Plot point on ggplot2 smoothing regression on vline intersection - r

Question

I want to create a (time-series) plot out of 40 million data points in order to show two regression lines with two specific events on each of it (first occurrence of an optimum in time-series).
Currently, I draw the regression lines and add a geom_vline to it to indicate the event.
As I want to be independent from colours in the plot, it would be beneficial if I could just plot the marker geom_vline as a point on the regression line.
Do you have any idea how to solve this using ggplot2?
My current approach is this here (replaced data points with test data):
library(ggplot2)
# Generate data
m1 <- "method 1"
m2 <- "method 2"
data1 <- data.frame(Time=seq(100), Value=sample(1000, size=100), Type=rep(as.factor(m1), 100))
data2 <- data.frame(Time=seq(100), Value=sample(1000, size=100), Type=rep(as.factor(m2), 100))
df <- rbind(data1, data2)
rm(data1, data2)
# Calculate first minima for each Type
m1_intercept <- df[which(df$Type == m1), ][which.min(df[which(df$Type == m1), ]$Value),]
m2_intercept <- df[which(df$Type == m2), ][which.min(df[which(df$Type == m2), ]$Value),]
# Plot regression and vertical lines
p1 <- ggplot(df, aes(x=Time, y=Value, group=Type, colour=Type), linetype=Type) +
geom_smooth(se=F) +
geom_vline(aes(xintercept=m1_intercept$Time, linetype=m1_intercept$Type)) +
geom_vline(aes(xintercept=m2_intercept$Time, linetype=m2_intercept$Type)) +
scale_linetype_manual(name="", values=c("dotted", "dashed")) +
guides(colour=guide_legend(title="Regression"), linetype=guide_legend(title="First occurrence of optimum")) +
theme(legend.position="bottom")
ggsave("regression.png", plot=p1, height=5, width=7)
which generates this plot:
My desired plot would be something like this:
So my questions are
Does it make sense to indicate a minimum value on a regression line? The values y-axis position would be in fact wrong but just to indicate the timepoint?
If yes, how can I achieve such a behaviour?
If no, what would you think could be better?
Thank you very much in advance!
Robin

Answer 1

If you first run your ggplot() call with only geom_smooth(), you can access plotted values through ggplot_build(), which we then can use to plot points on the two fitted lines. Example:
# Create initial plot
p1<-ggplot(df, aes(x=Time, y=Value, colour=Type)) +
geom_smooth(se=F)
# Now we can access the fitted values
smooths <- ggplot_build(p1)$data[[1]]
smooths_1 <- smooths[smooths$group==1,] # First group (method 1)
smooths_2 <- smooths[smooths$group==2,] # Second group (method 2)
# Then we find the closest plotted values to the minima
smooth_1_x <- smooths_1$x[which.min(abs(smooths_1$x - m1_intercept$Time))]
smooth_2_x <- smooths_2$x[which.min(abs(smooths_2$x - m2_intercept$Time))]
# Subset the previously defined datasets for respective closest values
point_data1 <- smooths_1[smooths_1$x==smooth_1_x,]
point_data2 <- smooths_1[smooths_2$x==smooth_2_x,]
Now we use point_data1 and point_data2 to place the points on your plot:
ggplot(df, aes(x=Time, y=Value, colour=Type)) +
geom_smooth(se=F) +
geom_point(data=point_data1, aes(x=x, y=y), colour = "red",size = 5) +
geom_point(data=point_data2, aes(x=x, y=y), colour = "red", size = 5)
To reproduce this plot, you can use set.seed(42) for your data generation step.