Добавить отдельную легенду geom_vline

У меня есть этот кадр данных:Добавить отдельную легенду geom_vline

dput(df) 
structure(list(X = c(337L, 338L, 339L, 340L, 341L, 342L, 343L, 
344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L, 
355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 
366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 
377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L, 
388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L, 
399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 
410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L, 
421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 
432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 
443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L, 
454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L, 
465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L, 
476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 
487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L, 
498L, 499L, 500L, 501L, 502L, 503L, 504L, 841L, 842L, 843L, 844L, 
845L, 846L, 847L, 848L, 849L, 850L, 851L, 852L, 853L, 854L, 855L, 
856L, 857L, 858L, 859L, 860L, 861L, 862L, 863L, 864L, 865L, 866L, 
867L, 868L, 869L, 870L, 871L, 872L, 873L, 874L, 875L, 876L, 877L, 
878L, 879L, 880L, 881L, 882L, 883L, 884L, 885L, 886L, 887L, 888L, 
889L, 890L, 891L, 892L, 893L, 894L, 895L, 896L, 897L, 898L, 899L, 
900L, 901L, 902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L, 
911L, 912L, 913L, 914L, 915L, 916L, 917L, 918L, 919L, 920L, 921L, 
922L, 923L, 924L, 925L, 926L, 927L, 928L, 929L, 930L, 931L, 932L, 
933L, 934L, 935L, 936L, 937L, 938L, 939L, 940L, 941L, 942L, 943L, 
944L, 945L, 946L, 947L, 948L, 949L, 950L, 951L, 952L, 953L, 954L, 
955L, 956L, 957L, 958L, 959L, 960L, 961L, 962L, 963L, 964L, 965L, 
966L, 967L, 968L, 969L, 970L, 971L, 972L, 973L, 974L, 975L, 976L, 
977L, 978L, 979L, 980L, 981L, 982L, 983L, 984L, 985L, 986L, 987L, 
988L, 989L, 990L, 991L, 992L, 993L, 994L, 995L, 996L, 997L, 998L, 
999L, 1000L, 1001L, 1002L, 1003L, 1004L, 1005L, 1006L, 1007L, 
1008L, 1345L, 1346L, 1347L, 1348L, 1349L, 1350L, 1351L, 1352L, 
1353L, 1354L, 1355L, 1356L, 1357L, 1358L, 1359L, 1360L, 1361L, 
1362L, 1363L, 1364L, 1365L, 1366L, 1367L, 1368L, 1369L, 1370L, 
1371L, 1372L, 1373L, 1374L, 1375L, 1376L, 1377L, 1378L, 1379L, 
1380L, 1381L, 1382L, 1383L, 1384L, 1385L, 1386L, 1387L, 1388L, 
1389L, 1390L, 1391L, 1392L, 1393L, 1394L, 1395L, 1396L, 1397L, 
1398L, 1399L, 1400L, 1401L, 1402L, 1403L, 1404L, 1405L, 1406L, 
1407L, 1408L, 1409L, 1410L, 1411L, 1412L, 1413L, 1414L, 1415L, 
1416L, 1417L, 1418L, 1419L, 1420L, 1421L, 1422L, 1423L, 1424L, 
1425L, 1426L, 1427L, 1428L, 1429L, 1430L, 1431L, 1432L, 1433L, 
1434L, 1435L, 1436L, 1437L, 1438L, 1439L, 1440L, 1441L, 1442L, 
1443L, 1444L, 1445L, 1446L, 1447L, 1448L, 1449L, 1450L, 1451L, 
1452L, 1453L, 1454L, 1455L, 1456L, 1457L, 1458L, 1459L, 1460L, 
1461L, 1462L, 1463L, 1464L, 1465L, 1466L, 1467L, 1468L, 1469L, 
1470L, 1471L, 1472L, 1473L, 1474L, 1475L, 1476L, 1477L, 1478L, 
1479L, 1480L, 1481L, 1482L, 1483L, 1484L, 1485L, 1486L, 1487L, 
1488L, 1489L, 1490L, 1491L, 1492L, 1493L, 1494L, 1495L, 1496L, 
1497L, 1498L, 1499L, 1500L, 1501L, 1502L, 1503L, 1504L, 1505L, 
1506L, 1507L, 1508L, 1509L, 1510L, 1511L, 1512L), variable = 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("As", "Cd_totale", "Cr_totale" 
), class = "factor"), Area = 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 1L, 6L, 
6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 4L, 5L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 
3L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 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, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 4L, 4L, 4L, 
4L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 1L, 1L, 6L, 
6L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 
6L, 6L, 1L, 1L, 6L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 2L, 3L, 
3L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
1L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("candiano", "Lido_Spina", "Porto_Corsini", 
"Punta_Marina", "Sito1", "Sito2"), class = "factor"), Campione = structure(c(40L, 
39L, 38L, 155L, 37L, 36L, 153L, 50L, 51L, 156L, 34L, 152L, 73L, 
154L, 75L, 76L, 157L, 41L, 42L, 43L, 44L, 45L, 35L, 47L, 48L, 
49L, 6L, 7L, 13L, 21L, 162L, 164L, 166L, 2L, 8L, 46L, 14L, 165L, 
167L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 12L, 33L, 
150L, 151L, 111L, 74L, 3L, 9L, 15L, 18L, 4L, 10L, 158L, 159L, 
160L, 161L, 17L, 20L, 1L, 126L, 127L, 128L, 52L, 53L, 54L, 55L, 
56L, 57L, 58L, 22L, 163L, 139L, 140L, 141L, 142L, 143L, 144L, 
145L, 146L, 147L, 32L, 149L, 109L, 110L, 72L, 84L, 112L, 113L, 
114L, 115L, 116L, 77L, 16L, 19L, 5L, 11L, 82L, 83L, 61L, 85L, 
86L, 87L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 59L, 60L, 
123L, 62L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 148L, 108L, 70L, 
71L, 137L, 124L, 125L, 98L, 99L, 88L, 89L, 117L, 78L, 79L, 80L, 
81L, 122L, 91L, 138L, 97L, 90L, 136L, 100L, 101L, 94L, 102L, 
92L, 93L, 96L, 69L, 103L, 95L, 105L, 119L, 107L, 104L, 118L, 
120L, 121L, 106L, 106L, 109L, 105L, 110L, 108L, 121L, 122L, 102L, 
111L, 107L, 146L, 147L, 104L, 149L, 150L, 112L, 148L, 103L, 145L, 
120L, 117L, 4L, 10L, 123L, 18L, 125L, 126L, 127L, 124L, 129L, 
118L, 119L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 128L, 
101L, 60L, 61L, 62L, 23L, 24L, 25L, 26L, 27L, 28L, 151L, 113L, 
114L, 115L, 116L, 3L, 9L, 15L, 78L, 79L, 80L, 81L, 82L, 83L, 
84L, 85L, 86L, 87L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 
137L, 138L, 139L, 140L, 100L, 59L, 142L, 143L, 144L, 165L, 62L, 
63L, 64L, 65L, 66L, 67L, 152L, 153L, 154L, 155L, 156L, 157L, 
158L, 159L, 160L, 161L, 40L, 16L, 19L, 5L, 11L, 17L, 20L, 88L, 
89L, 90L, 13L, 21L, 162L, 164L, 166L, 2L, 8L, 12L, 14L, 141L, 
58L, 22L, 163L, 74L, 167L, 76L, 77L, 39L, 41L, 42L, 68L, 69L, 
70L, 71L, 72L, 73L, 7L, 75L, 49L, 50L, 51L, 52L, 53L, 43L, 44L, 
45L, 46L, 1L, 6L, 34L, 48L, 36L, 37L, 38L, 56L, 57L, 54L, 55L, 
29L, 35L, 32L, 33L, 30L, 31L, 47L, 66L, 64L, 62L, 67L, 103L, 
65L, 101L, 63L, 58L, 59L, 60L, 102L, 62L, 99L, 100L, 77L, 37L, 
38L, 39L, 40L, 41L, 42L, 43L, 44L, 61L, 48L, 49L, 50L, 51L, 52L, 
53L, 54L, 55L, 56L, 57L, 12L, 14L, 22L, 163L, 137L, 138L, 165L, 
167L, 23L, 24L, 25L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 
76L, 36L, 3L, 9L, 15L, 78L, 79L, 80L, 81L, 82L, 45L, 46L, 47L, 
86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 8L, 135L, 136L, 
97L, 98L, 34L, 139L, 140L, 141L, 142L, 26L, 27L, 28L, 29L, 30L, 
31L, 32L, 33L, 17L, 35L, 154L, 155L, 156L, 18L, 4L, 10L, 16L, 
19L, 83L, 84L, 85L, 110L, 20L, 1L, 6L, 7L, 13L, 21L, 162L, 164L, 
166L, 95L, 96L, 109L, 11L, 111L, 112L, 113L, 114L, 115L, 143L, 
104L, 105L, 106L, 107L, 108L, 5L, 149L, 123L, 124L, 125L, 126L, 
127L, 157L, 158L, 159L, 160L, 161L, 121L, 134L, 153L, 150L, 151L, 
152L, 130L, 131L, 128L, 129L, 148L, 144L, 132L, 2L, 116L, 133L, 
122L, 146L, 147L, 120L, 145L, 118L, 119L, 117L), .Label = c("A_1", 
"A_2", "A_LS", "A_PC", "A_PM", "B_1", "B1_1", "B1_2", "B1_LS", 
"B1_PC", "B1_PM", "B_2", "B2_1", "B2_2", "B2_LS", "B2_PC", "B2_PM", 
"B_LS", "B_PC", "B_PM", "C_1", "C_2", "C386", "C387", "C388", 
"C389", "C390", "C391", "C392", "C393", "C394", "C395", "C396", 
"C397", "C398", "C399", "C400", "C401", "C402", "C403", "C404", 
"C405", "C406", "C407", "C408", "C409", "C410", "C411", "C412", 
"C413", "C414", "C415", "C416", "C417", "C418", "C419", "C420", 
"C421", "C422", "C423", "C424", "C425", "C426", "C427", "C428", 
"C429", "C430", "C431", "C432", "C433", "C434", "C435", "C436", 
"C437", "C438", "C439", "C440", "C441", "C442", "C443", "C444", 
"C445", "C446", "C447", "C448", "C449", "C450", "C451", "C452", 
"C453", "C454", "C455", "C456", "C457", "C458", "C459", "C460", 
"C461", "C462", "C463", "C464", "C465", "C466", "C467", "C468", 
"C469", "C470", "C471", "C472", "C473", "C474", "C475", "C476", 
"C477", "C478", "C479", "C480", "C481", "C482", "C483", "C484", 
"C485", "C486", "C487", "C488", "C489", "C490", "C491", "C492", 
"C493", "C494", "C495", "C496", "C497", "C498", "C499", "C500", 
"C501", "C502", "C503", "C504", "C505", "C506", "C507", "C508", 
"C509", "C510", "C511", "C512", "C513", "C514", "C515", "C516", 
"C517", "C518", "C519", "C520", "C521", "C522", "C523", "C524", 
"D_1", "D_2", "E_1", "E_2", "F_1", "F_2"), class = "factor"), 
    zona = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 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, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
    1L, 1L, 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, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("campione", 
    "controllo"), class = "factor"), value = c(7.75, 7.83, 8, 
    9, 7.2, 7.5, 6.5, 6.4, 6.2, 8, 7.75, 7.6, 8.5, 7, 8.25, 8.25, 
    9, 7, 7.5, 8.17, 8.75, 6.67, 7.6, 5.83, 6.75, 5.6, 9.48, 
    8.35, 8.37, 7.5, 5.5, 6.45, 6.22, 9.3, 8.62, 7, 6, 6.17, 
    5.71, 9, 8.75, 13.5, 7.75, 7.6, 8.33, 8, 8.75, 7.4, 8, 8.17, 
    6.17, 7, 8.5, 8, 8.45, 7.82, 6, 8.7, 10.1, 8.64, 9, 8, 6.6, 
    6.6, 7, 7.66, 9.19, 7.67, 10, 8, 6.2, 6.2, 6.25, 7, 6, 6, 
    6.4, 7, 7.75, 8, 7, 7, 9, 9, 7.8, 7, 6.17, 7, 8.25, 7, 8.6, 
    6.6, 8.25, 8, 8, 6.5, 6.75, 6.2, 6, 8.25, 6, 8.38, 9.16, 
    7.7, 8, 8, 5.6, 7.67, 7.67, 6.33, 9, 7.5, 7.33, 6.8, 7, 7, 
    8, 6, 5.8, 7, 6, 6, 5.8, 7.25, 8.8, 8.5, 8, 8.25, 7.75, 8.4, 
    8.5, 8.25, 7.25, 6, 7, 7, 7, 6.33, 7, 7, 8, 7.25, 6.67, 7.33, 
    5, 6, 7, 8, 7, 8, 8, 8, 8, 8, 7.67, 8, 8, 8.25, 7, 9.5, 8, 
    6, 8.2, 7, 6.5, 7, 6, 7.8, 0.12, 0.28, 0.12, 0.1, 0.24, 0.1, 
    0.1, 0.12, 0.15, 0.22, 0.13, 0.1, 0.11, 0.1, 0.12, 0.13, 
    0.13, 0.11, 0.1, 0.1, 0.1, 0.15, 0.14, 0.1, 0.15, 0.1, 0.1, 
    0.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.09, 0.1, 0.1, 0.09, 0.1, 
    0.11, 0.1, 0.08, 0.1, 0.1, 0.11, 0.1, 0.1, 0.15, 0.13, 0.15, 
    0.1, 0.16, 0.13, 0.12, 0.1, 0.1, 0.12, 0.1, 0.14, 0.14, 0.025, 
    0.12, 0.12, 0.11, 0.12, 0.13, 0.13, 0.11, 0.09, 0.1, 0.08, 
    0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.13, 0.1, 0.1, 0.1, 0.11, 
    0.09, 0.15, 0.1, 0.1, 0.1, 0.11, 0.09, 0.12, 0.12, 0.12, 
    0.12, 0.16, 0.1, 0.1, 0.1, 0.1, 0.2, 0.15, 0.1, 0.14, 0.12, 
    0.15, 0.025, 0.16, 0.18, 0.16, 0.025, 0.15, 0.08, 0.09, 0.1, 
    0.15, 0.17, 0.11, 0.05, 0.05, 0.12, 0.15, 0.12, 0.025, 0.1, 
    0.11, 0.17, 0.16, 0.12, 0.05, 0.12, 0.12, 0.13, 0.17, 0.13, 
    0.14, 0.12, 0.12, 0.11, 0.12, 0.13, 0.15, 0.12, 0.12, 0.1, 
    0.1, 0.12, 0.1, 0.13, 0.13, 0.18, 0.1, 0.14, 0.12, 0.1, 0.1, 
    0.18, 0.2, 0.15, 0.1, 0.11, 0.13, 0.11, 0.24, 0.16, 0.13, 
    0.15, 0.15, 0.22, 0.1, 85.75, 63.25, 43.6, 84.5, 84, 95.4, 
    98, 35.6, 79.6, 71.6, 73.2, 84, 54, 89, 99, 94.75, 70.6, 
    92.25, 84.83, 78.5, 70.17, 92.5, 94.67, 93.75, 75.2, 64.75, 
    66, 75.6, 82.4, 80.2, 78.8, 81, 79, 75.6, 79.2, 85.9, 57, 
    93.9, 97.9, 96.25, 84, 53.9, 56.1, 108.5, 111.75, 104.5, 
    78.25, 82.75, 87.25, 86.05, 85, 96, 102.25, 100.5, 100, 77, 
    84.3, 83.9, 52, 90.67, 85.75, 76.67, 86.33, 93.67, 70.5, 
    86.6, 77.67, 86.33, 74, 73.67, 86.67, 87.5, 72, 89.67, 93, 
    95, 93.5, 96, 88, 91, 86, 104.5, 90, 86.5, 85, 100.25, 81.25, 
    93.2, 109.75, 105, 104, 87.8, 99.75, 92.67, 47, 88.2, 73, 
    95, 94, 98.7, 100.4, 91.5, 63, 94.2, 89.33, 90.33, 83.67, 
    75.6, 86.8, 99.7, 90, 88.7, 88.4, 99.8, 76.4, 57.8, 52.5, 
    93, 91, 108, 91.5, 105, 98, 69.5, 79.75, 68.6, 103, 81, 90, 
    101.2, 102.6, 96.6, 100.8, 81, 90, 65, 79, 102.67, 102, 107, 
    107.5, 93, 70.2, 70.2, 67, 90.33, 71.5, 61.17, 64.6, 84.8, 
    87.5, 96.67, 76, 101, 100.75, 97.8, 77.4, 83.4, 79, 79, 55, 
    59.33, 98, 95.25, 82, 87, 70, 91), LCB_pelite = c(17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 
    17, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 
    0.2, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
    50, 50, 50, 50, 50), LCB = c(25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 
    0.35, 0.35, 0.35, 0.35, 0.35, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
    100, 100, 100, 100, 100, 100), LCL = c(32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 
    32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 
    0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
    360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360)), .Names = c("X", 
"variable", "Area", "Campione", "zona", "value", "LCB_pelite", 
"LCB", "LCL"), class = "data.frame", row.names = c(NA, -504L))

и что код, который я использовал для создания следующего участка (со старыми данными, не с ними):

ggplot(df)+ 
    stat_ecdf(aes(x=value, color=Area))+ 
    geom_vline(aes(xintercept=LCB_pelite), size=2, color="red")

enter image description here

Что я хотел бы сделать, так это добавить в легенду ссылку на geom_vline.

Другими словами, я хотел бы иметь 2 легенды: первый - это тот, который автоматически добавляется ggplot, а второй должен иметь другой заголовок и символ geom_vline.

Я также попытался сделать следующий хак:

ggplot(df)+ 
    stat_ecdf(aes(x=value, color=Area))+ 
    geom_vline(aes(xintercept=LCB_pelite, color="Limit"), size=2)

со следующим результатом:

enter image description here

но есть 2 проблемы: во-первых, что цвет в случайном порядке выбирается ggplot и вторая проблема заключается в том, что символ находится внутри старой легенды, в то время как мне это нужно в отдельной легенде.

Я надеюсь, что я ясно ..

Благодарности

EDIT

Я сделал некоторые «хаки» в мой dataframe и теперь он выглядит (ссе dput сверху).

Тогда я расплавить:

require("reshape2") 
df_melt<-(melt(df))

и я написал этот кусок кода:

require("ggplot2") 
ggplot(df, aes(x=value, color=zona, linetype = factor(LCB_pelite)))+ 
    stat_ecdf()+ 
    facet_wrap(~variable, scales="free_x") + 
    geom_vline(aes(xintercept=LCB_pelite), color="red", linetype="dashed") + 
#  geom_vline(aes(xintercept=LCB), color="orangered", linetype="dashed") + 
#  geom_vline(aes(xintercept=LCL), color="blue", linetype="dashed") + 
    scale_linetype(name = "Limit") + 
    guides(linetype = guide_legend(override.aes = list(colour = "red")))

Что мне нужно сейчас:

добавить другой 2 geom_vline (см. Код сверху) в легенде
ad (LCB_pelite, LCB, LCL, а не 0,2, 17 и 50)

Возможно ли это с помощью ggplot?

Спасибо!

источник

2015-02-24 matteo

У вас может быть отдельная легенда, если вы указываете тип линии.Однако vline также показать в цвете легенды, и я не могу найти способ подавить, что:

ggplot(df)+ 
    stat_ecdf(aes(x=value, color=Area))+ 
    geom_vline(aes(xintercept = LCB_pelite, linetype = "vline1"), 
      color="red", size=2, show_guide = TRUE) + 
    scale_linetype_discrete(name = "vline")

resulting plot

источник

2015-02-24 12:50:32 Roland

благодарит @Roland за ответ. Да, ваш ответ может быть решением. Но что, если у меня есть более 1 'geom_vline' ?! В этом случае ваше решение не подойдет ... но спасибо в любом случае !! – matteo

Вы можете добавить linetype эстетику к ggplot вызова, а затем исправить цвет в легенде, используя guides/override.aes:

ggplot(df, aes(x = value, color = Area, linetype = factor(LCB_pelite))) + 
    stat_ecdf() + 
    geom_vline(aes(xintercept = LCB_pelite), color = "red") + 
    scale_linetype(name = "Limit") + 
    guides(linetype = guide_legend(override.aes = list(colour = "red")))

Пожалуйста, обратите внимание на правописание в override.aes: коло U г с у (по какой-то причине не «цвет» работы здесь ...)

enter image description here

источник

2015-02-24 13:11:50 Henrik

Спасибо, ваше решение решает проблему. Еще один сложный вопрос. Что делать, если у меня есть другой столбец (например, 'LCL') с другими значениями, которые я хочу построить как' geom_vline', и создать его в легенде 'Limit' до 50? – matteo

Так что я почти там. я вновь реорганизован данные из выше этой части кода:

gf_melt<-melt(gf, id=c("variable", "Area", "Campione", "zona", "value"), variable.name="limite")

я переименовывать некоторые переменные («значение» появляется два раза), и я получил это:

head(gf_melt) 
variable  Area Campione  zona variable_value  limite value 
1  As candiano  C403 campione   7.75 LCB_pelite 17 
2  As candiano  C402 campione   7.83 LCB_pelite 17 
3  As candiano  C401 campione   8.00 LCB_pelite 17 
4  As candiano  C518 campione   9.00 LCB_pelite 17 
5  As candiano  C400 campione   7.20 LCB_pelite 17 
6  As candiano  C399 campione   7.50 LCB_pelite 17

Я могу теперь использовать limite в color в geom_vline:

ggplot(gf_melt)+ 
    stat_ecdf(aes(x=variable_value, color=zona))+ 
    facet_wrap(~variable, scales="free_x")+ 
    geom_vline(aes(xintercept=value, color=limite

, чтобы получить этот участок:

enter image description here ))

одна последняя вещь ..

как может теперь отдельный LCB, LCB_pelite и LCL в другой легенде с другим названием?

источник

2015-02-26 17:21:06 matteo

Добавить отдельную легенду geom_vline

ответ

Смежные вопросы