rollify.R

# devtools::install_github("business-science/tibbletime")
library(tibbletime)
#> 
#> Attaching package: 'tibbletime'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, tibbletime, stats
#> lag():    dplyr, stats

# Create a rolling version of any function
mean_roll_5 <- rollify(~mean(.x),    window = 5)
mean_roll_8 <- rollify(~mean(.x),    window = 8)
cor_roll_5 <-  rollify(~cor(.x, .y), window = 5)

# Load up Facebook stock prices
data(FB)

# Just a common call to `mutate`, but using rolling functions!
FB %>% 
  mutate(rolling_mean_5 = mean_roll_5(adjusted),
         rolling_mean_8 = mean_roll_8(adjusted),
         rolling_cor_5  = cor_roll_5(adjusted, high))
#> # A tibble: 1,008 x 11
#>    symbol       date  open  high   low close    volume adjusted
#>     <chr>     <date> <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#>  1     FB 2013-01-02 27.44 28.18 27.42 28.00  69846400    28.00
#>  2     FB 2013-01-03 27.88 28.47 27.59 27.77  63140600    27.77
#>  3     FB 2013-01-04 28.01 28.93 27.83 28.76  72715400    28.76
#>  4     FB 2013-01-07 28.69 29.79 28.65 29.42  83781800    29.42
#>  5     FB 2013-01-08 29.51 29.60 28.86 29.06  45871300    29.06
#>  6     FB 2013-01-09 29.67 30.60 29.49 30.59 104787700    30.59
#>  7     FB 2013-01-10 30.60 31.45 30.28 31.30  95316400    31.30
#>  8     FB 2013-01-11 31.28 31.96 31.10 31.72  89598000    31.72
#>  9     FB 2013-01-14 32.08 32.21 30.62 30.95  98892800    30.95
#> 10     FB 2013-01-15 30.64 31.71 29.88 30.10 173242600    30.10
#> # ... with 998 more rows, and 3 more variables: rolling_mean_5 <dbl>,
#> #   rolling_mean_8 <dbl>, rolling_cor_5 <dbl>