在ggplot2中匹配图例项目和颜色，其中一些geom_segment不包含在图例中 [英] matching legend items and colours in ggplot2 where some geom_segment are not included in legend
问题描述
我似乎无法让我的图例标签和颜色在ggplot2中正确匹配。我有一些geom_segments，我不想包含在图例中。我尝试了各种选择，没有工作。现有的问题似乎都没有处理不在标签上标注某些元素的问题，所以也许这增加了复杂性。代码如下：
library（distr）
library（ggplot2）
Percent_values< ; -c（0.5,0.75,0.9,0.95,0.995,0.999）
Dist1_mean = 20219
Dist1_CV = 3235/20219
Dist1_SDEV< -Dist1_mean * Dist1_CV
Dist1_parm2< -sqrt （log（1 + Dist1_CV ^ 2））
Dist1_parm1 <-log（Dist1_mean） - （Dist1_parm2 ^ 2）/ 2
Dist1_quant <-qlnorm（Percent_values，meanlog = Dist1_parm1，sdlog = Dist1_parm2）
＃现在在均值，中位数和选定百分位数处绘制垂直线的CDF
a1 <-stat_function（fun =
plnorm，args = list（meanlog = Dist1_parm1，sdlog = Dist1_parm2），geom =line ，
color =blue，size = 1.25）
lowerx <-0
upperx< -1.1 * Dist1_quant [6]
plot1< -ggplot（data.frame x = c（lowerx，upperx）），aes（x = x））+ a1
plot1 < -
plot1 + scale_x_continuous（name =Value）+ scale_y_continuous（name =Cumulative
概率）
#add平均垂直线和相关水平线到轴
mean_yvalue <-plnorm（Dist1_mean，meanlog = Dist1_parm1，sdlog = dist1_parm2）
plot1 <-plot1 + geom_segment（aes（x = Dist1_mean，y = 0，
xend = Dist1_mean，yend = mean_yvalue，color =red），size = 1.25）
plot1 < -
plot1 + geom_segment（aes（x = 0，y = mean_yvalue，xend = Dist1_mean，
yend = mean_yvalue，color =red），size = 1.25，linetype =dotted ，show.legend =
FALSE）
＃和第75百分位数
perc <-0.75
p75 <-Dist1_quant [2]
plot1 < -
plot1 + geom_segment（aes（x = p75，y = 0，xend = p75，
yend = perc，color =green），size = 1.25）
plot1< -plot1 + geom_segment（aes（x = 0，y = perc，xend = p75，
yend = perc，color =green），size = 1.25，linetype =dotted，show.legend = FALSE）
#and 99.5th
perc2 <-0.995
p995 <-Dist1_quant [6]
plot1 < -
plot1 + geom_segment（aes（x = p995，y = 0，xend = p995，
$ plot1 + geom_segment（aes（x = 0，y = perc2，xend = p995，
，$ b yend = perc2，color =orange），size = 1.25） yend = perc2，color =orange），size = 1.25，linetype =dotted，show.legend = FALSE）
plot1< -plot1 + ggtitle（累积密度函数scale_colour_discrete（name =，labels = c（Lognormal，Mean，75th
％ile，99.5th％ ile））
plot1
这会产生一个图表（我似乎无法）a）当我想要四个（另外99.5％ile加入这三个）时，只有三个图例项目（Lognormal，Mean和75th％ile），并且三个项目是分别是红色，绿色和蓝色，而我想对数正态为蓝色，平均值为红色，第75个为绿色，第99.5个为橙色。虚线应该保留在图上，但不会出现在图例中。
我做错了什么？据推测，这与美学和scale_colour_discrete有关，但我无法弄清楚该做什么。任何帮助将不胜感激。
谢谢。
添加到
但是，如果您打算尝试一个包含大量组件和信息丰富的传奇的复杂图表，这只是一个小小的习惯改变，可能会为您节省大量的挫折。 / p>
I can't seem to get my legend labels and colours to match correctly in ggplot2. I have some geom_segments that I don't want to include in the legend. I have tried various options and none work. None of the existing questions seem to deal with the issue of not labelling some elements on the plot, so maybe that is adding complexity. The code is below:
library("distr")
library("ggplot2")
Percent_values<-c(0.5,0.75,0.9,0.95,0.995,0.999)
Dist1_mean=20219
Dist1_CV=3235/20219
Dist1_SDEV<-Dist1_mean*Dist1_CV
Dist1_parm2<-sqrt(log(1+Dist1_CV^2))
Dist1_parm1<-log(Dist1_mean)-(Dist1_parm2^2)/2
Dist1_quant<-qlnorm(Percent_values,meanlog=Dist1_parm1,sdlog=Dist1_parm2)
#Now draw CDF with vertical line at mean, median and chosen percentile
a1<-stat_function(fun =
plnorm,args=list(meanlog=Dist1_parm1,sdlog=Dist1_parm2),geom="line",
colour="blue",size=1.25)
lowerx<-0
upperx<-1.1*Dist1_quant[6]
plot1<-ggplot(data.frame(x = c(lowerx, upperx)), aes(x = x))+a1
plot1<-
plot1+scale_x_continuous(name="Value")+scale_y_continuous(name="Cumulative
probability")
#add mean vertical line and associated horizontal line to axis
mean_yvalue<-plnorm(Dist1_mean,meanlog=Dist1_parm1,sdlog=Dist1_parm2)
plot1<-plot1+geom_segment(aes(x=Dist1_mean,y=0,
xend=Dist1_mean,yend=mean_yvalue,colour="red"),size=1.25)
plot1<-
plot1+geom_segment(aes(x=0,y=mean_yvalue,xend=Dist1_mean,
yend=mean_yvalue,colour="red"),size=1.25,linetype="dotted",show.legend =
FALSE)
#and 75th percentile
perc<-0.75
p75<-Dist1_quant[2]
plot1<-
plot1+geom_segment(aes(x=p75,y=0,xend=p75,
yend=perc,colour="green"),size=1.25)
plot1<-plot1+geom_segment(aes(x=0,y=perc,xend=p75,
yend=perc,colour="green"),size=1.25,linetype="dotted",show.legend = FALSE)
#and 99.5th
perc2<-0.995
p995<-Dist1_quant[6]
plot1<-
plot1+geom_segment(aes(x=p995,y=0,xend=p995,
yend=perc2,colour="orange"),size=1.25)
plot1<-
plot1+geom_segment(aes(x=0,y=perc2,xend=p995,
yend=perc2,colour="orange"),size=1.25,linetype="dotted",show.legend = FALSE)
plot1<-plot1+ggtitle("Cumulative density function of estimated future claims
outgo")+
scale_colour_discrete(name="", labels=c("Lognormal", "Mean","75th
%ile","99.5th %ile"))
plot1
This produces a chart (that I can't seem to load for some reason) which a) only has three legend items ("Lognormal", "Mean" and "75th %ile") when I want four (additionally "99.5th %ile" added to these three) and the three items are coloured red, green and blue, respectively, whereas I want Lognormal as blue, Mean as red, 75th as green and 99.5th as orange. The dotted lines should remain on the plot, but not appear in the legend.
What am I doing wrong? Presumably it is something to do with aesthetics and "scale_colour_discrete", but I can't work out what to do. Any help would be much appreciated.
Thanks.
To add to the answer by www, I find that it saves a lot of risk of confusion if you don't create aesthetic mappings to the names of colors. Instead use a meaningful label like "mean" for the mean line. That way if you change your mind about color selection later you don't end up with something absurd like a manual scale that maps "blue" to "orange".
Generally, aesthetic mappings should not map to colors directly, but rather should assign values that will be mapped to colors later (either manually or automatically).
EDIT:
At bdemarest's suggestion, I'm adding some more explanation and code. Here's what www's refactoring of the OP's code would look like:
library("distr")
library("ggplot2")
#calculations
Percent_values <- c(0.5,0.75,0.9,0.95,0.995,0.999)
Dist1_mean = 20219
Dist1_CV = 3235 / 20219
Dist1_SDEV <- Dist1_mean*Dist1_CV
Dist1_parm2 <- sqrt(log(1+Dist1_CV^2))
Dist1_parm1 <- log(Dist1_mean)-(Dist1_parm2^2)/2
Dist1_quant <- qlnorm(Percent_values, meanlog=Dist1_parm1,
sdlog=Dist1_parm2)
lowerx <- 0
upperx <- 1.1*Dist1_quant[6]
mean_yvalue <- plnorm(Dist1_mean, meanlog=Dist1_parm1,
sdlog=Dist1_parm2)
perc <- 0.75
p75 <- Dist1_quant[2]
perc2 <- 0.995
p995 <- Dist1_quant[6]
#plot
ggplot(data.frame(x = c(lowerx, upperx)), aes(x = x))+
stat_function(fun=plnorm,
args=list(meanlog=Dist1_parm1,
sdlog=Dist1_parm2),
geom="line",
aes(colour="logn"), # logn label
size=1.25) +
scale_x_continuous(name="Value")+
scale_y_continuous(name="Cumulative probability") +
geom_segment(aes(x=Dist1_mean, y=0,
xend=Dist1_mean,
yend=mean_yvalue,
colour="mean"), # mean label
size=1.25) +
geom_segment(aes(x=0, y=mean_yvalue,
xend=Dist1_mean,
yend=mean_yvalue,
colour="mean"), # mean label
size=1.25, linetype="dotted",
show.legend = FALSE) +
geom_segment(aes(x=p75, y=0,
xend=p75, yend=perc,
colour="75th"), # 75th percentile label
size=1.25) +
geom_segment(aes(x=0, y=perc, xend=p75,
yend=perc,
colour="75th"), # 75th percentile label
size=1.25, linetype="dotted",
show.legend = FALSE) +
geom_segment(aes(x=p995, y=0, xend=p995,
yend=perc2,
colour="995th"), # 99.5th percentile label
size=1.25) +
geom_segment(aes(x=0, y=perc2, xend=p995,
yend=perc2,
colour="995th"), # 99.5th percentile label
size=1.25, linetype="dotted",
show.legend = FALSE) +
ggtitle("Cumulative density function of estimated future claims outgo") +
scale_colour_manual(name="",
# labels map onto colors and pretty labels
values=c("logn"="blue",
"mean"="red",
"75th"="green",
"995th"="orange"),
labels=c("logn"="Lognormal",
"mean"="Mean",
"75th"="75th %ile",
"995th"="99.5th %ile"))
Note that in the aes mappings, color names have been replaced with descriptive labels (e.g., "red" became "mean" and "blue" became "logn"), and the scale_colour_manual call has been changed to map these labels onto both colors and pretty labels for the legend. That way, if you later decide to make the 75th percentile segments purple instead of green, you just have to change the color mapping in the scale_colour_manual call at the bottom -- you don't have to dig through the code and find the right geom_segments to change, and, if you're too lazy to do that, you won't end up with something horrible, like:
scale_colour_manual(values=c("blue"="blue",
"red"="red",
"green"="purple", # !!!???
"orange"="orange"))
Instead, you (very intuitively) change the color of the 75th components to be purple:
scale_colour_manual(values=c("logn"="blue",
"mean"="red",
"75th"="purple", # makes sense
"995th"="orange"))
Changing the code to use meaningful labels in the aesthetic mappings simply makes it cleaner, more intuitive, and easier to read and maintain. It does not change the final output of the graph:
But if you're going to be attempting a complicated graph with lots of components and an information-packed legend, this is a small change of habit that will likely save you a great deal of frustration down the road.
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