将形状编号映射到图例 [英] Mapping shape number to legend

问题描述

我在ggplot2的语法上需要一些帮助.我有一些看起来像这样的数据:

 >dput(SOData)结构(列表(化合物= c("cmpd1"，"cmpd2"，"cmpd3"，"cmpd2"，"cmpd3"，"cmpd3"，"cmpd4"，"cmpd5"，"cmpd6"，"cmpd1"，"cmpd5"，"cmpd6"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd4"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd5"，"cmpd7"，"cmpd7"，"cmpd4"，"cmpd4"，"cmpd4"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd7"，"cmpd3"，"cmpd5"，"cmpd3"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd5"，"cmpd3"，"cmpd5"，"cmpd7"，"cmpd3"，"cmpd7"，"cmpd8"，"cmpd8"，"cmpd8"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd7"，"cmpd1"，"cmpd2"，"cmpd3"，"cmpd2"，"cmpd3"，"cmpd3"，"cmpd4"，"cmpd5"，"cmpd6"，"cmpd1"，"cmpd5"，"cmpd6"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd4"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd2"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd1"，"cmpd5"，"cmpd7"，"cmpd7"，"cmpd4"，"cmpd4"，"cmpd4"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd7"，"cmpd3"，"cmpd5"，"cmpd3"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd5"，"cmpd3"，"cmpd5"，"cmpd7"，"cmpd3"，"cmpd7"，"cmpd8"，"cmpd8"，"cmpd8"，"cmpd6"，"cmpd5"，"cmpd6"，"cmpd7")，变量=结构(c(1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L，2L)，类=因数"，.Label = c("Avg SS(pA)"，最小峰值(pA)"))，值= c(274，109，175，113，86，121，80，112、311、110、101、312、97、419、494、454、169、80、114、119，105、392、207、103、84、102、100、86、96、79、339、356、394，317、227、158、54、136、104、107、86、58、66、84、72、90、111，95、134、89、285、50、69、78、89、249、90、80、62、248、72、85，96、97、108、85、31、53、482、551、388、323、59、74、233、193，206、162、79、97、21、72、170、144、57、21、68、94、310、223，262、191、116、107、108、116、149、185、153、76、99、111、103，129、119、395、181、203、293、192、340、74、130、107、132、284，93、72、92、140、75、57、71、63、141、154、21、52、50、106、63，184、369、89、223、173、120、111、191、298、62、65、72、325，286，194，339，128，91，110)，Conc = c("10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"0.1"，.3"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，.3"，"1.00"，"10.0"，"10.0"，"10.0"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"0.1"，"0.1"，"1.00"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"0.1"，.3"，.3"，.3"，"10.0"，"1.00"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"10.0"，"0.1"，"10.0"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"0.1"，"10.0"，"1.00"，"0.1"，"10.0"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"0.1"，.3"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"1.00"，"0.1"，.3"，"1.00"，"10.0"，"10.0"，"10.0"，"10.0"，"10.0"，"10.0"，"1.00"，"1.00"，"0.1"，"0.1"，"1.00"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"0.1"，.3"，.3"，.3"，"10.0"，"1.00"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"10.0"，"0.1"，"10.0"，"1.00"，"1.00"，"0.1"，"10.0"，"10.0"，"1.00"，"0.1"，"0.1"，"10.0"，"1.00"，"0.1"，"10.0"，"1.00"，"0.1"，"10.0"))，.Names = c("Compound"，"variable"，"value"，"Conc")，row.names = c(NA，-150L)，class = c("data.table"，"data.frame")、. internal.selfref =<指针:0x0000000000110788>) 

我的GGplot看起来像这样:

  FinalPlot = ggplot(data = SOData，aes(x = Conc，y = value，color = variable))+geom_point(大小= 2，aes(x = Conc，y =值，颜色=变量)，形状= 16)+stat_summary(aes(group = variable)，fun.y = mean，geom ='point'，size = 4，shape = 9)+scale_shape_manual('Legend'，values = c(9,16))+facet_wrap(〜复合)+ylab('Current(pA)')+xlab('浓度(µM)')+主题(text = element_text(size = 14，face ='bold'))+主题(strip.text.x = element_text(大小= 16，脸='粗体'))+主题() 

关于情节:

每个方面都是一个单独的复合词.在每个方面，我都需要给定条件(变量)的所有值，然后是每个变量的均值.现在我有geom_point映射值，然后stat_summary做平均值.每个都有一个单独的形状编号.这可以正常工作，但图例不提供任何信息.如何拆分图例以将点颜色显示为变量，然后将形状样式显示为Raw或Mean?

解决方案

如果要在图例中显示某些内容，通常 必须将其添加到 aes .您可以简单地将形状映射到信息丰富的名称，然后在您的 scale_shape_manual 调用中将该名称映射到实际形状.

  ggplot(data = SOData，aes(x = Conc，y = value，color = variable))+geom_point(大小= 2，aes(x = Conc，y =值，颜色=变量，形状='原始'))+stat_summary(aes(group = variable，shape ='mean')，fun.y = mean，geom ='point'，size = 4)+scale_shape_manual('Legend'，values = c(mean = 9，raw = 16))+facet_wrap(〜复合)+ylab('Current(pA)')+xlab('浓度(µM)')+主题(text = element_text(size = 14，face ='bold'))+主题(strip.text.x = element_text(大小= 16，面='粗体')) 

(请注意， minimum 示例可能包含更小的数据集，使用data.frame代替data.table，没有构面，没有标签，也没有主题调用.)

I need some help in the grammar of ggplot2. I have some Data that looks like this:

> dput(SOData)
structure(list(Compound = c("cmpd1", "cmpd2", "cmpd3", "cmpd2", 
"cmpd3", "cmpd3", "cmpd4", "cmpd5", "cmpd6", "cmpd1", "cmpd5", 
"cmpd6", "cmpd1", "cmpd1", "cmpd1", "cmpd1", "cmpd2", "cmpd2", 
"cmpd1", "cmpd1", "cmpd1", "cmpd1", "cmpd2", "cmpd2", "cmpd2", 
"cmpd2", "cmpd2", "cmpd2", "cmpd4", "cmpd1", "cmpd1", "cmpd1", 
"cmpd1", "cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd2", 
"cmpd2", "cmpd1", "cmpd1", "cmpd1", "cmpd1", "cmpd5", "cmpd7", 
"cmpd7", "cmpd4", "cmpd4", "cmpd4", "cmpd6", "cmpd5", "cmpd6", 
"cmpd5", "cmpd6", "cmpd7", "cmpd3", "cmpd5", "cmpd3", "cmpd6", 
"cmpd5", "cmpd6", "cmpd5", "cmpd3", "cmpd5", "cmpd7", "cmpd3", 
"cmpd7", "cmpd8", "cmpd8", "cmpd8", "cmpd6", "cmpd5", "cmpd6", 
"cmpd7", "cmpd1", "cmpd2", "cmpd3", "cmpd2", "cmpd3", "cmpd3", 
"cmpd4", "cmpd5", "cmpd6", "cmpd1", "cmpd5", "cmpd6", "cmpd1", 
"cmpd1", "cmpd1", "cmpd1", "cmpd2", "cmpd2", "cmpd1", "cmpd1", 
"cmpd1", "cmpd1", "cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd2", 
"cmpd2", "cmpd4", "cmpd1", "cmpd1", "cmpd1", "cmpd1", "cmpd2", 
"cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd2", "cmpd1", 
"cmpd1", "cmpd1", "cmpd1", "cmpd5", "cmpd7", "cmpd7", "cmpd4", 
"cmpd4", "cmpd4", "cmpd6", "cmpd5", "cmpd6", "cmpd5", "cmpd6", 
"cmpd7", "cmpd3", "cmpd5", "cmpd3", "cmpd6", "cmpd5", "cmpd6", 
"cmpd5", "cmpd3", "cmpd5", "cmpd7", "cmpd3", "cmpd7", "cmpd8", 
"cmpd8", "cmpd8", "cmpd6", "cmpd5", "cmpd6", "cmpd7"), variable = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), class = "factor", .Label = c("Avg SS (pA)", 
"Min Peak (pA)")), value = c(274, 109, 175, 113, 86, 121, 80, 
112, 311, 110, 101, 312, 97, 419, 494, 454, 169, 80, 114, 119, 
105, 392, 207, 103, 84, 102, 100, 86, 96, 79, 339, 356, 394, 
317, 227, 158, 54, 136, 104, 107, 86, 58, 66, 84, 72, 90, 111, 
95, 134, 89, 285, 50, 69, 78, 89, 249, 90, 80, 62, 248, 72, 85, 
96, 97, 108, 85, 31, 53, 482, 551, 388, 323, 59, 74, 233, 193, 
206, 162, 79, 97, 21, 72, 170, 144, 57, 21, 68, 94, 310, 223, 
262, 191, 116, 107, 108, 116, 149, 185, 153, 76, 99, 111, 103, 
129, 119, 395, 181, 203, 293, 192, 340, 74, 130, 107, 132, 284, 
93, 72, 92, 140, 75, 57, 71, 63, 141, 154, 21, 52, 50, 106, 63, 
184, 369, 89, 223, 173, 120, 111, 191, 298, 62, 65, 72, 325, 
286, 194, 339, 128, 91, 110), Conc = c("10.0", "10.0", "10.0", 
"1.00", "1.00", "0.1", ".3", "10.0", "10.0", "1.00", "1.00", 
"1.00", "0.1", "10.0", "10.0", "10.0", "10.0", "1.00", "1.00", 
"1.00", "0.1", "10.0", "10.0", "10.0", "1.00", "1.00", "1.00", 
"0.1", ".3", "1.00", "10.0", "10.0", "10.0", "10.0", "10.0", 
"10.0", "1.00", "1.00", "0.1", "0.1", "1.00", "1.00", "0.1", 
"0.1", "10.0", "1.00", "0.1", ".3", ".3", ".3", "10.0", "1.00", 
"1.00", "0.1", "0.1", "10.0", "1.00", "10.0", "0.1", "10.0", 
"1.00", "1.00", "0.1", "10.0", "10.0", "1.00", "0.1", "0.1", 
"10.0", "1.00", "0.1", "10.0", "1.00", "0.1", "10.0", "10.0", 
"10.0", "10.0", "1.00", "1.00", "0.1", ".3", "10.0", "10.0", 
"1.00", "1.00", "1.00", "0.1", "10.0", "10.0", "10.0", "10.0", 
"1.00", "1.00", "1.00", "0.1", "10.0", "10.0", "10.0", "1.00", 
"1.00", "1.00", "0.1", ".3", "1.00", "10.0", "10.0", "10.0", 
"10.0", "10.0", "10.0", "1.00", "1.00", "0.1", "0.1", "1.00", 
"1.00", "0.1", "0.1", "10.0", "1.00", "0.1", ".3", ".3", ".3", 
"10.0", "1.00", "1.00", "0.1", "0.1", "10.0", "1.00", "10.0", 
"0.1", "10.0", "1.00", "1.00", "0.1", "10.0", "10.0", "1.00", 
"0.1", "0.1", "10.0", "1.00", "0.1", "10.0", "1.00", "0.1", "10.0"
)), .Names = c("Compound", "variable", "value", "Conc"), row.names = c(NA, 
-150L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000000000110788>)

My GGplot looks like so:

    FinalPlot = ggplot(data=SOData,aes(x=Conc,y=value,color=variable))+
geom_point(size =2,aes(x=Conc,y=value,color=variable), shape=16)+
stat_summary(aes(group=variable),fun.y=mean,geom='point',size=4,shape=9)+
scale_shape_manual('Legend',values =c(9,16))+
facet_wrap(~Compound)+
ylab('Current (pA)')+
xlab('Concentration (µM)')+
theme(text= element_text(size=14,face='bold'))+
theme(strip.text.x = element_text(size = 16,face = 'bold'))+
theme()

About the plot:

Each facet will be an individual compound. In each facet, I want all the values for a given condition(variable), then the mean of each variable. Right now what I have geom_point mapping the values then stat_summary doing the mean. each with a separate shape number. This works fine, but the legend is uninformative. How can I split the legend to show the point color as variable, then the shape style to be Raw or Mean?

解决方案

If you want something to show up in the legend, you generally will have to add it to the aes. You can simply map the shape to an informative name, and map that name in your scale_shape_manual call to an actual shape.

ggplot(data=SOData,aes(x=Conc,y=value,color=variable))+
  geom_point(size =2,aes(x=Conc,y=value,color=variable, shape='raw'))+
  stat_summary(aes(group=variable,shape='mean'),fun.y=mean,geom='point',size=4)+
  scale_shape_manual('Legend',values =c(mean = 9, raw = 16))+
  facet_wrap(~Compound)+
  ylab('Current (pA)')+
  xlab('Concentration (µM)')+
  theme(text= element_text(size=14,face='bold'))+
  theme(strip.text.x = element_text(size = 16,face = 'bold'))

(Please note that a minimal example could contain a much smaller data set, with a data.frame instead of a data.table, no facets, no labels and no theme calls.)

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