The main function is gl. It is a variant of glue to simplify
building regression formulas given vectors of variable names.
library(glueformula)
# Example: build a formula for ivreg
# with gf
contr = c("x1","x2","x3") # exogenous control variables
instr = c(contr,"z1","z2") # instruments
gf(q ~ p + {contr} | {instr})
Another helper function is varnames_snippet. It just uses the colnames of a data frame to create a code snippet with a vector of all variable names. Here is an example:
df = dplyr::tibble(x=1,y=5,z=4,`a b`=4)
varnames_snippet(df)
This prints (and on Windows copies to your clipboard) the following code snippet:
c("x", "y", "z", "`a b`")You can then manually select the used variables in your regression by deleting the unused variables. With many variables, this can be much quicker than writing down all used variables.
The easiest way to install glueformula is from my Github powered package repository by running:
install.packages("glueformula",repos = c("https://skranz-repo.github.io/drat/",getOption("repos")))Alteneratively, you can run the following code to install the source package from Github:
if (!require(glue)) install.packages("glue")
if (!require(remotes)) install.packages("remotes")
remotes::install_github("skranz/glueformula")
If the Github installation fails because unimportant warnings are converted to errors run before the installation:
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")