Skip to contents

Quickly use any function as a rolling function and apply to multiple .periods. Works with dplyr groups too.

Usage

tk_augment_slidify(
  .data,
  .value,
  .period,
  .f,
  ...,
  .align = c("center", "left", "right"),
  .partial = FALSE,
  .names = "auto"
)

Arguments

.data

A tibble.

.value

One or more column(s) to have a transformation applied. Usage of tidyselect functions (e.g. contains()) can be used to select multiple columns.

.period

One or more periods for the rolling window(s)

.f

A summary [function / formula],

...

Optional arguments for the summary function

.align

Rolling functions generate .period - 1 fewer values than the incoming vector. Thus, the vector needs to be aligned. Select one of "center", "left", or "right".

.partial

.partial 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.

.names

A vector of names for the new columns. Must be of same length as .period. Default is "auto".

Value

Returns a tibble object describing the timeseries.

Details

tk_augment_slidify() scales the slidify_vec() function to multiple time series .periods. See slidify_vec() for examples and usage of the core function arguments.

See also

Augment Operations:

Underlying Function:

  • slidify_vec() - The underlying function that powers tk_augment_slidify()

Examples

library(tidyverse)
library(tidyquant)
library(timetk)

# Single Column | Multiple Rolling Windows
FANG %>%
    select(symbol, date, adjusted) %>%
    group_by(symbol) %>%
    tk_augment_slidify(
        .value   = contains("adjust"),
        # Multiple rolling windows
        .period  = c(10, 30, 60, 90),
        .f       = AVERAGE,
        .partial = TRUE,
        .names   = str_c("MA_", c(10, 30, 60, 90))
    ) %>%
    ungroup()
#> # A tibble: 4,032 × 7
#>    symbol date       adjusted MA_10 MA_30 MA_60 MA_90
#>    <chr>  <date>        <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 FB     2013-01-02     28    28.9  30.0  29.7  29.1
#>  2 FB     2013-01-03     27.8  29.3  30.1  29.7  29.1
#>  3 FB     2013-01-04     28.8  29.6  30.2  29.7  29.0
#>  4 FB     2013-01-07     29.4  29.7  30.2  29.6  29.0
#>  5 FB     2013-01-08     29.1  29.8  30.3  29.6  29.0
#>  6 FB     2013-01-09     30.6  30.0  30.3  29.5  28.9
#>  7 FB     2013-01-10     31.3  30.2  30.3  29.4  28.9
#>  8 FB     2013-01-11     31.7  30.3  30.2  29.4  28.8
#>  9 FB     2013-01-14     31.0  30.4  30.1  29.3  28.8
#> 10 FB     2013-01-15     30.1  30.6  30.1  29.3  28.7
#> # … with 4,022 more rows

# Multiple Columns | Multiple Rolling Windows
FANG %>%
    select(symbol, date, adjusted, volume) %>%
    group_by(symbol) %>%
    tk_augment_slidify(
        .value  = c(adjusted, volume),
        .period  = c(10, 30, 60, 90),
        .f       = AVERAGE,
        .partial = TRUE
    ) %>%
    ungroup()
#> # A tibble: 4,032 × 12
#>    symbol date       adjusted    volume adjusted_roll_10 volume_roll_10
#>    <chr>  <date>        <dbl>     <dbl>            <dbl>          <dbl>
#>  1 FB     2013-01-02     28    69846400             28.9      73357200 
#>  2 FB     2013-01-03     27.8  63140600             29.3      76494229.
#>  3 FB     2013-01-04     28.8  72715400             29.6      78132200 
#>  4 FB     2013-01-07     29.4  83781800             29.7      80438933.
#>  5 FB     2013-01-08     29.1  45871300             29.8      89719300 
#>  6 FB     2013-01-09     30.6 104787700             30.0      90267930 
#>  7 FB     2013-01-10     31.3  95316400             30.2      87979540 
#>  8 FB     2013-01-11     31.7  89598000             30.3      85671150 
#>  9 FB     2013-01-14     31.0  98892800             30.4      82817300 
#> 10 FB     2013-01-15     30.1 173242600             30.6      83120150 
#> # … with 4,022 more rows, and 6 more variables: adjusted_roll_30 <dbl>,
#> #   volume_roll_30 <dbl>, adjusted_roll_60 <dbl>, volume_roll_60 <dbl>,
#> #   adjusted_roll_90 <dbl>, volume_roll_90 <dbl>