Question

我正在尝试在ggplot2中绘制一个方面，但我很难得到不同方面的内部排序。数据如下所示：

head(THAT_EXT)
  ID          FILE           GENRE      NODE
1 CKC_1823_01  CKC          Novels    better
2 CKC_1824_01  CKC          Novels    better
3  EW9_192_03  EW9 Popular Science    better
4  H0B_265_01  H0B Popular Science       sad
5  CS2_231_03  CS2  Academic Prose desirable
6    FED_8_05  FED  Academic Prose   certain

str(THAT_EXT)
'data.frame':   851 obs. of  4 variables:
 $ ID   : Factor w/ 851 levels "A05_122_01","A05_277_07",..: 345 346 439 608 402 484 319 395 228 5 ...
 $ FILE : Factor w/ 241 levels "A05","A06","A0K",..: 110 110 127 169 120 135 105 119 79 2 ...
 $ GENRE: Factor w/ 5 levels "Academic Prose",..: 4 4 5 5 1 1 1 5 1 5 ...
 $ NODE : Factor w/ 115 levels "absurd","accepted",..: 14 14 14 89 23 16 59 59 18 66 ...

部分问题在于无法获得正确的排序。以下是我使用的NODE排序代码：

THAT_EXT <- within(THAT_EXT, 
               NODE <- factor(NODE, 
                              levels=names(sort(table(NODE), 
                                                decreasing=TRUE))))

当我用下面的代码绘制时，我会得到一个图表，其中NODE未在各个GENRE中正确排序，因为不同的NODE在不同的GENRE中更常见：

p1 <- 
ggplot(THAT_EXT, aes(x=NODE)) +
geom_bar() +
scale_x_discrete("THAT_EXT", breaks=NULL) + # supress tick marks on x axis
facet_wrap(~GENRE)

The resulting plot with the wrong ordering of NODE

我想要的是每个方面都有NODE按特定GENRE的降序排序。有人能帮忙吗？

structure(list(ID = structure(c(1L, 2L, 3L, 4L, 10L, 133L, 137L, 
138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 
149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 
160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 
171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 
182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 
193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L, 
204L, 205L, 206L, 207L, 208L, 212L, 213L, 214L, 215L, 216L, 217L, 
218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L, 
229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L, 
240L, 241L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 
276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 290L, 291L, 
298L, 299L, 300L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 
313L, 314L, 315L, 316L, 317L, 318L, 319L, 327L, 328L, 329L, 330L, 
331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 
342L, 343L, 344L, 345L, 346L, 347L, 348L, 352L, 353L, 354L, 355L, 
356L, 357L, 358L, 359L, 360L, 349L, 350L, 351L, 361L, 362L, 363L, 
364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 
375L, 376L, 377L, 378L, 379L, 380L, 381L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 41L, 42L, 43L, 44L, 45L, 
46L, 50L, 54L, 72L, 73L, 74L, 75L, 76L, 90L, 91L, 92L, 97L, 98L, 
102L, 115L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 209L, 
210L, 211L, 242L, 243L, 244L, 245L, 246L, 289L, 292L, 293L, 294L, 
295L, 296L, 297L, 301L, 302L, 311L, 312L, 320L, 321L, 322L, 323L, 
324L, 325L, 326L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 5L, 
6L, 7L, 8L, 9L, 11L, 37L, 38L, 39L, 40L, 47L, 48L, 49L, 51L, 
52L, 53L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 
66L, 67L, 68L, 69L, 70L, 71L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 
84L, 85L, 86L, 87L, 88L, 89L, 93L, 94L, 95L, 96L, 99L, 100L, 
101L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 
113L, 114L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 
134L, 135L, 136L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 
255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 
266L, 285L, 286L, 287L, 288L), .Label = c("A05_122_01", "A05_277_07", 
"A05_400_01", "A05_99_01", "A06_1283_02", "A06_1389_01", "A06_1390_01", 
"A06_1441_02", "A06_884_03", "A0K_1190_03", "A77_1684_01", "A8K_525_03", 
"A8K_582_01", "A8K_645_01", "A8K_799_01", "A90_341_02", "A90_496_01", 
"A94_217_01", "A94_472_01", "A94_477_03", "A9M_164_01", "A9M_259_03", 
"A9N_199_01", "A9N_489_01", "A9N_591_01", "A9R_173_01", "A9R_425_02", 
"A9W_536_02", "AA5_121_01", "AAE_203_01", "AAE_243_01", "AAE_412_01", 
"AAW_14_03", "AAW_244_02", "AAW_297_04", "AAW_365_04", "ADG_1398_01", 
"ADG_1500_01", "ADG_1507_01", "ADG_1516_01", "AHB_336_01", "AHB_421_01", 
"AHJ_1090_02", "AHJ_619_01", "AR3_340_01", "AR3_91_03", "ARF_879_01", 
"ARF_985_01", "ARF_991_02", "ARK_1891_01", "ASL_33_04", "ASL_43_01", 
"ASL_9_01", "AT7_1031_01", "B09_1162_01", "B09_1475_01", "B09_1493_01", 
"B09_1539_01", "B0G_197_01", "B0G_320_01", "B0N_1037_01", "B0N_624_01", 
"B0N_645_02", "B0N_683_01", "B3G_313_04", "B3G_320_03", "B3G_398_02", 
"B7M_1630_01", "B7M_1913_01", "BNN_746_02", "BNN_895_01", "BP7_2426_01", 
"BP7_2777_01", "BP7_2898_01", "BP9_410_01", "BP9_599_01", "BPK_829_01", 
"C93_1407_02", "C9A_181_01", "C9A_196_01", "C9A_365_01", "C9A_82_02", 
"C9A_9_01", "CB9_306_02", "CB9_63_04", "CB9_86_01", "CBJ_439_01", 
"CBJ_702_02", "CBJ_705_01", "CCM_320_01", "CCM_665_01", "CCM_669_02", 
"CCN_1036_02", "CCN_1078_01", "CCN_1119_01", "CCN_784_01", "CCW_2284_02", 
"CCW_2349_03", "CE7_242_02", "CE7_284_01", "CE7_39_01", "CEB_1675_01", 
"CER_145_03", "CER_23_01", "CER_235_02", "CER_378_10", "CET_1056_02", 
"CET_680_01", "CET_705_01", "CET_797_01", "CET_838_01", "CET_879_05", 
"CET_946_03", "CET_986_01", "CEY_2977_01", "CJ3_107_02", "CJ3_114_03", 
"CJ3_20_01", "CJ3_81_01", "CK2_112_01", "CK2_22_01", "CK2_392_01", 
"CK2_42_01", "CK2_75_01", "CKC_1776_01", "CKC_1777_01", "CKC_1823_01", 
"CKC_1824_01", "CKC_1860_01", "CKC_1883_01", "CKC_1883_02", "CKC_2127_01", 
"CMN_1439_02", "CRM_5767_01", "CRM_5770_03", "CRM_5789_01", "CS2_110_01", 
"CS2_131_01", "CS2_139_01", "CS2_187_01", "CS2_187_03", "CS2_231_03", 
"CS2_249_02", "CS2_301_01", "CS2_35_01", "CS2_58_02", "EV6_16_01", 
"EV6_206_02", "EV6_240_01", "EV6_244_02", "EV6_28_01", "EV6_30_01", 
"EV6_32_01", "EV6_450_01", "EV6_69_01", "EV6_80_01", "EV6_91_01", 
"FAC_1019_01", "FAC_1026_01", "FAC_1027_01", "FAC_1235_01", "FAC_1269_05", 
"FAC_1270_05", "FAC_1393_01", "FAC_1406_03", "FAC_933_01", "FAC_950_01", 
"FAC_960_01", "FED_105_01", "FED_120_02", "FED_21_02", "FED_281_02", 
"FED_302_02", "FED_53_01", "FED_8_05", "FEF_498_03", "FEF_674_03", 
"FR2_410_01", "FR2_557_02", "FR2_593_01", "FR2_691_01", "FR4_232_01", 
"FR4_331_01", "FR4_346_01", "FS7_818_01", "FS7_919_01", "FU0_368_02", 
"FYT_1138_01", "FYT_1183_01", "FYT_901_05", "G08_1336_01", "G1E_385_01", 
"G1N_824_01", "G1N_860_01", "G1N_868_01", "G1N_975_01", "GU5_854_01", 
"GUJ_423_01", "GUJ_501_01", "GUJ_611_01", "GUJ_629_03", "GUJ_700_01", 
"GV0_10_01", "GV0_104_01", "GV0_111_01", "GV0_122_01", "GV0_160_01", 
"GV0_232_02", "GV2_1465_01", "GV2_1899_01", "GV6_2683_01", "GW6_297_01", 
"GW6_306_05", "GW6_307_01", "GW6_322_01", "GW6_330_02", "GW6_335_01", 
"GW6_338_01", "GW6_367_02", "GW6_373_01", "GW6_407_01", "GW6_411_01", 
"GW6_413_01", "GW6_421_01", "GW6_423_01", "GW6_424_01", "GW6_428_01", 
"GW6_447_01", "GWM_480_01", "GWM_533_02", "GWM_554_02", "GWM_554_03", 
"GWM_609_01", "GWM_609_04", "GWM_610_01", "GWM_730_01", "GWM_731_01", 
"GWM_738_01", "GWM_804_06", "GWM_815_01", "GWM_832_03", "GVP_179_01", 
"GVP_211_01", "GVP_393_02", "GVP_443_02", "GVP_710_01", "H0B_171_04", 
"H0B_216_01", "H0B_265_01", "H0B_32_01", "H0B_361_03", "H0B_365_01", 
"H0B_369_01", "H0B_74_01", "H0B_93_01", "H10_1002_01", "H10_1032_04", 
"H10_653_01", "H10_803_01", "H10_824_01", "H10_825_03", "H10_881_01", 
"H10_986_01", "H78_851_04", "H78_891_01", "H78_946_04", "H79_1959_19", 
"H7S_110_05", "H7S_130_06", "H7S_131_03", "H7S_131_04", "H7S_146_01", 
"H7S_148_01", "H7S_164_01", "H7S_179_01", "H7S_54_01", "H7S_56_05", 
"H7S_62_03", "H7S_79_01", "H7S_8_01", "H7S_81_01", "H7S_83_01", 
"H7S_87_01", "H7S_92_03", "H7X_1028_02", "H7X_1091_01", "H7X_691_01", 
"H7X_695_01", "H8H_2917_01", "H8K_153_01", "H8K_55_01", "H8M_1897_01", 
"H8M_2104_02", "H8T_3316_03", "H98_3204_01", "H98_3410_01", "H98_3490_02", 
"H9R_130_02", "H9R_39_01", "H9S_1297_01", "HA2_3107_02", "HA2_3284_01", 
"HPY_754_04", "HPY_785_09", "HPY_799_03", "HPY_807_04", "HPY_830_04", 
"HPY_838_02", "HPY_843_01", "HPY_869_11", "HR7_190_01", "HR7_440_01", 
"HTP_540_01", "HTP_585_01", "HTP_588_05", "HTP_593_01", "HTP_601_01", 
"HTP_613_01", "HTP_648_02", "HTW_197_01", "HTW_494_01", "HTW_750_01", 
"HWL_2770_01", "HWL_2919_01", "HWM_45_01", "HWM_45_02", "HXY_1047_03", 
"HXY_701_01", "HXY_781_01", "HXY_783_01", "HXY_784_01", "HXY_836_01", 
"HXY_931_01", "HXY_963_01", "HXY_972_01", "HXY_985_03", "HY6_1024_01", 
"HY6_1025_01", "HY6_1164_01", "HY6_1223_01", "HY6_988_03", "HY6_989_01", 
"HY8_160_01", "HY8_164_01", "HY8_292_03", "HY8_316_01", "HY9_778_03", 
"HY9_845_02", "HYX_235_08", "HYX_245_01", "HYX_88_01", "J12_1474_02", 
"J12_1492_01", "J12_1571_01", "J12_1845_01", "J14_341_01", "J18_597_04", 
"J18_698_02", "J18_759_01", "J18_828_01", "J3R_197_01", "J3R_219_02", 
"J3R_277_04", "J3T_267_01", "J3T_269_02", "J3T_57_02", "J41_41_02", 
"J41_58_03", "J9B_133_03", "J9B_341_02", "J9B_341_03", "J9D_147_05", 
"J9D_218_01", "J9D_411_01", "J9D_616_01", "J9D_616_02", "JNB_563_02", 
"JT7_118_01", "JT7_129_02", "JT7_218_02", "JT7_344_02", "JXS_3663_01", 
"JXU_407_01", "JXU_468_02", "JXU_559_01", "JXV_1439_04", "JXV_1592_01", 
"JY1_100_01"), class = "factor"), GENRE = structure(c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L), .Label = c("Academic Prose", "Conversation", "News", 
"Novels", "Popular Science"), class = "factor"), NODE = structure(c(9L, 
10L, 10L, 10L, 4L, 10L, 71L, 35L, 49L, 6L, 5L, 15L, 28L, 44L, 
64L, 64L, 28L, 28L, 18L, 18L, 32L, 18L, 58L, 10L, 72L, 28L, 18L, 
10L, 64L, 10L, 35L, 64L, 64L, 69L, 8L, 10L, 50L, 69L, 49L, 49L, 
15L, 69L, 10L, 49L, 8L, 64L, 49L, 10L, 69L, 18L, 61L, 67L, 67L, 
61L, 57L, 69L, 11L, 10L, 64L, 10L, 59L, 61L, 49L, 10L, 59L, 1L, 
61L, 35L, 54L, 54L, 39L, 44L, 61L, 64L, 69L, 1L, 23L, 49L, 49L, 
8L, 69L, 49L, 69L, 49L, 49L, 69L, 35L, 49L, 49L, 49L, 35L, 10L, 
49L, 48L, 10L, 49L, 11L, 44L, 50L, 11L, 50L, 69L, 49L, 10L, 59L, 
68L, 47L, 69L, 49L, 35L, 29L, 8L, 49L, 50L, 35L, 10L, 35L, 8L, 
35L, 8L, 10L, 35L, 10L, 10L, 10L, 35L, 44L, 61L, 35L, 44L, 28L, 
47L, 39L, 39L, 49L, 61L, 43L, 60L, 19L, 10L, 10L, 10L, 44L, 44L, 
62L, 44L, 10L, 59L, 10L, 61L, 1L, 53L, 33L, 10L, 8L, 8L, 64L, 
64L, 10L, 57L, 61L, 64L, 66L, 19L, 61L, 64L, 10L, 10L, 8L, 19L, 
35L, 28L, 10L, 61L, 35L, 42L, 35L, 28L, 32L, 64L, 10L, 18L, 28L, 
25L, 35L, 35L, 10L, 18L, 10L, 22L, 55L, 28L, 10L, 1L, 55L, 51L, 
1L, 38L, 28L, 28L, 33L, 10L, 44L, 29L, 16L, 8L, 28L, 69L, 32L, 
10L, 61L, 20L, 35L, 10L, 28L, 10L, 32L, 10L, 46L, 59L, 64L, 35L, 
66L, 2L, 35L, 28L, 30L, 18L, 69L, 32L, 10L, 28L, 17L, 36L, 64L, 
61L, 10L, 64L, 33L, 3L, 37L, 26L, 28L, 64L, 44L, 28L, 64L, 64L, 
6L, 6L, 64L, 50L, 32L, 8L, 64L, 50L, 28L, 24L, 18L, 47L, 35L, 
40L, 24L, 55L, 44L, 22L, 1L, 49L, 44L, 18L, 45L, 63L, 64L, 35L, 
12L, 35L, 10L, 35L, 10L, 10L, 10L, 44L, 44L, 44L, 65L, 44L, 55L, 
32L, 49L, 64L, 39L, 69L, 1L, 60L, 7L, 14L, 44L, 33L, 10L, 19L, 
10L, 70L, 53L, 8L, 61L, 61L, 44L, 61L, 65L, 28L, 68L, 69L, 27L, 
61L, 28L, 72L, 34L, 61L, 32L, 10L, 49L, 35L, 49L, 10L, 10L, 69L, 
39L, 40L, 19L, 59L, 53L, 49L, 49L, 44L, 49L, 35L, 49L, 61L, 61L, 
1L, 10L, 28L, 49L, 35L, 49L, 61L, 50L, 69L, 35L, 61L, 35L, 50L, 
10L, 28L, 69L, 61L, 21L, 69L, 29L, 35L, 35L, 35L, 11L, 69L, 8L, 
41L, 56L, 35L, 61L, 69L, 49L, 49L, 49L, 1L, 13L, 64L, 64L, 52L, 
44L, 64L, 64L, 50L, 49L, 69L, 11L, 59L, 49L, 31L), .Label = c("apparent", 
"appropriate", "awful", "axiomatic", "best", "better", "breathtaking", 
"certain", "characteristic", "clear", "conceivable", "convenient", 
"crucial", "cruel", "desirable", "disappointing", "emphatic", 
"essential", "evident", "expected", "extraordinary", "fair", 
"fortunate", "Funny", "good", "great", "imperative", "important", 
"impossible", "incredible", "inescapable", "inevitable", "interesting", 
"ironic", "likely", "Likely", "lucky", "ludicrous", "natural", 
"necessary", "needful", "notable", "noteworthy", "obvious", "odd", 
"paradoxical", "plain", "plausible", "possible", "probable", 
"proper", "relevant", "remarkable", "revealing", "right", "Sad", 
"self-evident", "sensible", "significant", "striking", "surprising", 
"symptomatic", "terrible", "true", "typical", "understandable", 
"unexpected", "unfortunate", "unlikely", "unreasonable", "untrue", 
"vital"), class = "factor")), .Names = c("ID", "GENRE", "NODE"
), class = "data.frame", row.names = c(NA, -388L))

Answer 1

正如我已提到的那样：facet_wrap并非用于具有个别尺度。至少我找不到解决方案。因此，在scale_x_discrete中设置标签并未带来所需的结果。

但这是我的解决方法：

library(plyr)
library(ggplot2)

nodeCount <- ddply( df, c("GENRE", "NODE"), nrow )
nodeCount$factors <- paste( nodeCount$GENRE, nodeCount$NODE, sep ="." )
nodeCount <- nodeCount[ order( nodeCount$GENRE, nodeCount$V1, decreasing=TRUE  ), ]
nodeCount$factors <- factor( nodeCount$factors, levels=nodeCount$factors )
head(nodeCount)

              GENRE       NODE V1                    factors
121 Popular Science   possible 14   Popular Science.possible
128 Popular Science surprising 11 Popular Science.surprising
116 Popular Science     likely  9     Popular Science.likely
132 Popular Science   unlikely  9   Popular Science.unlikely
103 Popular Science      clear  7      Popular Science.clear
129 Popular Science       true  5       Popular Science.true

g <- ggplot( nodeCount, aes( y=V1, x = factors ) ) +
  geom_bar() +
  scale_x_discrete( breaks=NULL ) + # supress tick marks on x axis
  facet_wrap( ~GENRE, scale="free_x" ) +
  geom_text( aes( label = NODE, y = V1+2 ), angle = 45, vjust = 0, hjust=0, size=3 )

给出了：

enter image description here

faceg ggplot2的内部排序

1 个答案: