Predicted value from lm not on the regression line in ggplot2

[Normal_distribution]

So I'm doing a very simple scatter plot of a data set and impose a linear trendline to it. Then I highlight a particular observation as well as the predicted value from a linear model, but for whatever reason the predicted value is NOT on the trendline that ggplot2 creates. Not sure what I'm doing wrong here. A reprex is below. Any thoughts would be greatly appreciated. Thanks!

library(tidyverse)
library(modelr)
library(ggrepel)
filter_data <- mtcars %>% arrange(wt) %>% rownames_to_column() %>% rename(model = rowname) %>% 
  add_predictions(lm(mpg~wt, data=.)) # add predicted values from a linear model to the dataframe
corona <- filter_data %>% filter(model =="Toyota Corona") # observation of interest

# highlight a data point
example<- filter_data %>%
  ggplot()+
  geom_point(aes(x = wt, mpg)) +
  geom_smooth(mapping = aes(x = wt, mpg), method = lm, se = FALSE) +
  geom_point(data=corona,aes(x = wt, mpg),color="red")+ # the true data point in red
  geom_point(data=corona,aes(x = wt, y=pred),color="purple")+ # the predicted value in purple
  xlim(2.0,3.0)+ylim(20,25)

# looking at the resulting figure, the purple point is NOT on the line. What's happening?

[nirgrahamuk]

from the ggplot2 documentation on xlim,ylim and limits

By default, any values outside the limits specified are replaced with NA . Be warned that this will remove data outside the limits and this can produce unintended results. For changing x or y axis limits without dropping data observations, see coord_cartesian() .

so by applying your xlim and ylim you altered the inline geom_smooth of your ggplot, try coord_cartesian instead.

[Normal_distribution]

Thank you so much! That fixes it though I'm not entirely sure why xlim and ylim caused the issue in the first place.