How to add new legends to complicated scatter plot using ggplot2
I built a simple linear regression model, and produced some predicted values using the model. However, I am more interested in visualizing it on the graph, but I do not know how to add a legend to highlight original mpg values as 'black' and new predicted values as "red".
Data used in this example is mtcars dataset from datasets package
library(ggplot2)
library(datasets)
library(broom)
# Build a simple linear model between hp and mpg
m1<-lm(hp~mpg,data=mtcars)
# Predict new `mpg` given values below
new_mpg = data.frame(mpg=c(23,21,30,28))
new_hp<- augment(m1,newdata=new_mpg)
# plot new predicted values in the graph along with original mpg values
ggplot(data=mtcars,aes(x=mpg,y=hp)) + geom_point(color="black") + geom_smooth(method="lm",col=4,se=F) +
geom_point(data=new_hp,aes(y=.fitted),color="red")
scatter plot
Here is one idea. You can combine the predicted and observed data in the same data frame and then create the scatter plot to generate the legend. The following code is an extension of your existing code.
# Prepare the dataset
library(dplyr)
new_hp2 <- new_hp %>%
select(mpg, hp = .fitted) %>%
# Add a label to show it is predicted data
mutate(Type = "Predicted")
dt <- mtcars %>%
select(mpg, hp) %>%
# Add a label to show it is observed data
mutate(Type = "Observed") %>%
# Combine predicted data and observed data
bind_rows(new_hp2)
# plot the data
ggplot(data = dt, aes(x = mpg, y = hp, color = factor(Type))) +
geom_smooth(method="lm", col = 4, se = F) +
geom_point() +
scale_color_manual(name = "Type", values = c("Black", "Red"))
这是另一种没有dplyr ：
ggplot() +
geom_point(data = mtcars, aes(x = mpg, y = hp, colour = "Obs")) +
geom_point(data = new_hp, aes(x = mpg, y = .fitted, colour = "Pred")) +
scale_colour_manual(name="Type",
values = c("black", "red")) +
geom_smooth(data = mtcars, aes(x = mpg, y = hp),
method = "lm", col = 4, se = F)