olsrr | Tools for developing linear regression models | Testing library

I try to analyze all the variables in my data set to see which set of variables is describing my dependent variable StockPrice the best. The following code is the one I use to do so:

I did linear regression analysis between the response variable(y) and predictor variables in the surgical data set considering pindex as a confounding variable. I aim to plot the response variable(y) against the experimentally determined values of the predictor variables and to this end, I am Successful. However, could not able to indicate the estimated regression and p-value in the ggplot2. In the code below, trying to do the analysis and the plot. It would be much appreciated if someone could show me how to indicate the estimated regression and p-values inside the ggplot2.

My problem is for the mtcars data set in R, I need to create all possible additive linear regression models where I'm regressing on the mpg variable. The null model is easy, as there's

10 choose 0 ways to get the null model, and 10 choose 1 ways to create a SLR on mpg; 10 choose 2 ways to create a two variable regression on mpg; 10 choose 3 ways to create a SLR on mpg; etc.,

So in total, as this is equivalent to summing across the 10th row in Pascal's Triangle, the total models I need to consider comes out to be 1,024.

Now, the other tricky part is I need to somehow store each model in some separate object so that all the 2 variable models are grouped together, all the three variable models are grouped together, etc, on top of also storing all them together (though perhaps there's a more efficient way to do this). The reason for this is my task is to look at all of these models, take their AIC scores and their Mallow's Cp scores, store those in a data frame, and then sort those scores from lowest to highest and keep the top 10. On top of this, I need to also be able to store, see, and have access to/use the two best 1-variable models through the two best 10-variable models because I need to provide the r-squared values and adjusted r-squared values for these various models along with the error mean square value. I'm still pretty/relatively new to R/coding in general, but I provide my attempt below: