I'm trying to plot a facets in ggplot2 but I struggle to get the internal ordering of the different facets right. The data looks like this:
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 ...
Part of the problem is that can't get the sorting right. Here is the code for the sorting of NODE that I use:
THAT_EXT <- within(THAT_EXT,
NODE <- factor(NODE,
levels=names(sort(table(NODE),
decreasing=TRUE))))
When I plot this with the code below I get a graphs in which the NODE is not correctly sorted in the individual GENREs since different NODEs are more frequent in different GENREs:
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)
What I want is for every facet to have NODE sorted in decreasing order for that particular GENRE. Can anyone help with this?
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))
As I mentioned already: facet_wrap is not intended for having individual scales. At least I didn't find a solution. Hence, setting the labels in scale_x_discrete did not bring the desired result.
But this my workaround:
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 )
Which gives:
Related
I have a ggplot related question, which should be easy but I could not find the answer yet. I am trying to plot a faceted plot with the code below and this dataset (11 kB).
ggplot(plot.dat, aes(x = estimate, y = reorder(countryyear, estimate))) +
geom_point() +
geom_segment(aes(x=conf.low, xend=conf.high, yend=countryyear)) +
facet_grid(. ~ facet) +
xlab("Random Effect Estimate") +
ylab("") + scale_x_continuous(breaks=c(seq(0, 5, 1)), limits=c(0, 5)) +
ggtitle("Random Slopes in Country*Year Groups from Northwestern Europe") +
theme_minimal() + theme(plot.title = element_text(hjust = 0.5))
I would like countryyear to be organized by the values of estimate in the Extreme Right facet. Not quite sure how to order by values of a specific facet. Any ideas are welcome! Thanks.
Update: Here is the dput structure of a random subset of the dataset. It has some missing values, but it should work for the sake of the example. I also updated the download link above, that has the full version.
structure(list(estimate = c(1.41056902925372, 0.854859208455895,
1.16012834593894, 0.871339033194504, 0.803272289946221, 1.17540386134493,
0.996313357490551, 1.49940694539732, 1.33773365908762, 2.7318703090905,
1.19131935418045, 1.12765907711738, 0.746741192261761, 0.985847015192172,
0.912357310925342, 1.11582763712164, 1.21854572824977, 0.675712547978394,
0.566955524699616, 1.32611743759365, 0.519648352294682, 0.591013596394243,
1.30944973684044, 0.613722269599125, 1.13293279727271, 0.950788678552604,
1.1599446923567, 1.11493952112913, 0.95336321045095, 1.39002327097034,
0.794207546872633, 0.788545101449259, 1.01096883872495, 0.897407203907834,
1.38391605229103, 1.35754760293107, 1.0718508539761, 0.542191158958878,
0.757132752456427, 1.44172863221312, 1.04842251986171, 0.77260404885379,
0.879288027642055, 1.09372353598088, 0.745484830381145, 1.21211217249353,
0.628009608902132, 1.34864488674734), countryyear = structure(c(1L,
2L, 4L, 5L, 7L, 9L, 10L, 12L, 13L, 26L, 28L, 29L, 31L, 32L, 34L,
36L, 37L, 39L, 40L, 57L, 59L, 60L, 62L, 63L, 65L, 67L, 68L, 70L,
71L, 73L, 75L, 76L, 89L, 90L, 92L, 94L, 95L, 103L, 104L, 106L,
108L, 109L, 111L, 128L, 130L, 132L, 133L, 135L), .Label = c("AT02",
"AT04", "AT06", "AT14", "AT16", "BE02", "BE04", "BE06", "BE08",
"BE10", "BE12", "BE14", "BE16", "BG06", "BG08", "BG10", "BG12",
"CH14", "CZ02", "CZ04", "CZ08", "CZ10", "CZ12", "CZ14", "CZ16",
"DE02", "DE04", "DE06", "DE08", "DE10", "DE12", "DE14", "DE16",
"DK02", "DK04", "DK06", "DK08", "DK10", "DK12", "DK14", "EE04",
"EE06", "EE08", "EE10", "EE12", "EE14", "EE16", "ES02", "ES04",
"ES06", "ES08", "ES10", "ES12", "ES14", "ES16", "FI02", "FI04",
"FI06", "FI08", "FI10", "FI12", "FI14", "FI16", "FR06", "FR08",
"FR10", "FR12", "FR14", "FR16", "GB02", "GB04", "GB06", "GB08",
"GB10", "GB12", "GB14", "GB16", "GR02", "GR04", "GR08", "GR10",
"HU02", "HU06", "HU08", "HU10", "HU12", "HU14", "HU16", "IE02",
"IE04", "IE06", "IE08", "IE10", "IE12", "IE14", "IE16", "IT04",
"IT12", "IT16", "LT10", "LT12", "LT14", "NL02", "NL04", "NL06",
"NL08", "NL10", "NL12", "NL14", "NL16", "NO14", "PL02", "PL04",
"PL06", "PL08", "PL10", "PL12", "PL14", "PL16", "PT02", "PT04",
"PT06", "PT08", "PT10", "PT12", "PT14", "PT16", "SE02", "SE04",
"SE06", "SE08", "SE10", "SE12", "SE14", "SE16", "SI02", "SI04",
"SI06", "SI08", "SI10", "SI12", "SI14", "SI16", "SK04", "SK06",
"SK08", "SK10", "SK12"), class = "factor"), facet = structure(c(1L,
3L, 1L, 4L, 5L, 3L, 4L, 1L, 1L, 1L, 5L, 5L, 4L, 5L, 3L, 1L, 2L,
4L, 5L, 2L, 1L, 4L, 2L, 5L, 2L, 3L, 4L, 3L, 2L, 5L, 5L, 4L, 2L,
5L, 4L, 5L, 3L, 1L, 4L, 5L, 3L, 5L, 4L, 1L, 5L, 2L, 4L, 1L), .Label = c("Intercept",
"Extreme Left", "Center", "Right", "Extreme Right"), class = "factor"),
conf.low = c(1.16824810706745, 0.686215051613965, 0.910277310292764,
0.591705078386698, 0.37357342399703, 0.947951001435781, 0.663296044193037,
1.18794112232166, 1.06645119085865, 2.33578182814618, 0.580210898576738,
0.564235690522211, 0.530859530342114, 0.516191258265551,
0.730992343373883, 0.862424540370486, 0.827891784352444,
0.427638276259852, 0.275692447335368, 0.829763907986328,
0.370078643492081, 0.321852705445509, 0.83550621863293, 0.289836810427436,
0.847226120408727, 0.780056160572728, 0.873143885861924,
0.869757467125519, 0.615741777890997, 0.649483531741787,
0.349657606457465, 0.523294407847395, 0.670109418373736,
0.36656743494149, 0.952201390937053, 0.777207016700884, 0.888128473009524,
0.397085597526946, 0.479828726362257, 0.614533313431094,
0.813336887981082, 0.3129232351085, 0.61435321820328, 0.854801028643867,
0.346698059397102, 0.805414039007076, 0.434676644041643,
1.07780736338027), conf.high = c(1.70315275860739, 1.06494933995261,
1.47855797769819, 1.28312522319126, 1.7272277157504, 1.45743211956315,
1.49652679976667, 1.8925358720741, 1.67802460909168, 3.19512520208851,
2.44607918797515, 2.25369471581694, 1.05041423643869, 1.8828182806291,
1.13872035780431, 1.44368725318228, 1.79353596677755, 1.06769546329854,
1.16593171156554, 2.11938292490653, 0.729667639003753, 1.08526995489865,
2.05223919950836, 1.29954170985538, 1.51498719434776, 1.15888977865399,
1.54095070825389, 1.4292376699955, 1.47610807594453, 2.97492484321718,
1.80395225460704, 1.18824770090216, 1.52521060717706, 2.19697554354282,
2.01136404338166, 2.37122858469145, 1.29357889999432, 0.740322123703373,
1.19469713534712, 3.38237391450413, 1.35145693795059, 1.90755095606211,
1.25847381058047, 1.39942645489832, 1.60297301142912, 1.82417470710871,
0.907332092210651, 1.68753999308876)), row.names = c(1L,
9L, 17L, 25L, 33L, 41L, 49L, 57L, 65L, 128L, 136L, 144L, 152L,
160L, 168L, 176L, 184L, 192L, 200L, 283L, 291L, 299L, 307L, 315L,
323L, 331L, 339L, 347L, 355L, 363L, 371L, 379L, 442L, 450L, 458L,
466L, 474L, 512L, 520L, 528L, 536L, 544L, 552L, 640L, 648L, 656L,
664L, 672L), class = "data.frame")
For two example dataframes:
gp <- structure(list(gp.code = structure(c(1L, 3L, 5L, 13L, 6L, 20L,
10L, 19L, 17L, 12L, 2L, 18L, 7L, 16L, 15L, 4L, 8L, 143L, 14L,
9L, 11L, 33L, 23L, 113L, 102L, 97L, 83L, 122L, 77L, 111L, 29L,
68L, 142L, 56L, 118L, 115L, 78L, 58L, 104L, 71L, 43L, 121L, 32L,
110L, 53L, 70L, 123L, 61L, 87L, 48L, 73L, 100L, 37L, 141L, 114L,
34L, 89L, 81L, 98L, 92L, 63L, 50L, 60L, 47L, 125L, 145L, 145L,
93L, 93L, 99L, 99L, 138L, 138L, 137L, 86L, 139L, 91L, 146L, 79L,
103L, 31L, 124L, 22L, 76L, 26L, 108L, 105L, 116L, 84L, 136L,
67L, 106L, 52L, 95L, 51L, 27L, 82L, 130L, 101L, 107L, 133L, 62L,
42L, 117L, 112L, 85L, 69L, 49L, 46L, 45L, 120L, 38L, 39L, 55L,
96L, 80L, 75L, 44L, 35L, 109L, 41L, 24L, 59L, 54L, 144L, 65L,
28L, 25L, 119L, 66L, 74L, 36L, 57L, 21L, 135L, 134L, 132L, 140L,
64L, 127L, 129L, 128L, 131L, 72L, 88L, 40L, 30L, 94L, 90L, 126L
), .Label = c("E82002", "E82014", "E82018", "E82019", "E82023",
"E82031", "E82037", "E82040", "E82041", "E82055", "E82058", "E82059",
"E82060", "E82062", "E82071", "E82077", "E82084", "E82095", "E82107",
"E82113", "M85001", "M85002", "M85005", "M85007", "M85008", "M85009",
"M85011", "M85013", "M85015", "M85019", "M85020", "M85021", "M85024",
"M85025", "M85030", "M85031", "M85037", "M85041", "M85042", "M85043",
"M85047", "M85048", "M85051", "M85052", "M85055", "M85056", "M85058",
"M85059", "M85062", "M85064", "M85065", "M85070", "M85074", "M85076",
"M85077", "M85078", "M85079", "M85084", "M85086", "M85088", "M85092",
"M85097", "M85098", "M85107", "M85111", "M85113", "M85115", "M85116",
"M85118", "M85127", "M85128", "M85134", "M85136", "M85141", "M85142",
"M85145", "M85146", "M85153", "M85154", "M85156", "M85167", "M85171",
"M85174", "M85176", "M85177", "M85178", "M85179", "M85600", "M85611",
"M85624", "M85634", "M85642", "M85652", "M85655", "M85669", "M85671",
"M85679", "M85684", "M85686", "M85693", "M85694", "M85699", "M85701",
"M85713", "M85715", "M85716", "M85717", "M85721", "M85730", "M85733",
"M85735", "M85736", "M85749", "M85753", "M85756", "M85757", "M85770",
"M85774", "M85776", "M85782", "M85783", "M85794", "M85797", "M85801",
"M88020", "M88021", "M89001", "M89002", "M89008", "M89009", "M89012",
"M89013", "M89021", "M89026", "M89027", "Y00412", "Y00471", "Y00492",
"Y01680", "Y02567", "Y02571", "Y02620", "Y02639", "Y02893", "Y02961",
"Y02963"), class = "factor"), cqc.rating = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 5L, 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, 4L, 2L, 1L, 1L, 1L, 1L,
5L, 1L, 5L, 1L, 5L, 1L, 5L, 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, 5L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("good", "inadequate", "not rated",
"oustanding", "requires improvement"), class = "factor")), .Names = c("gp.code",
"cqc.rating"), row.names = c(NA, 150L), class = "data.frame")
df <- structure(list(gp.code = structure(c(1L, 4L, 6L, 14L, 7L, 2L,
21L, 11L, 20L, 18L, 13L, 3L, 19L, 22L, 8L, 17L, 16L, 5L, 9L,
148L, 15L, 10L, 12L, 37L, 25L, 127L, 114L, 109L, 77L, 135L, 98L,
87L, 125L, 79L, 32L, 147L, 64L, 132L, 129L, 88L, 67L, 118L, 68L,
93L, 49L, 82L, 134L, 26L, 35L, 124L, 61L, 81L, 136L, 33L, 71L,
54L, 102L, 46L, 84L, 112L, 43L, 146L, 128L, 24L, 38L, 103L, 95L,
110L, 105L, 74L, 57L, 70L, 53L, 138L, 117L, 39L, 94L, 116L, 149L,
111L, 144L, 106L, 143L, 145L, 101L, 104L, 150L, 89L, 115L, 34L,
137L, 23L, 29L, 86L, 28L, 75L, 83L, 122L, 60L, 66L, 119L, 99L,
130L, 142L, 65L, 78L, 59L, 107L, 120L, 56L, 31L, 58L, 30L, 72L,
96L, 139L, 113L, 121L, 140L, 73L, 48L, 131L, 126L, 42L, 100L,
76L, 80L, 141L, 55L, 52L, 36L, 51L, 133L, 44L, 45L, 63L, 40L,
92L, 108L, 90L, 85L, 50L, 41L, 123L, 91L, 47L, 27L, 69L, 62L,
97L), .Label = c("E82002", "E82004", "E82014", "E82018", "E82019",
"E82023", "E82031", "E82037", "E82040", "E82041", "E82055", "E82058",
"E82059", "E82060", "E82062", "E82071", "E82077", "E82084", "E82095",
"E82107", "E82113", "E82663", "M85002", "M85003", "M85005", "M85006",
"M85007", "M85009", "M85010", "M85011", "M85014", "M85015", "M85018",
"M85020", "M85021", "M85023", "M85024", "M85025", "M85028", "M85029",
"M85030", "M85036", "M85037", "M85041", "M85042", "M85045", "M85047",
"M85048", "M85051", "M85052", "M85055", "M85056", "M85058", "M85059",
"M85062", "M85063", "M85064", "M85065", "M85070", "M85072", "M85074",
"M85076", "M85077", "M85078", "M85081", "M85082", "M85084", "M85085",
"M85086", "M85088", "M85092", "M85094", "M85097", "M85098", "M85100",
"M85105", "M85108", "M85115", "M85116", "M85118", "M85127", "M85128",
"M85133", "M85136", "M85142", "M85145", "M85146", "M85153", "M85154",
"M85156", "M85159", "M85163", "M85164", "M85166", "M85167", "M85171",
"M85172", "M85174", "M85176", "M85177", "M85178", "M85179", "M85611",
"M85634", "M85642", "M85652", "M85669", "M85671", "M85679", "M85684",
"M85686", "M85693", "M85694", "M85699", "M85701", "M85706", "M85711",
"M85713", "M85715", "M85716", "M85717", "M85721", "M85730", "M85733",
"M85735", "M85736", "M85749", "M85753", "M85756", "M85757", "M85770",
"M85774", "M85782", "M85783", "M85794", "M85797", "M85801", "M88020",
"M89009", "M89021", "Y00159", "Y00412", "Y00471", "Y00492", "Y01680",
"Y02571", "Y02620", "Y02639", "Y02961", "Y02963"), class = "factor"),
antibiotic = c(1.23248149651249, 1.19804465710497, 0.753794802511325,
0.85669917849255, 0.806766970145873, 1.2944351625755, 0.79749081458912,
0.949915803767271, 1.28676136005656, 0.861894948337942, 0.98944777231592,
0.77976175611218, 1.0802092104795, 1.18992427754597, 0.922230847446508,
1.00968448247105, 1.00925275017575, 1.13856339619023, 1.29658868391219,
3.43992412181159, 0.9405259515181, 1.04536664449872, 0.857195681526592,
1.36040902899291, 1.1555007762595, 1.23099411388522, 1.2921619764172,
1.20896911806371, 0.90601414991211, 1.48026866615811, 0.865283503864064,
1.34285564503446, 0.919419926661631, 1.41915312988514, 1.2330635342805,
3.66834851140276, 1.2803964023984, 0.777309332259057, 1.16760007845018,
0.903108177347766, 1.07415817045842, 1.76503145582347, 0.662906258393768,
1.11922205065869, 1.45743378132416, 1.40338387936522, 1.56356764856955,
1.21554707497369, 0.765459254266153, 1.02985290952772, 0.747988215118069,
1.28199535302764, 0.791630491986821, 1.45457105212014, 1.5360908424018,
1.36219759497743, 1.2823181822961, 1.16445352400409, 0.867251210987798,
0.93449947713661, 0.972235945064716, 0.952976072770419, 1.01713285255742,
1.0094222885861, 0.875833539680039, 0.618892154842347, 0.472595751806604,
0.496879988390655, 1.50731245234776, 1.04907441178441, 0.894164623526121,
0.658261298693029, 0.726078998206472, 1.02776752877325, 1.19666179452119,
0.97476267236602, 0.0127648710748021, 1.17439331625073, 5.8393330107237,
1.59645232815965, 0.487542408650236, 1.14865894544346, 0.729495610858418,
0.475652186678803, 0.810665743225695, 1.55727483921682, 0.509032628956674,
1.08248967413256, 0.829656197645062, 0.883813971368163, 1.1606344950849,
0.643888106444113, 0.658542420310134, 0.788100265873058,
0.999993653251755, 0.549776366766276, 1.00900222339709, 0.759174545084884,
0.732601429257463, 0.811032584239922, 0.992078825347759,
0.916336303170667, 0.924425842068231, 0.833487920775124,
1.2048401786876, 1.0710312446967, 1.15996384388112, 0.802575397465166,
0.827940641127218, 0.988964351312201, 0.810501627167164,
0.972188732451928, 1.21663117141513, 0.648182525899754, 1.24597821683072,
1.25013278566623, 1.16685772173495, 0.878810966942241, 1.21188990166584,
1.05209718360933, 0.928089616209815, 1.51726626492982, 0.955522092040987,
1.14598540145985, 0.992072220256482, 1.17856657930143, 0.487420516416757,
1.12018266962542, 0.999491890919433, 1.10449907263643, 1.38308178076077,
1.0848078324396, 0.735665641476272, 0.815600508556523, 1.04175344119065,
1.63317262657807, 0.941009543029732, 0.945643608300648, 0.785026349264038,
1.11186113789778, 0.931541465655869, 0.950426305389678, 1.12222589692599,
1.75509240895922, 1.39836663546273, 1.11387374264761, 1.42177823010633,
0.957155370021804, 1.48242155040868, 1.1388984391116)), .Names = c("gp.code",
"antibiotic"), row.names = c(NA, 150L), class = "data.frame")
I wish to merge the data in gp to df. This is a sample of my data, the full version is about 8000 records.
I normally use the code:
new <- merge(df, gp, by=c("gp.code"), all.x=T)
But when I run this, you can see it retrieves 154 records in the 'new' dataframe. As I understand it, the all.x=TRUE refers to all of the records in the df dataframe - why is it picking up more rows of data? If I change it to all.y=TRUE it gets 150 records. When I run this on my full dataset, I cannot back to the number of rows in df (using all.x or all.y = T), just with the additional merged column.
What am I doing wrong? Is there another function which is more appropriate?
I have function in R to get mean of anything, which is any column name of dataframe
mean_anything <- function(directory, anything){
files_full <- list.files(directory, full.names=TRUE)
seethis <- lapply(files_full, read.csv)
output <- do.call(rbind, seethis)
mean(output$anything, na.rm=TRUE)
}
If I invoke mean_anything("diet_data", "Age") I get
[1] NA
Warning message:
In mean.default(output$anything, na.rm = TRUE) :
argument is not numeric or logical: returning NA
However, if I replace
mean(output$anything, na.rm=TRUE)
with
mean(output$Age, na.rm=TRUE)
Then the function will output [1] 36.4
I tried using single and double quotes around anything, I tried output[anything], how to fix?
dput(output)
structure(list(Patient.Name = 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, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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("Andy", "David", "John", "Mike", "Steve"), class = "factor"),
Age = c(30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 35L, 35L, 35L, 35L,
35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L,
35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L,
35L, 35L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L,
40L, 40L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L,
55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L,
55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L), Weight = c(140L,
140L, 140L, 139L, 138L, 138L, 138L, 138L, 138L, 138L, 138L,
138L, 137L, 137L, 138L, 139L, 139L, 137L, 137L, 137L, 137L,
137L, 137L, 135L, 135L, 135L, 135L, 135L, 135L, 135L, 210L,
209L, 209L, 209L, 209L, 209L, 209L, 208L, 208L, 208L, 208L,
208L, 208L, 207L, 206L, 206L, 206L, 205L, 205L, 205L, 205L,
204L, 204L, 204L, 203L, 203L, 202L, 202L, 202L, 201L, 175L,
175L, 175L, 175L, 175L, 175L, 175L, 175L, 175L, 175L, 175L,
175L, 175L, 175L, 175L, 175L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 177L, 188L, 188L, 188L, 188L, 189L,
189L, 189L, 189L, 189L, 189L, 189L, 189L, 189L, 189L, 190L,
190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 192L, 192L,
192L, 192L, 192L, 192L, 192L, 225L, 225L, 225L, 224L, 224L,
224L, 223L, 223L, 223L, 223L, 223L, 222L, 221L, 221L, 221L,
220L, 220L, 219L, 219L, 219L, 218L, 217L, 217L, 217L, 216L,
215L, 215L, 214L, 214L, 214L), Day = c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L)), .Names = c("Patient.Name", "Age", "Weight",
"Day"), row.names = c(NA, 150L), class = "data.frame")
I solved it.
mean(output[,anything], na.rm=TRUE)
i.e.
mean_anything <- function(directory, anything){
files_full <- list.files(directory, full.names=TRUE)
seethis <- lapply(files_full, read.csv)
output <- do.call(rbind, seethis)
mean(output[,anything], na.rm=TRUE)
}
Where anything is any column in the dataframe
I would like to make a bubble plot using ggplot2 in R. My code and data are found below.
Please leave the colors as they are. I am having difficulties in scaling positive and negative values equally. For example, -3 is scaled smaller than +3. I would like negatives and positives to be scaled proportionately irrespective of sign.
Identify negative from positive values using some kind of outline linetype for bubbles and include it in the legend.
Also remove the "Mean" part of the legend.
Thanks very much for your great help.
#=====================================================================
library(ggplot2)
if (dev.cur() == 1) x11(width=8,height=6)
par(mfcol=c(1,1))
p<-ggplot(site.resiudal, aes(x=Eastings, y=Northings, size=Mean,label=site.resiudal$Site,legend = FALSE))+
#theme(legend.position="none")+
geom_point(shape=21)+
geom_point(aes(colour = factor(Region)))+
scale_area(range=c(1,15))+
scale_alpha(guide = 'none')+
scale_x_continuous(name="Longitude", limits=c(-120,-95))+
scale_y_continuous(name="Latitude", limits=c(48,61))+
geom_text(size=4)+
scale_colour_manual(name="Region",labels = c("A", "B","C","D", "E"),values = c("1" = "firebrick3","2" = "palegreen4","3" = "sandybrown","4" = "red","5" = "gray0"))+
theme(legend.title = element_text(colour="black", size=16, face="plain"))+
theme(legend.text = element_text(colour="black", size = 16, face = "plain"))
p
#Data[["sign"]] = ifelse(Data[["Mean"]] >= 0, "positive", "negative")
#=================================================
structure(list(Site = structure(c(101L, 102L, 105L, 107L, 108L,
110L, 111L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 16L, 20L, 47L, 52L, 53L, 55L, 91L, 92L, 93L, 94L, 95L,
96L, 99L, 15L, 17L, 18L, 19L, 21L, 114L, 23L, 26L, 36L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L,
73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L,
86L, 87L, 88L, 89L, 98L, 100L, 103L, 104L, 106L, 109L, 112L,
113L, 115L, 116L, 117L, 119L, 42L, 44L, 46L, 48L, 49L, 50L, 51L,
54L, 56L, 57L, 58L, 90L, 97L, 118L, 120L, 22L, 24L, 25L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 37L, 38L, 39L, 40L, 41L,
43L, 45L), .Label = c("G100", "G101", "G102", "G103", "G104",
"G105", "G106", "G107", "G108", "G109", "G110", "G111", "G112",
"G113", "G114", "G115", "G116", "G117", "G118", "G119", "G120",
"GG10", "GG11", "GG12", "GG13", "GG14", "GG15", "GG16", "GG17",
"GG18", "GG19", "GG20", "GG21", "GG22", "GG23", "GG24", "GG25",
"GG26", "GG27", "GG28", "GG29", "GG30", "GG31", "GG32", "GG33",
"GG34", "GG35", "GG36", "GG37", "GG38", "GG39", "GG40", "GG41",
"GG42", "GG43", "GG44", "GG45", "GG46", "GG47", "GG48", "GG49",
"GG50", "GG51", "GG52", "GG53", "GG54", "GG55", "GG56", "GG57",
"GG58", "GG59", "GG60", "GG61", "GG62", "GG63", "GG64", "GG65",
"GG66", "GG67", "GG68", "GG69", "GG70", "GG71", "GG72", "GG73",
"GG74", "GG75", "GG76", "GG77", "GG78", "GG79", "GG80", "GG81",
"GG82", "GG83", "GG84", "GG85", "GG86", "GG87", "GG88", "GG89",
"GG90", "GG91", "GG92", "GG93", "GG94", "GG95", "GG96", "GG97",
"GG98", "GG99", "GGG1", "GGG2", "GGG3", "GGG4", "GGG5", "GGG6",
"GGG7", "GGG8", "GGG9"), class = "factor"), Name = structure(c(53L,
87L, 29L, 92L, 36L, 76L, 102L, 103L, 119L, 2L, 9L, 11L, 45L,
47L, 49L, 54L, 90L, 30L, 105L, 66L, 78L, 107L, 81L, 42L, 41L,
43L, 59L, 110L, 24L, 27L, 56L, 61L, 64L, 118L, 40L, 21L, 44L,
70L, 108L, 25L, 58L, 98L, 83L, 5L, 19L, 26L, 31L, 38L, 55L, 60L,
71L, 74L, 75L, 85L, 95L, 120L, 109L, 1L, 67L, 20L, 50L, 63L,
106L, 111L, 116L, 62L, 6L, 99L, 114L, 73L, 84L, 89L, 93L, 97L,
115L, 80L, 10L, 12L, 88L, 79L, 15L, 17L, 33L, 35L, 94L, 100L,
3L, 16L, 37L, 101L, 117L, 8L, 39L, 48L, 86L, 113L, 23L, 13L,
69L, 96L, 104L, 32L, 65L, 82L, 14L, 22L, 18L, 46L, 68L, 72L,
77L, 91L, 112L, 4L, 7L, 28L, 51L, 57L, 52L, 34L), .Label = c("ANEROID",
"ARBORG", "ATHABASCA", "BANFF", "BANGOR", "BATTLEFORD", "BEAVER MINES",
"BEAVERLODGE", "BERENS RIVER", "BIRTLE", "BISSETT", "BRANDON",
"BUFFALO NARROWS", "CALGARY", "CALMAR", "CAMPSIE", "CAMROSE",
"CARWAY", "CEYLON", "CHAPLIN", "CHURCHILL", "CLARESHOLM", "COLD LAKE",
"COLLINS BAY", "CORONATION", "COTE", "CREE LAKE", "CROWSNEST",
"CYPRESS RIVER", "DAUPHIN", "DAVIDSON", "DRUMHELLER", "EDMONTON",
"EDSON", "ELK POINT", "EMERSON AUT", "ENILDA-BERG", "ESTEVAN",
"FAIRVIEW", "FLIN FLON", "FORT CHIPEWYAN", "FORT MCMURRAY", "FORT VERMILION",
"GILLAM", "GIMLI", "GLEICHEN", "GRAND RAPIDS", "GRANDE PRAIRIE",
"GREAT FALLS", "HIGH POINT", "HIGHWOOD", "HINTON VALLEY", "HUDSON BAY",
"INDIAN BAY", "INDIAN HEAD", "ISLAND FALLS", "JASPER WARDEN",
"JENNER", "KEG RIVER RS", "KELLIHER", "KEY LAKE", "KINDERSLEY",
"KLINTONEL", "LA RONGE", "LACOMBE 2", "LANGRUTH WEST", "LEADER",
"LETHBRIDGE", "LOON LAKE", "LYNN LAKE", "MANOR", "MEDICINE HAT",
"MELFORT", "MOOSE JAW", "MOOSOMIN", "MORDEN", "MOUNTAIN VIEW",
"NEEPAWA MURRAY", "NINETTE", "NIPAWIN", "NORWAY HOUSE", "OLDS",
"ONEFOUR", "OUTLOOK", "PASWEGIN", "PEACE RIVER", "PELLY", "PIERSON",
"PILGER", "PINAWA WNRE", "PINCHER CREEK ", "PORTAGE PRAIRIE",
"PRINCE ALBERT", "RANFURLY", "REGINA", "ROCKY MT HOUSE ", "SASKATOON",
"SCOTFIELD", "SCOTT", "SION", "SLAVE LAKE", "SPRAGUE", "STEINBACH",
"STETTLER NORTH", "SWAN RIVER", "SWIFT CURRENT", "THE PAS", "THOMPSON",
"TONKIN", "URANIUM CITY ", "VAL-MARIE", "VAUXHALL", "WABASCA RS",
"WASECA", "WASKESIU LAKE", "WEST POPLAR", "WHITECOURT", "WHITESAND DAM",
"WINNIPEG", "YELLOW GRASS"), class = "factor"), Mean = c(-0.020525899,
0.333863493, 0.210353772, NA, NA, 0.093520458, 0.341295298, NA,
-0.175074657, 0.09834825, 0.075610648, NA, -0.117503802, 0.18309367,
0.25246942, 0.221329766, 0.072167004, -0.094766032, NA, NA, 0.19783711,
-0.166351357, -0.0996169, -0.038555432, -0.028092042, 0.297855371,
0.108263891, 0.002057761, 0.327731415, NA, 0.180100638, 0.193837736,
-0.003306948, 0.178881894, 0.3655509, -0.235975798, -0.176154056,
-0.080433735, -0.110955273, -0.228010105, 0.048103255, -0.116681527,
-0.073042421, NA, NA, 0.035356012, 0.297171565, -0.197834719,
0.036412958, 0.055218077, NA, -0.236229087, 0.265211081, 0.271625885,
-0.293179359, 0.113744571, -0.207770026, 0.100471248, -0.071569464,
NA, NA, NA, -0.052716493, 0.057385851, 0.090340517, -0.30456625,
-0.234420722, 0.082287977, 0.009973663, NA, -0.06405062, 0.074703356,
-0.208329196, -0.272401078, 0.217991554, -0.043619919, -0.208901155,
-0.020022401, 0.111495318, NA, 0.38239749, 0.199136959, -0.177740258,
NA, 0.147515615, 0.309306538, 0.298741467, 0.068170296, NA, -0.02102765,
0.001754313, -0.010196512, 0.108254156, -0.228183063, -0.196261239,
NA, -0.167054722, 0.039949534, 0.154337034, -0.020855461, 0.136010278,
NA, 0.096997744, NA, -0.241963754, 0.660176529, 0.423554314,
0.190305726, -0.210778787, -0.261148915, NA, 0.054264129, -0.098706619,
-0.138776994, NA, NA, NA, -0.113823745, 0.373292721, -0.047060083
), Eastings = c(-102.5800018, -101.8700027, -99.08000183, -98.26999664,
-97.23000336, -98.08000183, -95.59999847, -96.76999664, -97.23000336,
-97.08000183, -97.02999878, -95.69999695, -97.01999664, -99.27999878,
-96, -95.19999695, -96.06999969, -100.0500031, -101.2300034,
-98.80000305, -99.56999969, -101.0999985, -97.84999847, -111.2200012,
-111.1200027, -116.0299988, -117.6200027, -108.4800034, -103.6999969,
-107.1299973, -102.3499985, -105.6200027, -105.2699966, -103.1500015,
-101.8799973, -94.06999969, -94.72000122, -101.0800018, -97.87000275,
-111.4499969, -111.1999969, -111.3499985, -110.4700012, -102.2799988,
-104.6500015, -101.7799988, -105.9800034, -102.9700012, -103.6500015,
-103.75, -102.0999985, -105.5500031, -101.6699982, -103.9199982,
-104.6699982, -104.1800003, -102.2300034, -107.3000031, -109.5,
-106.6500015, -107.9300003, -108.9199982, -107.7300034, -107.8499985,
-106.3799973, -109.1800003, -108.25, -108.8300018, -109.4000015,
-104.5999985, -107.0500031, -105.1500015, -105.6699982, -106.7200012,
-106.0699997, -104, -101.0500031, -99.94999695, -101.2699966,
-99.65000153, -113.8499985, -112.8199997, -113.5800018, -111.0699997,
-111.7300034, -114.1200027, -113.2799988, -114.6800003, -116.3000031,
-114.7799988, -115.7799988, -119.4000015, -118.5299988, -118.8799973,
-117.4499969, -113.8300018, -110.2799988, -108.4300003, -109.0999985,
-114.9199982, -112.7200012, -112.8700027, -113.75, -114.0999985,
-114.0199966, -113.7300034, -113.3799973, -113.0500031, -112.8000031,
-110.7200012, -113.6299973, -113.9800034, -112.1299973, -115.5500031,
-114.1800003, -114.4800034, -114.3700027, -118.0299988, -117.5299988,
-116.4499969), Northings = c(52.88000107, 52.08000183, 49.54999924,
49.95000076, 49, 49.18000031, 49.02000046, 49.52999878, 49.91999817,
50.93000031, 52.34999847, 51.02999878, 50.63000107, 53.15000153,
50.47000122, 49.61999893, 50.18000031, 51.09999847, 52.11999893,
50.41999817, 50.15000153, 53.97000122, 53.97000122, 56.65000153,
58.77000046, 58.38000107, 57.75, 59.56999969, 58.18000031, 57.34999847,
55.52999878, 57.25, 55.15000153, 56.22999954, 54.77000046, 58.72999954,
56.34999847, 56.86999893, 55.79999924, 52.06999969, 50.72000122,
51.58000183, 49.11999893, 50.90000153, 49.38000107, 51.52000046,
51.27000046, 49.22000122, 50.54999924, 51.25, 49.61999893, 50.33000183,
50.13000107, 51.97999954, 50.43000031, 49.81999969, 51.20000076,
49.72000122, 50.90000153, 50.47000122, 50.97999954, 49.68000031,
50.27000046, 49.36999893, 49, 51.52000046, 52.77000046, 52.36999893,
53.13000107, 52.81999969, 51.47999954, 52.41999817, 53.22000122,
52.16999817, 53.91999817, 53.33000183, 50.43000031, 49.91999817,
49.18000031, 49.41999817, 53.27999878, 53.02999878, 53.31999969,
53.88000107, 53.41999817, 53.88000107, 54.72000122, 54.13000107,
55.41999817, 55.27999878, 54.15000153, 55.20000076, 56.08000183,
55.18000031, 56.22999954, 55.97000122, 54.41999817, 55.83000183,
54.04999924, 52.41999817, 52.33000183, 51.47000122, 52.45000076,
51.77999878, 51.11999893, 49.93000031, 49, 50.88000107, 49.63000107,
50.02000046, 49.13000107, 49.52000046, 50.04999924, 51.20000076,
49.47000122, 49.63000107, 50.54999924, 52.93000031, 53.40000153,
53.58000183), Region = c(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, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L)), .Names = c("Site",
"Name", "Mean", "Eastings", "Northings", "Region"), class = "data.frame", row.names = c(NA,
-120L))
#============================================
use abs()
eg
instead of size=Mean use size=abs(Mean)
Then, you can track the sign using shape (or some other aesthetic, but color and size are already taken)
replace:
geom_point(shape=21)+
geom_point(aes(colour = factor(Region))) +
with the single line
geom_point(aes(shape=factor(sign(Mean)), colour = factor(Region))) +
If you'd like, you can also add lines such as
scale_shape_discrete(name="Mean Is", breaks=c(-1, 1), labels=c("Negative", "Positive"))
guides(size=FALSE)
I have a data frame like this:
structure(list(x = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 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,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L,
76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L,
89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 112L,
113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 123L, 124L,
125L, 127L, 128L, 129L, 130L, 132L, 133L, 134L, 135L, 136L, 137L,
138L, 139L, 140L, 141L, 142L, 143L, 145L, 146L, 147L, 148L, 149L,
150L, 151L, 152L, 153L, 154L, 155L, 158L, 160L, 163L, 164L, 166L,
167L, 169L, 170L, 173L, 174L, 178L, 179L, 181L, 182L, 183L, 186L,
187L, 191L, 192L, 193L, 194L, 197L, 198L, 200L, 205L, 208L, 209L,
213L, 214L, 216L, 217L, 220L, 222L, 223L, 225L, 229L, 233L, 235L,
237L, 242L, 243L, 244L, 251L, 253L, 254L, 255L, 261L, 262L, 263L,
264L, 267L, 268L, 269L, 270L, 276L, 281L, 282L, 284L, 285L, 287L,
289L, 293L, 295L, 297L, 299L, 301L, 306L, 308L, 315L, 317L, 318L,
320L, 327L, 330L, 336L, 337L, 345L, 346L, 355L, 359L, 376L, 377L,
379L, 384L, 387L, 388L, 402L, 405L, 408L, 415L, 420L, 421L, 427L,
428L, 429L, 430L, 437L, 438L, 439L, 440L, 446L, 448L, 453L, 456L,
469L, 472L, 476L, 478L, 481L, 483L, 486L, 487L, 488L, 497L, 500L,
502L, 504L, 507L, 512L, 525L, 530L, 531L, 543L, 546L, 550L, 578L,
581L, 598L, 601L, 680L, 689L, 693L, 712L, 728L, 746L, 768L, 790L,
794L, 840L, 851L, 861L, 928L, 969L, 1010L, 1180L, 1698L), freq = c(29186L,
12276L, 5851L, 3938L, 3133L, 1894L, 1157L, 820L, 597L, 481L,
398L, 297L, 269L, 251L, 175L, 176L, 153L, 130L, 117L, 108L, 93L,
83L, 58L, 84L, 60L, 43L, 59L, 51L, 57L, 53L, 38L, 38L, 32L, 35L,
28L, 27L, 29L, 22L, 24L, 29L, 30L, 23L, 26L, 19L, 19L, 25L, 14L,
22L, 16L, 12L, 15L, 14L, 11L, 13L, 18L, 10L, 17L, 20L, 7L, 9L,
2L, 8L, 12L, 8L, 7L, 10L, 10L, 9L, 6L, 6L, 9L, 5L, 11L, 4L, 5L,
5L, 10L, 4L, 6L, 1L, 4L, 7L, 3L, 4L, 3L, 2L, 3L, 5L, 7L, 2L,
2L, 3L, 2L, 4L, 7L, 1L, 3L, 5L, 5L, 3L, 5L, 2L, 2L, 2L, 3L, 2L,
5L, 7L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 2L, 2L, 1L,
3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 1L, 4L, 3L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 4L, 4L, 1L, 2L,
2L, 4L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 4L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("x",
"freq"), row.names = c(NA, -296L), class = "data.frame")
After the x value of 130, there are missing values. Is there a way I make this a continuous data frame in increments of 1 i.e. from 1 to 1698, populate the entire list and set the elements that do not have a value here as 0? What I mean is:
1,2
4,5
5,7
should be converted to:
1,2
2,0
3,0
4,5
5,7
Any suggestions?
You can also use merge (assuming your data is strored in l):
l <- merge(l,data.frame(x = 1:1698),all = TRUE,by = "x")
l$freq[is.na(l$freq)] <- 0
I'd create a data set of values that aren't covered by column x and then create a dataframe of those values and assign 0 to the freq of all of these x values. Then rbind and order by x.
#I called your data dat
y <- 1:max(dat$x)
dat2 <- data.frame(x=y[!y%in%dat$x], freq=0)
dat3 <- rbind(dat, dat2)
dat4 <- dat3[order(dat3$x), ] #could stop here
rownames(dat4) <- NULL #but I hate non sequential row names
dat4