如何将所有数据添加到facet_wrap ggplot标准错误图（其中facet包裹已用于表示因子）

Question

我正在尝试绘制一个多窗格图形，该图形显示各种生物多样性指标的标准误差图。我有来自五条河流的数据，但我想独立显示五条河流（当前在facet_warp函数中调用），但我想添加第六个图，该图将所有数据组合为标准误差（该图是该行中的最终图）共 6 个）。我可以将第六张图绘制为散点图或小 SE，但格式与其他图不同。任何帮助将不胜感激。

提前非常感谢！我用于一项指标的代码如下。

sum = summarySE(Summer, 
                measurevar="Feve", 
                groupvars=c("Habitat","River"))

str(sum)

pd = position_dodge(.5)

fig1 <- ggplot(sum, aes(x=Habitat, 
                        y=Feve, 
                        colour = Habitat)) + 
  geom_errorbar(aes(ymin=Feve-se, 
                    ymax=Feve+se), 
                width=.2, size=0.7, position=pd) +
  geom_point(position = pd, size = 6)


Feve1<- fig1 +
  xlab("Habitat") + ylab("FEve")  + theme_bw()  + facet_wrap(~River, ncol=5)+
  scale_colour_manual(values=Plot_colours) +
  theme(axis.text.x = element_text(angle = 0, hjust = 0.5, size=20), 
        axis.text.y = element_text(size=20),
        strip.text = element_text(size=20), 
        axis.title.x =element_text(size=25),
        axis.title.y =element_text(size=25),
        plot.tag=element_text(size=30),
        panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"), 
        legend.key=element_blank(),
        legend.position = c("none"), 
        legend.text=element_text(size=20), 
        legend.title=element_text(size=25)) + labs(tag = "e)")

dput()函数的数据结构如下：

structure(list(Habitat = c("Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", "Gravel", 
"Gravel", "Gravel", "Gravel", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Sand", 
"Sand", "Sand", "Sand", "Sand", "Sand", "Sand", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt", 
"Silt", "Silt", "Silt", "Silt", "Silt", "Silt", "Silt"), River = c("Mill", 
"Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", 
"Mill", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", 
"Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", 
"Mill", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool", 
"Wool", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool", 
"Wool", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", 
"Mill", "Mill", "Mill", "Gadder", "Gadder", "Gadder", "Gadder", 
"Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", 
"Gadder", "Gadder", "Gadder", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Mill", "Mill", "Mill", "Mill", 
"Mill", "Mill", "Mill", "Mill", "Mill", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool", 
"Wool", "Wool", "Wool", "Wool", "Wool", "Mill", "Mill", "Mill", 
"Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Gadder", 
"Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", "Gadder", 
"Gadder", "Gadder", "Gadder", "Gadder", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Little Stour", "Little Stour", 
"Little Stour", "Little Stour", "Mill", "Mill", "Mill", "Mill", 
"Mill", "Mill", "Mill", "Mill", "Mill", "Mill", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Frome", "Frome", "Frome", "Frome", 
"Frome", "Frome", "Frome", "Wool", "Wool", "Wool", "Wool", "Wool", 
"Wool", "Wool", "Wool", "Wool", "Wool", "Wool", "Wool"), Feve = c(0.506977035, 
0.658168517, 0.5552127, 0.674176982, 0.568126029, 0.470484617, 
0.546243187, 0.557337037, 0.549162764, 0.51482439, 0.296783107, 
0.540411614, 0.441655613, 0.748657363, 0.586072179, 0.562687945, 
0.679463256, 0.640924761, 0.76043005, 0.570535503, 0.345381789, 
0.7466591, 0.697030182, 0.397968864, 0.370615261, 0.512819438, 
0.251546229, 0.570256251, 0.346010274, 0.243989438, 0.879881015, 
0.578277252, 0.586240417, 0.653396257, 0.641415819, 0.524595729, 
0.536800538, 0.519825276, 0.521801451, 0.465289634, 0.838068847, 
0.487272624, 0.665124783, 0.629608162, 0.400335866, 0.538598014, 
0.614481336, 0.50953854, 0.564571301, 0.434708346, 0.603286877, 
0.389340894, 0.451569201, 0.664220591, 0.625448998, 0.47830779, 
0.610677015, 0.592997649, 0.389771325, 0.345401462, 0.368971704, 
0.515001741, 0.49651484, 0.49955372, 0.538803174, 0.430966396, 
0.601647539, 0.450264668, 0.510230055, 0.455519866, 0.382308378, 
0.541156034, 0.48855963, 0.454064886, 0.628266636, 0.588126731, 
0.694832118, 0.721497295, 0.652815016, 0.614719973, 0.629744193, 
0.556498854, 0.599436173, 0.537701923, 0.634997581, 0.422792666, 
0.54448257, 0.61526252, 0.644073418, 0.463178528, 0.53049777, 
0.73680085, 0.654108793, 0.508193606, 0.784465676, 0.625011416, 
0.370492072, 0.615810028, 0.758132891, 0.733838212, 0.699321368, 
0.680547958, 0.631187485, 0.580031627, 0.669025786, 0.578582091, 
0.720102863, 0.610043844, 0.705463595, 0.736388029, 0.626156558, 
0.665129554, 0.736158535, 0.635527589, 0.667806898, 0.719755199, 
0.516756193, 0.743297471, 0.385352895, 0.032755073, 0.370695443, 
0.411080389, 0.610129264, 0.44115061, 0.436587861, 0.646474891, 
0.4632019, 0.544676459, 0.70722696, 0.775250617, 0.386924944, 
0.681213071, 0.785576735, 0.653825379, 0.765341039, 0.531730415, 
0.496468683, 0.568045574, 0.559047785, 0.618799013, 0.595831336, 
0.62825441, 0.566092305, 0.424579031, 0.475876404, 0.456873779, 
0.547629079, 0.650585859, 0.573124305, 0.723337968, 0.5208022, 
0.682698101, 0.639284704, 0.633086197, 0.561406514, 0.680181535, 
0.459038805, 0.471462813, 0.731018956, 0.634841981, 0.721187209, 
0.702791349, 0.744408386, 0.595889197, 0.48472181, 0.560199898, 
0.608437475, 0.707858755, 0.675909255, 0.735408435, 0.435542404, 
0.6053562, 0.595449887, 0.520702662, 0.645871555, 0.557698442, 
0.771899899, 0.508251962, 0.694331707, 0.549159191, 0.53994213, 
0.547164043, 0.644919934, 0.699196417, 0.735875306, 0.667984496, 
0.630353871, 0.718764016, 0.398504512, 0.668611595, 0.436300968, 
0.562561037, 0.656273832, 0.71630428, 0.626932262, 0.733307825, 
0.669013893, 0.837176403, 0.298035491, 0.810612337, 0.777539406, 
0.790902907, 0.713633147, 0.626837346, 0.571953853, 0.745992741, 
0.533352203, 0.755656202, 0.548742714, 0.327365258, 0.72424353, 
0.528402042, 0.540123857, 0.688264186, 0.541088378, 0.675038588, 
0.71309915, 0.651328765, 0.56050516, 0.632870384, 0.480536671, 
0.549664771, 0.58966238, 0.648690841, 0.543502869, 0.583063433, 
0.522673603, 0.656145405, 0.389555701, 0.52203683, 0.560684461, 
0.469447718, 0.639486253, 0.594082402, 0.57815447, 0.633922141, 
0.480927001, 0.605528679, 0.665749149, 0.401690615, 0.344411136, 
0.382189728, 0.681118444, 0.519816427, 0.47681727, 0.470170962, 
0.478776858, 0.694232011, 0.696928503, 0.705804165, 0.691504978, 
0.771564279, 0.634355679, 0.540054854, 0.580389632, 0.648298349, 
0.599541806, 0.375901167, 0.471454503, 0.361917446, 0.487022267, 
0.597901339)), class = "data.frame", row.names = c(NA, -262L))

Answer 1

尝试使用内置数据集而不是原始数据来回答问题总是有点不太令人满意，但我们似乎没有什么选择。

让我们使用内置数据集

mtcars

，但将

cyl

和

gears

设置为因子变量来模拟您自己的 x 轴和分面变量：

library(tidyverse)

mtcars2 <- mtcars %>%
  rownames_to_column('model') %>%
  mutate(across(c(cyl, gear), factor))

如果我们用齿轮分面进行绘图，我们会得到以下绘图：

ggplot(mtcars2, aes(cyl, mpg)) +
  geom_errorbar(stat = 'summary', fun.data = 'mean_se', width = 0.4,
                linewidth = 1, aes(color = cyl)) +
  geom_point(position = position_jitter(0.2), alpha = 0.5) +
  facet_wrap(.~gear, ncol = 2) +
  theme_bw()

现在，如果我们想添加一个包含所有数据的额外分面，我们可以通过将数据帧的第二个副本行绑定到原始数据帧上来实现此目的，其中分面变量被单个字符串替换

"all"

:

ggplot(rbind(mtcars2, within(mtcars2, gear <- 'all')), aes(cyl, mpg)) +
  geom_errorbar(stat = 'summary', fun.data = 'mean_se', width = 0.4,
                linewidth = 1, aes(color = cyl)) +
  geom_point(position = position_jitter(0.2), alpha = 0.5) +
  facet_wrap(.~gear) +
  theme_bw()