ggplot2使用geom_line手动指定颜色 [英] ggplot2 manually specifying colour with geom_line

问题描述

我试图绘制下面的图表，并想手动指定颜色。

我需要绘制基因型，因为有多个基因型属于同样Bgrnd_All，我希望他们在绘制的行中单独出现。

但是，我想通过Bgrnd_All颜色的线，具体是在顺序/颜色在scale_fill_manual中使用。

当我这样做时，scale_fill_manual中的值不会覆盖geom_line中定义的现有颜色。

[下面的图表的数据] [1] https://www.dropbox.com/s/9nmu87wkh2yqfxn/ summary_200_exp2.csv？dl = 0

  pd<  -  position_dodge（1）
 
 ggplot（data = summary.200.exp2，aes（x = Time，y = Length，color = Genotype，group = Genotype））+ 
 geom_errorbar（aes（ymin = Length-se，ymax = Length + se ），color =black，width = 1，position = pd）+ 
 geom_line（aes（color = Bgrnd_All），position = pd，size = 1）+ 
 scale_x_continuous ，17,22,41,89））+＃使用抽样时的间隔
 scale_fill_manual（values = c（Avalon =＃000066，Av_A =＃663399，Av_B =＃339999，Cadenza = CC0033，Cad_A =FF6600，Cad_B =FF9933）+ 
 ylab（叶段宽度（mm））+ 
 xlab （axis.title = element_text（size = 14，face =bold），
 axis.text = element_text（size = 14），
 strip.text.y = element_text（size = 14）） 
  

---

A code>的数据：

  summary.200.exp2<  -  structure（list（X = 1:40 ，
基因型=结构（c（1L，1L，1L，1L，1L，2L，2L，2L，2L，2L，3L，3L，3L，3L，3L，4L，4L，4L，4L，4L ，5L，5L，5L，5L，5L，5L，6L，6L，6L，6L，6L，7L，7L，7L，7L，7L，8L，8L，8L，8L，8L） ，4.41，7.50，7.59，8.51，8.77，Avalon，Cadenza，class =factor），
 Time = c（0L，17L，22L ，41L，89L，0L，17L，22L，41L，89L，0L，17L，22L，41L，89L，0L，17L，22L，41L，89L，0L，17L，22L，41L，89L，0L，17L，22L 41L，41L，41L，41L，89L），
 Bgrnd_All =结构（c（4L，4L，4L，4L，4L，5L，5L， 5L，5L，5L，5L，5L，5L，5L，5L，5L，5L，4L，4L，4L，4L，4L，3L，3L， （Av_A，Av_B，Avalon，Cad_A，Cad_B，Cadenza），class =3，3L，3L，3L，6L，6L，6L，6L，6L）因子），
 N = c（43L，48L，44L，47L，48L，22L，21L，26L，27L，25L，36L，24L，44L，48L，45L，50L，26L，52L， 53L，38L，52L，52L，49L，50L，39L，39L，42L，38L，42L，84L，42L，84L，42L，42L，50L，26L，53L，27L，27L），
长度= c （1.17423255813953，1.58852083333333，1.71263636363636，1.86736170212766，2.0331875，1.07563636363636，1.49866666666667，1.48734615384615，1.66796296296296，2.15416，1.08716666666667，1.09858333333333，1.24593181818182，1.30827083333333，1.81537777777778，1.15672，1.8475，1.96815384615385，2.01822222222222，2.5057358490566，1.14697368421053，1.40276923076923，1.49832692307692，1.76981632653061，2.27954 ，1.18312820512821，1.75928205128205，1.86195238095238，1.91426315789474，2.26883333333333，1.10839285714286，1.97902380952381，2.03271428571429，2.15685714285714，2.8227380952381，1.08658，1.68880769230769，1.7277358490566，1.9232962962963，2.13466666666667），
 SD = C（0.218740641945063，0​​.357307960001092，0.377931031662453，0.416137123383518，0.440003996899158， 0.176915784499843，0.426273190962478，0.305677731254037，0.450036449932454，0.48642939535627，0.15212823538055，0.175160775008132，0.293836087650785，0.282464815326021，0.346608194369436，0.211422397593258，0.408328617659845，0.413460118977535，0.419730221832425，0.508692484972064，0.217587942685885，0.207510416973071，0.245473270071832，0.377310585673427，0.536134471785516，0.159925670150259，0.298319411009668，0.338847829173593，0.296186727462412，0.445638589029855， 0.162594700328365，0.308723610551514，0.318831396748337，0.381781291715339，0.402059458017902，0.167826451905484，0.257140275994371，0.338637947743116，0.362428434825926，0.343680867174096），
 SE = C（0.0333576351702583，0.0515729617225566，0.0569752467571038，0.0606998379642952，0.06350910651356，0.0377185719899813，0.0930204363959963，0.0599483352513503，0.0866095551712153，0.097285879071254，0.0253547058967583 ，0.0357545434766975，0.0442974569365289，0.040770284291269，0.0516692989445678，0.0298996422065822，0.0800798303617661，0.0573366022820362，0.0571180485063685，0.0698742866122227，0.0352974252834232，0.0287765172534354，0.0340410177692235，0.053901512239061，0.0758208641254813，0.0256086023072023，0.0477693365291991，0.052285355168868，0.0480478318490224，0.0687635271596866，0.0177405362346046，0.0476370873204908，0.0347873573697084，0.0589101322645314，0.0620391212561054，0.0237342444409691 ，0.0504293571163821，0.046515499476421，0.0697493848029077，0.0661414137260961），
 CI = C（0.0673184331863912，0.103751416510302，0.114901535684132，0.122182436693452，0.127763842564108，0.0784400645137227，0.194037230170767，0.123465907623535，0.178028490322197，0.200788185881879，0.0514727894594648，0.0739639084701291，0.0893343358495282，0.0820192326650262，0.104132629687123，0.0600855805773719， 0.164927497928001，0.11510803218647，0.11456429705202，0.140213013986381，0.0715193770736051，0.0577712690042106，0.0683401947985261，0.108376253996364，0.152367731004308，0.0518419050566429，0.0967039660836575，0.105592416917608，0.0973541547573791，0.138870760371045，0.0352852130493688，0.0962050495562246，0.06919065466693，0.118971425682342，0.125290547146885，0.0476957499005439，0.103861205171753，0.0933401784102089，0.143371913789607，0.135955623027448）） 
 .Names = c（X，Genotype，Time，Bgrnd_All，N，Length，sd，se，ci），class =data .frame，row.names = c（NA，-40L））
  




解决方案

如@juba在注释中所述，应该使用 scale_colour_manual 而不是 scale_fill_manual 。此外，你试图绘制多条线和错误线在一个情节。

使用facet的一个例子（和你的代码的一些简化）：

  ggplot（summary.200.exp2，aes（x = Time，y = Length，group = Genotype））+ 
 geom_line（aes（color = Bgrnd_All），size = 1）+ 
 geom_errorbar（aes（ymin = Length-se，ymax = Length + se，color = Bgrnd_All），width = 2）+ 
 scale_x_continuous（Time，breaks = c（0,17,22,41,89））+ 
 scale_colour_manual（values = c（Avalon =＃000066，Av_A =＃663399，Av_B =＃ （叶片段宽度（mm））+ 
 theme_bw（）+ 
（339999，Cadenza =＃CC0033，Cad_A =＃FF6600，Cad_B =＃FF9933 
 theme（axis.title = element_text（size = 14，face =bold），axis.text = element_text（size = 10））+ 
 facet_wrap（〜Bgrnd_All，ncol = 3）
  

这给出：

I'm trying to plot the graph below, and want to manually specify colours.

I need to plot by genotype, since there are multiple genotypes belonging to the same Bgrnd_All, and I want them to come up separately in the lines plotted.

However, I want to colour the lines by Bgrnd_All, and specifically in the order/colour I use in scale_fill_manual.

When I do this, the values in scale_fill_manual do not overwrite the existing colour as defined in geom_line. How can I do this?

I'd be grateful for pointers.

[Data for graph below][1]https://www.dropbox.com/s/9nmu87wkh2yqfxn/summary_200_exp2.csv?dl=0

    pd <- position_dodge(1)

ggplot(data=summary.200.exp2, aes(x=Time, y=Length, colour=Genotype, group=Genotype)) + 
  geom_errorbar(aes(ymin=Length - se, ymax=Length + se), colour="black", width=1, position=pd) +
  geom_line(aes(colour=Bgrnd_All), position=pd, size =1) +
  scale_x_continuous(breaks=c(0,17,22,41,89)) + #using breaks of when sampled
  scale_fill_manual(values=c(Avalon="#000066",Av_A="#663399",Av_B="#339999",Cadenza="CC0033",Cad_A="FF6600",Cad_B="FF9933"))+
  ylab("leaf segment width (mm)") +
  xlab("Time") +
  theme(axis.title = element_text(size=14,face="bold"), 
        axis.text = element_text(size=14),
        strip.text.y = element_text(size=14))

---

A dput of the data:

summary.200.exp2 <- structure(list(X = 1:40,
                               Genotype = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L), .Label = c("4.18", "4.41", "7.50", "7.59", "8.51", "8.77", "Avalon", "Cadenza"), class = "factor"), 
                               Time = c(0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L, 0L, 17L, 22L, 41L, 89L), 
                               Bgrnd_All = structure(c(4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L), .Label = c("Av_A", "Av_B", "Avalon", "Cad_A", "Cad_B", "Cadenza"), class = "factor"), 
                               N = c(43L, 48L, 44L, 47L, 48L, 22L, 21L, 26L, 27L, 25L, 36L, 24L, 44L, 48L, 45L, 50L, 26L, 52L, 54L, 53L, 38L, 52L, 52L, 49L, 50L, 39L, 39L, 42L, 38L, 42L, 84L, 42L, 84L, 42L, 42L, 50L, 26L, 53L, 27L, 27L), 
                               Length = c(1.17423255813953, 1.58852083333333, 1.71263636363636, 1.86736170212766, 2.0331875, 1.07563636363636, 1.49866666666667, 1.48734615384615, 1.66796296296296, 2.15416, 1.08716666666667, 1.09858333333333, 1.24593181818182, 1.30827083333333, 1.81537777777778, 1.15672, 1.8475, 1.96815384615385, 2.01822222222222, 2.5057358490566, 1.14697368421053, 1.40276923076923, 1.49832692307692, 1.76981632653061, 2.27954, 1.18312820512821, 1.75928205128205, 1.86195238095238, 1.91426315789474, 2.26883333333333, 1.10839285714286, 1.97902380952381, 2.03271428571429, 2.15685714285714, 2.8227380952381, 1.08658, 1.68880769230769, 1.7277358490566, 1.9232962962963, 2.13466666666667), 
                               sd = c(0.218740641945063, 0.357307960001092, 0.377931031662453, 0.416137123383518, 0.440003996899158, 0.176915784499843, 0.426273190962478, 0.305677731254037, 0.450036449932454, 0.48642939535627, 0.15212823538055, 0.175160775008132, 0.293836087650785, 0.282464815326021, 0.346608194369436, 0.211422397593258, 0.408328617659845, 0.413460118977535, 0.419730221832425, 0.508692484972064, 0.217587942685885, 0.207510416973071, 0.245473270071832, 0.377310585673427, 0.536134471785516, 0.159925670150259, 0.298319411009668, 0.338847829173593, 0.296186727462412, 0.445638589029855, 0.162594700328365, 0.308723610551514, 0.318831396748337, 0.381781291715339, 0.402059458017902, 0.167826451905484, 0.257140275994371, 0.338637947743116, 0.362428434825926, 0.343680867174096), 
                               se = c(0.0333576351702583, 0.0515729617225566, 0.0569752467571038, 0.0606998379642952, 0.06350910651356, 0.0377185719899813, 0.0930204363959963, 0.0599483352513503, 0.0866095551712153, 0.097285879071254, 0.0253547058967583, 0.0357545434766975, 0.0442974569365289, 0.040770284291269, 0.0516692989445678, 0.0298996422065822, 0.0800798303617661, 0.0573366022820362, 0.0571180485063685, 0.0698742866122227, 0.0352974252834232, 0.0287765172534354, 0.0340410177692235, 0.053901512239061, 0.0758208641254813, 0.0256086023072023, 0.0477693365291991, 0.052285355168868, 0.0480478318490224, 0.0687635271596866, 0.0177405362346046, 0.0476370873204908, 0.0347873573697084, 0.0589101322645314, 0.0620391212561054, 0.0237342444409691, 0.0504293571163821, 0.046515499476421, 0.0697493848029077, 0.0661414137260961), 
                               ci = c(0.0673184331863912, 0.103751416510302, 0.114901535684132, 0.122182436693452, 0.127763842564108, 0.0784400645137227, 0.194037230170767, 0.123465907623535, 0.178028490322197, 0.200788185881879, 0.0514727894594648, 0.0739639084701291, 0.0893343358495282, 0.0820192326650262, 0.104132629687123, 0.0600855805773719, 0.164927497928001, 0.11510803218647, 0.11456429705202, 0.140213013986381, 0.0715193770736051, 0.0577712690042106, 0.0683401947985261, 0.108376253996364, 0.152367731004308, 0.0518419050566429, 0.0967039660836575, 0.105592416917608, 0.0973541547573791, 0.138870760371045, 0.0352852130493688, 0.0962050495562246, 0.06919065466693, 0.118971425682342, 0.125290547146885, 0.0476957499005439, 0.103861205171753, 0.0933401784102089, 0.143371913789607, 0.135955623027448)), 
                          .Names = c("X", "Genotype", "Time", "Bgrnd_All", "N", "Length", "sd", "se", "ci"), class = "data.frame", row.names = c(NA, -40L))

解决方案

As stated by @juba in the comments, you should use scale_colour_manual instead of scale_fill_manual. Moreover, you are trying to plot to many lines and errorbars in one plot. They overlap each other to much and it is therefore hard to distuinguish between the lines/errorbars.

An example with the use of facetting (and some simplification of your code):

ggplot(summary.200.exp2, aes(x=Time, y=Length, group=Genotype)) + 
  geom_line(aes(colour=Bgrnd_All), size =1) +
  geom_errorbar(aes(ymin=Length-se, ymax=Length+se, colour=Bgrnd_All), width=2) +
  scale_x_continuous("Time", breaks=c(0,17,22,41,89)) + 
  scale_colour_manual(values=c(Avalon="#000066",Av_A="#663399",Av_B="#339999",Cadenza="#CC0033",Cad_A="#FF6600",Cad_B="#FF9933"))+
  ylab("leaf segment width (mm)") +
  theme_bw() +
  theme(axis.title = element_text(size=14,face="bold"), axis.text = element_text(size=10)) +
  facet_wrap(~Bgrnd_All, ncol=3)

this gives:

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