plot_ly 為 scatter3d 添加兩行而不是 1

Question

我有一些數據，我想比較一段時間內的輸出。 我可以使用plot_ly創建以下線圖。

library(plotly)
plot_ly(data,
        x = ~x,
        y = ~z,
        z = ~y,
        type = 'scatter3d',
        mode = 'lines',
        color = ~z)

但是，它使 I 直線回到開始日期。

當我將我的結果與 2D ggplot 版本進行比較時，我得到了不同的圖。

data %>% 
  ggplot(aes(x = x, y = y)) +
  geom_line() +
  facet_wrap(~z, ncol = 1)

如何修復plot_ly圖並將垂直曲面添加到 0。即線圖下方的所有顏色。

數據：

data <- structure(list(x = structure(c(17167, 17167, 17167, 17168, 17168, 
17168, 17169, 17169, 17169, 17170, 17170, 17170, 17171, 17171, 
17171, 17172, 17172, 17172, 17173, 17173, 17173, 17174, 17174, 
17174, 17175, 17175, 17175, 17176, 17176, 17176, 17177, 17177, 
17177, 17178, 17178, 17178, 17179, 17179, 17179, 17180, 17180, 
17180, 17181, 17181, 17181, 17182, 17182, 17182, 17183, 17183, 
17183, 17184, 17184, 17184, 17185, 17185, 17185, 17186, 17186, 
17186, 17187, 17187, 17187, 17188, 17188, 17188, 17189, 17189, 
17189, 17190, 17190, 17190, 17191, 17191, 17191, 17192, 17192, 
17192, 17193, 17193, 17193, 17194, 17194, 17194, 17195, 17195, 
17195, 17196, 17196, 17196, 17197, 17197, 17197, 17198, 17198, 
17198, 17199, 17199, 17199, 17200, 17200, 17200, 17201, 17201, 
17201, 17202, 17202, 17202, 17203, 17203, 17203, 17204, 17204, 
17204, 17205, 17205, 17205, 17206, 17206, 17206, 17207, 17207, 
17207, 17208, 17208, 17208, 17209, 17209, 17209, 17210, 17210, 
17210, 17211, 17211, 17211, 17212, 17212, 17212, 17213, 17213, 
17213, 17214, 17214, 17214, 17215, 17215, 17215, 17216, 17216, 
17216, 17217, 17217, 17217, 17218, 17218, 17218, 17219, 17219, 
17219, 17220, 17220, 17220, 17221, 17221, 17221, 17222, 17222, 
17222, 17223, 17223, 17223, 17224, 17224, 17224, 17225, 17225, 
17225, 17226, 17226, 17226, 17167, 17167, 17167, 17168, 17168, 
17168, 17169, 17169, 17169, 17170, 17170, 17170, 17171, 17171, 
17171, 17172, 17172, 17172, 17173, 17173, 17173, 17174, 17174, 
17174, 17175, 17175, 17175, 17176, 17176, 17176, 17177, 17177, 
17177, 17178, 17178, 17178, 17179, 17179, 17179, 17180, 17180, 
17180, 17181, 17181, 17181, 17182, 17182, 17182, 17183, 17183, 
17183, 17184, 17184, 17184, 17185, 17185, 17185, 17186, 17186, 
17186, 17187, 17187, 17187, 17188, 17188, 17188, 17189, 17189, 
17189, 17190, 17190, 17190, 17191, 17191, 17191, 17192, 17192, 
17192, 17193, 17193, 17193, 17194, 17194, 17194, 17195, 17195, 
17195, 17196, 17196, 17196, 17197, 17197, 17197, 17198, 17198, 
17198, 17199, 17199, 17199, 17200, 17200, 17200, 17201, 17201, 
17201, 17202, 17202, 17202, 17203, 17203, 17203, 17204, 17204, 
17204, 17205, 17205, 17205, 17206, 17206, 17206, 17207, 17207, 
17207, 17208, 17208, 17208, 17209, 17209, 17209, 17210, 17210, 
17210, 17211, 17211, 17211, 17212, 17212, 17212, 17213, 17213, 
17213, 17214, 17214, 17214, 17215, 17215, 17215, 17216, 17216, 
17216, 17217, 17217, 17217, 17218, 17218, 17218, 17219, 17219, 
17219, 17220, 17220, 17220, 17221, 17221, 17221, 17222, 17222, 
17222, 17223, 17223, 17223, 17224, 17224, 17224, 17225, 17225, 
17225, 17226, 17226, 17226), class = "Date"), y = c(-0.0367045477032661, 
0.429456221696455, 0.298946768045425, -0.0530501157045364, 0.657595156284515, 
0.284923911094666, -0.0538917072117329, 0.6460880288505, 0.291709721088409, 
0.0252184998244047, 0.635886635805946, 0.284828245639801, 0.0362892858684063, 
0.603276582609396, 0.284290611743927, -0.0255751628428698, 0.557980910583865, 
0.284395158290863, -0.0429346822202206, 0.546278891444672, 0.30083292722702, 
-0.0245261415839195, 0.267402382858563, 0.283861696720123, -0.0316767357289791, 
0.240410132973921, 0.303045958280563, -0.0293124560266733, 0.237432341498788, 
0.303045958280563, -0.0878232195973396, 0.258405393280555, 0.303519457578659, 
-0.0591422282159328, 0.276141530892346, 0.297644734382629, -0.0768811851739883, 
0.272345736564603, 0.303971320390701, -0.0658016055822372, 0.277077610546257, 
0.303574740886688, -0.0666064769029617, 0.30387060175417, 0.296770751476288, 
-0.0657989829778671, 0.289401514746714, 0.307651847600937, -0.0698227062821388, 
0.289136817271356, 0.303574740886688, -0.0645555704832077, 0.308249935485946, 
0.29633766412735, -0.075448676943779, 0.320676689676475, 0.296770751476288, 
-0.0534204207360744, 0.291023617784958, 0.296585887670517, -0.0816904157400131, 
0.293582299200352, 0.303971320390701, -0.0887914896011353, 0.317414778226521, 
0.303574740886688, -0.111749045550823, 0.517643463914283, 0.295290470123291, 
-0.111155368387699, 0.572040039405692, 0.301303833723068, -0.119236908853054, 
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-0.0246341452002525, 0.346893597918097, 0.300775051116943, -0.0238024983555079, 
0.424945826351177, 0.283530950546265, 0.0195363350212574, 0.3771753582987, 
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-0.015153992921114, 0.235662241815589, 0.632501840591431, -0.0502956174314022, 
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0.0864016264677048, 0.154617818188854, 0.70279586315155, 0.0997499749064445, 
0.0426971155684441, 0.694870948791504, 0.127143919467926, 0.0791654114145786, 
0.710166811943054, 0.185667186975479, 0.186871806392446, 0.623484969139099, 
0.144664570689201, 0.139920372632332, 0.653435468673706, 0.118960104882717, 
0.153784470516257, 0.649945735931396, 0.142046749591827, 0.119161713286303, 
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-0.084382064640522, 0.156942268717103, 0.620253801345825, -0.0469883792102337, 
0.12639589223545, 0.620192766189575, -0.0486434809863567, 0.137116887490265, 
0.620373368263245, -0.0528004430234432, 0.099452295107767, 0.612701892852783, 
-0.169763207435608, 0.129708995227702, 0.625861763954163, -0.110179461538792, 
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0.617315530776978, -0.0833431631326675, 0.14575181400869, 0.677693128585815, 
-0.169824779033661, 0.125805306248367, 0.655001640319824, -0.129096671938896, 
0.139818842406385, 0.651273250579834), z = structure(c(17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 
17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 
13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 12L, 17L, 13L, 
12L, 17L, 13L, 12L, 17L, 13L, 12L), .Label = c("Barometer", "Holiday", 
"Humidity", "Month 1", "Month 10", "Month 11", "Month 12", "Month 2", 
"Month 4", "Month 7", "Month 8", "Protocol Active", "Rest Variables", 
"Scattered Clouds", "Temp", "Weekday", "Wind"), class = "factor")), class = "data.frame", row.names = c(NA, 
-360L))

Answer 1

關於行，它不會在年底回去，事情是，在X數據軸重復，讓你擁有180倍的值，然后同樣的180個值算賬，該行實際上是在數據的中間. 解決這個問題的一種方法是制作兩個不同的散點並將它們添加到同一個圖形中。

關於y = 0的表面，我並沒有真正在 R 上使用 plotly 並且我現在沒有時間來測試它，但我認為它應該看起來像這樣

fig <- plot_ly(data,
        x = ~x,
        y = ~z,
        z = ~y,
        type = 'scatter3d',
        mode = 'lines',
        color = ~z)

fig <- fig %>% add_surface(
        x = c(min(~x), max(~x)),
        y = c(0, 0),
        z = c(c(min(~z), min(~z)), c(max(~z), max(~z))))

再一次，我並沒有真正使用 R，但我希望你能明白這個想法。 處理多個跟蹤時的一個好方法是從一個空圖 ( fig <- plot_ly() ) 開始，然后開始按需添加跟蹤。