Using R's lm on a dataframe with a list of predictors

Using the formula notation y ~ . specifies that you want to regress y on all of the other variables in the dataset.

df = data.frame(y = 1:10, x1 = runif(10), x2 = rnorm(10))
# fits a model using x1 and x2
fit <- lm(y ~ ., data = df) 
# Removes the column containing x1 so regression on x2 only
fit <- lm(y ~ ., data = df[, -2]) 

There is an alternative to Dason's answer, for when you want to specify the columns, to exclude, by name. It is to use subset(), and specify the select argument:

df = data.frame(y = 1:10, x1 = runif(10), x2 = rnorm(10))
fit = lm(y ~ ., data = subset(df, select=-x1))

Trying to use data[,-c("x1")] fails with "invalid argument to unary operator".

It can extend to excluding multiple columns: subset(df, select = -c(x1,x2))

And you can still use numeric columns:

df = data.frame(y = 1:10, x1 = runif(10), x2 = rnorm(10))
fit = lm(y ~ ., data = subset(df, select = -2))

(That is equivalent to subset(df, select=-x1) because x1 is the 2nd column.)

Naturally you can also use this to specify the columns to include.

df = data.frame(y = 1:10, x1 = runif(10), x2 = rnorm(10))
fit = lm(y ~ ., data = subset(df, select=c(y,x2)) )

(Yes, that is equivalent to lm(y ~ x2, df) but is distinct if you were then going to be using step(), for instance.)