Dataset coke.dta contains the choice between Coke and Pepsi by 1140 shoppers from scanner data. The variable pratio represents the relative price of Coke compared to Pepsi—for example, if pratio=1.1, it indicates that the price of Coke was 10% higher than that of Pepsi. Disp_Coke and disp_pepsi are indicator variables if the product was on special display at the time of the purchase.
(a) Estimate a model that predicts the probability of purchasing Coke (Coke==1) as a function of price ratio and display (both Coke and Pepsi).
What kind of R function do i use to make this kind of model? (the picture is just to show how the dataset looks)
What you're asking here seems dramatically outside the scope of a simple stackoverflow question.
The answer to your question is almost certainly the function lm from the stats package which will perform linear regression on your dataset, but knowing whether it is or not is an entire process of data analysis itself.
I recommend reading this article on performing linear regression modeling in R http://r-statistics.co/Linear-Regression.html
It can walk you through the basic steps of linear regression modeling far better than I can here, and there is a link at the bottom to another article that discusses the besic assumptions required for linear regression modeling to be an appropriate choice for your data.
Edit: I decieded to just link that article here too: http://r-statistics.co/Assumptions-of-Linear-Regression.html