使用ggplot可视化正/负时间序列数据的优雅方法？

Question

我有一些时间序列数据，表示随着时间推移的一系列数据的累积总和，基本上是流入和流出市场的资金。有些是积极的，有些是消极的，但不同的数据线当然总和为整个市场的资金流量。我一直在考虑如何使用ggplot将其可视化，到目前为止，小倍数似乎是最清晰的方法 - 请参阅下面的图像和代码。

是否有人使用R和（最好）ggplot对此类数据进行引人注目的可视化建议？我尝试过使用geom_area，但这种情况非常混乱，即使在使用stack关键字后，我也似乎找不到清晰显示每个数据系列的方法。

require(ggplot2)
require(scales)
require(gridExtra)

mymelt <- structure(list(mydate = structure(c(15340, 15340, 15340, 15340,
15340, 15340, 15340, 15340, 15340, 15340, 15340, 15340, 15371,
15371, 15371, 15371, 15371, 15371, 15371, 15371, 15371, 15371,
15371, 15371, 15400, 15400, 15400, 15400, 15400, 15400, 15400,
15400, 15400, 15400, 15400, 15400, 15431, 15431, 15431, 15431,
15431, 15431, 15431, 15431, 15431, 15431, 15431, 15431, 15461,
15461, 15461, 15461, 15461, 15461, 15461, 15461, 15461, 15461,
15461, 15461, 15492, 15492, 15492, 15492, 15492, 15492, 15492,
15492, 15492, 15492, 15492, 15492, 15522, 15522, 15522, 15522,
15522, 15522, 15522, 15522, 15522, 15522, 15522, 15522, 15553,
15553, 15553, 15553, 15553, 15553, 15553, 15553, 15553, 15553,
15553, 15553), class = "Date"), variable = c("b", "bc", "f",
"in", "it", "l", "of", "o", "pr", "s", "total", "tr", "b", "bc",
"f", "in", "it", "l", "of", "o", "pr", "s", "total", "tr", "b",
"bc", "f", "in", "it", "l", "of", "o", "pr", "s", "total", "tr",
"b", "bc", "f", "in", "it", "l", "of", "o", "pr", "s", "total",
"tr", "b", "bc", "f", "in", "it", "l", "of", "o", "pr", "s",
"total", "tr", "b", "bc", "f", "in", "it", "l", "of", "o", "pr",
"s", "total", "tr", "b", "bc", "f", "in", "it", "l", "of", "o",
"pr", "s", "total", "tr", "b", "bc", "f", "in", "it", "l", "of",
"o", "pr", "s", "total", "tr"), value = c(-23, 6.90000000000001,
459.799999999999, -403.6, -56.1, -95, -13.8, 32.6, 121.5, -15.7,
26.2000000000007, 12.5, -25.1, 238.3, 1047.2, -803.2, -151.5,
-260.5, -59.6, -93.8, 461.5, -37.7, 26.7999999999993, -288.8,
-46.4, 249, 1289.8, -783.2, -188.1, -414.9, -77.7, -61, 928.4,
-36.8, 17.4000000000015, -841.7, -46.5, 276.2, 1384.8, -541.1,
-71.8999999999999, -433.3, -61.3, -28.3, 494.699999999999, -23.4,
-14.5999999999985, -964.5, -46.1, 376.2, 1020.1, -119.4, 56.8000000000001,
-447.7, -9.50000000000001, 14.2, -9.20000000000164, 2.5, -42.7999999999993,
-880.6, -52.9, 345.5, 892.599999999999, -241.8, 144.3, -428.2,
-3.30000000000001, 91.9, -294.800000000002, -5.19999999999999,
-42.1999999999971, -490.1, -64.5, 379.7, 679.299999999999, -143.1,
185.9, -419.8, -4.30000000000001, 182.4, -421.900000000002, 1.80000000000001,
-59.8999999999978, -435.2, -80.2, 422.2, 645.499999999998, -391.4,
76.6000000000001, -387.4, -1.70000000000001, 211.2, -131.500000000002,
-10.6, -40.8999999999978, -393.6), fill = c("#A4D3EE80", "#A478AB80",
"#01AEF080", "#8DC73F80", "#F8931D80", "#FFAAAA80", "#8C8C8C",
"#D38D5F80", "#23238E80", "#77B9B780", "#C8373780", "#EEDD8280",
"#A4D3EE80", "#A478AB80", "#01AEF080", "#8DC73F80", "#F8931D80",
"#FFAAAA80", "#8C8C8C", "#D38D5F80", "#23238E80", "#77B9B780",
"#C8373780", "#EEDD8280", "#A4D3EE80", "#A478AB80", "#01AEF080",
"#8DC73F80", "#F8931D80", "#FFAAAA80", "#8C8C8C", "#D38D5F80",
"#23238E80", "#77B9B780", "#C8373780", "#EEDD8280", "#A4D3EE80",
"#A478AB80", "#01AEF080", "#8DC73F80", "#F8931D80", "#FFAAAA80",
"#8C8C8C", "#D38D5F80", "#23238E80", "#77B9B780", "#C8373780",
"#EEDD8280", "#A4D3EE80", "#A478AB80", "#01AEF080", "#8DC73F80",
"#F8931D80", "#FFAAAA80", "#8C8C8C", "#D38D5F80", "#23238E80",
"#77B9B780", "#C8373780", "#EEDD8280", "#A4D3EE80", "#A478AB80",
"#01AEF080", "#8DC73F80", "#F8931D80", "#FFAAAA80", "#8C8C8C",
"#D38D5F80", "#23238E80", "#77B9B780", "#C8373780", "#EEDD8280",
"#A4D3EE80", "#A478AB80", "#01AEF080", "#8DC73F80", "#F8931D80",
"#FFAAAA80", "#8C8C8C", "#D38D5F80", "#23238E80", "#77B9B780",
"#C8373780", "#EEDD8280", "#A4D3EE80", "#A478AB80", "#01AEF080",
"#8DC73F80", "#F8931D80", "#FFAAAA80", "#8C8C8C", "#D38D5F80",
"#23238E80", "#77B9B780", "#C8373780", "#EEDD8280")), .Names = c("mydate",
"variable", "value", "fill"), row.names = c(NA, 96L), class = "data.frame")

myvals <- mymelt[mymelt$mydate == mymelt$mydate[nrow(mymelt)],] ## last date in mymelt should always be same as plotenddate as we subset earlier
mymelt <- within(mymelt, variable <- factor(variable, as.character(myvals[order(myvals$value, decreasing = T),]$variable), ordered = TRUE))

p <- ggplot(mymelt, aes(x = mydate, y = value)) +
     geom_area(aes(fill = variable), position = "stack") +
     facet_wrap(~ variable, ncol = 4) +
     theme(axis.text.x = element_text(size = 8, angle = 90, colour = "grey50")) +
     theme()
print(p)

Answer 1

通常我会建议您水平堆叠面板，以便每个时间序列都有共同的x轴。但是，如果您不想将scales更改为@GavinSimpson建议，则无法使用。在这种情况下，最好将面板放在彼此旁边，但删除一些不必要的数据墨水（参见Tufte, 2001）。

通常，您不需要图例，因为面板名称已经告诉您变量的名称。这也消除了对彩虹色的需求。我也会避免使用geom_area并使用geom_line代替 - 你的效果仍然突出，而不会过度填充具有沉重几何区域的情节。之后会有一些细小的细节 - 删除小网格以降低网格密度，更改轴文本大小，减小geom_line的宽度。您将主题更改为theme_bw以删除所有灰色废话。最后，如果在这种特定情况下，绘图的高度将大约是其宽度的50％，这将有所帮助。此解决方案的唯一问题是x轴上的日期标签非常小。

p <- ggplot(mymelt, aes(x = mydate, y = value)) +
  geom_line(lwd=0.3) +
  facet_grid(. ~ variable) +
  theme_bw() +
  theme(axis.text.x = element_text(size = 5, angle = 90),
        axis.text.y = element_text(size = 8),
        axis.title.x = element_text(vjust = 0),
        axis.ticks = element_blank(), 
        panel.grid.minor = element_blank())
print(p)
ggsave(plot=p, filename="plot.png", width = 8, height = 4)