If you are tired of doing the following:

dat <- mtcars |>
  filter(am == 1)
lm(mpg ~ wt + hp, data=dat)

and would like to do this instead:

mtcars |> 
  filter(am == 1) |>
  zlm(mpg ~ wt + hp)

then this little package might be something for you.

zfit makes it easier to use a piped workflow with functions that don’t have the “correct” order of parameters (the first parameter of the function does not match the object passing through the pipe).

The issue is especially prevalent with model fitting functions, such as when passing and processing a data.frame (or tibble) before passing them to lm() or similar functions. The pipe passes the data object into the first parameter of the function, but the conventional estimation functions expect a formula to be the first parameter.

This package addresses the issue with three functions that make it trivial to construct a pipe-friendly version of any function:

• zfunction() reorders the arguments of a function. Just pass the name of a function, and the name of the parameter that should receive the piped argument, and it returns a version of the function with that parameter coming first.

• zfold() creates a fold (a wrapper) around a function with the reordered arguments. This is sometimes needed instead of a simple reordering, for example for achieving correct S3 dispatch, and for functions that report its name or other information in output.

• zfitter() takes any estimation function with the standard format of a formula and data parameter, and returns a version suitable for us in pipes (with the data parameter coming first). Internally, it simply calls the zfold() function to create a fold around the fitter function.

The package also includes ready made wrappers around the most commonly used estimation functions. zlm()and zglm() correspond to lm() and glm(), and zlogit(), zprobit(), and zpoisson(), use glm() to perform logistic or poisson regression within a pipe.

Finally, the package includes the zprint() function, which is intended to simplify the printing of derived results, such as summary(), within the pipe, without affecting the modeling result itself.

Install the release version from CRAN with:

install.packages("zfit")

Install the development version from GitHub with:

remotes::install_github("torfason/zfit")

The examples below assume that the following packages are loaded:

library(zfit)
library(dplyr)

The most basic use of the functions in this package is to pass a data.frame/tibble to zlm():

cars |> zlm(speed ~ dist)

Often, it is useful to process the data.frame/tibble before passing it to zlm():

iris |>
  filter(Species=="setosa") |>
  zlm(Sepal.Length ~ Sepal.Width + Petal.Width)

The zprint() function provides a simple way to “tee” the piped object for printing a derived object, but then passing the original object onward through the pipe. The following code pipes an estimation model object into zprint(summary). This means that the summary() function is called on the model being passed through the pipe, and the resulting summary is printed. However, zprint(summary) then returns the original model object, which is assigned to m (instead of assigning the summary object):

m <- iris |>
  filter(Species=="setosa") |>
  zlm(Sepal.Length ~ Sepal.Width + Petal.Width) |>
  zprint(summary)

The zprint() function is quite useful within an estimation pipeline to print a summary of an object without returning the summary (using zprint(summary) as above), but it can also be used independently from estimation models, such as to print a summarized version of a tibble within a pipeline before further processing, without breaking the pipeline:

sw_subset <- starwars |>
  zprint(count, homeworld, sort=TRUE) |> # prints counts by homeworld
  filter(homeworld=="Tatooine")
sw_subset  # sw_subset is ungrouped, but filtered by homeworld