We walk through the basics of `litr`

with a simple
example.

Suppose we want to create a package called `rleastsquares`

that does least squares. To do so, we will create a .Rmd file called
something like `create-rleastsquares.Rmd`

. We recommend
creating this from a template:

```
rmarkdown::draft("create-rleastsquares.Rmd",
template = "make-an-r-package",
package = "litr")
```

When you work from template, you’ll notice some special lines in the yaml:

```
knit: litr::render
output: litr::litr_html_document
params:
package_name: "rhello" # <-- change this to your package name
package_parent_dir: "." # <-- relative to this file's location
```

This is what will lead to an R package being created when you press
“Knit”. We’ll replace `"rhello"`

with
`"rleastsquares"`

.

## Package setup

Note:Every R package needs a DESCRIPTION file. We’ll start by filling in the relevant information.

```
usethis::create_package(
path = ".",
fields = list(
Package = param$package_name,
Version = "0.0.0.9000",
Title = "Fit Least Squares",
Description = "A package that fits least squares.",
`Authors@R` = person(
given = "First",
family = "Last",
email = "you@gmail.com",
role = c("aut", "cre")
)
)
)
usethis::use_mit_license(copyright_holder = "F. Last")
```

## Writing a function for the package

Since this is an R markdown file, we can use latex to explain our code, provide derivations, etc.

Suppose we have a response vector \(y\in\mathbb R^n\) and a data matrix \(X\in\mathbb R^{n\times p}\).

We want to find the solution to the problem

\[ \min_{\beta\in\mathbb R^p}\|y-X\beta\|^2 \]

We’ll assume that \(X\) is full rank with \(n > p\). We know that the solution is given by

\[ \hat\beta=(X^TX)^{-1}X^Ty. \]

We’ll write a function that does exactly that!

```
#' Get the OLS solution
#'
#' @param y our response, which is an n-vector
#' @param X our data matrix, which is n by p
#' @export
do_least_squares <- function(y, X) {
if(nrow(X) != length(y))
stop("The number of rows of X must match the length of y.")
as.numeric(solve(crossprod(X), crossprod(X, y)))
}
```

Note:Code chunks whose first line starts with`#'`

are added to the package. If you’re not familiar with`roxygen2`

, see here for more.

Now that we’ve defined `do_least_squares()`

, let’s try it
out!

```
set.seed(123)
n <- 100
p <- 1
x <- cbind(1, matrix(rnorm(n*p), n, p))
beta_star <- c(2, 0.5)
sigma <- 0.1
y <- x %*% beta_star + sigma * rnorm(n)
```

Note:This code chunk does not start with`#'`

, so it is not added to the package.

`betahat <- do_least_squares(y, x)`

Let’s see how this compares to `lm`

’s answer.

`fit_lm <- lm(y ~ x[, 2])`

`fit_lm$coefficients`

```
## (Intercept) x[, 2]
## 1.9897197 0.4947528
```

Compare that to…

`betahat`

`## [1] 1.9897197 0.4947528`

Ok, `do_least_squares()`

appears to be working. Let’s
define a formal unit test based on the example above.

```
testthat::test_that("do_least_squares() works", {
set.seed(123)
n <- 100
p <- 1
x <- cbind(1, matrix(rnorm(n*p), n, p))
beta_star <- c(2, 0.5)
sigma <- 0.1
y <- x %*% beta_star + sigma * rnorm(n)
fit_lm <- lm(y ~ x[, 2])
# do lm and our function give the same coefficient vector?
testthat::expect_equal(do_least_squares(y, x),
as.numeric(fit_lm$coefficients))
# do we get the desired error when there is a length mismatch?
testthat::expect_error(do_least_squares(y[-1], x), "must match")
})
```

`## Test passed 🎊`

Note:Code chunks that have one or more lines starting with`test_that(`

or`testthat::test_that(`

are added to the package as tests.

## Some fancier features

### Finer control over where in the package your code is sent

As noted above, `litr`

detects whether to send a code
chunk to the package based on whether it starts with `#'`

or
has `test_that`

in it. However, sometimes you’ll want finer
control. In this case you can override the behavior by explicitly
specifying the target location. To do so, use a code chunk option of the
form `send_to="R/myfile.R"`

. This will add your code to that
particular file (either creating it if need be or else appending it).
There are two primary use cases for this feature: (a) when you don’t
want to use `roxygen2`

to document a function and (b) when
you want several functions to appear together in the same .R file.

### Using a function from a different package

Imagine we wanted to actually use a function from another package in
our own. For example, perhaps we want to use `lsfit()`

from
the `stats`

package:

```
#' Get the OLS solution using lsfit()
#'
#' @param y our response, which is an n-vector
#' @param X our data matrix, which is n by p
#' @export
do_least_squares_with_lsfit <- function(y, X) {
fit <- stats::lsfit(x = X, y = y, intercept = FALSE)
return(as.numeric(fit$coefficients))
}
```

And then we also update the DESCRIPTION file with this package dependence:

`usethis::use_package("stats")`

To use a function from another package, simply use the

`pkg::`

prefix when calling it and then add`usethis::use_package("pkg")`

to include the package dependency.

### Including a dataset in your package

There’s a template for that.

### Using `Rcpp`

in your package

There’s a template for that.

### Including a README, vignettes, and `pkgdown`

site

There’s a template for that.

### Defining your package with `bookdown`

If you’re writing a large package, it may be convenient to define it
across multiple .Rmd files. You can use `bookdown`

for this,
which leads to a nice looking online book with multiple chapters.
There’s a template for that.

## Documenting the package

We finish by running a commands that will turn the
`royxgen2`

into traditional documentation files in our R
package.

`litr::document() # <-- use instead of devtools::document()`