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首页 > 解决方案 > 在ggplot中为特定颜色着色最大，最小和中值价格路径

r - 在ggplot中为特定颜色着色最大，最小和中值价格路径

问题描述

目前我有一个包含 20 个模拟价格路径的数据表“test_data”。数据片段如下所示：

Month variable     value
1       V1         1.0000000
2       V1         1.0107850
3       V1         1.0265488
4       V1         1.0636623
5       V1         1.0941731
6       V1         1.1097758
7       V1         1.1116170
8       V1         1.1229932
9       V1         1.1779669
10      V1         1.1610330
11      V1         1.1408806
1       V2         1.0000000
2       V2         1.0109236
3       V2         1.0053303
4       V2         1.0126677
5       V2         1.0461632
6       V2         1.0747788
7       V2         1.0997711
8       V2         1.1196782
9       V2         1.1354743
10      V2         1.1602896
11      V2         1.1596831
1       V3         1.0000000
2       V3         1.0572598
3       V3         1.0374854

20 条价格路径 V（1 到 20）中的每一条都从 1 开始，并以某个值结束。我使用 ggplot 绘制了 20 条价格路径

ggplot(test_data, aes(Month,value, col=variable)) + 
  geom_line()+
  ggtitle("Simulated Price Paths") +
  theme_light() +
  theme(legend.title = element_blank()) +
  theme(legend.position = "none")

给出了下图

在此处输入图像描述

我想要三个特定的价格路径，颜色为黑色，线条更粗：

- 价格路径以第 11 个月的最高值结束

- 第 11 个月的最低路径结束值

- 第 11 个月的中位数

我是 ggplot 的新手，完全不确定如何实现这一点。感谢所有帮助！

标签： rggplot2colors

解决方案

您需要有另一列来指定要变黑的变量。您可以创建另一个具有该列的数据框，也可以将该列添加到原始数据框中。我认为在这种情况下，只有一个单独的数据框会更容易，这样您就不必玩弄这些scale_color_*功能了。仅供参考，您应该以文本格式提供数据，以便人们更轻松地提供帮助。R 中的dput函数使您可以轻松地做到这一点。

library(tidyverse)
test_data = expand.grid(Month = 1:11,variable=paste0('V',1:20),stringsAsFactors = F) %>%
  mutate(value=runif(nrow(.)))
dput(test_data) # the data is shown in code cell below the graph

# make a df that says which variables have the max/min/median month 11 value
foo = test_data %>% filter(Month == 11) %>%
  # order the value column
  arrange(value) %>%
  # add columns to find min, max, and "median" the middle value
  mutate(row_num = row_number(),
         new_col = if_else(row_num == 1,'min',
                           if_else(row_num ==  nrow(.),'max',
                                   # may have to play with median because no value guaranteed to exactly equal the median
                                   if_else(row_num == round(nrow(.)/2),'median','other')))) %>%
  select(variable,new_col)

# add that new variable to the data based on the variable column
test_data_with_classifier = test_data %>%
  left_join(foo,by='variable') %>%
  filter(new_col %in% c('min','max','median'))

# build your initial plot but remove the color aesthetic and data from the ggplot call
# so it doesn't apply to second geom_line call
ggplot(mapping=aes(Month,value)) +
  # original geom_line
  geom_line(aes(color=variable),data=test_data) +
  ggtitle("Simulated Price Paths") +
  theme_light() +
  theme(legend.title = element_blank()) +
  theme(legend.position = "none") +
  # new geom_line to plot the black min,max,median data
  geom_line(data=test_data_with_classifier,mapping=aes(group=new_col),
            color='black',size=1)

新图

structure(list(Month = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L), variable = c("V1", "V1", "V1", "V1", "V1", "V1", "V1", 
"V1", "V1", "V1", "V1", "V2", "V2", "V2", "V2", "V2", "V2", "V2", 
"V2", "V2", "V2", "V2", "V3", "V3", "V3", "V3", "V3", "V3", "V3", 
"V3", "V3", "V3", "V3", "V4", "V4", "V4", "V4", "V4", "V4", "V4", 
"V4", "V4", "V4", "V4", "V5", "V5", "V5", "V5", "V5", "V5", "V5", 
"V5", "V5", "V5", "V5", "V6", "V6", "V6", "V6", "V6", "V6", "V6", 
"V6", "V6", "V6", "V6", "V7", "V7", "V7", "V7", "V7", "V7", "V7", 
"V7", "V7", "V7", "V7", "V8", "V8", "V8", "V8", "V8", "V8", "V8", 
"V8", "V8", "V8", "V8", "V9", "V9", "V9", "V9", "V9", "V9", "V9", 
"V9", "V9", "V9", "V9", "V10", "V10", "V10", "V10", "V10", "V10", 
"V10", "V10", "V10", "V10", "V10", "V11", "V11", "V11", "V11", 
"V11", "V11", "V11", "V11", "V11", "V11", "V11", "V12", "V12", 
"V12", "V12", "V12", "V12", "V12", "V12", "V12", "V12", "V12", 
"V13", "V13", "V13", "V13", "V13", "V13", "V13", "V13", "V13", 
"V13", "V13", "V14", "V14", "V14", "V14", "V14", "V14", "V14", 
"V14", "V14", "V14", "V14", "V15", "V15", "V15", "V15", "V15", 
"V15", "V15", "V15", "V15", "V15", "V15", "V16", "V16", "V16", 
"V16", "V16", "V16", "V16", "V16", "V16", "V16", "V16", "V17", 
"V17", "V17", "V17", "V17", "V17", "V17", "V17", "V17", "V17", 
"V17", "V18", "V18", "V18", "V18", "V18", "V18", "V18", "V18", 
"V18", "V18", "V18", "V19", "V19", "V19", "V19", "V19", "V19", 
"V19", "V19", "V19", "V19", "V19", "V20", "V20", "V20", "V20", 
"V20", "V20", "V20", "V20", "V20", "V20", "V20"), value = c(0.3207616454456, 
0.763921687379479, 0.816522044595331, 0.749660312896594, 0.59870492015034, 
0.598363760858774, 0.724914398510009, 0.209003237076104, 0.17182691837661, 
0.724648888222873, 0.971318073105067, 0.369079714873806, 0.766701503191143, 
0.80729194264859, 0.511416860157624, 0.944176482968032, 0.0978654811624438, 
0.716958795208484, 0.38553729141131, 0.327095735818148, 0.918577790725976, 
0.865334192989394, 0.834687531692907, 0.638412003172562, 0.199683424085379, 
0.109074046369642, 0.584284831071272, 0.675479859812185, 0.713516661897302, 
0.0794180030934513, 0.384025492705405, 0.828940884210169, 0.699888165574521, 
0.735423399368301, 0.907396653899923, 0.248153271153569, 0.65956291789189, 
0.606529506854713, 0.516759118298069, 0.425723660970107, 0.162580149481073, 
0.610716496128589, 0.829937220551074, 0.00947263417765498, 0.511702742660418, 
0.652116083307192, 0.590301762800664, 0.909111243207008, 0.952054579043761, 
0.799994048662484, 0.158715128898621, 0.809286607196555, 0.0451009501703084, 
0.725396532332525, 0.0322660498786718, 0.112002467270941, 0.613799379440024, 
0.362014023819938, 0.271398104028776, 0.215616049477831, 0.307330428389832, 
0.757429565768689, 0.689513321500272, 0.408647255739197, 0.343476827954873, 
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-220L))

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