使用来自不同数据集的geom_point和geom_smooth添加图例

Question

我很难为具有黄土回归的geom_point图设置正确的图例，同时使用了2个数据集

我得到了一个数据集，该数据集汇总了一天中的活动，然后在同一张图上作了绘图，记录了每小时和每天的所有活动，以及用黄土函数平滑的回归曲线，以及每天的每个小时。

更准确地说，这是第一个代码的示例，返回的图没有图例，这正是我所期望的：

# first graph, which is given what I expected but with no legend
p <- ggplot(dat1, aes(x = Hour, y = value)) +
  geom_point(color = "darkgray", size = 1) +
  geom_point(data = dat2, mapping = aes(x = Hour, y = mean), 
             color = 20, size = 3) + 
  geom_smooth(method = "loess", span = 0.2, color = "red", fill = "blue")

和图形（灰色表示每小时，每天的所有数据。红色曲线表示黄土回归。蓝色点表示每小时的平均值）

当我尝试设置图例时，我无法用两种点（灰色数据，均值蓝色）和黄土曲线（红色）的解释来绘制图例。请参阅以下我尝试过的示例。

# second graph, which is given what I expected + the legend for the loess that 
# I wanted but with not the dot legend

p <- ggplot(dat1, aes(x = Hour, y = value)) +
  geom_point(color = "darkgray", size = 1) +
  geom_point(data = dat2, mapping = aes(x = Hour, y = mean), 
             color = "blue", size = 3) +
  geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +  
  scale_color_identity(name = "legend model", guide = "legend", 
                       labels = "loess regression \n with confidence interval")

我只获得了曲线的好传说

和另一个审判：

# I tried to combine both date set into a single one as following but it did not 
# work at all and I really do not understand how the legends works in ggplot2 
# compared to the normal plots

A <- rbind(dat1, dat2)
p <- ggplot(A, aes(x = Heure, y = value, color = variable)) +
  geom_point(data = subset(A, variable == "data"), size = 1) +
  geom_point(data = subset(A, variable == "Moy"), size = 3) +
  geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +
  scale_color_manual(name = "légende", 
                     labels = c("Data", "Moy", "loess regression \n with confidence interval"), 
                     values = c("darkgray", "royalblue", "red"))

似乎所有图例设置都以一种“怪异”的方式混合在一起，一个是用灰线覆盖的灰色点，然后是蓝色和红色（对于3个标签）相同。所有人都有蓝色的背景：

Answer 1

如果您需要标记均值，则可能需要有点创意，因为在ggplot中手动添加图例并不容易。

我模拟的东西看起来像下面的数据。

dat1 = data.frame(
       Hour = rep(1:24,each=10),
       value = c(rnorm(60,0,1),rnorm(60,2,1),rnorm(60,1,1),rnorm(60,-1,1))
)
# classify this as raw data
dat1$Data = "Raw"
# calculate mean like you did
dat2 <- dat1 %>% group_by(Hour) %>% summarise(value=mean(value))
# classify this as mean
dat2$Data = "Mean"

# combine the data frames
plotdat <- rbind(dat1,dat2)
# add a dummy variable, we'll use it later
plotdat$line = "Loess-Smooth"

我们首先制作基本点图：

ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
  geom_point() + 
  scale_color_manual(values=c("blue","darkgray"))+
  scale_size_manual(values=c(3,1),guide=FALSE)

请注意尺寸，我们将指南设置为FALSE，因此它不会出现。现在我们添加黄土平滑，引入图例的一种方法是引入线型，并且由于只有一组，因此您将只有一个变量：

ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
  geom_point() + 
  scale_color_manual(values=c("blue","darkgray"))+
  scale_size_manual(values=c(3,1),guide=FALSE)+
  geom_smooth(data=subset(plotdat,Data="Raw"),
  aes(linetype=line),size=1,alpha=0.3,
         method = "loess", span = 0.2, color = "red", fill = "blue")