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I would like to change the proportion of the graph covered by the "zoom". If we look at the following image we are at a ratio "graph:zoom" around 1:2 I would like instead a ratio 2:1. In other words, what I want is to reduce the height of the zoom. How should I proceed?
Here is my code
require(dplyr)
require(tidyverse)
require(ggforce)
HY <- DebitH %>%
ggplot(aes(x = Date, y = Débit_horaire)) +
geom_point(alpha = 6/10, size = 0.3, color = "blue") +
geom_line(alpha = 4/10, size = 0.5, color = "blue") +
labs(x = "Date", y = "Débit Horaire (m3/s)")+
theme_bw() +
theme(panel.grid.major.y = element_line(color = "grey",
size = 0.25,
linetype = 2),
legend.position = "none") +
facet_zoom(xlim = as.POSIXct(c("2021-01-15 00:00:00","2021-02-15 00:00:00")))
And here you will find a sample of the data
DebitH <-
structure(list(Date = structure(c(1610233200, 1610236800, 1610240400,
1610244000, 1610247600, 1610251200, 1610254800, 1610258400, 1610262000,
1610265600, 1610269200, 1610272800, 1610276400, 1610280000, 1610283600,
1610287200, 1610290800, 1610294400, 1610298000, 1610301600, 1610305200,
1610308800, 1610312400, 1610316000, 1610319600, 1610323200, 1610326800,
1610330400, 1610334000, 1610337600, 1610341200, 1610344800, 1610348400,
1610352000, 1610355600, 1610359200, 1610362800, 1610366400, 1610370000,
1610373600, 1610377200, 1610380800, 1610384400, 1610388000, 1610391600,
1610395200, 1610398800, 1610402400, 1610406000, 1610409600, 1610413200,
1610416800, 1610420400, 1610424000, 1610427600, 1610431200, 1610434800,
1610438400, 1610442000, 1610445600, 1610449200, 1610452800, 1610456400,
1610460000, 1610463600, 1610467200, 1610470800, 1610474400, 1610478000,
1610481600, 1610485200, 1610488800, 1610492400, 1610496000, 1610499600,
1610503200, 1610506800, 1610510400, 1610514000, 1610517600, 1610521200,
1610524800, 1610528400, 1610532000, 1610535600, 1610539200, 1610542800,
1610546400, 1610550000, 1610553600, 1610557200, 1610560800, 1610564400,
1610568000, 1610571600, 1610575200, 1610578800, 1610582400, 1610586000,
1610589600, 1610593200, 1610596800, 1610600400, 1610604000, 1610607600,
1610611200, 1610614800, 1610618400, 1610622000, 1610625600, 1610629200,
1610632800, 1610636400, 1610640000, 1610643600, 1610647200, 1610650800,
1610654400, 1610658000, 1610661600, 1610665200, 1610668800, 1610672400,
1610676000, 1610679600, 1610683200, 1610686800, 1610690400, 1610694000,
1610697600, 1610701200, 1610704800, 1610708400, 1610712000, 1610715600,
1610719200, 1610722800, 1610726400, 1610730000, 1610733600, 1610737200,
1610740800, 1610744400, 1610748000, 1610751600, 1610755200, 1610758800,
1610762400, 1610766000, 1610769600, 1610773200, 1610776800, 1610780400,
1610784000, 1610787600, 1610791200, 1610794800, 1610798400, 1610802000,
1610805600, 1610809200, 1610812800, 1610816400, 1610820000, 1610823600,
1610827200, 1610830800, 1610834400, 1610838000, 1610841600, 1610845200,
1610848800, 1610852400, 1610856000, 1610859600, 1610863200, 1610866800,
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1610892000, 1610895600, 1610899200, 1610902800, 1610906400, 1610910000,
1610913600, 1610917200, 1610920800, 1610924400, 1610928000, 1610931600,
1610935200, 1610938800, 1610942400, 1610946000, 1610949600, 1610953200,
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1610978400, 1610982000, 1610985600, 1610989200, 1610992800, 1610996400,
1.611e+09, 1611003600, 1611007200, 1611010800, 1611014400, 1611018000,
1611021600, 1611025200, 1611028800, 1611032400, 1611036000, 1611039600,
1611043200, 1611046800, 1611050400, 1611054000, 1611057600, 1611061200,
1611064800, 1611068400, 1611072000, 1611075600, 1611079200, 1611082800,
1611086400, 1611090000, 1611093600, 1611097200, 1611100800, 1611104400,
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1611172800, 1611176400, 1611180000, 1611183600, 1611187200, 1611190800,
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1611561600, 1611565200, 1611568800, 1611572400, 1611576000, 1611579600,
1611583200, 1611586800, 1611590400, 1611594000, 1611597600, 1611601200,
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1611626400, 1611630000, 1611633600, 1611637200, 1611640800, 1611644400,
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1611864000, 1611867600, 1611871200, 1611874800, 1611878400, 1611882000,
1611885600, 1611889200, 1611892800, 1611896400, 1611900000, 1611903600,
1611907200, 1611910800, 1611914400, 1611918000, 1611921600, 1611925200,
1611928800, 1611932400, 1611936000, 1611939600, 1611943200, 1611946800,
1611950400, 1611954000, 1611957600, 1611961200, 1611964800, 1611968400,
1611972000, 1611975600, 1611979200, 1611982800, 1611986400, 1611990000,
1611993600, 1611997200, 1612000800, 1612004400, 1612008000, 1612011600,
1612015200, 1612018800, 1612022400, 1612026000, 1612029600, 1612033200,
1612036800, 1612040400, 1612044000, 1612047600, 1612051200, 1612054800,
1612058400, 1612062000, 1612065600, 1612069200, 1612072800, 1612076400,
1612080000, 1612083600, 1612087200, 1612090800, 1612094400, 1612098000,
1612101600, 1612105200, 1612108800, 1612112400, 1612116000, 1612119600,
1612123200, 1612126800, 1612130400, 1612134000, 1612137600, 1612141200,
1612144800, 1612148400, 1612152000, 1612155600, 1612159200, 1612162800,
1612166400, 1612170000, 1612173600, 1612177200, 1612180800, 1612184400,
1612188000, 1612191600, 1612195200, 1612198800, 1612202400, 1612206000,
1612209600, 1612213200, 1612216800, 1612220400, 1612224000, 1612227600,
1612231200, 1612234800, 1612238400, 1612242000, 1612245600, 1612249200,
1612252800, 1612256400, 1612260000, 1612263600, 1612267200, 1612270800,
1612274400, 1612278000, 1612281600, 1612285200, 1612288800, 1612292400,
1612296000, 1612299600, 1612303200, 1612306800, 1612310400, 1612314000,
1612317600, 1612321200, 1612324800, 1612328400, 1612332000, 1612335600,
1612339200, 1612342800, 1612346400, 1612350000, 1612353600, 1612357200,
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1612404000, 1612407600, 1612411200, 1612414800, 1612418400, 1612422000,
1612425600, 1612429200, 1612432800, 1612436400, 1612440000, 1612443600,
1612447200, 1612450800, 1612454400, 1612458000, 1612461600, 1612465200,
1612468800, 1612472400, 1612476000, 1612479600, 1612483200, 1612486800,
1612490400, 1612494000, 1612497600, 1612501200, 1612504800, 1612508400,
1612512000, 1612515600, 1612519200, 1612522800, 1612526400, 1612530000,
1612533600, 1612537200, 1612540800, 1612544400, 1612548000, 1612551600,
1612555200, 1612558800, 1612562400, 1612566000, 1612569600, 1612573200,
1612576800, 1612580400, 1612584000, 1612587600, 1612591200, 1612594800,
1612598400, 1612602000, 1612605600, 1612609200, 1612612800, 1612616400,
1612620000, 1612623600, 1612627200, 1612630800, 1612634400, 1612638000,
1612641600, 1612645200, 1612648800, 1612652400, 1612656000, 1612659600,
1612663200, 1612666800, 1612670400, 1612674000, 1612677600, 1612681200,
1612684800, 1612688400, 1612692000, 1612695600, 1612699200, 1612702800,
1612706400, 1612710000, 1612713600, 1612717200, 1612720800, 1612724400,
1612728000, 1612731600, 1612735200, 1612738800, 1612742400, 1612746000,
1612749600, 1612753200, 1612756800, 1612760400, 1612764000, 1612767600,
1612771200, 1612774800, 1612778400, 1612782000, 1612785600, 1612789200,
1612792800, 1612796400, 1612800000, 1612803600, 1612807200, 1612810800,
1612814400, 1612818000, 1612821600, 1612825200, 1612828800, 1612832400,
1612836000, 1612839600, 1612843200, 1612846800, 1612850400, 1612854000,
1612857600, 1612861200, 1612864800, 1612868400, 1612872000, 1612875600,
1612879200, 1612882800, 1612886400, 1612890000, 1612893600, 1612897200,
1612900800, 1612904400, 1612908000, 1612911600, 1612915200, 1612918800,
1612922400, 1612926000, 1612929600, 1612933200, 1612936800, 1612940400,
1612944000, 1612947600, 1612951200, 1612954800, 1612958400, 1612962000,
1612965600, 1612969200, 1612972800, 1612976400, 1612980000, 1612983600,
1612987200, 1612990800, 1612994400, 1612998000, 1613001600, 1613005200,
1613008800, 1613012400, 1613016000, 1613019600, 1613023200, 1613026800,
1613030400, 1613034000, 1613037600, 1613041200, 1613044800, 1613048400,
1613052000, 1613055600, 1613059200, 1613062800, 1613066400, 1613070000,
1613073600, 1613077200, 1613080800, 1613084400, 1613088000, 1613091600,
1613095200, 1613098800, 1613102400, 1613106000, 1613109600, 1613113200,
1613116800, 1613120400, 1613124000, 1613127600, 1613131200, 1613134800,
1613138400, 1613142000, 1613145600, 1613149200, 1613152800, 1613156400,
1613160000, 1613163600, 1613167200, 1613170800, 1613174400, 1613178000,
1613181600, 1613185200, 1613188800, 1613192400, 1613196000, 1613199600,
1613203200, 1613206800, 1613210400, 1613214000, 1613217600, 1613221200,
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1613268000, 1613271600, 1613275200, 1613278800, 1613282400, 1613286000,
1613289600, 1613293200, 1613296800, 1613300400, 1613304000, 1613307600,
1613311200, 1613314800, 1613318400, 1613322000, 1613325600, 1613329200,
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1613354400, 1613358000, 1613361600, 1613365200, 1613368800, 1613372400,
1613376000, 1613379600, 1613383200, 1613386800, 1613390400, 1613394000,
1613397600, 1613401200, 1613404800, 1613408400, 1613412000, 1613415600,
1613419200, 1613422800, 1613426400, 1613430000, 1613433600, 1613437200,
1613440800, 1613444400, 1613448000, 1613451600, 1613455200, 1613458800,
1613462400, 1613466000, 1613469600, 1613473200, 1613476800, 1613480400,
1613484000, 1613487600, 1613491200, 1613494800, 1613498400, 1613502000,
1613505600, 1613509200, 1613512800, 1613516400, 1613520000, 1613523600,
1613527200, 1613530800, 1613534400, 1613538000, 1613541600, 1613545200,
1613548800, 1613552400, 1613556000, 1613559600, 1613563200, 1613566800,
1613570400, 1613574000, 1613577600, 1613581200, 1613584800, 1613588400,
1613592000, 1613595600, 1613599200, 1613602800, 1613606400, 1613610000,
1613613600, 1613617200, 1613620800, 1613624400, 1613628000, 1613631600,
1613635200, 1613638800, 1613642400, 1613646000, 1613649600, 1613653200,
1613656800, 1613660400, 1613664000, 1613667600, 1613671200, 1613674800,
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1613700000, 1613703600, 1613707200, 1613710800, 1613714400, 1613718000,
1613721600, 1613725200, 1613728800, 1613732400, 1613736000, 1613739600,
1613743200, 1613746800, 1613750400, 1613754000, 1613757600, 1613761200,
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1613786400, 1613790000, 1613793600, 1613797200, 1613800800, 1613804400,
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1613959200, 1613962800, 1613966400, 1613970000, 1613973600, 1613977200,
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1614045600, 1614049200, 1614052800, 1614056400, 1614060000, 1614063600,
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1614434400, 1614438000, 1614441600, 1614445200, 1614448800, 1614452400,
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1614499200, 1614502800, 1614506400, 1614510000, 1614513600, 1614517200,
1614520800, 1614524400, 1614528000, 1614531600, 1614535200, 1614538800,
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1614672000, 1614675600, 1614679200, 1614682800, 1614686400, 1614690000,
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1614736800, 1614740400, 1614744000, 1614747600, 1614751200, 1614754800,
1614758400, 1614762000, 1614765600, 1614769200, 1614772800, 1614776400,
1614780000, 1614783600, 1614787200, 1614790800, 1614794400, 1614798000,
1614801600, 1614805200, 1614808800, 1614812400, 1614816000, 1614819600,
1614823200, 1614826800, 1614830400, 1614834000, 1614837600, 1614841200,
1614844800, 1614848400, 1614852000, 1614855600, 1614859200, 1614862800,
1614866400, 1614870000, 1614873600, 1614877200, 1614880800, 1614884400,
1614888000, 1614891600, 1614895200, 1614898800), tzone = "UTC", class = c("POSIXct",
"POSIXt")), Débit_horaire = c(5.052, 5.036, 5.009, 4.982, 4.958,
4.937, 4.917, 4.885, 4.858, 4.834, 4.804, 4.786, 4.769, 4.753,
4.741, 4.722, 4.713, 4.702, 4.689, 4.68, 4.669, 4.664, 4.658,
4.645, 4.623, 4.595, 4.567, 4.539, 4.51, 4.48, 4.443, 4.413,
4.381, 4.352, 4.327, 4.318, 4.291, 4.27, 4.268, 4.268, 4.265,
4.257, 4.254, 4.258, 4.27, 4.283, 4.295, 4.299, 4.303, 4.307,
4.305, 4.308, 4.32, 4.334, 4.355, 4.396, 4.457, 4.531, 4.624,
4.738, 4.879, 5.057, 5.28, 5.558, 5.863, 6.186, 6.562, 6.958,
7.327, 7.555, 7.638, 7.638, 7.577, 7.452, 7.291, 7.089, 6.867,
6.646, 6.469, 6.301, 6.161, 6.043, 5.946, 5.872, 5.807, 5.752,
5.702, 5.67, 5.658, 5.65, 5.647, 5.656, 5.692, 5.74, 5.786, 5.839,
5.91, 6.003, 6.124, 6.256, 6.374, 6.489, 6.596, 6.686, 6.72,
6.725, 6.706, 6.684, 6.646, 6.61, 6.578, 6.568, 6.554, 6.54,
6.529, 6.535, 6.538, 6.542, 6.553, 6.531, 6.547, 6.54, 6.534,
6.51, 6.504, 6.486, 6.462, 6.444, 6.429, 6.412, 6.395, 6.373,
6.365, 6.354, 6.344, 6.334, 6.326, 6.315, 6.303, 6.295, 6.295,
6.298, 6.296, 6.298, 6.289, 6.273, 6.258, 6.238, 6.225, 6.219,
6.21, 6.213, 6.216, 6.216, 6.22, 6.222, 6.229, 6.233, 6.236,
6.239, 6.238, 6.248, 6.258, 6.271, 6.292, 6.311, 6.33, 6.342,
6.348, 6.358, 6.36, 6.365, 6.366, 6.359, 6.353, 6.342, 6.333,
6.327, 6.33, 6.327, 6.319, 6.326, 6.332, 6.324, 6.34, 6.356,
6.373, 6.402, 6.443, 6.487, 6.537, 6.571, 6.582, 6.582, 6.563,
6.54, 6.515, 6.478, 6.442, 6.41, 6.372, 6.331, 6.304, 6.277,
6.246, 6.218, 6.205, 6.199, 6.193, 6.186, 6.187, 6.203, 6.233,
6.274, 6.315, 6.362, 6.4, 6.432, 6.459, 6.484, 6.512, 6.539,
6.544, 6.548, 6.543, 6.529, 6.525, 6.593, 6.638, 6.713, 6.831,
6.968, 7.142, 7.37, 7.656, 7.924, 8.117, 8.181, 8.262, 8.273,
8.25, 8.243, 8.183, 8.13, 8.073, 8.021, 7.953, 7.888, 7.819,
7.765, 7.733, 7.703, 7.675, 7.663, 7.661, 7.673, 7.701, 7.739,
7.8, 7.843, 7.902, 7.973, 8.06, 8.136, 8.216, 8.342, 8.429, 8.585,
8.694, 8.838, 9.026, 9.38, 9.815, 10.294, 10.786, 11.347, 11.859,
12.233, 12.637, 12.966, 13.283, 13.685, 13.908, 14.334, 14.721,
15.149, 15.581, 16.521, 17.244, 17.842, 18.501, 19.313, 20.18,
20.831, 21.707, 22.559, 23.352, 24.222, 25.223, 25.933, 26.78,
27.336, 28.231, 28.845, 29.255, 29.805, 30.559, 30.458, 30.79,
30.01, 30.098, 29.579, 29.405, 29.149, 28.992, 28.879, 28.596,
28.513, 27.866, 27.839, 27.721, 27.259, 27.385, 26.951, 26.759,
26.467, 26.188, 25.971, 25.564, 25.572, 25.392, 25.243, 25.029,
24.91, 24.723, 24.44, 24.286, 24.039, 23.907, 23.594, 23.454,
23.31, 23.152, 22.881, 22.773, 22.481, 22.202, 21.956, 21.718,
21.385, 21.212, 20.804, 20.526, 20.251, 19.879, 19.599, 19.357,
19.033, 18.914, 18.647, 18.546, 18.301, 18.163, 17.975, 17.763,
17.563, 17.43, 17.19, 17.061, 16.821, 16.673, 16.518, 16.312,
16.137, 16.007, 15.833, 15.698, 15.528, 15.349, 15.195, 15.025,
14.867, 14.718, 14.57, 14.357, 14.224, 14.021, 13.917, 13.79,
13.584, 13.405, 13.267, 13.115, 13, 12.925, 12.786, 12.678, 12.518,
12.44, 12.342, 12.221, 12.138, 12.006, 11.919, 11.84, 11.776,
11.701, 11.605, 11.54, 11.484, 11.371, 11.321, 11.252, 11.177,
11.123, 11.066, 11.028, 10.961, 10.917, 10.842, 10.808, 10.706,
10.66, 10.588, 10.534, 10.496, 10.439, 10.434, 10.407, 10.393,
10.326, 10.289, 10.243, 10.188, 10.134, 10.09, 10.07, 10.039,
10.089, 10.19, 10.407, 10.695, 11.179, 11.769, 12.402, 13.126,
13.95, 14.598, 15.244, 15.748, 16.186, 16.655, 16.938, 17.177,
17.321, 17.365, 17.337, 17.256, 17.256, 17.163, 17.1, 17.143,
17.363, 17.495, 17.883, 18.542, 19.075, 19.628, 20.129, 20.61,
20.944, 21.508, 22.023, 22.557, 23.16, 23.827, 24.4, 24.849,
25.24, 25.387, 25.282, 25.309, 25.296, 25.562, 25.898, 26, 26.274,
26.225, 26.295, 26.394, 26.274, 26.071, 26.072, 26.035, 26.211,
26.383, 26.303, 26.501, 26.474, 26.513, 26.728, 26.825, 27.036,
27.121, 27.443, 27.65, 27.775, 27.689, 27.79, 28.197, 27.82,
28.144, 27.998, 27.789, 27.686, 26.934, 26.954, 26.612, 26.222,
26.192, 25.722, 25.616, 25.531, 25.292, 25.189, 24.797, 24.832,
24.664, 24.525, 24.343, 24.419, 24.284, 24.044, 24.054, 23.818,
23.651, 23.619, 23.587, 23.589, 23.397, 23.314, 23.144, 22.834,
22.846, 22.571, 22.437, 22.204, 22.036, 21.734, 21.588, 21.283,
21.018, 20.798, 20.394, 20.138, 19.926, 19.679, 19.477, 19.19,
18.914, 18.683, 18.514, 18.243, 18.11, 17.981, 17.873, 17.723,
17.655, 17.56, 17.496, 17.444, 17.353, 17.342, 17.289, 17.221,
17.182, 17.05, 16.966, 16.87, 16.649, 16.514, 16.457, 16.305,
16.172, 16.121, 16.223, 16.198, 16.412, 16.695, 16.984, 17.443,
17.808, 18.403, 19.015, 19.509, 19.919, 20.47, 20.922, 21.258,
21.561, 21.636, 21.557, 21.321, 21.223, 20.979, 20.834, 20.559,
20.501, 20.434, 20.312, 20.226, 20.08, 19.971, 19.846, 19.8,
19.712, 19.652, 19.59, 19.502, 19.442, 19.378, 19.261, 19.197,
19.193, 19.159, 19.119, 19.053, 18.966, 18.916, 18.877, 18.774,
18.71, 18.623, 18.501, 18.422, 18.277, 18.145, 18.057, 17.947,
17.828, 17.73, 17.56, 17.472, 17.347, 17.187, 17.019, 16.896,
16.784, 16.676, 16.496, 16.395, 16.255, 16.154, 16.125, 16.037,
15.981, 15.889, 15.897, 15.811, 15.786, 15.723, 15.584, 15.535,
15.433, 15.333, 15.291, 15.213, 15.091, 15.02, 14.928, 14.845,
14.804, 14.916, 15.406, 15.627, 16, 16.414, 16.809, 17.147, 17.457,
17.723, 17.973, 18.175, 18.371, 18.551, 18.613, 18.581, 18.389,
18.036, 17.58, 17.179, 16.751, 16.425, 16.129, 15.928, 15.791,
15.61, 15.487, 15.389, 15.363, 15.355, 15.312, 15.326, 15.301,
15.324, 15.291, 15.266, 15.184, 15.102, 15.056, 15.008, 14.947,
14.899, 14.84, 14.82, 14.819, 14.781, 14.766, 14.768, 14.715,
14.696, 14.631, 14.54, 14.455, 14.344, 14.209, 14.103, 14.027,
13.931, 13.848, 13.769, 13.662, 13.611, 13.493, 13.424, 13.358,
13.286, 13.232, 13.132, 13.034, 12.98, 12.937, 12.86, 12.76,
12.694, 12.604, 12.561, 12.466, 12.361, 12.257, 12.138, 12.025,
11.95, 11.863, 11.767, 11.675, 11.554, 11.473, 11.38, 11.287,
11.198, 11.125, 11.074, 11.007, 10.96, 10.906, 10.85, 10.768,
10.693, 10.62, 10.513, 10.435, 10.33, 10.226, 10.147, 10.045,
9.956, 9.844, 9.758, 9.669, 9.596, 9.542, 9.488, 9.453, 9.423,
9.359, 9.331, 9.332, 9.307, 9.279, 9.242, 9.204, 9.147, 9.079,
8.982, 8.892, 8.814, 8.74, 8.659, 8.602, 8.531, 8.45, 8.412,
8.35, 8.284, 8.243, 8.202, 8.168, 8.133, 8.111, 8.08, 8.062,
8.044, 8.024, 8.007, 7.975, 7.924, 7.867, 7.782, 7.71, 7.624,
7.544, 7.463, 7.373, 7.297, 7.233, 7.166, 7.103, 7.052, 7.024,
6.985, 6.938, 6.917, 6.914, 6.909, 6.893, 6.9, 6.903, 6.917,
6.891, 6.853, 6.803, 6.749, 6.694, 6.639, 6.565, 6.483, 6.42,
6.37, 6.32, 6.282, 6.252, 6.226, 6.204, 6.214, 6.215, 6.166,
6.155, 6.134, 6.133, 6.158, 6.175, 6.181, 6.175, 6.167, 6.148,
6.116, 6.082, 6.046, 6.019, 5.983, 5.962, 5.933, 5.912, 5.891,
5.864, 5.85, 5.831, 5.822, 5.806, 5.806, 5.813, 5.828, 5.856,
5.876, 5.907, 5.931, 5.97, 6.023, 6.055, 6.072, 6.069, 6.06,
6.052, 6.028, 6.015, 5.987, 5.965, 5.937, 5.902, 5.869, 5.836,
5.798, 5.77, 5.756, 5.767, 5.812, 5.869, 5.935, 6.029, 6.119,
6.188, 6.235, 6.292, 6.332, 6.383, 6.455, 6.575, 6.634, 6.617,
6.543, 6.441, 6.336, 6.241, 6.162, 6.08, 6.011, 5.942, 5.885,
5.83, 5.785, 5.756, 5.729, 5.691, 5.666, 5.628, 5.581, 5.531,
5.478, 5.431, 5.383, 5.327, 5.278, 5.23, 5.193, 5.159, 5.119,
5.086, 5.057, 5.034, 5.016, 4.995, 4.912, 4.963, 4.987, 5.019,
5.099, 5.21, 5.313, 5.476, 5.692, 5.883, 6.152, 6.457, 6.633,
6.623, 6.445, 6.218, 5.996, 5.813, 5.667, 5.548, 5.461, 5.379,
5.303, 5.24, 5.168, 5.122, 5.078, 5.044, 5.004, 4.973, 4.943,
4.911, 4.875, 4.848, 4.823, 4.794, 4.772, 4.75, 4.727, 4.711,
4.693, 4.668, 4.652, 4.639, 4.627, 4.617, 4.608, 4.593, 4.573,
4.56, 4.549, 4.544, 4.541, 4.532, 4.512, 4.504, 4.493, 4.474,
4.465, 4.45, 4.438, 4.417, 4.407, 4.396, 4.387, 4.381, 4.375,
4.361, 4.351, 4.337, 4.334, 4.33, 4.325, 4.32, 4.311, 4.312,
4.305, 4.291, 4.274, 4.257, 4.248, 4.233, 4.23, 4.222, 4.216,
4.215, 4.2, 4.19, 4.184, 4.177, 4.172, 4.161, 4.152, 4.145, 4.141,
4.136, 4.133, 4.126, 4.114, 4.099, 4.087, 4.078, 4.064, 4.05,
4.043, 4.035, 4.025, 4.013, 4.002, 3.989, 3.979, 3.97, 3.966,
3.958, 3.943, 3.94, 3.932, 3.928, 3.918, 3.91, 3.901, 3.88, 3.874,
3.866, 3.849, 3.831, 3.818, 3.804, 3.795, 3.787, 3.781, 3.779,
3.773, 3.767, 3.763, 3.757, 3.754, 3.751, 3.745, 3.738, 3.729,
3.731, 3.724, 3.723, 3.72, 3.72, 3.709, 3.707, 3.709, 3.707,
3.685, 3.668, 3.667, 3.682, 3.671, 3.652, 3.644, 3.635, 3.632,
3.626, 3.617, 3.614, 3.604, 3.598, 3.591, 3.586, 3.58, 3.578,
3.57, 3.564, 3.558, 3.547, 3.534, 3.524, 3.514, 3.499, 3.481,
3.476, 3.459, 3.449, 3.44, 3.43, 3.414, 3.41, 3.411, 3.417, 3.431,
3.455, 3.496, 3.53, 3.568, 3.604, 3.645, 3.685, 3.717, 3.728,
3.727, 3.69, 3.635, 3.592, 3.558, 3.52, 3.498, 3.472, 3.44, 3.413,
3.393, 3.37, 3.353, 3.321, 3.287, 3.261, 3.24, 3.237, 3.23, 3.227,
3.225, 3.223, 3.233, 3.242, 3.247, 3.234, 3.208, 3.181, 3.154,
3.134, 3.123, 3.109, 3.097, 3.091, 3.091, 3.085, 3.08, 3.074,
3.073, 3.07, 3.046, 3.052, 3.053, 3.049, 3.046, 3.029, 3.028,
3.018, 3.003, 2.997, 2.981, 2.971, 2.957, 2.946, 2.937, 2.931,
2.926, 2.916, 2.907, 2.904, 2.893, 2.887, 2.876, 2.866, 2.861,
2.854, 2.851, 2.85, 2.855, 2.865, 2.86, 2.859, 2.855, 2.843,
2.838, 2.828, 2.806, 2.793, 2.782, 2.775, 2.77, 2.759, 2.752,
2.744, 2.737, 2.729, 2.723, 2.719, 2.711, 2.703, 2.693, 2.697,
2.7, 2.699, 2.695, 2.693, 2.693, 2.692, 2.69, 2.681, 2.67, 2.658,
2.642, 2.637, 2.63, 2.623, 2.614, 2.611, 2.604, 2.604, 2.598,
2.591, 2.593, 2.594, 2.591, 2.585, 2.602, 2.601, 2.597, 2.59,
2.583, 2.582, 2.569, 2.562, 2.551, 2.544, 2.536, 2.537, 2.523,
2.516, 2.513, 2.515, 2.516, 2.516, 2.519, 2.518, 2.517, 2.516,
2.52, 2.534, 2.566, 2.607, 2.625, 2.675, 2.703, 2.742, 2.782,
2.827, 2.956)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-1297L))
You could set the size of the zoom panel relative to the main plot via the argument zoom.size which defaults to 2, i.e. the zoom panel is twice the size of the main panel. Hence, to achieve your desired result set zoom.size=.5:
require(dplyr)
require(tidyverse)
require(ggforce)
DebitH %>%
ggplot(aes(x = Date, y = Débit_horaire)) +
geom_point(alpha = 6 / 10, size = 0.3, color = "blue") +
geom_line(alpha = 4 / 10, size = 0.5, color = "blue") +
labs(x = "Date", y = "Débit Horaire (m3/s)") +
theme_bw() +
theme(
panel.grid.major.y = element_line(
color = "grey",
size = 0.25,
linetype = 2
),
legend.position = "none"
) +
facet_zoom(xlim = as.POSIXct(c("2021-01-15 00:00:00", "2021-02-15 00:00:00")), zoom.size = .5)
I am conducting some regression analysis and I need to first create a Bartik Instrument to use as an IV. Essentially, I have 10 decile groups of the income distribution. These are both at a global level and a country level (as there is an unbalanced panel of countries in the dataset). I want to grow each country's decile groups according to worldwide changes.
The image above represents the world and shows the percentage of people in each income decile on the left-hand side. On the right are the calculated percentage changes for each income decile between years. There are 10 columns all up for the 10 deciles.
The image below shows the country's decile groups. The starting year will be 1990 for each country (ie, the beginning decile proportion for each decile will be the year 1990 for each country. This serves as the "weight" in all of the statistics). Then, each decile will grow at the same percentage change as the global level.
For example, if dp1 is 1.92 in 1990 for the country Afghanistan, 1991 will be calculated from the global percentage change between 1990 and 1991. Because the global change was -2.857%, the predicted value of dp1 in 1991 for Afghanistan will be 1.865. This value will then be used in the calculation for predicting 1992.
The issue is, it needs to start at 1990 for each country and end in the final predicted year of 2019. I cannot just use a mutate function as it won't recognize that each country restarts in 1990.
Any guidance on this issue will be greatly appreciated. Please let me know if you need to see any more of the data as it is all open source and can therefore be freely shared.
Dput of the world data frame:
structure(list(Entity = c("World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World"), Year = c(1990,
1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020), Code = c("WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD"), gini = c("69.95", "70.18", "70.07", "69.88",
"69.79", "69.51", "69.16", "68.93", "68.98", "68.83", "68.76",
"68.43", "68.1", "67.68", "67.24", "66.79", "66.22", "65.57",
"64.9", "63.8", "63.28", "62.92", "62.54", "62.11", "61.68",
"61.47", "61.12", "60.92", "60.81", "60.65", "60.6"), palma = c(14.44,
14.74, 14.2, 13.7, 13.28, 12.95, 12.46, 12.31, 12.12, 12.04,
12.12, 11.74, 11.49, 11.18, 10.91, 10.55, 10.08, 9.67, 9.27,
8.53, 8.29, 8.12, 7.91, 7.74, 7.47, 7.36, 7.25, 7.11, 7.07, 6.95,
6.97), dp1 = c(0.35, 0.34, 0.35, 0.36, 0.36, 0.38, 0.38, 0.38,
0.39, 0.38, 0.38, 0.39, 0.38, 0.38, 0.39, 0.39, 0.4, 0.4, 0.4,
0.43, 0.43, 0.43, 0.43, 0.43, 0.45, 0.45, 0.44, 0.43, 0.44, 0.44,
0.43), dp2 = c(0.71, 0.72, 0.76, 0.78, 0.82, 0.82, 0.84, 0.86,
0.87, 0.9, 0.88, 0.89, 0.9, 0.92, 0.93, 0.93, 0.95, 0.98, 1.01,
1.07, 1.09, 1.1, 1.1, 1.14, 1.14, 1.14, 1.17, 1.19, 1.18, 1.19,
1.2), dp3 = c(1.09, 1.06, 1.1, 1.18, 1.19, 1.25, 1.29, 1.31,
1.3, 1.33, 1.31, 1.36, 1.38, 1.39, 1.43, 1.48, 1.5, 1.54, 1.59,
1.69, 1.72, 1.74, 1.79, 1.81, 1.83, 1.88, 1.89, 1.88, 1.94, 1.95,
1.94), dp4 = c(1.52, 1.5, 1.59, 1.64, 1.74, 1.75, 1.82, 1.82,
1.9, 1.89, 1.88, 1.93, 1.97, 2, 2.02, 2.07, 2.18, 2.24, 2.29,
2.44, 2.48, 2.51, 2.57, 2.58, 2.71, 2.72, 2.73, 2.82, 2.76, 2.81,
2.8), dp5 = c(2.11, 2.15, 2.27, 2.34, 2.42, 2.51, 2.53, 2.56,
2.6, 2.64, 2.7, 2.72, 2.74, 2.8, 2.92, 2.99, 3.05, 3.1, 3.2,
3.44, 3.52, 3.57, 3.65, 3.77, 3.76, 3.86, 3.93, 3.96, 3.95, 4.03,
4.04), dp6 = c(3.23, 3.18, 3.25, 3.38, 3.44, 3.52, 3.6, 3.64,
3.66, 3.74, 3.68, 3.87, 3.98, 4.08, 4.07, 4.14, 4.35, 4.54, 4.68,
4.85, 5.02, 5.11, 5.2, 5.29, 5.48, 5.43, 5.54, 5.56, 5.62, 5.6,
5.57), dp7 = c(5.49, 5.42, 5.43, 5.42, 5.37, 5.41, 5.5, 5.66,
5.49, 5.57, 5.67, 5.73, 5.86, 6.03, 6.23, 6.23, 6.49, 6.63, 6.91,
7.12, 7.37, 7.38, 7.59, 7.72, 7.84, 7.94, 7.92, 8.02, 8, 8.05,
8.13), dp8 = c(10.96, 10.76, 10.3, 10.04, 9.78, 9.73, 9.78, 9.82,
9.67, 9.61, 9.7, 9.75, 9.73, 10, 10.18, 10.5, 10.55, 10.88, 11.04,
11.32, 11.4, 11.63, 11.62, 11.72, 11.78, 11.82, 12.05, 11.85,
12.1, 12.08, 12.12), dp9 = c(21.51, 21.26, 20.81, 20.53, 20.22,
20.17, 20.15, 20.03, 19.9, 19.75, 19.77, 19.7, 19.88, 19.72,
19.74, 19.75, 19.69, 19.71, 19.75, 19.51, 19.48, 19.49, 19.39,
19.36, 19.23, 19.14, 19.05, 19.37, 19.25, 19.3, 19.38), dp10 = c(52.93,
53.51, 54.05, 54.24, 54.58, 54.39, 54.02, 53.85, 54.14, 54.13,
53.96, 53.6, 53.13, 52.61, 52.04, 51.45, 50.77, 49.93, 49.08,
48.07, 47.44, 46.98, 46.61, 46.14, 45.75, 45.56, 45.23, 44.9,
44.72, 44.52, 44.37), `dp1_PChangeFrom-1` = c(NA, -0.0285714285714284,
0.0294117647058822, 0.0285714285714286, 0, 0.0555555555555556,
0, 0, 0.0263157894736842, -0.0256410256410257, 0, 0.0263157894736842,
-0.0256410256410257, 0, 0.0263157894736842, 0, 0.0256410256410257,
0, 0, 0.0749999999999999, 0, 0, 0, 0, 0.0465116279069768, 0,
-0.0222222222222222, -0.0227272727272727, 0.0232558139534884,
0, -0.0227272727272727), `dp2_PChangeFrom-1` = c(NA, 0.0140845070422535,
0.0555555555555556, 0.0263157894736842, 0.0512820512820512, 0,
0.024390243902439, 0.0238095238095238, 0.0116279069767442, 0.0344827586206897,
-0.0222222222222222, 0.0113636363636364, 0.0112359550561798,
0.0222222222222222, 0.0108695652173913, 0, 0.0215053763440859,
0.0315789473684211, 0.0306122448979592, 0.0594059405940595, 0.0186915887850467,
0.00917431192660551, 0, 0.0363636363636362, 0, 0, 0.0263157894736842,
0.0170940170940171, -0.00840336134453782, 0.00847457627118645,
0.00840336134453782), `dp3_PChangeFrom-1` = c(NA, -0.0275229357798165,
0.0377358490566038, 0.0727272727272726, 0.00847457627118645,
0.0504201680672269, 0.032, 0.0155038759689923, -0.00763358778625955,
0.0230769230769231, -0.0150375939849624, 0.0381679389312977,
0.014705882352941, 0.00724637681159421, 0.0287769784172662, 0.034965034965035,
0.0135135135135135, 0.0266666666666667, 0.0324675324675325, 0.0628930817610062,
0.0177514792899408, 0.0116279069767442, 0.0287356321839081, 0.0111731843575419,
0.0110497237569061, 0.0273224043715846, 0.00531914893617022,
-0.0052910052910053, 0.0319148936170213, 0.00515463917525774,
-0.00512820512820513), `dp4_PChangeFrom-1` = c(NA, -0.0131578947368421,
0.0600000000000001, 0.031446540880503, 0.0609756097560976, 0.00574712643678161,
0.04, 0, 0.0439560439560439, -0.00526315789473685, -0.0052910052910053,
0.0265957446808511, 0.0207253886010363, 0.0152284263959391, 0.01,
0.0247524752475247, 0.0531400966183576, 0.0275229357798165, 0.0223214285714285,
0.0655021834061135, 0.0163934426229508, 0.0120967741935483, 0.0239043824701195,
0.00389105058365768, 0.0503875968992248, 0.00369003690036909,
0.00367647058823522, 0.0329670329670329, -0.0212765957446809,
0.0181159420289856, -0.00355871886121005), `dp5_PChangeFrom-1` = c(NA,
0.018957345971564, 0.0558139534883721, 0.0308370044052863, 0.0341880341880342,
0.037190082644628, 0.00796812749003985, 0.0118577075098815, 0.015625,
0.0153846153846154, 0.0227272727272727, 0.00740740740740741,
0.00735294117647059, 0.021897810218978, 0.0428571428571429, 0.0239726027397261,
0.0200668896321069, 0.0163934426229509, 0.0322580645161291, 0.0749999999999999,
0.0232558139534884, 0.0142045454545454, 0.0224089635854342, 0.0328767123287672,
-0.00265251989389927, 0.0265957446808511, 0.0181347150259068,
0.00763358778625949, -0.00252525252525247, 0.020253164556962,
0.00248138957816372), `dp6_PChangeFrom-1` = c(NA, -0.0154798761609907,
0.0220125786163522, 0.04, 0.0177514792899408, 0.0232558139534884,
0.0227272727272727, 0.0111111111111111, 0.0054945054945055, 0.0218579234972678,
-0.0160427807486631, 0.0516304347826087, 0.0284237726098191,
0.0251256281407035, -0.00245098039215681, 0.017199017199017,
0.0507246376811594, 0.0436781609195403, 0.0308370044052863, 0.0363247863247863,
0.0350515463917526, 0.0179282868525898, 0.0176125244618395, 0.0173076923076923,
0.0359168241965974, -0.00912408759124101, 0.0202578268876612,
0.00361010830324902, 0.0107913669064749, -0.00355871886121005,
-0.00535714285714274), `dp7_PChangeFrom-1` = c(NA, -0.0127504553734062,
0.00184501845018446, -0.00184162062615097, -0.00922509225092248,
0.00744878957169461, 0.0166358595194085, 0.0290909090909091,
-0.0300353356890459, 0.0145719489981785, 0.0179533213644524,
0.0105820105820107, 0.0226876090750436, 0.0290102389078498, 0.033167495854063,
0, 0.0417335473515248, 0.0215716486902927, 0.0422322775263952,
0.0303907380607815, 0.0351123595505618, 0.00135685210312073,
0.0284552845528455, 0.0171277997364954, 0.0155440414507772, 0.0127551020408164,
-0.00251889168765749, 0.0126262626262626, -0.00249376558603486,
0.00625000000000009, 0.00993788819875777), `dp8_PChangeFrom-1` = c(NA,
-0.0182481751824818, -0.0427509293680297, -0.0252427184466021,
-0.0258964143426295, -0.0051124744376277, 0.00513874614594028,
0.00408997955010234, -0.0152749490835031, -0.00620475698035165,
0.00936524453694067, 0.00515463917525781, -0.00205128205128201,
0.0277492291880781, 0.018, 0.031434184675835, 0.00476190476190483,
0.0312796208530806, 0.014705882352941, 0.0253623188405798, 0.00706713780918729,
0.0201754385964913, -0.00085984522785912, 0.00860585197934608,
0.00511945392491457, 0.00339558573853998, 0.0194585448392555,
-0.0165975103734441, 0.0210970464135021, -0.00165289256198344,
0.00331125827814562), `dp9_PChangeFrom-1` = c(NA, -0.0116225011622501,
-0.0211665098777047, -0.0134550696780393, -0.015099853872382,
-0.00247279920870411, -0.000991571641051221, -0.00595533498759293,
-0.00649026460309548, -0.00753768844221098, 0.00101265822784808,
-0.0035407182599899, 0.00913705583756344, -0.00804828973843059,
0.00101419878296144, 0.000506585612968671, -0.00303797468354424,
0.00101574403250379, 0.00202942668696089, -0.0121518987341771,
-0.00153767298821123, 0.000513347022587167, -0.00513083632632108,
-0.0015471892728211, -0.0067148760330578, -0.00468018720748829,
-0.00470219435736676, 0.0167979002624672, -0.0061951471347445,
0.00259740259740263, 0.00414507772020717), `dp10_PChangeFrom-1` = c(NA,
0.0109578688834309, 0.0100915716688469, 0.00351526364477345,
0.00626843657817102, -0.00348112861854155, -0.00680272108843533,
-0.00314698259903743, 0.00538532961931289, -0.000184706316956003,
-0.00314058747459822, -0.00667160859896218, -0.00876865671641789,
-0.00978731413514028, -0.0108344421212697, -0.011337432744043,
-0.0132167152575316, -0.0165452038605476, -0.0170238333667134,
-0.0205786471067644, -0.0131058872477637, -0.00969645868465432,
-0.00787569178373771, -0.0100836730315383, -0.00845253576072823,
-0.00415300546448082, -0.00724319578577712, -0.00729604244970149,
-0.00400890868596881, -0.00447227191413228, -0.00336927223719689
)), row.names = c(NA, -31L), class = "data.frame")
dput of the countries data frame::
structure(list(Year = numeric(0), Entity = character(0), Code = character(0),
gini = character(0), palma = numeric(0), dp1 = numeric(0),
dp2 = numeric(0), dp3 = numeric(0), dp4 = numeric(0), dp5 = numeric(0),
dp6 = numeric(0), dp7 = numeric(0), dp8 = numeric(0), dp9 = numeric(0),
dp10 = numeric(0)), class = c("grouped_df", "tbl_df", "tbl",
"data.frame"), row.names = integer(0), groups = structure(list(
Entity = character(0), Year = numeric(0), .rows = structure(list(), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = integer(0), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
I have a data set with 7 features including the date column where my dependent variables are NORTH and YORKSANDTHEHUMBER and the rest are independent variables. I want to automate the process where I take my first dependent feature NORTH and run it against all the independent variables in a univariate manner so that the first model gives me the result for NORTH and x1, second for NORTH and x2 and so on via using for loop but I couldn't make the sense. Can anyone please guide me in this?
Data:
structure(list(Date = structure(c(289094400, 297043200, 304992000,
312854400, 320716800, 328665600, 336614400, 344476800, 352252800,
360201600, 368150400, 376012800, 383788800, 391737600, 399686400,
407548800, 415324800, 423273600, 431222400, 439084800, 446947200,
454896000, 462844800, 470707200, 478483200, 486432000, 494380800,
502243200, 510019200, 517968000, 525916800, 533779200, 541555200,
549504000, 557452800, 565315200, 573177600, 581126400, 589075200,
596937600, 604713600, 612662400, 620611200, 628473600, 636249600,
644198400, 652147200, 660009600, 667785600, 675734400, 683683200,
691545600, 699408000, 707356800, 715305600, 723168000, 730944000,
738892800, 746841600, 754704000, 762480000, 770428800, 778377600,
786240000, 794016000, 801964800, 809913600, 817776000, 825638400,
833587200, 841536000, 849398400, 857174400, 865123200, 873072000,
880934400, 888710400, 896659200, 904608000, 912470400, 920246400,
928195200, 936144000, 944006400, 951868800, 959817600, 967766400,
975628800, 983404800, 991353600, 999302400, 1007164800, 1014940800,
1022889600, 1030838400, 1038700800, 1046476800, 1054425600, 1062374400,
1070236800, 1078099200, 1086048000, 1093996800, 1101859200, 1109635200,
1117584000, 1125532800, 1133395200, 1141171200, 1149120000, 1157068800,
1164931200, 1172707200, 1180656000, 1188604800, 1196467200, 1204329600,
1212278400, 1220227200, 1228089600, 1235865600, 1243814400, 1251763200,
1259625600, 1267401600, 1275350400, 1283299200, 1291161600, 1298937600,
1306886400, 1314835200, 1322697600, 1330560000, 1338508800, 1346457600,
1354320000, 1362096000, 1370044800, 1377993600, 1385856000, 1393632000,
1401580800, 1409529600, 1417392000, 1425168000, 1433116800, 1441065600,
1448928000, 1456790400, 1464739200, 1472688000, 1480550400, 1488326400,
1496275200, 1504224000, 1512086400, 1519862400, 1527811200, 1535760000,
1543622400, 1551398400, 1559347200, 1567296000, 1575158400, 1583020800,
1590969600, 1598918400, 1606780800, 1614556800, 1622505600, 1630454400,
1638316800), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
Industrialproduction = c(8.2, 8.79, 0.94, 1.53, -3.18, -8.66,
-8.96, -11.93, -8.14, -4.5, 1.53, 2.06, 2.39, 2.02, 2.01,
1.68, 2.16, 2.15, 3.77, 5.95, 3.58, 0.81, -1.58, -1.72, 3.62,
9.78, 8.51, 3.49, 1.97, -1.02, 1.92, 6.13, 3.87, 3.54, 2.76,
4.19, 4.73, 4.84, 6.64, 3.88, 2.05, 1.36, 0.53, 1.47, 1.61,
3.22, -1.45, -2.76, -3.83, -5.06, -4.01, -1.76, -0.27, -0.82,
2.23, 0.69, 1.38, 2.07, 2.32, 4.1, 4.61, 5.68, 6.13, 5.91,
2.85, 1.66, 1, 0.37, 2.52, 1.26, 1.24, 1.48, 0.37, 2.24,
2.7, 4.38, 7.6, 3.89, 0.84, -0.82, -0.46, 5.61, 9.48, 5.06,
1.95, 2.1, 1.08, 6.27, 1.46, 2.28, 3.21, 3.37, 12.94, -1.06,
-2.07, -6.22, -5.19, 6.65, 6.78, 4.35, -2.69, -1.31, -2.08,
3.44, -3.08, -0.92, -1.62, -0.91, 8.32, 2.57, 4.33, 2.44,
1.52, -1.3, -4.94, -3.97, -3.59, -1.83, 1.77, -1.86, -4.86,
-5.07, -7.55, -5.37, -0.33, -1.2, -0.11, -1.11, -8.39, -5.4,
-5.52, -4.16, 0.12, -0.7, -0.58, -0.59, 0.48, 3.87, 5.29,
7.91, 7.21, -0.45, -2.23, -1.86, 4.19, 5.9, 5.94, 2.45, 0,
-0.75, -1.08, 1.63, -3.28, -0.22, 3.49, 1.07, 1.53, 5.3,
4.21, 6.14, 10.24, 2.26, 0.71, -1.3, -8.9, -12.36, -5.02,
-2.83, 3.76, 9.86, 1.9, 0.94), Householdconsumption = c(30.09,
32.53, 33.35, 35.23, 37.18, 37.59, 38.89, 39.82, 41.56, 42.7,
43.74, 45.03, 46.19, 46.95, 48.29, 49.84, 51.26, 52.15, 53.5,
54.36, 55.4, 56.7, 57.05, 58.88, 60.09, 61.44, 63.27, 64.74,
66.63, 68.35, 69.55, 70.81, 72.3, 74.29, 76.65, 78.82, 81.51,
83.81, 86.53, 88.4, 90.29, 92.46, 93.95, 95.99, 97.85, 100.83,
102.42, 104.05, 106.08, 107.79, 109.33, 110.63, 111.71, 113.52,
114.9, 116.02, 118.31, 119.4, 122.27, 124.05, 125.13, 125.99,
127.59, 129.19, 130.16, 132.29, 135.06, 136.61, 139.34, 142.14,
144.59, 146.95, 149.43, 151.71, 155.34, 156.37, 158.39, 160.69,
164.47, 164.41, 167.54, 169.48, 170.09, 172.51, 176.26, 177.61,
179.44, 180.28, 182.96, 184.01, 186.83, 186.34, 188.79, 190.18,
191.94, 194.56, 196.46, 198.86, 201.75, 203.09, 205.24, 208.26,
210.84, 213.9, 216.18, 217.54, 220.61, 222.9, 223.67, 227.66,
230.62, 232.57, 234.8, 237.82, 241.91, 244.47, 248.84, 248.63,
248.14, 243.9, 241.46, 239.04, 240.72, 243.03, 241.87, 248,
249.95, 251.91, 254.92, 254.81, 257.17, 261.11, 262.28, 265.29,
266.74, 271.42, 274.28, 277.61, 282.48, 282.94, 285.76, 290.21,
292.88, 294.9, 296.07, 299.14, 302.58, 302.82, 309.63, 313.2,
318.64, 320.87, 323.41, 325.57, 326.56, 329.67, 335.95, 337.61,
341.08, 345.09, 346.16, 350.18, 350.23, 347.89, 339.85, 270.86,
325.65, 320.28, 311.3, 341.24, 354.61, 361.47), Investmentgrowth = c(17.3,
22.73, 25.8, 29.99, 21.59, 15.49, 11.11, 6.04, 4.23, 4.42,
4.28, 3.51, 6.53, 8.81, 10.52, 12.63, 14.6, 8.04, 7.42, 10.72,
11.15, 16.11, 15.45, 11.36, 18.41, 8.32, 8.99, 8.18, 0.86,
5.04, 9.07, 14.27, 11.11, 19.61, 23.14, 19.47, 27.16, 24.6,
17.45, 16.17, 20.57, 17.01, 17.76, 15.36, 8.28, 7.05, 2.92,
2.83, -3.08, -4.32, -7.48, -6.69, -3.71, -4.64, -3.87, -4.88,
-1.72, -0.38, 1.97, 4.65, 2.84, 2.98, 3.68, 2.88, 0.69, 3.5,
4.91, 5.66, 11.3, 13.85, 10.87, 4.01, -5.63, -8.06, -3.81,
3.94, 10.74, 9.14, 3.83, 3.36, 3.29, 3.24, 7.59, 3.43, 7.05,
13.14, 1.12, 7.68, 4.22, 1.34, 9.27, 0.78, 0.66, -1.52, 4.17,
12.34, 11.74, 5.2, 1.89, -1.56, 2.26, 5.89, 5.79, 4.84, 3.44,
7.15, 7.27, 7.31, 6.11, 5.7, 8.15, 6.96, 7.79, 10.05, 2.71,
9.61, 4.63, 2.72, 1.13, -6.1, -8.98, -14.36, -9.8, -11.41,
-3.13, 1.28, 3.81, 9.18, 1.62, 2.05, 2.14, 2.03, 7.32, 3.88,
0.09, 3.44, -1.27, 6.8, 10.41, 5.73, 12.93, 7.89, 6.8, 7.92,
8.2, 9.32, 6.18, 7.39, 5.22, 6.07, 9.44, 5.64, 6.8, 7.2,
4.77, 6.83, 3.74, 1.63, 2.59, 1.17, 4.39, 3.28, 3.78, 2.18,
-1.93, -19.78, -7.51, -2.54, -0.99, 23.33, 6.54, 4.25), ConsumerPriceIndex = c(24.88,
25.94, 27.55, 28.28, 29.79, 31.39, 31.92, 32.55, 33.55, 34.94,
35.55, 36.48, 37.02, 38.14, 38.14, 38.45, 38.73, 39.54, 40.1,
40.49, 40.76, 41.57, 41.99, 42.35, 43.25, 44.46, 44.46, 44.74,
45.06, 45.58, 45.81, 46.42, 46.88, 47.49, 47.72, 48.14, 48.44,
49.43, 49.83, 50.33, 50.82, 52.02, 52.42, 53.11, 53.91, 55.6,
56.69, 57.09, 57.59, 60.27, 60.67, 61.27, 61.67, 62.56, 62.56,
62.86, 63.16, 64.05, 64.45, 64.35, 64.55, 65.35, 65.45, 65.64,
66.24, 67.04, 67.34, 67.63, 68.03, 68.63, 68.93, 69.13, 69.13,
69.82, 70.22, 70.32, 70.7, 71.3, 71.5, 71.8, 71.9, 72.3,
72.4, 72.6, 72.3, 72.9, 73.1, 73.2, 73, 74.1, 74.1, 74, 74.1,
74.6, 74.8, 75.2, 75.3, 75.4, 75.9, 76.2, 76.1, 76.6, 76.7,
77.4, 77.5, 78.1, 78.6, 78.9, 78.9, 80.1, 80.5, 81.3, 81.4,
82, 81.9, 83, 83.4, 85.2, 86.1, 85.5, 85.8, 86.7, 87.1, 88,
88.7, 89.5, 89.8, 91.2, 92.2, 93.3, 94.4, 95.1, 95.4, 95.5,
96.5, 97.6, 98.1, 98.3, 99.1, 99.6, 99.7, 100.2, 100.3, 100.1,
99.7, 100.2, 100.2, 100.3, 100.2, 100.6, 101.1, 101.9, 102.7,
103.5, 104.3, 105, 105.1, 105.9, 106.6, 107.1, 107, 107.9,
108.4, 108.5, 108.6, 108.8, 109.2, 109.4, 109.7, 111.4, 112.4,
114.7), NORTH = c(4.06976744186047, 5.51675977653633, 7.2799470549305,
4.75015422578655, 4.59363957597172, 3.15315315315317, 1.2008733624454,
-0.377562028047452, -0.108283703302655, 0.650406504065032,
0.969305331179318, 0.106666666666688, 3.09003729355352, 2.11886304909562,
2.32793522267207, 5.68743818001977, -1.46934955545156, 3.95611702127658,
5.19438987619354, -0.0912012507600199, 2.81677896109541,
3.97412590369087, 1.30118326353028, 3.31553807249226, 1.32872294960955,
2.93700394923507, 0.908853875665812, 1.81241002546971, -1.3414545718222,
4.81772747317361, -3.4743890895067, 4.63823913990992, 0.857370960463727,
1.78620594713658, 0.527472527472524, -4.05973562947765, -0.136726966764838,
3.16657890117607, 5.95161125667812, 8.01002055498458, 10.5501040737437,
13.4138468987035, 2.93371279497212, 8.84291046495554, -6.87764606265876,
2.90741287990725, 3.71548486856639, 1.23317430567388, -1.1153443739474,
4.31313207880924, -1.64273763383666, 0.751373343751978, -3.21877014345816,
1.16314882913623, -3.59065232516701, -4.65283582701413, 4.98489115166134,
3.18459755147199, -3.72875180849018, 2.20137289784552, -4.22488416879167,
-0.706371260732776, -2.33320725244584, -2.77596063540517,
9.48636128308308, -2.15172116987927, -5.71766285746257, 1.92271571537407,
0.655934629757954, 4.01517293049256, -2.89270965830984, 3.910032505864,
-1.31616434600239, 1.51533020314829, 3.09793915477058, 1.00146317751519,
-0.516295759142123, 4.36356154298765, -0.254418667464494,
-1.38015492270122, -0.375369475589906, 3.79511767246943,
1.67693295616696, 0.197127124553074, -1.01758464617007, 5.70477696100394,
-1.37564670926045, 1.39335708665185, 2.29473337483174, -1.40489357721877,
10.7514355294201, -0.403985348024547, -0.0106181613732362,
10.6504339189417, 7.72602065226992, 6.66622841015428, 7.3618861388054,
7.20852539277177, 7.17954849482943, 5.47999408979134, 9.96115783870405,
6.960515961579, 4.82626274289161, -0.428385428540776, 1.6283388103162,
2.07440844957785, -0.707412409361252, -4.9247119657169, 4.3311229522328,
2.53158682305453, -0.8800288960527, 2.40275362264064, 0.67520264383003,
3.97711266595697, 0.00749650524863867, -0.990038901876062,
-0.63991866618197, -2.00199671222057, -5.15098853828302,
-3.65317386916235, -4.67277715297035, -0.564594703469009,
3.29526766976492, 0.0888482310529472, -0.524228981506815,
3.04012050839788, -1.53185447929528, -0.338917708381546,
-2.5450727924491, 3.36238295093309, -0.918735392055365, -0.766840492430499,
-0.767135363240273, 0.0468961039030733, 1.51618073336643,
-2.02356670927575, -1.11584500803018, 2.45568937824186, 0.989863990072745,
-0.4214032191629, 2.8219393653178, 4.51474479784726, -2.49049271581373,
-0.41346860604498, 3.13864420514751, -0.0877964623534655,
-0.674347043417658, -0.143267961613368, -0.243406512930108,
0.0402054219496719, 0.12912750657269, 0.168664845016241,
-0.713623226415894, 1.49163339466038, 1.57747101133233, -2.10536689354583,
3.12980292320487, -0.90833324273064, -1.71375697178543, 0.582188469928239,
2.89692448021907, 0.0768238907010953, -1.53392147948349,
1.23622644511851, -0.0506227154778281, 0.327869614383542,
2.62019966395382, 3.48629495563575, 0.593740862165774, 4.09560684327741,
2.32207959691005, 0.506809670097958), YORKSANDTHEHUMBER = c(4.0121120363361,
5.45851528384282, 9.52380952380951, 6.04914933837431, 3.03030303030299,
5.42099192618225, 2.78993435448577, -0.53219797764768, 1.97966827180309,
1.15424973767052, 0.466804979253115, -1.96179659266907, 2.42232754081095,
0.719794344473031, -0.306278713629415, 3.37941628264209,
2.74393263992076, 3.91920555341303, 1.91585099967527, 0.892125625853447,
2.91888477848958, 3.78293078507868, 0.109815847271484, 6.83486625601216,
0.722691730511011, 3.56008625759656, -0.227160867754524,
2.69419041475355, -1.17134094520194, 2.78546324684064, 1.01487759630426,
1.54843356139717, 4.15602836879435, 4.43619773934357, -0.309698451507728,
-1.45519947678222, -1.09839057574248, 9.08267346664877, 11.8913598474363,
13.9511229623114, 9.71243848306475, 7.66524473371739, 6.46801731884651,
-2.26736490763654, -4.35729847494552, -2.93870179974964,
-7.72353426221536, -7.01127302722023, 2.02543627323513, 2.51245245873873,
0.712134856164617, -2.74951902189779, 3.20525370229387, -2.17225212432703,
0.304311135936791, -5.21962007478405, -1.22771231792975,
5.62676205566459, -0.0988236572110239, 0.865912760888606,
-3.71050647202427, 1.5475703474865, -3.43233328040058, -2.86288061069106,
-0.551968808874026, 2.05442655433966, 0.388675938226524,
-2.60493926554792, -2.23312255163324, 5.04817095211292, 1.43656632546456,
2.53687507970646, -2.37376845704496, 4.95419269721737, 2.5486061891899,
-0.64046817419928, 1.75846231104579, 0.542834308795226, -0.322606591645488,
-2.67961743436791, 3.57498650723638, 2.89743475977992, 1.28567849851333,
1.828392232888, -0.335580970541442, 5.34860062451308, -2.98213938289875,
3.55468980520775, 2.76514398982056, 3.45832186518539, 1.32470422187813,
2.79428923624948, 3.8093136923264, 9.02544568216825, 7.65854560247412,
11.0775256253873, -0.658987130155868, 10.726463566155, 5.35747018223358,
4.66387144397987, 5.14763674355188, 10.581371911713, 3.46926043870116,
-0.000369065205607915, 0.924675325682334, 3.681119585314,
-0.0731638011738147, 0.690177922935143, 1.33427941484383,
2.65734876034112, 1.62515008951355, 1.48038293242949, 0.494192527588077,
2.39510739408179, 0.818557817036399, -1.1083492547105, -1.89465779498896,
-3.74953204588813, -3.7238074999174, -4.9788025925358, -4.65464963206228,
3.34588197167384, 2.20886725349025, 1.99954661835316, -0.777545762347822,
3.58681336123701, -2.96757202302368, -3.36310924643208, 2.01483012871867,
2.4154475314586, -0.642314624781054, -2.0920093049768, -1.73904001349183,
1.69071701857513, 0.201962934561265, -2.66472457335063, 0.323680874793625,
1.37879437405697, 3.26467995053582, 2.21645486418079, -0.646736928898328,
2.06516965491332, 1.8250141624007, -1.68545096699093, -0.818973277015041,
4.05215303886115, -1.16233786449552, -1.56747999678074, 0.67708495662531,
2.92754908797974, 1.50505329502891, -1.12667258046976, -0.765034978617734,
2.67854615526131, -0.306294171526678, 0.175047038539941,
1.56451236885344, 0.618844724791642, 3.34585295985361, -1.76420421213768,
-0.079420811764984, 1.56942028744185, 0.407910173531572,
-0.268243129544691, 2.57107118459526, -0.758721256899304,
3.03713057699041, 2.68699850192726, 1.88666482868311, 4.78697689266296,
2.43248653386118, 1.27252711337855)), row.names = c(NA, -172L
), class = "data.frame")
Code:
library(tseries)
library(dplyr)
# ARDL MODELING AND FORECASTING
in_sampleARDL <- data %>%
dplyr::filter(Date < '2020-03-01')
out_sampleARDL <-data %>%
dplyr::filter(Date >= '2020-03-01')
auto_ardl(NORTH~Householdconsumption,
data = in_sampleARDL, max_order = 4, selection = 'BIC')
pred1 <-forecast(ardlDlm(formula = NORTH ~ diff(Householdconsumption),
data = in_sampleARDL, p =3)
, x =out_sampleARDL$NORTH, h = 4)
error1 = out_sampleARDL$NORTH[1:2]- pred1[["forecasts"]]
mean(error1^2)
auto_ardl(NORTH~Industrialproduction,
data = in_sampleARDL, max_order = 4, selection = 'BIC')
pred2 <-forecast(ardlDlm(formula = NORTH ~ Industrialproduction,
data = in_sampleARDL, p =3)
, x =out_sampleARDL$NORTH, h = 4)
error2 = out_sampleARDL$NORTH[1:4]- pred2[["forecasts"]]
mean(error2^2)
I have data with several data agregations (Year, Quarter, Month).
I try to leave a space between each Date aggregation in x-axis label.
I obtain this for the moment :
And I want to obtain :
Here my data :
My dataframe (dput) :
r8_plot = structure(list(DATE = c(2016L, 2017L, 2018L, 201701L, 201702L,
201703L, 201704L, 201801L, 201802L, 201803L, 201804L, 201801L,
201802L, 201803L, 201804L, 201805L, 201806L, 201807L, 201808L,
201809L, 201810L, 201811L, 201812L, 201844L, 201845L, 201846L,
201847L, 201848L, 201849L, 201850L), Var1 = c(6.64, 6.21, 6.53,
6.31, 6.01, 6.36, 6.17, 6.76, 6.37, 6.68, 6.27, 7.5, 6.49, 6.4,
6.54, 6.18, 6.37, 5.98, 6.37, 7.48, 6.6, 5.97, 6.25, 5.42, 6.18,
5.81, 6.46, 6.36, 6.05, 6.35), Var2 = c(2.38, 2.25, 2.36, 2.22,
2.52, 1.98, 2.27, 2.44, 2.31, 2.27, 2.41, 2.53, 2.25, 2.51, 2.35,
2.42, 2.19, 2.51, 1.91, 2.38, 2.34, 2.29, 2.68, 2.15, 1.89, 2.6,
2.52, 2.37, 2.97, 2.71), Var3 = c(4.26, 3.96, 4.17, 4.09, 3.5,
4.38, 3.9, 4.32, 4.06, 4.4, 3.86, 4.96, 4.23, 3.9, 4.19, 3.77,
4.18, 3.47, 4.46, 5.1, 4.26, 3.68, 3.57, 3.27, 4.29, 3.2, 3.95,
3.99, 3.09, 3.64), Var4 = c(35.84, 36.17, 36.08, 35.2, 41.86,
31.17, 36.76, 36.07, 36.27, 34.07, 38.43, 33.78, 34.76, 39.18,
35.95, 39.07, 34.35, 42.04, 29.91, 31.8, 35.48, 38.38, 42.86,
39.72, 30.53, 44.85, 38.94, 37.24, 48.98, 42.63), Var5 = c("Y",
"Y", "Y", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "W", "W", "W", "W",
"W", "W", "W"), Var6 = structure(1:30, .Label = c("2016", "2017",
"2018", "Q1-2017", "Q2-2017", "Q3-2017", "Q4-2017", "Q1-2018",
"Q2-2018", "Q3-2018", "Q4-2018", "M01-2018", "M02-2018", "M03-2018",
"M04-2018", "M05-2018", "M06-2018", "M07-2018", "M08-2018", "M09-2018",
"M10-2018", "M11-2018", "M12-2018", "W44-2018", "W45-2018", "W46-2018",
"W47-2018", "W48-2018", "W49-2018", "W50-2018"), class = "factor"),
Var7 = c(7.1, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.6,
6.6, 6.7, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6,
6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6)), .Names = c("DATE",
"Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7"), row.names = c(NA,
30L), class = "data.frame")
r8_plot$Var6 = factor(r8_plot$Var6, labels = unique(r8_plot$Var6), levels=unique(r8_plot$Var6))
library(plotly)
r8_plot %>% plot_ly(x = ~Var6) %>%
add_bars(y = ~Var1,marker = list(color = '#00802b'),
name = "Var1") %>%
add_bars(y = ~Var2,marker = list(color = '#ff9933'),
name = "Var2")%>%
add_lines(y = ~Var4,
name = "Var4",
yaxis = "y2", line = list(color = '#1a1aff'))%>%
add_lines(y = ~Var7,
name = "Var7",
yaxis = "y1")%>%
layout(barmode = "stack",
yaxis2 = list(overlaying = "y",side = "right"),barmode = "stack",xaxis = list(title = 'DATE'), yaxis = list(title = 'All quantity'), title ="Chart") %>% layout(height = 750, width = 1000, hovermode = 'closest',margin = list(b = 115))
Thanks in advance
You can add NaN y-values to any data frame and Plotly will interrupt the plot at this position.
For example
library(plotly)
data = data.frame(list(x = c(1, 2, NaN, 3, 4),
y = c(1, 2, NaN, 3, 4)))
plot_ly(data, x = ~x) %>%
add_lines(y = ~y)
will give you
For more complex data, like in the question, a function introducing the empty rows might be easier than doing it manually.
split_by_date <- function(data) {
data_length <- length(data[,1])
index <- 0
new_data <- data
new_line <- list(replicate(length(data), NaN))
for (i in 2:length(data$DATE)) {
if (substr(toString(data$Var6[[i]]), 1, 1) != substr(toString(data$Var6[[i - 1]]), 1, 1)) {
new_data <- rbind.data.frame(new_data[1:i + index - 1,], new_line[[1]], data[i:data_length,])
new_data$Var6[[index + i]] <- paste(replicate(index + 1, " "), collapse = " ")
index <- index + 1
rownames(new_data) <- 1:as.integer(data_length + index)
}
}
return(new_data)
}
We just need to make sure that the x-value is always unique, i.e. simply concatenate an increasing number of spaces. Otherwise we'll just get a single break in the graph.
Also another row x is introduced to help with plotting the x-values in the correct order.
r8_plot = structure(list(DATE = c(2016L, 2017L, 2018L, 201701L, 201702L, 201703L, 201704L, 201801L, 201802L, 201803L, 201804L, 201801L, 201802L, 201803L, 201804L, 201805L, 201806L, 201807L, 201808L, 201809L, 201810L, 201811L, 201812L, 201844L, 201845L, 201846L, 201847L, 201848L, 201849L, 201850L),
Var1 = c(6.64, 6.21, 6.53, 6.31, 6.01, 6.36, 6.17, 6.76, 6.37, 6.68, 6.27, 7.5, 6.49, 6.4, 6.54, 6.18, 6.37, 5.98, 6.37, 7.48, 6.6, 5.97, 6.25, 5.42, 6.18, 5.81, 6.46, 6.36, 6.05, 6.35),
Var2 = c(2.38, 2.25, 2.36, 2.22, 2.52, 1.98, 2.27, 2.44, 2.31, 2.27, 2.41, 2.53, 2.25, 2.51, 2.35, 2.42, 2.19, 2.51, 1.91, 2.38, 2.34, 2.29, 2.68, 2.15, 1.89, 2.6, 2.52, 2.37, 2.97, 2.71),
Var3 = c(4.26, 3.96, 4.17, 4.09, 3.5, 4.38, 3.9, 4.32, 4.06, 4.4, 3.86, 4.96, 4.23, 3.9, 4.19, 3.77, 4.18, 3.47, 4.46, 5.1, 4.26, 3.68, 3.57, 3.27, 4.29, 3.2, 3.95, 3.99, 3.09, 3.64),
Var4 = c(35.84, 36.17, 36.08, 35.2, 41.86, 31.17, 36.76, 36.07, 36.27, 34.07, 38.43, 33.78, 34.76, 39.18, 35.95, 39.07, 34.35, 42.04, 29.91, 31.8, 35.48, 38.38, 42.86, 39.72, 30.53, 44.85, 38.94, 37.24, 48.98, 42.63),
Var5 = c("Y", "Y", "Y", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "W", "W", "W", "W", "W", "W", "W"),
Var6 = c("2016", "2017", "2018", "Q1-2017", "Q2-2017", "Q3-2017", "Q4-2017", "Q1-2018", "Q2-2018", "Q3-2018", "Q4-2018", "M01-2018", "M02-2018", "M03-2018","M04-2018", "M05-2018", "M06-2018", "M07-2018", "M08-2018", "M09-2018", "M10-2018", "M11-2018", "M12-2018", "W44-2018", "W45-2018", "W46-2018", "W47-2018", "W48-2018", "W49-2018", "W50-2018"),
Var7 = c(7.1, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6,6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6)),
.Names = c("DATE", "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7"), row.names = c(NA, 30L), class = "data.frame")
plot <- split_by_date(r8_plot)
plot$x <- structure(1:length(plot$Var6), .Label = plot$Var6, class = "factor")
plot %>% plot_ly(x = ~x, height = 750, width = 1000) %>%
add_bars(y = ~Var1,
marker = list(color = '#00802b'),
name = "Var1") %>%
add_bars(y = ~Var2,
marker = list(color = '#ff9933'),
name = "Var2") %>%
add_lines(y = ~Var4,
name = "Var4",
yaxis = "y2",
line = list(color = '#1a1aff')) %>%
add_lines(y = ~Var7,
name = "Var7",
yaxis = "y1") %>%
layout(barmode = "stack",
xaxis = list(title = 'DATE', range = c(-0.1, 10)),
yaxis = list(title = 'All quantity'),
yaxis2 = list(overlaying = "y",
side = "right"),
title ="Chart",
hovermode = 'closest')
I am doing a hierarchial clustering on two parameters (X1, X2) for 50 different African countries. Thereby I want to identify for 5 different groups/clusters within the continent of Africa. I am using the following code to do so:
hc <- hclust(dist(df), method = "complete")
member <- cutree(hc, 5)
Now, I would like to use the information stored in member (i.e. the cluster-ID each country belongs to) in order to color a map of Africa so that each cluster is represented by another color. I know that there are many tutorials on coloring maps, like this. But I am wondering wether there is a specific map-coloring-approach tailored to the results of a hierarchial cluster analysis?
Has anybody ever done that? I am happy for any advice or tipp on how to achieve this in a most efficient way!
(Scaled) data looks as follows:
df <- structure(list(Country = structure(1:50, .Label = c("Angola",
"Benin", "Botswana", "Burkina Faso", "Burundi", "Cabo Verde",
"Cameroon", "Central African Republic", "Chad", "Comoros", "Congo",
"Cote d'Ivoire", "Democratic Republic of Congo", "Djibouti",
"Equatorial Guinea", "Eritrea", "Ethiopia", "Gabon", "Gambia",
"Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia",
"Madagascar", "Malawi", "Mali", "Mauritania", "Mauritius", "Mozambique",
"Namibia", "Niger", "Nigeria", "Reunion", "Rwanda", "Sao Tome and Principe",
"Senegal", "Seychelles", "Sierra Leone", "Somalia", "South Africa",
"South Sudan", "Sudan", "Swaziland", "Tanzania", "Togo", "Uganda",
"Zambia", "Zimbabwe"), class = "factor"), X1 = c(-0.18, -1.03,
0.6, 1.55, 0.22, 0.26, 0.76, 2.15, -1.43, 0.99, 1.79, -0.39,
1.73, 1.57, 1.11, -0.09, -1.49, -0.46, -0.48, -1.22, -0.78, -1.46,
-1.22, 0.35, 0.45, 1.29, -1.37, -0.61, 0.92, -1.3, 0.42, -1.18,
1.4, -0.83, 0.06, -0.76, -0.19, -0.37, -0.63, 0.64, 0.93, 0.33,
-0.76, -0.21, -0.59, -0.41, -0.74, 0.39, -1.1, 1.35), X2 = c(-0.22,
-0.42, 0.72, -0.59, -1.27, 0.64, -1.35, -1.4, -0.35, -1.43, 1.07,
-0.01, -0.51, 0.11, 1.14, -0.89, 0.77, 1.45, -1.67, -0.83, 0.71,
0.92, 1.63, 1.68, 0.23, -0.18, 0.07, 0.8, -0.02, 0.82, -0.72,
-0.41, -0.26, 0.02, -1.68, 1.67, 0.18, 0.98, 1.45, 0.31, -1.23,
-1.38, -0.63, 1.41, -0.12, 0, -1.3, -1.64, 0.21, 1.52)), .Names = c("Country",
"X1", "X2"), row.names = c(NA, -50L), class = "data.frame")
Using your data and plotly:
library(plotly)
hc <- hclust(dist(df), method = "complete")
df$member <- cutree(hc, 5)
#Grabbing the Africa Geo from a plotly example
g <- list(
scope = 'africa',
showframe = F,
showland = T,
landcolor = toRGB("grey90")
)
plot_ly(df, z = member, type = 'choropleth', mode = 'markers', locations = Country,
locationmode = 'country names') %>% layout(geo = g)
You can use the plotly settings found here: https://plot.ly/r/reference/
to switch to a non-continuous scale, but it so happens that the continuous scale works fine for plotting only 5. For more you'd want a diverging color scale.