tibbletime (version 0.1.9)

rollify: Create a rolling version of any function

Description

rollify returns a rolling version of the input function, with a rolling window specified by the user.

Usage

rollify(.f, window = 1, unlist = TRUE, na_value = NULL)

Arguments

.f

A function to modify, specified in one of the following ways:

• A named function, e.g. mean.

• An anonymous function, e.g. \(x) x + 1 or function(x) x + 1.

• A formula, e.g. ~ .x + 1. Only recommended if you require backward compatibility with older versions of R.

window

The window size to roll over

unlist

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.

na_value

A default value for the NA values at the beginning of the roll.

Details

The intended use of rollify is to turn a function into a rolling 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(), rollify creates a function that always returns output with the same length of the input, aligned right, and filled with NA unless otherwise specified by na_value.

The form of the .f argument is the same as the form that can be passed to purrr::map(). Use .x or . to refer to the first object to roll over, and .y to refer to the second object if required. The examples explain this further.

If optional arguments to the function are required, specify them in the call to rollify, and not in the call to the rolling version of the function. See the examples for more details.

See Also

purrr::safely, purrr::possibly

Examples

# Rolling mean --------------------------------------------------------------

data(FB)

# Turn the normal mean function into a rolling mean with a 5 row window
mean_roll_5 <- rollify(mean, window = 5)

dplyr::mutate(FB,
       normal_mean  = mean(adjusted),
       rolling_mean = mean_roll_5(adjusted))

# There's nothing stopping you from combining multiple rolling functions with
# different window sizes in the same mutate call
mean_roll_10 <- rollify(mean, window = 10)

dplyr::mutate(FB,
       rolling_mean_5  = mean_roll_5(adjusted),
       rolling_mean_10 = mean_roll_10(adjusted))

# Functions with multiple args and optional args ----------------------------

# With 2 args, use the purrr syntax of ~ and .x, .y
# Rolling correlation example
cor_roll <- rollify(~cor(.x, .y), window = 5)

dplyr::mutate(FB, 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 <- rollify(function(x, y, z) {
                         (mean(x) + mean(y) + mean(z)) / 3
                       },
                       window = 10)

# Or
avg_of_avgs <- rollify(~(mean(..1) + mean(..2) + mean(..3)) / 3,
                       window = 10)

dplyr::mutate(FB, 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 <- rollify(~mean(.x, na.rm = TRUE), window = 5)

dplyr::mutate(FB, roll_mean = roll_mean_na_rm(adjusted))

# Returning multiple values -------------------------------------------------

data(FB)

summary2 <- function(x) {
  unclass(summary(x))
}

# If the function returns >1 value, set the `unlist = FALSE` argument
# Running 5 number summary
summary_roll <- rollify(summary2, window = 5, unlist = FALSE)

FB_summarised <- dplyr::mutate(FB, summary_roll = summary_roll(adjusted))
FB_summarised$summary_roll[[5]]

# dplyr::bind_rows() is often helpful in these cases to get
# meaningful output

summary_roll <- rollify(~dplyr::bind_rows(summary2(.)), window = 5, unlist = FALSE)
FB_summarised <- dplyr::mutate(FB, summary_roll = summary_roll(adjusted))
FB_summarised %>%
  dplyr::filter(!is.na(summary_roll)) %>%
  tidyr::unnest(summary_roll)

# Rolling regressions -------------------------------------------------------

# Extending an example from R 4 Data Science on "Many Models".
# For each country in the gapminder data, calculate a linear regression
# every 5 periods of lifeExp ~ year
library(gapminder)

# Rolling regressions are easy to implement
lm_roll <- rollify(~lm(.x ~ .y), window = 5, unlist = FALSE)

gapminder %>%
  dplyr::group_by(country) %>%
  dplyr::mutate(rolling_lm = lm_roll(lifeExp, year))

# Rolling with groups -------------------------------------------------------

# One of the most powerful things about this is that it works with
# groups since `mutate` is being used
data(FANG)
FANG <- FANG %>%
  dplyr::group_by(symbol)

mean_roll_3 <- rollify(mean, window = 3)

FANG %>%
  dplyr::mutate(mean_roll = mean_roll_3(adjusted)) %>%
  dplyr::slice(1:5)