Question

我正在尝试从下面的两个ggplot图中排列一个分组的ggplot。我已附上一张希望实现的图像，但不确定如何实现。

我正在使用以下代码创建两个图；

Ra_plot %>%
  filter(date >= as_date("2016-12-01")) %>%
  ggplot(aes(x = date, y = adjusted, color = symbol)) +
  geom_line(size = 1) +
  facet_wrap(~ symbol, ncol = 2, scales = "free_y")


Ra_plot %>%
  filter(date >= as_date("2016-12-01")) %>%
  ggplot(aes(x = date)) +
  geom_line(aes(y = signal), color = "blue", linetype = 2) +
  geom_bar(aes(y = diff), stat = "identity", color = palette_light()[[1]]) +
  facet_wrap(~ symbol, ncol = 2, scale = "free_y")

我希望输出看起来像以下内容：

我应该考虑使用网格还是可以ggplot以我希望获得的方式处理地块。

数据：

Ra_plot <- structure(list(symbol = c("CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-018", 
    "CCT6-018", "CCT6-018", "CCT6-018", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-019", 
    "CCT6-019", "CCT6-019", "CCT6-019", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-020", 
    "CCT6-020", "CCT6-020", "CCT6-020", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", "CCT6-021", 
    "CCT6-021", "CCT6-021", "CCT6-021"), date = structure(c(17106, 
    17107, 17108, 17109, 17112, 17113, 17114, 17115, 17116, 17119, 
    17120, 17121, 17122, 17123, 17126, 17127, 17128, 17130, 17133, 
    17134, 17135, 17136, 17137, 17140, 17141, 17142, 17143, 17144, 
    17147, 17148, 17149, 17150, 17151, 17154, 17155, 17156, 17157, 
    17158, 17162, 17163, 17164, 17165, 17106, 17107, 17108, 17109, 
    17112, 17113, 17114, 17115, 17116, 17119, 17120, 17121, 17122, 
    17123, 17126, 17127, 17128, 17130, 17133, 17134, 17135, 17136, 
    17137, 17140, 17141, 17142, 17143, 17144, 17147, 17148, 17149, 
    17150, 17151, 17154, 17155, 17156, 17157, 17158, 17162, 17163, 
    17164, 17165, 17106, 17107, 17108, 17109, 17112, 17113, 17114, 
    17115, 17116, 17119, 17120, 17121, 17122, 17123, 17126, 17127, 
    17128, 17130, 17133, 17134, 17135, 17136, 17137, 17140, 17141, 
    17142, 17143, 17144, 17147, 17148, 17149, 17150, 17151, 17154, 
    17155, 17156, 17157, 17158, 17162, 17163, 17164, 17165, 17106, 
    17107, 17108, 17109, 17112, 17113, 17114, 17115, 17116, 17119, 
    17120, 17121, 17122, 17123, 17126, 17127, 17128, 17130, 17133, 
    17134, 17135, 17136, 17137, 17140, 17141, 17142, 17143, 17144, 
    17147, 17148, 17149, 17150, 17151, 17154, 17155, 17156, 17157, 
    17158, 17162, 17163, 17164, 17165), class = "Date"), adjusted = c(129.5, 
    127.169998, 120, 120.75, 122.150002, 124.220001, 123.18, 120.800003, 
    119.019997, 115.080002, 117.199997, 116.339996, 117.790001, 117.019997, 
    121.769997, 121.470001, 120.839996, 120.379997, 120.410004, 120.870003, 
    118.419998, 115.099998, 115.400002, 117.43, 117.309998, 117.949997, 
    118.910004, 119.68, 117.769997, 120.309998, 120.209999, 120.57, 
    119.870003, 119.239998, 119.089996, 119.040001, 117.400002, 117.269997, 
    118.010002, 116.919998, 116.349998, 115.050003, 785.409973, 765.559998, 
    767.030029, 755.049988, 784.929993, 787.75, 771.880005, 742.380005, 
    739.01001, 719.070007, 743.23999, 746.48999, 756.400024, 760.159973, 
    780, 785.330017, 780.119995, 780.369995, 766.77002, 762.52002, 
    750.570007, 743.650024, 740.340027, 759.359985, 764.719971, 770.419983, 
    767.330017, 768.659973, 760.119995, 774.340027, 768.820007, 761, 
    757.77002, 766, 771.219971, 770.599976, 766.340027, 760.590027, 
    771.400024, 772.130005, 765.150024, 749.869995, 123.300003, 122.339996, 
    122.139999, 122.029999, 124.580002, 124.339996, 122.190002, 115.419998, 
    114.779999, 113.379997, 113.589996, 115.190002, 115.029999, 115.209999, 
    117.959999, 118.040001, 117.690002, 117.410004, 116.93, 117.510002, 
    117, 117.220001, 120.809998, 119.160004, 124.57, 125.389999, 
    123.239998, 122.879997, 122.830002, 123.779999, 123.440002, 125, 
    124.220001, 125.449997, 125.120003, 126.5, 125.580002, 125.589996, 
    128.350006, 125.889999, 125.330002, 123.800003, 783.609985, 768.700012, 
    762.130005, 762.02002, 782.52002, 790.51001, 785.309998, 762.559998, 
    754.02002, 736.080017, 758.48999, 764.47998, 771.22998, 760.539978, 
    769.200012, 768.27002, 760.98999, 761.679993, 768.23999, 770.840027, 
    758.039978, 747.919983, 750.5, 762.52002, 759.109985, 771.190002, 
    776.419983, 789.289978, 789.27002, 796.099976, 797.070007, 797.849976, 
    790.799988, 794.200012, 796.419983, 794.559998, 791.26001, 789.909973, 
    791.549988, 785.049988, 782.789978, 771.820007), macd = c(0.775656910147982, 
    0.797346932149945, 0.521357938089917, 0.136184278050555, -0.189225922380254, 
    -0.562608723559488, -1.06956649261651, -1.58988420666739, -2.08672587969461, 
    -2.6328170184005, -3.1748828867045, -3.77297957133487, -4.21809665237388, 
    -4.57592546268422, -4.27842404747918, -4.05114131115747, -3.90961345620027, 
    -3.87769145767599, -3.77766883260927, -3.4366348094813, -3.02752419970859, 
    -2.50071754284896, -2.14121709554718, -1.68458637009835, -1.27877141907935, 
    -0.801211775630006, -0.662540261160538, -0.547616894476566, -0.690771598645834, 
    -0.681558984014052, -0.633303911869121, -0.537003121626467, -0.328646048125936, 
    0.0123666066978556, 0.269135086702899, 0.253527954587685, 0.253349884965104, 
    0.1755341017178, 0.105334423845704, -0.0848526621859413, -0.00907272762492584, 
    -0.170681933418881, -1.39741925937584, -1.64478151344726, -1.87486107309583, 
    -2.17220688902349, -2.19001170903169, -2.28360769820303, -2.67640945629708, 
    -3.16221761155032, -3.5538954065233, -4.03156809717544, -3.92095503463473, 
    -3.98267767647376, -3.9253330362836, -3.65723179383786, -3.31596185001577, 
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    0.398764035692856, 0.299756970784082, 0.121605790402302, 0.0904638856131124, 
    0.068062539742253, 0.197465459016377, 0.109452369702434, 10.4817336801601, 
    11.1994207189728, 10.5134236362317, 9.57977051479777, 8.71513088628475, 
    7.70434778379978, 6.63200568570297, 5.52150630281762, 4.32004033207796, 
    3.10122551434258, 1.84055267575476, 0.678550859590987, -0.403813234961548, 
    -1.3786972359436, -2.18723105892463, -3.02812455149013, -3.46836540638097, 
    -3.80898326755053, -3.97538200443274, -3.52390525737234, -3.04992862026117, 
    -2.49247111286869, -1.79454060399381, -1.28461918599766, -0.552431033430645, 
    0.143918173601332, 0.515710334381669, 0.83713465878188, 1.17454917884416, 
    1.56313432346815, 2.05628047529247, 2.55875179687635, 2.99654375306577, 
    3.23791459192655, 3.19505253612893, 3.2705556443696, 2.94623845670969, 
    2.62047722898282, 2.53846052040871, 2.39906310330718, 2.33218102596211, 
    2.14880396198645, 1.16142408778224, 1.11289796834209, 0.857634605608393, 
    0.502738295170069, 0.323644396136347, 0.141574969069547, -0.196560513454014, 
    -0.60702736767867, -0.974101509630498, -1.4149908864092, -1.6747989032932, 
    -1.79873019336693, -1.85871822912509, -1.86048330205084, -1.7397964129521, 
    -1.61630897125068, -1.68022385242601, -1.79567565807346, -1.83917135625697, 
    -1.61100237230263, -1.29781430231263, -0.875134042826042, -0.720760171699075, 
    -0.578602147304341, -0.529415074109341, -0.34664447684073, -0.231934605482187, 
    -0.106327159686415, 0.0650733179262897, 0.2677973262599, 0.507418932971704, 
    0.763304301879297, 1.09066442179129, 1.43169087317443, 1.71318302123171, 
    1.76256435620636, 1.94269528713715, 2.0152098411186, 2.07486304778541, 
    1.90576683657873, 1.76798761158086, 1.4902979731293), signal = c(0.471248889710887, 
    0.550665334378575, 0.585899192345393, 0.559732591031496, 0.485843882520468, 
    0.372709606752546, 0.19661512109012, -0.0493110445741803, -0.363051685164443, 
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    -2.63149854378179, -3.0443669131062, -3.37565300405519, -3.63340069844774, 
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    -0.135177603440485, -0.0477450025671785, 0.013194025175397, 0.0718529578422349, 
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    -0.0765150363908331, 0.144565444120117, 0.402096192881862, 0.715489320557442, 
    0.743227288311991, 0.756363727634752, 0.684784812258493, 0.719379923350961, 
    0.660340788866029, 0.593409990598406, 0.578579835963453, 0.607346996677893, 
    0.693351606078837, 0.784340922366611, 0.926226979045754, 1.04935276961959, 
    1.10197519566882, 0.929745534900301, 0.882207305072916, 0.738040023144109, 
    0.596908149641417, 0.272439949145061, 0.0230292452914631, -0.299064121086543
    )), row.names = c(NA, -168L), vars = "symbol", drop = TRUE, class = c("grouped_df", 
    "tbl_df", "tbl", "data.frame"), .Names = c("symbol", "date", 
    "adjusted", "macd", "signal", "diff"), indices = list(0:41, 42:83, 
        84:125, 126:167), group_sizes = c(42L, 42L, 42L, 42L), biggest_group_size = 42L, labels = structure(list(
        symbol = c("CCT6-018", "CCT6-019", "CCT6-020", "CCT6-021"
        )), row.names = c(NA, -4L), vars = "symbol", drop = TRUE, class = "data.frame", .Names = "symbol"))

编辑：我要问的问题与建议的链接不同。我已经应用了cowplot包，该包基本上并排绘制了两个图（根据上面的数据），这不是我要的。例如，应用cowplot::plot_grid(iris1, iris2, labels = "AUTO")可得出以下图：

这不是我所追求的。我正在尝试一些如何通过symbol重新布置图。数据看起来像;

# A tibble: 168 x 6
# Groups:   symbol [4]
   symbol   date       adjusted   macd  signal    diff
   <chr>    <date>        <dbl>  <dbl>   <dbl>   <dbl>
 1 CCT6-018 2016-11-01     130.  0.776  0.471   0.304 
 2 CCT6-018 2016-11-02     127.  0.797  0.551   0.247 
 3 CCT6-018 2016-11-03     120.  0.521  0.586  -0.0645
 4 CCT6-018 2016-11-04     121.  0.136  0.560  -0.424 
 5 CCT6-018 2016-11-07     122. -0.189  0.486  -0.675 
 6 CCT6-018 2016-11-08     124. -0.563  0.373  -0.935 
 7 CCT6-018 2016-11-09     123. -1.07   0.197  -1.27  
 8 CCT6-018 2016-11-10     121. -1.59  -0.0493 -1.54  
 9 CCT6-018 2016-11-11     119. -2.09  -0.363  -1.72  
10 CCT6-018 2016-11-14     115. -2.63  -0.742  -1.89  
# ... with 158 more rows

因此，我试图在第二张图上方绘制adjusted的{{1}}列（如第一张图所示）。

P.S。我正在使用symbol = CCT6-018-如果某些命令不起作用。

Answer 1

这是您要寻找的东西：

Ra_plot %>%
    gather(measure, value, adjusted, signal, diff) %>%
    mutate(dummy_facet = case_when(
        measure == "adjusted" ~ paste0("adjusted", symbol),
        measure == "signal" ~ paste0("sig_diff", symbol),
        measure == "diff" ~ paste0("sig_diff", symbol)
    )) %>%
    mutate(dummy_facet = factor(dummy_facet, levels = c("adjustedCCT6-018", "adjustedCCT6-019", "sig_diffCCT6-018", "sig_diffCCT6-019", "adjustedCCT6-020", "adjustedCCT6-021", "sig_diffCCT6-020", "sig_diffCCT6-021"))) %>%
    filter(date >= as_date("2016-12-01")) %>% 
    ggplot(aes(x = date, y = value))+
    geom_line(data = . %>% filter(measure == "adjusted"), aes(color = symbol), size = 1)+
    geom_line(data = . %>% filter(measure == "signal"), color = "blue", linetype = 2) +
    geom_bar(data = . %>% filter(measure == "diff"), stat = "identity", color = palette_light()[[1]])+
    facet_wrap(~dummy_facet, ncol = 2, scale = "free_y")+
    theme(legend.position = "none")

首先，我们gather您要绘制在一起的所有数据。接下来，我们创建一个虚拟facet变量，以确保您获得了所有想要的图。您可以不考虑虚拟变量和符号，但是，我个人只喜欢对一个变量进行改面，因为我不喜欢在绘图上具有两个方面标签（这就是为什么要在虚拟标签上粘贴符号）。我还将变量更改为一个因子，然后将级别设置为希望它们绘制的顺序。接下来，我们为每个图过滤合适的y值，然后按正常方式绘制。您需要稍微使用labeller参数来修正构面标签（如果您对此有疑问，请告诉我）。

按组排列ggplot图

1 个答案: