm fruit", "Soybeans", "Sugar cane"), class = "factor"), 
    value = c(0.173912, 0.033725, 0.381059, 5e-04, 0.002634, 
    3.2e-05, 2.68, 0.280529, 0.016081, 0.564918, 7.7e-05, 49.475895, 
    54.971626, 19.164718, 0.010413, 0.436194, 0.064546, 36.561497, 
    2.075983, 0.193416, 0.235669, 4.5e-05, 0.000798, 0.058342, 
    3.025392, 0.096921, 3.862775, 9.9e-05, 0.102181, 0.694238, 
    0.608671, 0.089536, 0.013037, 1.670432, 1.454, 0.690908, 
    0.16, 0.01253, 0.090263, 0.000204, 0.014856, 1.004181, 3.018872, 
    8.049439, 0.709584, 0.01174, 0.012265, 97.72186, 1.676421, 
    114.599168, 758.548292, 2.562569, 0.02, 1.533431, 0.0185, 
    0.487643, 0.24, 0.086511, 0.003185, 0.219435, 1.025, 2.227, 
    0.000503, 1.794697, 0.005314, 0.028375, 0.75, 0.16, 0.168, 
    0.686505, 2.246241, 3.110296, 0.027022, 1.288039, 14.0953, 
    7.7166, 0.09, 0.0058, 0.10334, 0.396506, 0.461004, 1.062589, 
    259.071, 0.671347, 13.149485, 104.404, 0.163478, 0.003203, 
    0.389033, 1.296199, 5.738006, 0.075685, 34.638019, 0.006231, 
    0.01173, 0.148057, 0.701698, 0.006865, 1.0958, 4.142143, 
    1.559638, 0.207765, 0.37387, 16.070729, 0.588105, 0.037012, 
    2.199755, 0.223325, 2.017574, 1.192601, 0.025, 2.225155, 
    0.0389, 1.3e-05, 5.3e-05, 0.000189, 0.004872, 0.044007, 0.429486, 
    5.460761, 1.436106, 3.275993, 0.035006, 9.030074, 7.1, 0.045, 
    15.260597, 0.904, 0.00571, 7.155699, 0.035936, 0.02, 0.090864, 
    5.737328, 8.116787, 0.084033, 1.384207, 0.000961, 1.458049, 
    14.12168, 0.412, 0.000658, 0.01119, 0.003489, 0.04522, 0.016752, 
    0.004037, 0.295172, 0.039051, 0.035063, 0.1425, 0.001812, 
    4.5476, 0.061, 1.965, 2.469763, 0.151762, 1.3624, 0.0033, 
    0.000349, 0.007286, 0.671953, 5.1e-05, 1.917395, 2.031743, 
    0.038, 33.758389, 0.768082, 0.842045, 0.30595, 0.00727, 0.081313, 
    0.006698, 0.004, 1.859037, 0.225, 1.501927, 0.675665, 2.4033, 
    0.00127, 5.379869, 6.811337, 0.162075, 28.72, 10.981, 306.069, 
    27.952, 158.343062, 0.542, 21.21295, 1.223086, 0.2, 7.561703, 
    0.185291, 2.6e-05, 0.007, 0.087583, 6.048499, 1.019781, 0.002539, 
    1.1312, 0.000167, 0.253, 1.497043, 0.022044, 0, 0.784695, 
    0.252319, 3.186, 0.002012, 4.751609, 0.003838, 0.653351, 
    0.003512, 1.192525, 0.00796, 1.76439, 0.003, 8.5e-05, 0.172666, 
    0.179364, 0.003356, 0.274072, 0.003418, 0.056974, 0.000695, 
    0.281, 0.021105, 4.3e-05, 3.041628, 3.464139, 0.208556, 2.964006, 
    0.072561, 101.7409, 0.02999, 0.000151, 2.811385, 0.007875, 
    0.368916, 0.197274, 0.014, 0.000442, 3.713331, 27.762481, 
    0.873518, 0.432927, 56.954993, 8.6e-05, 0.00274, 0.000126, 
    0.122399, 0.000679, 0.553115, 1.70392, 2.9, 1.909334, 0.20947, 
    10.370042, 2.300121, 0.029061, 3.234711, 0.116711, 0.009415, 
    0.175586, 0.408415, 0.0743, 0.005693, 7.096373, 0.038345, 
    0.252854, 10.42, 7.759426, 0.73, 1.497757, 0.007075, 0.001184, 
    5.7014, 0.000159, 73.401199, 0.13, 0.044486, 0.00011, 2.444456, 
    0.013237, 2.434698, 0.205183, 5.1559, 0.14043, 10.478, 6.608, 
    1.523204, 0.852022, 0.001581, 9.399617, 7.914909, 0.474792, 
    0.000666, 29.286893, 4.021592, 0.020297, 0.745123, 0.005479, 
    0.000164, 0.000693, 0.014219, 1.822545, 0.072587, 0.068992, 
    1.773, 0.047, 14.3261, 0.41637, 13.235748, 3.621344, 0.358417, 
    0.023934, 0.107877, 0.000633, 0.01778, 1.2e-05, 0.000964, 
    0.018412, 0.015079, 0.264342, 0.137775, 0.464979, 4.01837, 
    0.461272, 0.018, 0.211292, 0.077944, 1.066188, 0.102441, 
    0.272177, 0.007713, 0.285721, 0.065, 0.209189, 16.82, 1.316, 
    17.388177, 0.092, 3.775645, 0.004599, 0.000506, 0.195744, 
    0.014363, 0.654522, 0.048873, 5.828601, 8.4e-05, 0.001186, 
    6e-06, 0.125301, 0.162454, 0.005642, 0.18557, 3e-04, 1e-06, 
    0.218448, 2.3e-05, 4.96158, 14.591343, 0.054, 102.946001, 
    0.123014, 3.4e-05, 0.100224, 0.001033, 0.854689, 0.155058, 
    0.002154, 0.003221, 5.9, 0.14, 0.048072, 3.015316, 0.030689, 
    3.858456, 24.66875, 3.89937, 0.006432, 5.939737, 0.07548, 
    0.005741, 3.00108, 370.96039, 119.51849, 30.15301, 0.309, 
    1.316, 0.357097, 0.389427, 0.000904, 1.05, 0.452379, 0.008, 
    3.6, 5.109766, 0.101856, 18.356398, 2e-05, 0.042137, 0, 3.606549, 
    0.351416, 4.451253, 0.90099, 0.06, 3.583672)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -411L))

这是我的代码

crop_dataset %>%
  group_by(item, value) %>% 
  arrange(item, desc(value)) %>%
  group_by(item) %>% 
  top_n(10, value) %>%
  mutate(country = fct_reorder(country, value)) %>% 
  ggplot(aes(country, value)) +
  geom_point() +
  coord_flip() +
  facet_wrap(~item, scales = "free", ncol = 2) +
  labs(title = measure, y = "", x = measure)

这是输出：

我希望所有面板都可以排序，但这只是第一个。