Suppose I have a data frame in the environment, mydata, with three columns, A, B, C.
mydata = data.frame(A=c(1,2,3),
B=c(4,5,6),
C=c(7,8,9))
I can create a linear model with
lm(C ~ A, data=mydata)
I want a function to generalize this, to regress B or C on A, given just the name of the column, i.e.,
f = function(x){
lm(x ~ A, data=mydata)
}
f(B)
f(C)
or
g = function(x){
lm(mydata$x ~ mydata$A)
}
g(B)
g(C)
These solutions don't work. I know there is something wrong with the evaluation, and I have tried permutations of quo() and enquo() and !!, but no success.
This is a simplified example, but the idea is, when I have dozens of similar models to build, each fairly complicated, with only one variable changing, I want to do so without repeating the entire formula each time.