Я пытаюсь построить данные за несколько лет и среднее значение на одном графике.Мне бы хотелось, чтобы средняя линия (avg) и стандартные отклонения (StdH и StdL) были более жирными, чтобы выделять больше.
Я дошел до того, что могу рисовать их с разными цветами и типами линий, но мое кодирование, похоже, не работает специально для scale_size_manual () - даже когда я заставил другие ручные весы работать в том же режимепуть.Это просто не применяется или ничего не меняет ??Я не уверен, что мой синтаксис немного выключен?
Любая помощь будет признательна.
Код:
p1 <- ggplot(example, aes(x=day, y=value, colour = variable, shape=variable, linetype = variable,
fill = variable))
p1 <- p1 +geom_line()
p1 <- p1 + scale_shape_manual(name= "", values=
c(21,21,21,21,21,21,21,21,21,21),labels = c("2012", "2013", "2014", "2015",
"2016", "2017", "2018", "Avg", "StdH" , "StdL")) +
scale_linetype_manual(name= "", values= c("solid","solid","solid","solid","solid", "solid", "solid",
"solid","dotted", "dotted" ),
labels = c("2012", "2013", "2014", "2015",
"2016", "2017", "2018", "Avg","StdH" , "StdL")) +
scale_color_manual(name= "",values = c("lightpink1", "firebrick2", "red",
"khaki2", "darkgoldenrod2", "darkslategray3", "blue",
"black", "grey80",
"grey80"),labels = c("2012", "2013", "2014", "2015", "2016", "2017", "2018",
"Avg", "StdH" , "StdL"))+
scale_size_manual(name= "",values =
c(0.5,0.5,0.5,0.5,0.5,0.5,0.5,3,1,1),labels = c("2012", "2013", "2014",
"2015", "2016", "2017", "2018", "Avg", "StdH" , "StdL"))
Фиктивный набор данных:
example <- structure(list(day = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L), variable = structure(c(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, 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, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), .Label = c("2012",
"2013", "2014", "2015", "2016", "2017", "2018", "avg", "stdH",
"stdL"), class = "factor"), value = c(NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.813035011534722,
0.819399596857342, 0.828738664539958, 0.834710324143056, 0.844191607359277,
0.856100462892981, 0.861863779878472, 0.869605662396528, 0.878702257322222,
0.887130821185546, 0.899324228029167, 0.905628739333333, 0.914049177410987,
0.923330835359972, 0.930067979710709, 0.942030437145341, 0.948458332459722,
0.958359304605976, 0.969835481069589, 1.10938764625556, 1.12192300698819,
1.13473937973333, 1.14141483861181, 1.15061751442361, 1.16057298275972,
1.17090689060417, 1.18157552140208, 1.19986553628542, 1.21308408352153,
1.2266191703875, 1.23583940773611, 1.2494657257733, 1.25980365776111,
1.27269001772083, 1.28644381114028, 1.30061703170486, 1.31415782964236,
1.32427688976998, 0.830779163004933, 0.838186262363765, 0.859128417646401,
0.862724449693277, 0.871525131398038, 0.889826098448637, 0.891210156322401,
0.895077847238628, 0.895987598688284, 0.895820013531707, 0.911374309052301,
0.91266488286899, 0.910766739630252, 0.909109504993046, 0.913261098626404,
0.922103696808793, 0.927409952689993, 0.92591168405434, 0.941596485085614,
0.929119833825, 0.9444975486125, 0.948266898932639, 0.955056299104167,
0.963458828334955, 0.964581225203472, 0.972657548749817, 1.01873876981718,
1.03278145046042, 1.04546081641516, 1.05473343752431, 1.06352718637708,
1.0742666486442, 1.07891911047222, 1.08363550907302, 1.08906426304934,
1.09677758193194, 1.1046076705066, 1.10911954408125, 0.631248365554861,
0.641042829451008, 0.648293579827778, 0.655045395079167, 0.663208785114583,
0.666893115380125, 0.674189422488873, 0.681509924184028, 0.693503957159028,
0.702561419576389, 0.713188132266667, 0.724857766157639, 0.735444916902778,
0.745390341159028, 0.760225588979861, 0.771742594456944, 0.784025145670139,
0.79432079518125, 0.803671385588194, 0.637260062459986, 0.645136937039611,
0.651507199442887, 0.658422984122477, 0.671206412972822, 0.682619127953343,
0.690362939048611, 0.695130862961699, 0.699617322367361, 0.702218111697496,
0.712515244731707, 0.721580055017483, 0.727369178154969, 0.732949823962448,
0.744934685162613, 0.749011390347201, 0.757563485596087, 0.765955210908965,
0.77178278640612, 0.825138347105843, 0.835031030218735, 0.845112356687166,
0.851229048458991, 0.860701379933881, 0.870098835439713, 0.876865122848723,
0.890273098000025, 0.900076353713788, 0.907712544321304, 0.919625753665275,
0.927349672915107, 0.935227064419413, 0.941583878951304, 0.950802479878907,
0.960066032157983, 0.969141921675458, 0.977218749149916, 0.986713762000125,
1.00662096223506, 1.01846244020306, 1.03015819298715, 1.03619126491636,
1.04504445788237, 1.05466778026015, 1.06229870901887, 1.08183160804982,
1.0954292369126, 1.10626044728563, 1.11886959265124, 1.12621735838914,
1.13634521874989, 1.14334246002261, 1.15169875944251, 1.16252056967026,
1.17279340524912, 1.18256135017598, 1.19211693704488, 0.643655731976628,
0.651599620234415, 0.660066520387178, 0.666266832001628, 0.676358301985388,
0.685529890619276, 0.691431536678579, 0.698714587950224, 0.70472347051498,
0.709164641356977, 0.720381914679308, 0.728481987441075, 0.734108910088932,
0.739825297879997, 0.749906200315302, 0.757611494645704, 0.7654904381018,
0.771876148123849, 0.781310586955371)), row.names = c(1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L,
374L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L,
731L, 732L, 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L, 741L,
742L, 743L, 744L, 745L, 746L, 747L, 748L, 749L, 1096L, 1097L,
1098L, 1099L, 1100L, 1101L, 1102L, 1103L, 1104L, 1105L, 1106L,
1107L, 1108L, 1109L, 1110L, 1111L, 1112L, 1113L, 1114L, 1461L,
1462L, 1463L, 1464L, 1465L, 1466L, 1467L, 1468L, 1469L, 1470L,
1471L, 1472L, 1473L, 1474L, 1475L, 1476L, 1477L, 1478L, 1479L,
1826L, 1827L, 1828L, 1829L, 1830L, 1831L, 1832L, 1833L, 1834L,
1835L, 1836L, 1837L, 1838L, 1839L, 1840L, 1841L, 1842L, 1843L,
1844L, 2191L, 2192L, 2193L, 2194L, 2195L, 2196L, 2197L, 2198L,
2199L, 2200L, 2201L, 2202L, 2203L, 2204L, 2205L, 2206L, 2207L,
2208L, 2209L, 2556L, 2557L, 2558L, 2559L, 2560L, 2561L, 2562L,
2563L, 2564L, 2565L, 2566L, 2567L, 2568L, 2569L, 2570L, 2571L,
2572L, 2573L, 2574L, 2921L, 2922L, 2923L, 2924L, 2925L, 2926L,
2927L, 2928L, 2929L, 2930L, 2931L, 2932L, 2933L, 2934L, 2935L,
2936L, 2937L, 2938L, 2939L, 3286L, 3287L, 3288L, 3289L, 3290L,
3291L, 3292L, 3293L, 3294L, 3295L, 3296L, 3297L, 3298L, 3299L,
3300L, 3301L, 3302L, 3303L, 3304L), class = "data.frame")