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

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

Arguments

.f

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.

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

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))
#> # A tibble: 1,008 x 10 #> symbol date open high low close volume adjusted normal_mean #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-02 27.4 28.2 27.4 28 6.98e7 28 77.5 #>  2 FB 2013-01-03 27.9 28.5 27.6 27.8 6.31e7 27.8 77.5 #>  3 FB 2013-01-04 28.0 28.9 27.8 28.8 7.27e7 28.8 77.5 #>  4 FB 2013-01-07 28.7 29.8 28.6 29.4 8.38e7 29.4 77.5 #>  5 FB 2013-01-08 29.5 29.6 28.9 29.1 4.59e7 29.1 77.5 #>  6 FB 2013-01-09 29.7 30.6 29.5 30.6 1.05e8 30.6 77.5 #>  7 FB 2013-01-10 30.6 31.5 30.3 31.3 9.53e7 31.3 77.5 #>  8 FB 2013-01-11 31.3 32.0 31.1 31.7 8.96e7 31.7 77.5 #>  9 FB 2013-01-14 32.1 32.2 30.6 31.0 9.89e7 31.0 77.5 #> 10 FB 2013-01-15 30.6 31.7 29.9 30.1 1.73e8 30.1 77.5 #> # … with 998 more rows, and 1 more variable: rolling_mean <dbl>
# 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))
#> # A tibble: 1,008 x 10 #> symbol date open high low close volume adjusted rolling_mean_5 #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-02 27.4 28.2 27.4 28 6.98e7 28 NA #>  2 FB 2013-01-03 27.9 28.5 27.6 27.8 6.31e7 27.8 NA #>  3 FB 2013-01-04 28.0 28.9 27.8 28.8 7.27e7 28.8 NA #>  4 FB 2013-01-07 28.7 29.8 28.6 29.4 8.38e7 29.4 NA #>  5 FB 2013-01-08 29.5 29.6 28.9 29.1 4.59e7 29.1 28.6 #>  6 FB 2013-01-09 29.7 30.6 29.5 30.6 1.05e8 30.6 29.1 #>  7 FB 2013-01-10 30.6 31.5 30.3 31.3 9.53e7 31.3 29.8 #>  8 FB 2013-01-11 31.3 32.0 31.1 31.7 8.96e7 31.7 30.4 #>  9 FB 2013-01-14 32.1 32.2 30.6 31.0 9.89e7 31.0 30.7 #> 10 FB 2013-01-15 30.6 31.7 29.9 30.1 1.73e8 30.1 30.9 #> # … with 998 more rows, and 1 more variable: rolling_mean_10 <dbl>
# 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))
#> # A tibble: 1,008 x 9 #> symbol date open high low close volume adjusted running_cor #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-02 27.4 28.2 27.4 28 69846400 28 NA #>  2 FB 2013-01-03 27.9 28.5 27.6 27.8 63140600 27.8 NA #>  3 FB 2013-01-04 28.0 28.9 27.8 28.8 72715400 28.8 NA #>  4 FB 2013-01-07 28.7 29.8 28.6 29.4 83781800 29.4 NA #>  5 FB 2013-01-08 29.5 29.6 28.9 29.1 45871300 29.1 0.749 #>  6 FB 2013-01-09 29.7 30.6 29.5 30.6 104787700 30.6 0.805 #>  7 FB 2013-01-10 30.6 31.5 30.3 31.3 95316400 31.3 0.859 #>  8 FB 2013-01-11 31.3 32.0 31.1 31.7 89598000 31.7 0.884 #>  9 FB 2013-01-14 32.1 32.2 30.6 31.0 98892800 31.0 0.667 #> 10 FB 2013-01-15 30.6 31.7 29.9 30.1 173242600 30.1 0.379 #> # … with 998 more rows
# 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))
#> # A tibble: 1,008 x 9 #> symbol date open high low close volume adjusted avg_of_avgs #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-02 27.4 28.2 27.4 28 69846400 28 NA #>  2 FB 2013-01-03 27.9 28.5 27.6 27.8 63140600 27.8 NA #>  3 FB 2013-01-04 28.0 28.9 27.8 28.8 72715400 28.8 NA #>  4 FB 2013-01-07 28.7 29.8 28.6 29.4 83781800 29.4 NA #>  5 FB 2013-01-08 29.5 29.6 28.9 29.1 45871300 29.1 NA #>  6 FB 2013-01-09 29.7 30.6 29.5 30.6 104787700 30.6 NA #>  7 FB 2013-01-10 30.6 31.5 30.3 31.3 95316400 31.3 NA #>  8 FB 2013-01-11 31.3 32.0 31.1 31.7 89598000 31.7 NA #>  9 FB 2013-01-14 32.1 32.2 30.6 31.0 98892800 31.0 NA #> 10 FB 2013-01-15 30.6 31.7 29.9 30.1 173242600 30.1 29.7 #> # … with 998 more rows
# 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$adjusted[1] <- NA roll_mean_na_rm <- rollify(~mean(.x, na.rm = TRUE), window = 5) dplyr::mutate(FB, roll_mean = roll_mean_na_rm(adjusted))
#> # A tibble: 1,008 x 9 #> symbol date open high low close volume adjusted roll_mean #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-02 27.4 28.2 27.4 28 69846400 NA NA #>  2 FB 2013-01-03 27.9 28.5 27.6 27.8 63140600 27.8 NA #>  3 FB 2013-01-04 28.0 28.9 27.8 28.8 72715400 28.8 NA #>  4 FB 2013-01-07 28.7 29.8 28.6 29.4 83781800 29.4 NA #>  5 FB 2013-01-08 29.5 29.6 28.9 29.1 45871300 29.1 28.8 #>  6 FB 2013-01-09 29.7 30.6 29.5 30.6 104787700 30.6 29.1 #>  7 FB 2013-01-10 30.6 31.5 30.3 31.3 95316400 31.3 29.8 #>  8 FB 2013-01-11 31.3 32.0 31.1 31.7 89598000 31.7 30.4 #>  9 FB 2013-01-14 32.1 32.2 30.6 31.0 98892800 31.0 30.7 #> 10 FB 2013-01-15 30.6 31.7 29.9 30.1 173242600 30.1 30.9 #> # … with 998 more rows
# Returning multiple values ------------------------------------------------- data(FB) # If the function returns >1 value, set the `unlist = FALSE` argument # Running 5 number summary summary_roll <- rollify(summary, window = 5, unlist = FALSE) FB_summarised <- dplyr::mutate(FB, summary_roll = summary_roll(adjusted)) FB_summarised$summary_roll[[5]]
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's #> 27.77 28.51 28.91 28.75 29.15 29.42 1
# dplyr::bind_rows() is often helpful in these cases to get # meaningful output summary_roll <- rollify(~dplyr::bind_rows(summary(.)), 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)
#> # A tibble: 1,004 x 15 #> symbol date open high low close volume adjusted Min. `1st Qu.` #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 FB 2013-01-08 29.5 29.6 28.9 29.1 4.59e7 29.1 27.8 28.5 #>  2 FB 2013-01-09 29.7 30.6 29.5 30.6 1.05e8 30.6 27.8 28.8 #>  3 FB 2013-01-10 30.6 31.5 30.3 31.3 9.53e7 31.3 28.8 29.1 #>  4 FB 2013-01-11 31.3 32.0 31.1 31.7 8.96e7 31.7 29.1 29.4 #>  5 FB 2013-01-14 32.1 32.2 30.6 31.0 9.89e7 31.0 29.1 30.6 #>  6 FB 2013-01-15 30.6 31.7 29.9 30.1 1.73e8 30.1 30.1 30.6 #>  7 FB 2013-01-16 30.2 30.4 29.5 29.8 7.53e7 29.8 29.8 30.1 #>  8 FB 2013-01-17 30.1 30.4 30.0 30.1 4.03e7 30.1 29.8 30.1 #>  9 FB 2013-01-18 30.3 30.4 29.3 29.7 4.96e7 29.7 29.7 29.8 #> 10 FB 2013-01-22 29.8 30.9 29.7 30.7 5.52e7 30.7 29.7 29.8 #> # … with 994 more rows, and 5 more variables: Median <dbl>, Mean <dbl>, `3rd #> # Qu.` <dbl>, Max. <dbl>, `NA's` <dbl>
# 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))
#> # A tibble: 1,704 x 7 #> # Groups: country [142] #> country continent year lifeExp pop gdpPercap rolling_lm #> <fct> <fct> <int> <dbl> <int> <dbl> <list> #>  1 Afghanistan Asia 1952 28.8 8425333 779. <lgl [1]> #>  2 Afghanistan Asia 1957 30.3 9240934 821. <lgl [1]> #>  3 Afghanistan Asia 1962 32.0 10267083 853. <lgl [1]> #>  4 Afghanistan Asia 1967 34.0 11537966 836. <lgl [1]> #>  5 Afghanistan Asia 1972 36.1 13079460 740. <S3: lm> #>  6 Afghanistan Asia 1977 38.4 14880372 786. <S3: lm> #>  7 Afghanistan Asia 1982 39.9 12881816 978. <S3: lm> #>  8 Afghanistan Asia 1987 40.8 13867957 852. <S3: lm> #>  9 Afghanistan Asia 1992 41.7 16317921 649. <S3: lm> #> 10 Afghanistan Asia 1997 41.8 22227415 635. <S3: lm> #> # … with 1,694 more rows
# 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)
#> # A tibble: 20 x 9 #> # Groups: symbol [4] #> symbol date open high low close volume adjusted mean_roll #> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 AMZN 2013-01-02 256. 258. 253. 257. 3271000 257. NA #>  2 AMZN 2013-01-03 257. 261. 256. 258. 2750900 258. NA #>  3 AMZN 2013-01-04 258. 260. 257. 259. 1874200 259. 258. #>  4 AMZN 2013-01-07 263. 270. 263. 268. 4910000 268. 262. #>  5 AMZN 2013-01-08 267. 269. 264. 266. 3010700 266. 265. #>  6 FB 2013-01-02 27.4 28.2 27.4 28 69846400 28 NA #>  7 FB 2013-01-03 27.9 28.5 27.6 27.8 63140600 27.8 NA #>  8 FB 2013-01-04 28.0 28.9 27.8 28.8 72715400 28.8 28.2 #>  9 FB 2013-01-07 28.7 29.8 28.6 29.4 83781800 29.4 28.6 #> 10 FB 2013-01-08 29.5 29.6 28.9 29.1 45871300 29.1 29.1 #> 11 GOOG 2013-01-02 719. 727. 717. 723. 5101500 361. NA #> 12 GOOG 2013-01-03 725. 732. 721. 724. 4653700 361. NA #> 13 GOOG 2013-01-04 729. 741. 728. 738. 5547600 369. 364. #> 14 GOOG 2013-01-07 735. 739. 731. 735. 3323800 367. 366. #> 15 GOOG 2013-01-08 736. 736. 724. 733. 3364700 366. 367. #> 16 NFLX 2013-01-02 95.2 95.8 90.7 92.0 19431300 13.1 NA #> 17 NFLX 2013-01-03 92.0 97.9 91.5 96.6 27912500 13.8 NA #> 18 NFLX 2013-01-04 96.5 97.7 95.5 96.0 17761100 13.7 13.6 #> 19 NFLX 2013-01-07 96.4 102. 96.1 99.2 45550400 14.2 13.9 #> 20 NFLX 2013-01-08 100. 101. 96.8 97.2 24714900 13.9 13.9