英文:
How to create a function in R to add NBER recession shades to different facets in ggplot?
问题
让我们假设我想使用 ggplot 绘制不同股票的相同时间序列（例如股价），并且所有这些时间序列都具有自由刻度，因为这些股价的精确时间和幅度在不同股票之间允许不同。这很容易做到。
除此之外，我想添加NBER经济衰退期的阴影，因为在金融/经济实证工作中这是常见的。问题在于，并不是所有的面板都应该接收相同的阴影，因为时间段可能不同。
让我展示我的解决方案以及我遇到困难的地方。考虑主要数据集：
structure(list(date = structure(c(12053, 12084, 12112, 12143,12173, 12204, 12234, 12265, 12296, 12326, 12357, 12387, 12418,12449, 12478, 12509, 12539, 12570, 12600, 12631, 12662, 12692,12723, 12753, 12784, 12815, 12843, 12874, 12904, 12935, 12965,12996, 13027, 13057, 13088, 13118, 13149, 13180, 13208, 13239,13269, 13300, 13330, 13361, 13392, 13422, 13453, 13483, 13514,13545, 13573, 13604, 13634, 13665, 13695, 13726, 13757, 13787,13818, 13848, 13879, 13910, 13939, 13970, 14000, 14031, 14061,14092, 14123, 14153, 14184, 14214, 14245, 14276, 14304, 14335,14365, 14396, 14426, 14457, 14488, 14518, 14549, 14579, 14610,14641, 14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883,14914, 14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156,15187, 15218, 15248, 15279, 15309, 15340, 15371, 15400, 15431,15461, 15492, 15522, 15553, 15584, 15614, 15645, 15675, 15706,15737, 15765, 15796, 15826, 15857, 15887, 15918, 15949, 15979,16010, 16040, 16071, 16102, 16130, 16161, 16191, 16222, 16252,16283, 16314, 16344, 16375, 16405, 16436, 16467, 16495, 16526,16556, 16587, 16617, 16648, 16679, 16709, 16740, 16770, 16801,16832, 16861, 16892, 16922, 16953, 16983, 17014, 17045, 17075,17106, 17136, 17167, 17198, 17226, 17257, 17287, 17318, 17348,17379, 17410, 17440, 17471, 17501, 17532, 17563, 17591, 17622,17652, 17683, 17713, 17744, 17775, 17805, 17836, 17866, 17897,17928, 17956, 17987, 18017, 18048, 18078, 18109, 18140, 18170,18201, 18231, 18262, 18293, 18322, 18353, 18383, 18414, 18444,18475, 18506, 185<details><summary>英文:</summary>Let's say I want to plot the same time series (for example, stock prices) for different stocks using `ggplot`, all of them with free scales since the precise timing and magnitude of these stock prices are allowed to be different across stocks. That is easy to do.On top of that, I'd like to add NBER recession shades, as it's common in empirical work in Finance/Economics. The problem is that not all facets should receive the same shades since time periods might be different.Let me show my solution and where I am stuck at. Consider the main dataset:structure(list(date = structure(c(12053, 12084, 12112, 12143,12173, 12204, 12234, 12265, 12296, 12326, 12357, 12387, 12418,12449, 12478, 12509, 12539, 12570, 12600, 12631, 12662, 12692,12723, 12753, 12784, 12815, 12843, 12874, 12904, 12935, 12965,12996, 13027, 13057, 13088, 13118, 13149, 13180, 13208, 13239,13269, 13300, 13330, 13361, 13392, 13422, 13453, 13483, 13514,13545, 13573, 13604, 13634, 13665, 13695, 13726, 13757, 13787,13818, 13848, 13879, 13910, 13939, 13970, 14000, 14031, 14061,14092, 14123, 14153, 14184, 14214, 14245, 14276, 14304, 14335,14365, 14396, 14426, 14457, 14488, 14518, 14549, 14579, 14610,14641, 14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883,14914, 14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156,15187, 15218, 15248, 15279, 15309, 15340, 15371, 15400, 15431,15461, 15492, 15522, 15553, 15584, 15614, 15645, 15675, 15706,15737, 15765, 15796, 15826, 15857, 15887, 15918, 15949, 15979,16010, 16040, 16071, 16102, 16130, 16161, 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177.005172729492,193.970001220703, 190.679992675781, 190.679992675781), stock = c("Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Microsoft", "Microsoft", "Microsoft", "Microsoft", "Microsoft","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple", "Apple", "Apple", "Apple","Apple", "Apple", "Apple", "Apple")), row.names = c(NA, -440L), class = c("tbl_df", "tbl", "data.frame"))Now the secondary dataset:structure(list(Start = structure(c(11413, 13879, 18262), class = "Date"),End = structure(c(11656, 14425, 18382), class = "Date")), row.names = c(NA,-3L), spec = structure(list(cols = list(Start = structure(list(format = ""), class = c("collector_date", "collector")),End = structure(list(format = ""), class = c("collector_date","collector"))), default = structure(list(), class = c("collector_guess","collector")), delim = ","), class = "col_spec"), problems = <pointer: 0x123acf780>, class = c("spec_tbl_df","tbl_df", "tbl", "data.frame"))----------------My code:p1 <- final_df %>%ggplot() +geom_line(aes(x = date, y = close)) +facet_wrap(~stock, scales = "free")p1 + geom_rect(data = nber_date,aes(xmin = Start, xmax = End, ymin = -Inf, ymax = Inf),alpha = 0.2)The output is almost what I want but there is a problem:[![enter image description here][1]][1][1]: https://i.stack.imgur.com/KBPjK.pngFor Microsoft, there is empty space on the plot that shouldn't be there. It has shaded areas that shouldn't be in the plot since there is not actual data (the black line) for that area. I'd like to keep the plot for Apple exactly as it is but have a "trimmed" version for Microsoft in which I get the shading but the plot respects that there is no Microsoft data at that area.How to do some "adaptive" shading here? In a real application, I'd have many firms and sometimes very different values for the x axis.Thanks!</details># 答案1**得分**: 1你需要告诉 ggplot 在哪些"geom_rect"中显示哪些图例。一种方法是遍历每个股票，创建一个经过唯一过滤的 "nber_date" 的副本，并添加一个列来标识正确的股票。将这些数据框绑定在一起，然后将结果用作 "geom_rect" 图层的数据源。这比听起来复杂一些：```rlibrary(tidyverse)nber_labelled <- lapply(split(final_df, final_df$stock), function(x) {nber_date %>%filter(End > min(x$date) & Start < max(x$date)) %>%mutate(stock = first(x$stock))}) %>% bind_rows()
这将得到以下结果：
nber_labelled#> # A tibble: 4 x 3#> Start End stock#> <date> <date> <chr>#> 1 2001-04-01 2001-11-30 Apple#> 2 2008-01-01 2009-06-30 Apple#> 3 2020-01-01 2020-04-30 Apple#> 4 2008-01-01 2009-06-30 Microsoft
因此，现在当我们绘图时，会得到以下结果：
p1 + geom_rect(data = nber_labelled,aes(xmin = Start, xmax = End, ymin = -Inf, ymax = Inf),alpha = 0.2)
英文:
You have to tell ggplot which facets you want each geom_rect to appear in. One way to do this is to iterate through each stock and create a uniquely filtered copy of nber_date, and add a column to label it with the correct stock. Bind these data frames together and use the result as the source for your geom_rect layer. This is less complicated than it sounds:
library(tidyverse)nber_labelled <- lapply(split(final_df, final_df$stock), function(x) {nber_date %>%filter(End > min(x$date) & Start < max(x$date)) %>%mutate(stock = first(x$stock))}) %>% bind_rows()
This results in
nber_labelled#> # A tibble: 4 x 3#> Start End stock#> <date> <date> <chr>#> 1 2001-04-01 2001-11-30 Apple#> 2 2008-01-01 2009-06-30 Apple#> 3 2020-01-01 2020-04-30 Apple#> 4 2008-01-01 2009-06-30 Microsoft
So now when we plot, we get:
p1 + geom_rect(data = nber_labelled,aes(xmin = Start, xmax = End, ymin = -Inf, ymax = Inf),alpha = 0.2)
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