Running several regressions with changing outcome variable in R

Question

I have a dataframe (df) that looks like the following:

condition  dv1  dv2  dv3
1          2    4    3
2          5    7    4
3          7    1    2

In order to run several regressions simultaneously, I have used code like this:

dfdv <- df[,2:4]  
output <- lm(as.matrix(dfdv) ~ condition, data = df)
summary(output)

This gives me all the dv ~ condition regressions for dv1, dv2, and dv3. These are the regressions that are given in the output:

dv1 ~ condition
dv2 ~ condition
dv3 ~ condition

However, I would now like to control for different conditions in the regression. Specifically, I would like to figure out a way to run the following regressions efficiently.

dv1 ~ condition + dv1
dv1 ~ condition + dv2
dv1 ~ condition + dv3

I tried the following using a similar principle to what worked above, and it didn't work.

dfdv <- df[,2:4]  
output2 <- lm(dv1 ~ condition + as.matrix(dfdv), data = df)
summary(output2)

It gave me a single regression using all the dv's in that one regression as opposed to the regressions that I wanted. This is what it gave me:

dv1 ~ condition + dv1 + dv2 + dv3

Does anyone know how I can perform these regression analyses using simple code? I have several more in my actual dataset than I am including in this example.

Answer 1

We can use lapply for that. Loop through the column names that are 'dv', create formula with reformulate by specifying the response and dependent variable, and apply the lm

v1 <- grep('^dv\\d+$", names(df), value = TRUE)
lapply(v1, function(dv) lm(reformulate(c(dv, "condition"),
           dv), data = df))

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Or create the formula with paste

lapply(v1, function(dv) lm(paste0(dv, " ~ ", "condition + ", dv), data = df))