slidify: Create a rolling (sliding) version of any function

View source: R/dplyr-slidify.R

slidifyR Documentation

Create a rolling (sliding) version of any function


slidify returns a rolling (sliding) version of the input function, with a rolling (sliding) .period specified by the user.


  .period = 1,
  .align = c("center", "left", "right"),
  .partial = FALSE,
  .unlist = TRUE



A function, formula, or vector (not necessarily atomic).

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

  • For a single argument function, use .

  • For a two argument function, use .x and .y

  • For more arguments, use ..1, ..2, ..3 etc

This syntax allows you to create very compact anonymous functions.

If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. If a component is not present, the value of .default will be returned.


The period size to roll over


One of "center", "left" or "right".


Should the moving window be allowed to return partial (incomplete) windows instead of NA values. Set to FALSE by default, but can be switched to TRUE to remove NA's.


If the function returns a single value each time it is called, use .unlist = TRUE. If the function returns more than one value, or a more complicated object (like a linear model), use .unlist = FALSE to create a list-column of the rolling results.


The slidify() function is almost identical to tibbletime::rollify() with 3 improvements:

  1. Alignment ("center", "left", "right")

  2. Partial windows are allowed

  3. Uses slider under the hood, which improves speed and reliability by implementing code at C++ level

Make any function a Sliding (Rolling) Function

slidify() turns a function into a sliding version of itself for use inside of a call to dplyr::mutate(), however it works equally as well when called from purrr::map().

Because of it's intended use with dplyr::mutate(), slidify creates a function that always returns output with the same length of the input


Rolling / Sliding functions generate .period - 1 fewer values than the incoming vector. Thus, the vector needs to be aligned. Alignment of the vector follows 3 types:

  • center (default): NA or .partial values are divided and added to the beginning and end of the series to "Center" the moving average. This is common in Time Series applications (e.g. denoising).

  • left: NA or .partial values are added to the end to shift the series to the Left.

  • right: NA or .partial values are added to the beginning to shift the series to the Right. This is common in Financial Applications (e.g moving average cross-overs).

Allowing Partial Windows

A key improvement over tibbletime::slidify() is that timetk::slidify() implements .partial rolling windows. Just set .partial = TRUE.


See Also

Transformation Functions:

  • slidify_vec() - A simple vectorized function for applying summary functions to rolling windows.

Augmentation Functions (Add Rolling Multiple Columns):

  • tk_augment_slidify() - For easily adding multiple rolling windows to you data

Slider R Package:

  • slider::pslide() - The workhorse function that powers timetk::slidify()



FB <- FANG %>% filter(symbol == "FB")


# Turn the normal mean function into a rolling mean with a 5 row .period
mean_roll_5 <- slidify(mean, .period = 5, .align = "right")

FB %>%
    mutate(rolling_mean_5 = mean_roll_5(adjusted))

# Use `partial = TRUE` to allow partial windows (those with less than the full .period)
mean_roll_5_partial <- slidify(mean, .period = 5, .align = "right", .partial = TRUE)

FB %>%
    mutate(rolling_mean_5 = mean_roll_5_partial(adjusted))

# There's nothing stopping you from combining multiple rolling functions with
# different .period sizes in the same mutate call

mean_roll_10 <- slidify(mean, .period = 10, .align = "right")

FB %>%
    select(symbol, date, adjusted) %>%
        rolling_mean_5  = mean_roll_5(adjusted),
        rolling_mean_10 = mean_roll_10(adjusted)

# For summary operations like rolling means, we can accomplish large-scale
# multi-rolls with tk_augment_slidify()

FB %>%
    select(symbol, date, adjusted) %>%
        adjusted, .period = 5:10, .f = mean, .align = "right",
        .names = str_c("MA_", 5:10)


# One of the most powerful things about this is that it works with
# groups since `mutate` is being used

mean_roll_3 <- slidify(mean, .period = 3, .align = "right")

FANG %>%
    group_by(symbol) %>%
    mutate(mean_roll = mean_roll_3(adjusted)) %>%


# With 2 args, use the purrr syntax of ~ and .x, .y
# Rolling correlation example
cor_roll <- slidify(~cor(.x, .y), .period = 5, .align = "right")

FB %>%
    mutate(running_cor = cor_roll(adjusted, open))

# With >2 args, create an anonymous function with >2 args or use
# the purrr convention of ..1, ..2, ..3 to refer to the arguments
avg_of_avgs <- slidify(
    function(x, y, z) (mean(x) + mean(y) + mean(z)) / 3,
    .period = 10,
    .align = "right"

# Or
avg_of_avgs <- slidify(
    ~(mean(..1) + mean(..2) + mean(..3)) / 3,
    .period = 10,
    .align  = "right"

FB %>%
    mutate(avg_of_avgs = avg_of_avgs(open, high, low))

# Optional arguments MUST be passed at the creation of the rolling function
# Only data arguments that are "rolled over" are allowed when calling the
# rolling version of the function
FB$adjusted[1] <- NA

roll_mean_na_rm <- slidify(~mean(.x, na.rm = TRUE), .period = 5, .align = "right")

FB %>%
    mutate(roll_mean = roll_mean_na_rm(adjusted))


# Rolling regressions are easy to implement using `.unlist = FALSE`
lm_roll <- slidify(~lm(.x ~ .y), .period = 90, .unlist = FALSE, .align = "right")

FB %>%
    drop_na() %>%
    mutate(numeric_date = as.numeric(date)) %>%
    mutate(rolling_lm = lm_roll(adjusted, numeric_date)) %>%

timetk documentation built on June 1, 2022, 1:07 a.m.