我做了一个GGally-ggpairs-plot（见下文），现在有以下问题：
第一个变量PdKeyT的 y 轴标记有不正确的值（黄色），而是应该根据其 x 轴从负 25 标记加 75（绿色）。我无法弄清楚/解释原因，因为代码看起来没问题，尤其是其他变量被正确标记（每个变量在两个轴上的范围相同）。
有人对我有什么想法吗？
在图像下方，您将找到两个代码示例。
第一个仅显示 corr- 和 plot- 部分。
第二个是（应该是）完整的、可重复的样本，前面有（相当长的）df 部分。
这里是代码的 corr- 和 plot 部分：
CorrPoint_LevelvsBands <- round(cor(subset(loopsubset_created[c(45,16:25)])),2)
print(CorrPoint_LevelvsBands)
# Computing correlation matrix with p-values
CorrPoint_LevelvsBands_PVal <- cor_pmat(loopsubset_created[c(45,16:25)])
print(CorrPoint_LevelvsBands_PVal)
print(
ggpairs(loopsubset_created[c(45,16:25)],
title = paste("Corr. analysis"),
lower = list(continuous = wrap("cor",
size = 3)),
upper = list(
continuous = wrap("smooth",
alpha = 0.3,
size = 0.1))
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=8)) +
theme(axis.text.y = element_text(hjust = 1, size=8))
)
这是前面带有 df 的完整代码：
loopsubset_created<-structure(list(Site_ID = c("A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"
), Spot_Nr = c("1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1"), Transkt_Nr = c("2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2"), Point_Nr = c("8", "8", "8", "8",
"8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8",
"8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8",
"8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8", "8",
"8", "8", "8"), n = c(46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L,
46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L,
46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L,
46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L, 46L),
rank = 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), Date = c(20190208L, 20190213L, 20190215L,
20190218L, 20190223L, 20190228L, 20190302L, 20190305L, 20190315L,
20190320L, 20190322L, 20190325L, 20190330L, 20190401L, 20190416L,
20190419L, 20190421L, 20190501L, 20190506L, 20190524L, 20190531L,
20190603L, 20190618L, 20190625L, 20190628L, 20190705L, 20190710L,
20190720L, 20190730L, 20190804L, 20190809L, 20190814L, 20190817L,
20190827L, 20190903L, 20190911L, 20190913L, 20190916L, 20190921L,
20191008L, 20191023L, 20191026L, 20191110L, 20191130L, 20191205L,
20191210L), Point_ID = c("1026", "1026", "1026", "1026",
"1026", "1026", "1026", "1026", "1026", "1026", "1026", "1026",
"1026", "1026", "1026", "1026", "1026", "1026", "1026", "1026",
"1026", "1026", "1026", "1026", "1026", "1026", "1026", "1026",
"1026", "1026", "1026", "1026", "1026", "1026", "1026", "1026",
"1026", "1026", "1026", "1026", "1026", "1026", "1026", "1026",
"1026", "1026"), BLUE = c(1173, 1131, 888, 1045, 997, 938,
780, 832, 1248, 1147, 1124, 1114, 1156, 1140, 1265, 1375,
1244, 2250, 2375, 2000, 2700, 2652, 2087, 1303, 1637, 700,
1235, 1258, 1342, 1369, 1140, 1418, 1352, 1767, 1159, 1530,
1321, 1566, 1468, 1325, 1274, 1354, 2563, 2880, 1777, 1480
), GREEN = c(1242, 1265, 993, 1251, 1302, 1264, 1176, 1171,
1442, 1469, 1450, 1448, 1524, 1530, 1385, 1601, 1451, 2381,
2447, 2163, 3052, 2727, 2354, 1498, 1938, 817, 1427, 1476,
1586, 1535, 1388, 1895, 1619, 2172, 1395, 1731, 1525, 1829,
1716, 1538, 1477, 1583, 2954, 3139, 1815, 1571), RED = c(880,
992, 835, 947, 976, 918, 849, 826, 1285, 1190, 1249, 1213,
1273, 1327, 1322, 1659, 1495, 1617, 1746, 1991, 2204, 1870,
1880, 1166, 1694, 625, 1096, 1206, 1266, 1302, 1102, 1944,
1377, 1793, 1162, 1613, 1393, 1747, 1692, 1399, 1316, 1516,
2683, 2219, 1622, 1573), REDEDGE1 = c(634, 794, 670, 869,
953, 1057, 1028, 924, 1265, 1230, 1193, 1242, 1313, 1412,
1208, 1552, 1322, 1204, 1299, 1834, 1665, 1424, 1637, 1131,
1696, 606, 1052, 1256, 1351, 1344, 1168, 1859, 1367, 1716,
1234, 1562, 1321, 1694, 1773, 1389, 1397, 1600, 2285, 1676,
1448, 1350), REDEDGE2 = c(112, 198, 201, 300, 361, 594, 376,
332, 809, 511, 575, 615, 700, 861, 811, 1128, 807, 428, 331,
1255, 620, 731, 964, 636, 1253, 360, 520, 701, 869, 1104,
620, 1127, 837, 1164, 772, 1131, 838, 1220, 1313, 927, 903,
1267, 1098, 586, 932, 926), REDEDGE3 = c(107, 188, 185, 277,
377, 576, 317, 328, 842, 492, 565, 643, 722, 872, 842, 1070,
893, 504, 303, 1289, 639, 763, 987, 660, 1283, 406, 538,
744, 906, 1183, 698, 1168, 853, 1190, 802, 1170, 893, 1188,
1306, 904, 914, 1261, 1056, 537, 932, 940), BROADNIR = c(79,
155, 164, 193, 340, 453, 294, 250, 801, 408, 558, 531, 663,
834, 839, 1097, 932, 412, 305, 1181, 566, 677, 840, 448,
986, 229, 314, 559, 569, 1071, 400, 875, 581, 920, 446, 802,
662, 1073, 1131, 679, 674, 1005, 892, 428, 923, 1117), NIR = c(21,
55, 70, 145, 233, 402, 125, 189, 677, 225, 261, 380, 477,
535, 651, 797, 617, 305, 146, 1033, 399, 689, 923, 530, 1187,
347, 341, 690, 813, 1213, 535, 919, 787, 1041, 756, 982,
777, 1097, 1133, 880, 824, 1261, 651, 281, 751, 797), SWIR1 = c(92,
136, 95, 88, 237, 173, 134, 182, 392, 119, 121, 215, 219,
209, 302, 146, 149, 214, 155, 560, 241, 574, 634, 416, 981,
256, 390, 649, 840, 928, 582, 683, 901, 924, 940, 818, 804,
1045, 1099, 871, 964, 1265, 118, 136, 425, 390), SWIR2 = c(78,
85, 70, 81, 189, 128, 128, 128, 289, 96, 122, 130, 176, 163,
224, 119, 108, 136, 118, 441, 187, 464, 509, 288, 755, 173,
274, 444, 557, 658, 395, 504, 607, 668, 711, 567, 550, 752,
776, 598, 698, 959, 72, 97, 355, 290), PdKeyT = c(-10L, -20L,
-22L, -22L, -27L, -26L, -26L, -27L, -22L, -17L, -19L, -19L,
-23L, -23L, -5L, -9L, -9L, 54L, 53L, 40L, 60L, 43L, 19L,
15L, 15L, 15L, 13L, 8L, 9L, 7L, 7L, 9L, 6L, 8L, 6L, 12L,
9L, 4L, 2L, 2L, 3L, 2L, 75L, 43L, 28L, 13L)), row.names = 125:170, class = "data.frame")
library(dplyr)
library(ggplot2)
library(ggcorrplot)
library(GGally)
CorrPoint_LevelvsBands <- round(cor(subset(loopsubset_created[c(19,9:18)])),2)
print(CorrPoint_LevelvsBands)
# Computing correlation matrix with p-values
CorrPoint_LevelvsBands_PVal <- cor_pmat(loopsubset_created[c(19,9:18)])
print(CorrPoint_LevelvsBands_PVal)
print(
ggpairs(loopsubset_created[c(19,9:18)],
title = paste("Corr. analysis"),
lower = list(continuous = wrap("cor",
size = 3)),
upper = list(
continuous = wrap("smooth",
alpha = 0.3,
size = 0.1))
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=8)) +
theme(axis.text.y = element_text(hjust = 1, size=8))
)