Producing a "fuzzy" RD plot with ggplot2 - r

Question

My question is similar to this but the answers there will not work for me. Basically, I'm trying to produce a regression discontinuity plot with a "fuzzy" design that uses all the data for the treatment and control groups, but only plots the regression line within the "range" of the treatment and control groups.
Below, I've simulated some data and produced the fuzzy RD plot with base graphics. I'm hoping to replicate this plot with ggplot2. Note that the most important part of this is that the light blue regression line is fit using all the blue points, while the peach colored regression line is fit using all the red points, despite only being plotted over the ranges in which individuals were intended to receive treatment. That's the part I'm having a hard time replicating in ggplot.
I'd like to move to ggplot because I'd like to use faceting to produce this same plot across various units in which participants were nested. In the code below, I show a non-example using geom_smooth. When there's no fuzziness within a group, it works fine, but otherwise it fails. If I could get geom_smooth to be limited to only specific ranges, I think I'd be set. Any and all help is appreciated.
Simulate data
library(MASS)
mu <- c(0, 0)
sigma <- matrix(c(1, 0.7, 0.7, 1), ncol = 2)
set.seed(100)
d <- as.data.frame(mvrnorm(1e3, mu, sigma))
# Create treatment variable
d$treat <- ifelse(d$V1 <= 0, 1, 0)
# Introduce fuzziness
d$treat[d$treat == 1][sample(100)] <- 0
d$treat[d$treat == 0][sample(100)] <- 1
# Treatment effect
d$V2[d$treat == 1] <- d$V2[d$treat == 1] + 0.5
# Add grouping factor
d$group <- gl(9, 1e3/9)
Produce regression discontinuity plot with base
library(RColorBrewer)
pal <- brewer.pal(5, "RdBu")
color <- d$treat
color[color == 0] <- pal[1]
color[color == 1] <- pal[5]
plot(V2 ~ V1,
data = d,
col = color,
bty = "n")
abline(v = 0, col = "gray", lwd = 3, lty = 2)
# Fit model
m <- lm(V2 ~ V1 + treat, data = d)
# predicted achievement for treatment group
pred_treat <- predict(m,
newdata = data.frame(V1 = seq(-3, 0, 0.1),
treat = 1))
# predicted achievement for control group
pred_no_treat <- predict(m,
newdata = data.frame(V1 = seq(0, 4, 0.1),
treat = 0))
# Add predicted achievement lines
lines(seq(-3, 0, 0.1), pred_treat, col = pal[4], lwd = 3)
lines(seq(0, 4, 0.1), pred_no_treat, col = pal[2], lwd = 3)
# Add legend
legend("bottomright",
legend = c("Treatment", "Control"),
lty = 1,
lwd = 2,
col = c(pal[4], pal[2]),
box.lwd = 0)
non-example with ggplot
d$treat <- factor(d$treat, labels = c("Control", "Treatment"))
library(ggplot2)
ggplot(d, aes(V1, V2, group = treat)) +
geom_point(aes(color = treat)) +
geom_smooth(method = "lm", aes(color = treat)) +
facet_wrap(~group)
Notice the regression lines extending past the treatment range for groups 1 and 2.

Answer 1

There's probably a more graceful way to make the lines with geom_smooth, but it can be hacked together with geom_segment. Munge the data.frames outside of the plotting call if you like.
ggplot(d, aes(x = V1, y = V2, color = factor(treat, labels = c('Control', 'Treatment')))) +
geom_point(shape = 21) +
scale_color_brewer(NULL, type = 'qual', palette = 6) +
geom_vline(aes(xintercept = 0), color = 'grey', size = 1, linetype = 'dashed') +
geom_segment(data = data.frame(t(predict(m, data.frame(V1 = c(-3, 0), treat = 1)))),
aes(x = -3, xend = 0, y = X1, yend = X2), color = pal[4], size = 1) +
geom_segment(data = data.frame(t(predict(m, data.frame(V1 = c(0, 4), treat = 0)))),
aes(x = 0, xend = 4, y = X1, yend = X2), color = pal[2], size = 1)
Another option is geom_path:
df <- data.frame(V1 = c(-3, 0, 0, 4), treat = c(1, 1, 0, 0))
df <- cbind(df, V2 = predict(m, df))
ggplot(d, aes(x = V1, y = V2, color = factor(treat, labels = c('Control', 'Treatment')))) +
geom_point(shape = 21) +
geom_vline(aes(xintercept = 0), color = 'grey', size = 1, linetype = 'dashed') +
scale_color_brewer(NULL, type = 'qual', palette = 6) +
geom_path(data = df, size = 1)
For the edit with facets, if I understand what you want correctly, you can calculate a model for each group with lapply and predict for each group. Here I'm recombine with dplyr::bind_rows instead of do.call(rbind, ...) for the .id parameter to insert the group number from the list element name, though there are other ways to do the same thing.
df <- data.frame(V1 = c(-3, 0, 0, 4), treat = c('Treatment', 'Treatment', 'Control', 'Control'))
m_list <- lapply(split(d, d$group), function(x){lm(V2 ~ V1 + treat, data = x)})
df <- dplyr::bind_rows(lapply(m_list, function(x){cbind(df, V2 = predict(x, df))}), .id = 'group')
ggplot(d, aes(x = V1, y = V2, color = treat)) +
geom_point(shape = 21) +
geom_vline(aes(xintercept = 0), color = 'grey', size = 1, linetype = 'dashed') +
geom_path(data = df, size = 1) +
scale_color_brewer(NULL, type = 'qual', palette = 6) +
facet_wrap(~group)