CRAN status CRAN status shields R-CMD-check


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:

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 the development version from GitHub with:



The examples below assume that the following packages are loaded:


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) |>

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
sw_subset  # sw_subset is ungrouped, but filtered by homeworld