README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#",
  fig.path = "man/figures/README",
  out.width = "100%"
)
```

[zfunction()]: https://torfason.github.io/zfit/reference/zfunction.html
[zfitter()]: https://torfason.github.io/zfit/reference/zfitter.html
[zprint()]: https://torfason.github.io/zfit/reference/zprint.html

[zlm()]: https://torfason.github.io/zfit/reference/zlm.html
[zglm()]: https://torfason.github.io/zfit/reference/zglm.html

[zlogit()]: https://torfason.github.io/zfit/reference/zglm.html
[zprobit()]: https://torfason.github.io/zfit/reference/zglm.html
[zpoisson()]: https://torfason.github.io/zfit/reference/zglm.html

# zfit <a href='https://github.com/torfason/zfit/'><img src='man/figures/logo.png' align="right" width="140px" /></a>

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## TL;DR

If you are tired of doing the following:

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

and would like to do this instead:

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

then this little package might be something for you.

## Overview

```{r child="man/include/overview.Rmd"}
```


## Installation

Install the release version from [CRAN](https://CRAN.R-project.org/package=zfit) with:

``` r
install.packages("zfit")
```

Install the development version from [GitHub](https://github.com/torfason/zfit) with:

``` r
remotes::install_github("torfason/zfit")
```

## Examples

The examples below assume that the following packages are loaded:

``` r
library(zfit)
library(dplyr)
```

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

``` r
cars |> zlm(speed ~ dist)
```

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

``` r
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):

``` r
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:

``` r
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
```