slider provides a family of general purpose “sliding window”
functions. The API is purposefully *very* similar to purrr. The
goal of these functions is usually to compute rolling averages,
cumulative sums, rolling regressions, or other “window” based
computations.

There are 3 core functions in slider:

`slide()`

iterates over your data like`purrr::map()`

, but uses a sliding window to do so. It is type-stable, and always returns a result with the same size as its input.`slide_index()`

computes a rolling calculation*relative to an index*. If you have ever wanted to compute something like a “3 month rolling average” where the number of days in each month is irregular, you might like this function.`slide_period()`

is similar to`slide_index()`

in that it slides relative to an index, but it first breaks the index up into “time blocks”, like 2 month blocks of time, and then it slides over`.x`

using indices defined by those blocks.

Each of these core functions have the same variants as
`purrr::map()`

. For example, `slide()`

has
`slide_dbl()`

, `slide2()`

, and
`pslide()`

, along with the other combinations of these
variants that you might expect from having previously used purrr.

To learn more about these three functions, read the introduction vignette.

There are also a set of extremely fast specialized variants of
`slide_dbl()`

for the most common use cases. These include
`slide_sum()`

for rolling sums and `slide_mean()`

for rolling averages. There are index variants of each of these as well,
like `slide_index_sum()`

.

Install the released version from CRAN with:

`install.packages("slider")`

Install the development version from GitHub with:

`::pak("r-lib/slider") pak`

The help page
for `slide()`

has many examples, but here are a few:

`library(slider)`

The classic example would be to do a moving average.
`slide()`

handles this with a combination of the
`.before`

and `.after`

arguments, which control
the width of the window and the alignment.

```
# Moving average (Aligned right)
# "The current element + 2 elements before"
slide_dbl(1:5, ~mean(.x), .before = 2)
#> [1] 1.0 1.5 2.0 3.0 4.0
# Align left
# "The current element + 2 elements after"
slide_dbl(1:5, ~mean(.x), .after = 2)
#> [1] 2.0 3.0 4.0 4.5 5.0
# Center aligned
# "The current element + 1 element before + 1 element after"
slide_dbl(1:5, ~mean(.x), .before = 1, .after = 1)
#> [1] 1.5 2.0 3.0 4.0 4.5
```

With `Inf`

, you can do a “cumulative slide” to compute
cumulative expressions. I think of this as saying “give me everything
before the current element.”

```
slide(1:4, ~.x, .before = Inf)
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 1 2 3
#>
#> [[4]]
#> [1] 1 2 3 4
```

With `.complete`

, you can decide whether or not
`.f`

should be evaluated on incomplete windows. In the
following example, the requested window size is 3, but the first two
results are computed on windows of size 1 and 2 because partial results
are allowed by default. When `.complete`

is set to
`TRUE`

, the first two results are not computed.

```
slide(1:4, ~.x, .before = 2)
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 1 2 3
#>
#> [[4]]
#> [1] 2 3 4
slide(1:4, ~.x, .before = 2, .complete = TRUE)
#> [[1]]
#> NULL
#>
#> [[2]]
#> NULL
#>
#> [[3]]
#> [1] 1 2 3
#>
#> [[4]]
#> [1] 2 3 4
```

Unlike `purrr::map()`

, `slide()`

iterates over
data frames in a row wise fashion. Interestingly this means the default
of `slide()`

becomes a generic row wise iterator, with nice
syntax for accessing data frame columns.

There is a vignette specifically about this.

```
<- cars[1:4,]
mini_cars
slide(mini_cars, ~.x)
#> [[1]]
#> speed dist
#> 1 4 2
#>
#> [[2]]
#> speed dist
#> 1 4 10
#>
#> [[3]]
#> speed dist
#> 1 7 4
#>
#> [[4]]
#> speed dist
#> 1 7 22
slide_dbl(mini_cars, ~.x$speed + .x$dist)
#> [1] 6 14 11 29
```

This makes rolling regressions trivial!

```
library(tibble)
set.seed(123)
<- tibble(
df y = rnorm(100),
x = rnorm(100)
)
# Window size of 20 rows
# The current row + 19 before
# (see slide_index() for how to do this relative to a date vector!)
$regressions <- slide(df, ~lm(y ~ x, data = .x), .before = 19, .complete = TRUE)
df
15:25,]
df[#> # A tibble: 11 × 3
#> y x regressions
#> <dbl> <dbl> <list>
#> 1 -0.556 0.519 <NULL>
#> 2 1.79 0.301 <NULL>
#> 3 0.498 0.106 <NULL>
#> 4 -1.97 -0.641 <NULL>
#> 5 0.701 -0.850 <NULL>
#> 6 -0.473 -1.02 <lm>
#> 7 -1.07 0.118 <lm>
#> 8 -0.218 -0.947 <lm>
#> 9 -1.03 -0.491 <lm>
#> 10 -0.729 -0.256 <lm>
#> 11 -0.625 1.84 <lm>
```

In many business settings, the value you want to compute is tied to
some *index*, like a date vector. In these cases, you’ll probably
want to compute sliding windows relative to the index, and not using the
fixed window that `slide()`

provides. You can use
`slide_index()`

to pass in both `.x`

and an index,
`.i`

, and the window will be calculated relative to that
index.

Here, when computing a “2 day window”, you probably don’t want
`"2019-08-16"`

and `"2019-08-18"`

to be grouped
together. `slide()`

has no concept of an index, so when you
specify a window size of 2, it will group these two together.
`slide_index()`

, on the other hand, will do the right
thing.

```
<- 1:3
x <- as.Date(c("2019-08-15", "2019-08-16", "2019-08-18"))
i
# slide() has no concept of an "index"
slide(x, ~.x, .before = 1)
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 2 3
# "index aware"
slide_index(x, i, ~.x, .before = 1)
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 3
```

Essentially what happens is that when we get to
`"2019-08-18"`

, it “looks backwards” 1 day to set a window
boundary at `"2019-08-17"`

. Since the date at position 2,
`"2019-08-16"`

, is before `"2019-08-17"`

, it is
not included.

Powerfully, you can pass through any object to `.before`

that computes a value from `.i - .before`

. This means that
you could also have used a lubridate period object (which gets even more
interesting when you use `weeks()`

or
`months()`

):

```
slide_index(x, i, ~.x, .before = lubridate::days(1))
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] 1 2
#>
#> [[3]]
#> [1] 3
```

`slide_period()`

is different from
`slide_index()`

in that it first breaks the index into “time
blocks” and then slides over `.x`

relative to those blocks.
For example, in the monthly period slide below, `i`

is broken
up into 4 time blocks of “the current block of monthly data, plus one
block before this one”. The locations of those blocks are the locations
that are used to slice `.x`

with.

```
<- as.Date(c(
i "2019-01-29",
"2019-01-30",
"2019-02-05",
"2019-04-01",
"2019-05-10"
))
slide_period(i, i, "month", ~.x, .before = 1)
#> [[1]]
#> [1] "2019-01-29" "2019-01-30"
#>
#> [[2]]
#> [1] "2019-01-29" "2019-01-30" "2019-02-05"
#>
#> [[3]]
#> [1] "2019-04-01"
#>
#> [[4]]
#> [1] "2019-04-01" "2019-05-10"
```

One neat thing to notice is that `slide_period()`

is aware
of the distance between elements of `.i`

in the period you
specify. The practical implication of this is that in the above example,
group 3 with `2019-04-01`

did *not* include
`2019-02-05`

in it, because it is more than 1 month group
away.

This package is inspired heavily by SQL’s window functions. The API is similar, but more general because you can iterate over any kind of R object.

There have been multiple attempts at creating sliding window
functions (I personally created `rollify()`

, and worked a
little bit on `tsibble::slide()`

with Earo Wang).

`zoo::rollapply()`

`tibbletime::rollify()`

`tsibble::slide()`

I believe that slider is the next iteration of these. There are a few reasons for this:

To me, the API is more intuitive, and is more flexible because

`.before`

and`.after`

let you completely control the entry point (as opposed to fixed entry points like`"center"`

,`"left"`

, etc.It is objectively faster because it is written purely in C.

With

`slide_vec()`

you can return any kind of object, and are not limited to the suffixed versions:`_dbl`

,`_int`

, etc.It iterates rowwise over data frames, consistent with the vctrs framework.

I believe it is overall more consistent, backed by a theory that can always justify the sliding window generated by any combination of the parameters.

Earo and I have spoken, and we have mutually agreed that it would be
best to deprecate `tsibble::slide()`

in favor of
`slider::slide()`

.

Additionally, data.table’s
non-equi joins have been pretty much the only solution to the problem
that `slide_index()`

tries to solve. Their solution is robust
and quite fast, and has been a nice benchmark for slider. slider is
trying to solve a much narrower problem, so the API here is more
focused.

Like `purrr::map()`

, the core functions of slider, such as
`slide()`

and `slide_index()`

, are optimized in C
to be as fast as possible, but there is overhead involved in calling
`.f`

repeatedly. These functions are meant to be as
*general purpose* as possible, at the cost of some performance.
This means that slider can be used for more abstract computations, like
rolling regressions, or any other custom function that you want to use
in a rolling fashion.

slider also provides *specialized* functions for some of the
most common use cases, such as `slide_mean()`

, or
`slide_index_sum()`

. These compute their corresponding metric
at the C level, using a specialized algorithm, and are often much faster
than their `slide_dbl(x, fn)`

equivalent.

I’ve found the following references very useful to understand more about window functions:

Please note that the slider project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.