在 ggplot 中距 y 轴固定距离处标记 geom_bar 图
Labelling geom_bar plot at a fixed distance from y-axis in ggplot
我想将条形文字标签放置在距 y 轴固定距离的左对齐位置。我制作情节的代码如下，
ggplot(np4, aes(x = Ord, y = Value/1000, fill = Item)) +
geom_bar(stat = "identity") +
scale_y_continuous(expand = expansion(mult = c(.00, .6)), labels = comma) +
coord_flip() +
facet_wrap(~Year, scales = "free", drop = T, nrow = 2) +
labs(title = "Nepal's Export Commodities and Destinations, Mln USD",
caption = "Source: faostat") +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.caption = element_text(face = "italic")) +
geom_text(aes(label=paste0(Item,"-", Partner)), angle = 0,
vjust=.3, hjust = -.1, size=3)
以上代码生成的图像如下。文本跟随栏的尾端，但它们超出了绘图区域。它们由 scale_y_continuous(expand = expansion(mult = c(.00, .6))) 管理，但它只是在一定程度上是合理的。如果文本细节对条形同样重要，那么它们的位置应该全部对齐为左对齐，并且它们应该从最小条形的尾端开始。它们可以继续重叠在较大尺寸的条上，但它们的左对齐对称性对于传递所需信息更为重要。
如果用最少的代码行就能满足上述要求，我将不胜感激。
上图数据如下
structure(list(Year = c(1999, 1999, 1999, 1999, 1999, 1999, 1999,
1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 2003, 2003, 2003,
2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003,
2003, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,
2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010, 2010,
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012,
2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013,
2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014,
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,
2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018), Partner = c("Bangladesh",
"Bangladesh", "China, mainland", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "India", "India",
"USA", "India", "India", "India", "Portugal", "India", "India",
"India", "India", "India", "India", "India", "Unspecified Area",
"Bangladesh", "India", "India", "Bangladesh", "India", "India",
"India", "India", "Bangladesh", "India", "India", "India", "Turkey",
"UAE", "India", "India", "India", "India", "Bangladesh", "India",
"India", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "USA", "India", "India",
"Bangladesh", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "Thailand", "India",
"India", "Bangladesh", "India", "India", "India", "India", "India",
"India", "China, mainland", "India", "Thailand", "India", "Afghanistan",
"India", "India", "India", "India", "India", "India", "Bangladesh",
"India", "India", "India", "Afghanistan", "China, mainland",
"India", "India", "India", "Afghanistan", "India", "India", "India",
"India", "India", "Bangladesh", "Afghanistan", "India", "China, mainland",
"India", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "Afghanistan", "Bangladesh",
"India", "India", "India", "India", "India", "India", "India",
"USA", "India", "India", "India", "Bangladesh", "India", "India",
"India", "India", "India", "India", "USA", "India", "Vietnam",
"Malaysia", "India", "India", "India", "India", "India", "India",
"Bangladesh", "USA", "India", "India", "Vietnam", "India", "India",
"India", "China, mainland", "Malaysia", "India", "India", "India",
"India", "USA", "India", "India", "India", "India", "India",
"India", "USA", "France", "USA", "India"), Item = c("Lentils",
"Rice, milled", "Flour, wheat", "Cake, mustard", "Lentils", "Nutmeg, mace and cardamoms",
"Food prep nes", "Juice, orange, concentrated", "Ginger", "Flour, wheat",
"Crude materials", "Macaroni", "Food wastes", "Oil, rice bran",
"Oilseeds nes", "Lentils", "Nutmeg, mace and cardamoms", "Food prep nes",
"Sugar refined", "Tea", "Oil, vegetable origin nes", "Cake, rapeseed",
"Food wastes", "Macaroni", "Crude materials", "Ginger", "Fat nes, prepared",
"Lentils", "Oil, coconut (copra)", "Juice, orange, concentrated",
"Lentils", "Nutmeg, mace and cardamoms", "Crude materials", "Juice, fruit nes",
"Tea", "Wheat", "Food prep nes", "Juice, orange, single strength",
"Ginger", "Lentils", "Lentils", "Macaroni", "Nuts nes", "Food wastes",
"Juice, apple, single strength", "Lentils", "Crude materials",
"Nutmeg, mace and cardamoms", "Tea", "Beverages, non alcoholic",
"Nuts nes", "Ginger", "Food prep nes", "Juice, fruit nes", "Juice, orange, single strength",
"Macaroni", "Cake, rapeseed", "Oil, vegetable origin nes", "Lentils",
"Juice, apple, single strength", "Nutmeg, mace and cardamoms",
"Lentils", "Crude materials", "Tea", "Juice, fruit nes", "Nuts nes",
"Juice, orange, single strength", "Cake, rapeseed", "Macaroni",
"Ginger", "Juice, apple, single strength", "Juice, pineapple",
"Oil, vegetable origin nes", "Meat, cattle, boneless (beef & veal)",
"Food prep nes", "Nutmeg, mace and cardamoms", "Lentils", "Juice, fruit nes",
"Tea", "Crude materials", "Ginger", "Juice, orange, single strength",
"Cake, rapeseed", "Crude materials", "Juice, apple, single strength",
"Meat, cattle, boneless (beef & veal)", "Macaroni", "Food prep nes",
"Juice, pineapple", "Buffaloes", "Nutmeg, mace and cardamoms",
"Juice, fruit nes", "Tea", "Areca nuts", "Lentils", "Crude materials",
"Juice, orange, single strength", "Ginger", "Tobacco products nes",
"Crude materials", "Cake, rapeseed", "Juice, apple, single strength",
"Macaroni", "Food prep nes", "Juice, pineapple", "Nuts nes",
"Nutmeg, mace and cardamoms", "Juice, fruit nes", "Tea", "Lentils",
"Tobacco products nes", "Crude materials", "Crude materials",
"Juice, orange, single strength", "Cake, rapeseed", "Macaroni",
"Juice, apple, single strength", "Food wastes", "Ginger", "Juice, pineapple",
"Nutmeg, mace and cardamoms", "Juice, fruit nes", "Nuts nes",
"Tea", "Crude materials", "Tobacco products nes", "Lentils",
"Cake, rapeseed", "Juice, orange, single strength", "Food wastes",
"Ginger", "Juice, apple, single strength", "Macaroni", "Juice, pineapple",
"Pet food", "Nutmeg, mace and cardamoms", "Juice, fruit nes",
"Tea", "Lentils", "Cake, rapeseed", "Crude materials", "Macaroni",
"Juice, orange, single strength", "Ginger", "Juice, apple, single strength",
"Pet food", "Juice, pineapple", "Meat, cattle, boneless (beef & veal)",
"Tobacco products nes", "Oil, soybean", "Nutmeg, mace and cardamoms",
"Juice, fruit nes", "Tea", "Cake, rapeseed", "Crude materials",
"Lentils", "Pet food", "Juice, orange, single strength", "Macaroni",
"Meat, cattle, boneless (beef & veal)", "Ginger", "Juice, pineapple",
"Juice, apple, single strength", "Sugar confectionery", "Tobacco products nes",
"Beverages, non alcoholic", "Nutmeg, mace and cardamoms", "Oil, palm",
"Crude materials", "Pet food", "Food prep nes", "Oil, soybean",
"Feed, compound nes", "Ginger", "Cake, soybeans", "Spices nes",
"Oil, essential nes", "Oil, essential nes", "Food prep nes",
"Oil, essential nes"), Value = c(11649, 5283, 3988, 3961, 3788,
3592, 2454, 2372, 2289, 2203, 1650, 1516, 1381, 1333, 1296, 9346,
5816, 5230, 5071, 3963, 3723, 3597, 3264, 3004, 2916, 2845, 2732,
2645, 2422, 1889, 44045, 16949, 15539, 15404, 15119, 15059, 9961,
6512, 5208, 4716, 4583, 4320, 4285, 3382, 3314, 42515, 25401,
15801, 15169, 11555, 6549, 6157, 5937, 5517, 4348, 4003, 3481,
2941, 2399, 2250, 30460, 23839, 20133, 16490, 16047, 11100, 8780,
7463, 6444, 4809, 4110, 3473, 2705, 1841, 1780, 38868, 31320,
17720, 15734, 12839, 10745, 8013, 6426, 6324, 4158, 3530, 3374,
2969, 2887, 2699, 43224, 23404, 17065, 16966, 14896, 9645, 9294,
7997, 7589, 6549, 5556, 4619, 4227, 3688, 3588, 36906, 32832,
24910, 17843, 17767, 15799, 11098, 9488, 8291, 6243, 5807, 5021,
4076, 3827, 3418, 42866, 22956, 17090, 15966, 10380, 9116, 6567,
5807, 5049, 4686, 4411, 4221, 4114, 2482, 2237, 36324, 29671,
23268, 12133, 11531, 9581, 5997, 5594, 5144, 4123, 3702, 2762,
2669, 1775, 1362, 43440, 31226, 24649, 15143, 14893, 8555, 6221,
6187, 5539, 4528, 3913, 3620, 3082, 2141, 1665, 38863, 31839,
14575, 12746, 8417, 5903, 4552, 3845, 2721, 1311, 1208, 1141,
797, 538, 523), Ord = c(15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L,
7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L,
9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L,
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L,
11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L,
12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L,
13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L,
14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L,
15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L,
1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L,
2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L,
3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L,
4L, 3L, 2L, 1L)), row.names = c(NA, -180L), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), groups = structure(list(Year = c(1999,
2003, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018
), .rows = structure(list(1:15, 16:30, 31:45, 46:60, 61:75, 76:90,
91:105, 106:120, 121:135, 136:150, 151:165, 166:180), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -12L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
谢谢。
一个简单的方法是在 geom_text 中的 aes 中设置 y = 5 并将 scales = "fixed" 放在 facet_wrap 调用中：
ggplot(np4, aes(x = Ord, y = Value/1000, fill = Item)) +
geom_bar(stat = "identity") +
scale_y_continuous(expand = expansion(mult = c(.00, .6)), labels = comma) +
coord_flip() +
facet_wrap(~Year, drop = TRUE, nrow = 2) +
labs(title = "Nepal's Export Commodities and Destinations, Mln USD",
caption = "Source: faostat") +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.caption = element_text(face = "italic")) +
geom_text(aes(y = 5, label = paste0(Item,"-", Partner)), angle = 0,
vjust= 0.3, hjust = 0, size=3)
我想将条形文字标签放置在距 y 轴固定距离的左对齐位置。我制作情节的代码如下，
ggplot(np4, aes(x = Ord, y = Value/1000, fill = Item)) +
geom_bar(stat = "identity") +
scale_y_continuous(expand = expansion(mult = c(.00, .6)), labels = comma) +
coord_flip() +
facet_wrap(~Year, scales = "free", drop = T, nrow = 2) +
labs(title = "Nepal's Export Commodities and Destinations, Mln USD",
caption = "Source: faostat") +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.caption = element_text(face = "italic")) +
geom_text(aes(label=paste0(Item,"-", Partner)), angle = 0,
vjust=.3, hjust = -.1, size=3)
以上代码生成的图像如下。文本跟随栏的尾端，但它们超出了绘图区域。它们由 scale_y_continuous(expand = expansion(mult = c(.00, .6))) 管理，但它只是在一定程度上是合理的。如果文本细节对条形同样重要，那么它们的位置应该全部对齐为左对齐，并且它们应该从最小条形的尾端开始。它们可以继续重叠在较大尺寸的条上，但它们的左对齐对称性对于传递所需信息更为重要。
如果用最少的代码行就能满足上述要求，我将不胜感激。
上图数据如下
structure(list(Year = c(1999, 1999, 1999, 1999, 1999, 1999, 1999,
1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 2003, 2003, 2003,
2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003,
2003, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,
2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010, 2010,
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011,
2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011,
2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012,
2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013,
2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014,
2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,
2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018), Partner = c("Bangladesh",
"Bangladesh", "China, mainland", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "India", "India",
"USA", "India", "India", "India", "Portugal", "India", "India",
"India", "India", "India", "India", "India", "Unspecified Area",
"Bangladesh", "India", "India", "Bangladesh", "India", "India",
"India", "India", "Bangladesh", "India", "India", "India", "Turkey",
"UAE", "India", "India", "India", "India", "Bangladesh", "India",
"India", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "USA", "India", "India",
"Bangladesh", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "Thailand", "India",
"India", "Bangladesh", "India", "India", "India", "India", "India",
"India", "China, mainland", "India", "Thailand", "India", "Afghanistan",
"India", "India", "India", "India", "India", "India", "Bangladesh",
"India", "India", "India", "Afghanistan", "China, mainland",
"India", "India", "India", "Afghanistan", "India", "India", "India",
"India", "India", "Bangladesh", "Afghanistan", "India", "China, mainland",
"India", "India", "India", "India", "India", "India", "India",
"India", "India", "India", "India", "India", "Afghanistan", "Bangladesh",
"India", "India", "India", "India", "India", "India", "India",
"USA", "India", "India", "India", "Bangladesh", "India", "India",
"India", "India", "India", "India", "USA", "India", "Vietnam",
"Malaysia", "India", "India", "India", "India", "India", "India",
"Bangladesh", "USA", "India", "India", "Vietnam", "India", "India",
"India", "China, mainland", "Malaysia", "India", "India", "India",
"India", "USA", "India", "India", "India", "India", "India",
"India", "USA", "France", "USA", "India"), Item = c("Lentils",
"Rice, milled", "Flour, wheat", "Cake, mustard", "Lentils", "Nutmeg, mace and cardamoms",
"Food prep nes", "Juice, orange, concentrated", "Ginger", "Flour, wheat",
"Crude materials", "Macaroni", "Food wastes", "Oil, rice bran",
"Oilseeds nes", "Lentils", "Nutmeg, mace and cardamoms", "Food prep nes",
"Sugar refined", "Tea", "Oil, vegetable origin nes", "Cake, rapeseed",
"Food wastes", "Macaroni", "Crude materials", "Ginger", "Fat nes, prepared",
"Lentils", "Oil, coconut (copra)", "Juice, orange, concentrated",
"Lentils", "Nutmeg, mace and cardamoms", "Crude materials", "Juice, fruit nes",
"Tea", "Wheat", "Food prep nes", "Juice, orange, single strength",
"Ginger", "Lentils", "Lentils", "Macaroni", "Nuts nes", "Food wastes",
"Juice, apple, single strength", "Lentils", "Crude materials",
"Nutmeg, mace and cardamoms", "Tea", "Beverages, non alcoholic",
"Nuts nes", "Ginger", "Food prep nes", "Juice, fruit nes", "Juice, orange, single strength",
"Macaroni", "Cake, rapeseed", "Oil, vegetable origin nes", "Lentils",
"Juice, apple, single strength", "Nutmeg, mace and cardamoms",
"Lentils", "Crude materials", "Tea", "Juice, fruit nes", "Nuts nes",
"Juice, orange, single strength", "Cake, rapeseed", "Macaroni",
"Ginger", "Juice, apple, single strength", "Juice, pineapple",
"Oil, vegetable origin nes", "Meat, cattle, boneless (beef & veal)",
"Food prep nes", "Nutmeg, mace and cardamoms", "Lentils", "Juice, fruit nes",
"Tea", "Crude materials", "Ginger", "Juice, orange, single strength",
"Cake, rapeseed", "Crude materials", "Juice, apple, single strength",
"Meat, cattle, boneless (beef & veal)", "Macaroni", "Food prep nes",
"Juice, pineapple", "Buffaloes", "Nutmeg, mace and cardamoms",
"Juice, fruit nes", "Tea", "Areca nuts", "Lentils", "Crude materials",
"Juice, orange, single strength", "Ginger", "Tobacco products nes",
"Crude materials", "Cake, rapeseed", "Juice, apple, single strength",
"Macaroni", "Food prep nes", "Juice, pineapple", "Nuts nes",
"Nutmeg, mace and cardamoms", "Juice, fruit nes", "Tea", "Lentils",
"Tobacco products nes", "Crude materials", "Crude materials",
"Juice, orange, single strength", "Cake, rapeseed", "Macaroni",
"Juice, apple, single strength", "Food wastes", "Ginger", "Juice, pineapple",
"Nutmeg, mace and cardamoms", "Juice, fruit nes", "Nuts nes",
"Tea", "Crude materials", "Tobacco products nes", "Lentils",
"Cake, rapeseed", "Juice, orange, single strength", "Food wastes",
"Ginger", "Juice, apple, single strength", "Macaroni", "Juice, pineapple",
"Pet food", "Nutmeg, mace and cardamoms", "Juice, fruit nes",
"Tea", "Lentils", "Cake, rapeseed", "Crude materials", "Macaroni",
"Juice, orange, single strength", "Ginger", "Juice, apple, single strength",
"Pet food", "Juice, pineapple", "Meat, cattle, boneless (beef & veal)",
"Tobacco products nes", "Oil, soybean", "Nutmeg, mace and cardamoms",
"Juice, fruit nes", "Tea", "Cake, rapeseed", "Crude materials",
"Lentils", "Pet food", "Juice, orange, single strength", "Macaroni",
"Meat, cattle, boneless (beef & veal)", "Ginger", "Juice, pineapple",
"Juice, apple, single strength", "Sugar confectionery", "Tobacco products nes",
"Beverages, non alcoholic", "Nutmeg, mace and cardamoms", "Oil, palm",
"Crude materials", "Pet food", "Food prep nes", "Oil, soybean",
"Feed, compound nes", "Ginger", "Cake, soybeans", "Spices nes",
"Oil, essential nes", "Oil, essential nes", "Food prep nes",
"Oil, essential nes"), Value = c(11649, 5283, 3988, 3961, 3788,
3592, 2454, 2372, 2289, 2203, 1650, 1516, 1381, 1333, 1296, 9346,
5816, 5230, 5071, 3963, 3723, 3597, 3264, 3004, 2916, 2845, 2732,
2645, 2422, 1889, 44045, 16949, 15539, 15404, 15119, 15059, 9961,
6512, 5208, 4716, 4583, 4320, 4285, 3382, 3314, 42515, 25401,
15801, 15169, 11555, 6549, 6157, 5937, 5517, 4348, 4003, 3481,
2941, 2399, 2250, 30460, 23839, 20133, 16490, 16047, 11100, 8780,
7463, 6444, 4809, 4110, 3473, 2705, 1841, 1780, 38868, 31320,
17720, 15734, 12839, 10745, 8013, 6426, 6324, 4158, 3530, 3374,
2969, 2887, 2699, 43224, 23404, 17065, 16966, 14896, 9645, 9294,
7997, 7589, 6549, 5556, 4619, 4227, 3688, 3588, 36906, 32832,
24910, 17843, 17767, 15799, 11098, 9488, 8291, 6243, 5807, 5021,
4076, 3827, 3418, 42866, 22956, 17090, 15966, 10380, 9116, 6567,
5807, 5049, 4686, 4411, 4221, 4114, 2482, 2237, 36324, 29671,
23268, 12133, 11531, 9581, 5997, 5594, 5144, 4123, 3702, 2762,
2669, 1775, 1362, 43440, 31226, 24649, 15143, 14893, 8555, 6221,
6187, 5539, 4528, 3913, 3620, 3082, 2141, 1665, 38863, 31839,
14575, 12746, 8417, 5903, 4552, 3845, 2721, 1311, 1208, 1141,
797, 538, 523), Ord = c(15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L,
7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L,
9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L,
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L, 12L,
11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L, 13L,
12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L, 14L,
13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 15L,
14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L,
15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L,
1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L,
2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L,
3L, 2L, 1L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L,
4L, 3L, 2L, 1L)), row.names = c(NA, -180L), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), groups = structure(list(Year = c(1999,
2003, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018
), .rows = structure(list(1:15, 16:30, 31:45, 46:60, 61:75, 76:90,
91:105, 106:120, 121:135, 136:150, 151:165, 166:180), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -12L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
谢谢。
一个简单的方法是在 geom_text 中的 aes 中设置 y = 5 并将 scales = "fixed" 放在 facet_wrap 调用中：
ggplot(np4, aes(x = Ord, y = Value/1000, fill = Item)) +
geom_bar(stat = "identity") +
scale_y_continuous(expand = expansion(mult = c(.00, .6)), labels = comma) +
coord_flip() +
facet_wrap(~Year, drop = TRUE, nrow = 2) +
labs(title = "Nepal's Export Commodities and Destinations, Mln USD",
caption = "Source: faostat") +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.caption = element_text(face = "italic")) +
geom_text(aes(y = 5, label = paste0(Item,"-", Partner)), angle = 0,
vjust= 0.3, hjust = 0, size=3)