Creating data table of points above/below abline in ggplot2 - r

Question

Is it possible to identify data points above a geom_abline in ggplot, and to create a new data table separating these data points using data.table?
I have a panel dataset with 150 unique ID's, and have fit a fixed effects model using plm(). Here is a sample of the dataset:
data <- data.frame(ID = c(1,1,1,1,2,2,3,3,3),
year = c(1,2,3,4,1,2,1,2,3),
progenyMean = c(90,78,92,69,86,73,82,85,91),
damMean = c(89,89,72,98,95,92,94,87,89)
ID, year, progenyMean, damMean
1, 1, 70, 69
1, 2, 68, 69
1, 3, 72, 72
1, 4, 69, 68
2, 1, 76, 75
2, 2, 73, 80
3, 1, 72, 74
3, 2, 75, 67
3, 3, 71, 69
# Fixed Effects Model in plm
fixed <- plm(progenyMean ~ damMean, data, model= "within", index = c("ID","year"))
I have plotted the response progenyMean vs damMean using the following code:
plotFunction <- function(aggData, year){
ggplot(aggData, aes(x=damMeanCentered, y=progenyMean3Y)) +
geom_point() +
geom_abline(slope=fixed$coefficients, intercept=71.09, colour='dodgerblue1', size=1)
# The intercept 71.09 was calculated using the mean of fixef(fixed)
}
plotFunction(data, '(2005 - 2012)')
Is it possible to identify the points above/below the geom_abline in ggplot and create a new data table separating these data points using data.table?

Answer 1

It is not clear where the intercept came from, but nevertheless the trick is
add a predict to your dataset using the regression model (in your case fixed). Then filter out actual values that are higher than the predict.
library(dplyr)
data %>%
mutate(predict = predict(fixed, newdata = data)) %>%
filter(progenyMean > predict)

Answer 2

First make the predictions
data[,newpredict:=predict(fixed, newdata=data)]
It's not clear what you want the new data.table to look like but you'd get the values above predictions by doing
data[progencyMean>newpredict]
For below, you'd obviously just change the > to <.