Forword:我为自己的问题提供了一个相当令人满意的答案。我明白这是可以接受的做法。我自然希望能够提出建议和改进。
我的目的是绘制两个时间序列(存储在数据框中,日期存储为“日期”类),并根据一个是否在另一个之上,用两种不同的颜色填充数据点之间的区域。例如,绘制债券指数和股票指数,并在股票指数高于债券指数时用红色填充该区域,否则用蓝色填充该区域。
我已经将ggplot2
用于此目的,因为我对该软件包非常熟悉(作者:Hadley Wickham),但可以随意提出其他方法。我根据geom_ribbon()
包的ggplot2
函数编写了一个自定义函数。在早期,我遇到了与我在处理geom_ribbon()
函数和类'Date'
的对象方面缺乏经验相关的问题。下面的功能代表了我解决这些问题的努力,几乎可以肯定它是迂回,不可复杂,笨拙等等。所以我的问题是:请建议改进和/或替代方法。最终,在这里提供通用功能会很棒。
数据:
set.seed(123456789)
df <- data.frame(
Date = seq.Date(as.Date("1950-01-01"), by = "1 month", length.out = 12*10),
Stocks = 100 + c(0, cumsum(runif(12*10-1, -30, 30))),
Bonds = 100 + c(0, cumsum(runif(12*10-1, -5, 5))))
library('reshape2')
df <- melt(df, id.vars = 'Date')
自定义功能:
## Function to plot geom_ribbon for class Date
geom_ribbon_date <- function(data, group, N = 1000) {
# convert column of class Date to numeric
x_Date <- as.numeric(data[, which(sapply(data, class) == "Date")])
# append numeric date to dataframe
data$Date.numeric <- x_Date
# ensure fill grid is as fine as data grid
N <- max(N, length(x_Date))
# generate a grid for fill
seq_x_Date <- seq(min(x_Date), max(x_Date), length.out = N)
# ensure the grouping variable is a factor
group <- factor(group)
# create a dataframe of min and max
area <- Map(function(z) {
d <- data[group == z,];
approxfun(d$Date.numeric, d$value)(seq_x_Date);
}, levels(group))
# create a categorical variable for the max
maxcat <- apply(do.call('cbind', area), 1, which.max)
# output a dataframe with x, ymin, ymax, is. max 'dummy', and group
df <- data.frame(x = seq_x_Date,
ymin = do.call('pmin', area),
ymax = do.call('pmax', area),
is.max = levels(group)[maxcat],
group = cumsum(c(1, diff(maxcat) != 0))
)
# convert back numeric dates to column of class Date
df$x <- as.Date(df$x, origin = "1970-01-01")
# create and return the geom_ribbon
gr <- geom_ribbon(data = df, aes(x, ymin = ymin, ymax = ymax, fill = is.max, group = group), inherit.aes = FALSE)
return(gr)
}
用法:
ggplot(data = df, aes(x = Date, y = value, group = variable, colour = variable)) +
geom_ribbon_date(data = df, group = df$variable) +
theme_bw() +
xlab(NULL) +
ylab(NULL) +
ggtitle("Bonds Versus Stocks (Fake Data!)") +
scale_fill_manual('is.max', breaks = c('Stocks', 'Bonds'),
values = c('darkblue','darkred')) +
theme(legend.position = 'right', legend.direction = 'vertical') +
theme(legend.title = element_blank()) +
theme(legend.key = element_blank())
结果:
虽然stackoverflow上有相关的问题和答案,但我没有找到一个足够详细的用于我的目的。以下是一些有用的交流:
答案 0 :(得分:3)
也许我并不理解你的完整问题,但似乎一种相当直接的方法是将第三行定义为每个时间点的两个时间序列的最小值。然后geom_ribbon
被调用两次(对于Asset
的每个唯一值一次),以绘制由每个系列和最小线形成的带。代码可能如下所示:
set.seed(123456789)
df <- data.frame(
Date = seq.Date(as.Date("1950-01-01"), by = "1 month", length.out = 12*10),
Stocks = 100 + c(0, cumsum(runif(12*10-1, -30, 30))),
Bonds = 100 + c(0, cumsum(runif(12*10-1, -5, 5))))
library(reshape2)
library(ggplot2)
df <- cbind(df,min_line=pmin(df[,2],df[,3]) )
df <- melt(df, id.vars=c("Date","min_line"), variable.name="Assets", value.name="Prices")
sp <- ggplot(data=df, aes(x=Date, fill=Assets))
sp <- sp + geom_ribbon(aes(ymax=Prices, ymin=min_line))
sp <- sp + scale_fill_manual(values=c(Stocks="darkred", Bonds="darkblue"))
sp <- sp + ggtitle("Bonds Versus Stocks (Fake Data!)")
plot(sp)
这会生成以下图表:
答案 1 :(得分:1)
前一段时间我实际上有同样的问题,这里是related post。它定义了一个函数,用于查找两条线之间的交叉点,另一个函数在输入中获取数据框,然后使用matplot
和polygon
修改强>
这是代码,修改了一下以允许绘制最后一个多边形
set.seed(123456789)
dat <- data.frame(
Date = seq.Date(as.Date("1950-01-01"), by = "1 month", length.out = 12*10),
Stocks = 100 + c(0, cumsum(runif(12*10-1, -30, 30))),
Bonds = 100 + c(0, cumsum(runif(12*10-1, -5, 5))))
intersects <- function(x1, x2) {
seg1 <- which(!!diff(x1 > x2)) # location of first point in crossing segments
above <- x2[seg1] > x1[seg1] # which curve is above prior to crossing
slope1 <- x1[seg1+1] - x1[seg1]
slope2 <- x2[seg1+1] - x2[seg1]
x <- seg1 + ((x2[seg1] - x1[seg1]) / (slope1 - slope2))
y <- x1[seg1] + slope1*(x - seg1)
data.frame(x=x, y=y, pindex=seg1, pabove=(1:2)[above+1L])
# pabove is greater curve prior to crossing
}
fillColor <- function(data, addLines=TRUE) {
## Find points of intersections
ints <- intersects(data[,2], data[,3]) # because the first column is for Dates
intervals <- findInterval(1:nrow(data), c(0, ints$x))
## Make plot
matplot(data, type="n", col=2:3, lty=1, lwd=4,xaxt='n',xlab='Date')
axis(1,at=seq(1,dim(data)[1],length.out=12),
labels=data[,1][seq(1,dim(data)[1],length.out=12)])
legend("topright", c(colnames(data)[2], colnames(data)[3]), col=3:2, lty=1, lwd=2)
## Draw the polygons
for (i in seq_along(table(intervals))) {
xstart <- ifelse(i == 1, 0, ints$x[i-1])
ystart <- ifelse(i == 1, data[1,2], ints$y[i-1])
xend <- ints$x[i]
yend <- ints$y[i]
x <- seq(nrow(data))[intervals == i]
polygon(c(xstart, x, xend, rev(x)), c(ystart, data[x,2], yend, rev(data[x,3])),
col=ints$pabove[i]%%2+2)
}
# add end of plot
xstart <- ints[dim(ints)[1],1]
ystart <- ints[dim(ints)[1],2]
xend <- nrow(data)
yend <- data[dim(data)[1],2]
x <- seq(nrow(data))[intervals == max(intervals)]
polygon(c(xstart, x, xend, rev(x)), c(ystart, data[x,2], yend, rev(data[x,3])),
col=ints[dim(ints)[1]-1,4]%%2+2)
## Add lines for curves
if (addLines)
invisible(lapply(1:2, function(x) lines(seq(nrow(data)), data[,x], col=x%%2+2, lwd=2)))
}
## Plot the data
fillColor(dat,FALSE)
并且最终结果是(使用与问题相同的数据)
答案 2 :(得分:0)
@walts 的答案应该仍然是赢家,但在实施他的解决方案时,我给了它一个整洁的更新。
library(tidyverse)
set.seed(2345)
# fake data
raw_data <-
tibble(
date = as.Date("2020-01-01") + (1:40),
a = 95 + cumsum(runif(40, min = -20, max = 20)),
b = 55 + cumsum(runif(40, min = -1, max = 1))
)
# the steps
# the 'y' + 'min_line' + 'group' is the right granularity (by date) to
# create 2 separate ribbons
df <-
raw_data %>%
# find min of the two columns
mutate(min_line = pmin(a, b)) %>%
pivot_longer(c(a, b), names_to = "group", values_to = "y") %>%
print()
# the result
ggplot(data = df, aes(x = date, fill = group)) +
geom_ribbon(aes(ymax = y, ymin = min_line)) +
theme_classic()