我有一個長格式的資料框，我想僅使用因子的一個子集生成條形圖，并在同一圖表中還使用其他因子的資訊/資料添加點。
我想出了以下解決方案，但我想知道是否有更好的方法。
下面是一個例子：
rbind(df_fund_contributions,benmark_comp_returns) %>%
ggplot2::ggplot(aes(x = Date, y = Ra_contributions*100, fill =Fund)) #plot
geom_col()
geom_point(data = benmark_comp_returns, aes(color=Fund))
scale_color_manual(labels = c("Benchmark_Returns", 'portfolio_isa'), values = c("black", 'red'))
ylab('Returns Contributions (%)')
scale_fill_brewer(palette = "Paired")
scale_x_date(breaks = scales::breaks_pretty(10))
theme_minimal() theme(legend.position="bottom", text = element_text(size=20),
legend.title = element_blank())
我生成的圖表如下所示：
[![在此處輸入影像描述][1]][1]
我不明白為什么所有的傳說在各自的傳說中都有一個要點。我怎樣才能擺脫這個？
然后，最右邊的兩個圖例 (Benchmark_returns和portfolio_isa) 沒有很好地對齊。我想看看portoflio_isa下面的圖例Benchmar_Returns
有沒有更好的方法讓一個資料框可以使用因子的一個子集來做條形，而另一個子集來做，geom_point同時有更好的控制和更對齊的圖例？
資料
benmark_comp_returns <- structure(list(Date = structure(c(18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932), class = "Date"), Fund = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L), .Label = c("Benchmark_returns", "portfolio_isa"), class = "factor"),
Ra_contributions = c(0.0478973275493924, 0.0429625498691601,
-0.00987146529562977, 0.0410011423823866, 0.00941758614497523,
0.0349864600422998, -0.0223448750023872, 0.0381121681545589,
0.0351134695720898, 0.0166496661166204, 0.0531586687108598,
-0.0111559412001453, 0.0445469287928051, 0.00281101024533914,
0.0406282718668045, -0.0247869783939432, 0.0182891154197813,
0.0306387718131751)), row.names = c(NA, -18L), class = "data.frame")
df_fund_contributions <- structure(list(Date = structure(c(18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932, 18687, 18718, 18748, 18779,
18809, 18840, 18871, 18901, 18932), class = "Date"), Fund = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L), .Label = c("Artemis.UK",
"BG.American", "BG.European", "BG.Income", "BG.Pacific", "BG.Positive.Change",
"BNY.Investment", "Fidelity.Global.Tech", "MI.UK.Growth", "World.ex.UK"
), class = "factor"), Ra_contributions = c(0.00427165860999756,
0.00239847079026867, 0.00117754431202788, -0.00182894880661211,
0.0015714866119696, 0.00201985515304526, -0.000716111837747446,
0.00010844025664758, 0.000296559872361435, -0.00508106647547668,
0.00539584888044975, -0.00383796593852037, 0.00855760451422838,
-0.00105783414147886, 0.000502473932103786, -0.00329749205964847,
0.00209960811690113, 0.00183961510114417, 6.71423347435862e-05,
0.00403497203293068, -0.000284608500461858, 0.00233153961039023,
0.00146119835882152, 0.00315505857164022, -0.00417470041499501,
0.00138159592845111, 0.00343378138815176, 0.00245278797963633,
0.00441384714166171, -0.00064894253810821, 0.00358075762309507,
0.000857235410842261, 0.00280005532175731, -0.00250885316984295,
0.000953426797174473, 0.00363500835515063, -0.00685496500594374,
0.00588805087459376, -0.00243735627253794, 0.00752211168889483,
-0.00664016151247449, 0.00452144840571567, -0.00231800643829383,
0.00349181572848734, 0.00287501425724956, -0.00352018405882992,
0.0049448322743415, -0.00271660296964804, 0.0062422319547486,
0.00220134456831755, 0.00537823632154089, -0.00325469442031678,
0.000355838099185712, 0.00314657419344022, 0.00360353406021052,
0.00258097460780138, 0.000249327400845045, 0.00100446224081341,
0.00127957955088753, 0.00369878329082507, -0.00180152113372478,
0.00157127690034642, 0.00202363989457321, 0.00454903485057523,
0.00393549763466505, -0.00261753482244564, 0.00595399549768572,
-0.000685767558080919, 0.00461089490695632, -0.00194258549446136,
0.00202935948974536, 0.0050601619875501, 0.00692337793283104,
0.008156643520149, 0.00273205877224991, -0.000360455871006415,
0.00227382442135737, 0.00534524356313515, 0.000194645051589504,
-0.003185806335541, -8.25666847555917e-05, 0.0107961198005677,
0.00969975634580234, -0.00215785565985938, 0.0091873637594504,
0.00218940885990282, 0.00788171585200015, -0.00507956513557561,
0.00868991655727291, 0.00809432118772446)), row.names = c(NA,
-90L), class = "data.frame")
[1]: https://i.stack.imgur.com/mUqsF.png
uj5u.com熱心網友回復：
您的第一個問題很容易解決 - 分別將美學傳遞給每個 geom。
對于您的第二個問題，您可以guides為您的顏色美學指定其中的行數。
library(tidyverse)
ggplot()
geom_col(data = df_fund_contributions, aes(x = Date, y = Ra_contributions*100, fill =Fund))
geom_point(data = benmark_comp_returns, aes(x = Date, y = Ra_contributions*100, color=Fund))
scale_color_manual(labels = c("Benchmark_Returns", 'portfolio_isa'), values = c("black", 'red'))
scale_fill_brewer(palette = "Paired")
theme(legend.position="bottom",
legend.title = element_blank())
guides(color = guide_legend(nrow = 2))
uj5u.com熱心網友回復：
您詢問了如何可能使用 facet 使您的可視化更具吸引力。這是一個非常快速的想法。
library(tidyverse)
df_new <-
df_fund_contributions %>%
mutate(contrib = 100*Ra_contributions,
month = lubridate::month(Date, label = TRUE, abbr = TRUE))
ggplot(df_new)
geom_col(aes(x = Fund, y = contrib, fill = contrib >0))
scale_fill_brewer(palette = "Set1")
geom_hline(yintercept = 0)
facet_grid(~month)
coord_flip()
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