library(rlang)
library(dplyr)
library(tibbletime)
library(purrr)
data(FB)

# ------------------------------------------------------------------------
# Call mutate, but with the lhs being names and the rhs being expressions

mutate_double_sided <- function(.data, x) {

  dot_equal <- enquo(x)
  dot_expr  <- quo_get_expr(dot_equal)
  dot_env   <- quo_get_env(dot_equal)
  lhs <- dot_expr[[2]]
  rhs <- dot_expr[[3]]

  lhs_quos <- lapply(lhs, function(x) new_quosure(x))[-1]
  rhs_quos <- lapply(rhs, function(x) new_quosure(x))[-1]

  dots_list <- map2(lhs_quos, rhs_quos,  ~quos(!!quo_get_expr(.x) := !!.y))

  dots <- unlist(dots_list)

  mutate(.data, !!! dots)
}

mutate_double_sided(FB, .(adjusted2, clo) := .(adjusted+2, close))
#> # A tibble: 1,008 x 10
#>    symbol date        open  high   low close     volume adjusted adjusted2
#>    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>      <dbl>    <dbl>     <dbl>
#>  1 FB     2013-01-02  27.4  28.2  27.4  28.0  69846400.     28.0      30.0
#>  2 FB     2013-01-03  27.9  28.5  27.6  27.8  63140600.     27.8      29.8
#>  3 FB     2013-01-04  28.0  28.9  27.8  28.8  72715400.     28.8      30.8
#>  4 FB     2013-01-07  28.7  29.8  28.6  29.4  83781800.     29.4      31.4
#>  5 FB     2013-01-08  29.5  29.6  28.9  29.1  45871300.     29.1      31.1
#>  6 FB     2013-01-09  29.7  30.6  29.5  30.6 104787700.     30.6      32.6
#>  7 FB     2013-01-10  30.6  31.5  30.3  31.3  95316400.     31.3      33.3
#>  8 FB     2013-01-11  31.3  32.0  31.1  31.7  89598000.     31.7      33.7
#>  9 FB     2013-01-14  32.1  32.2  30.6  31.0  98892800.     31.0      33.0
#> 10 FB     2013-01-15  30.6  31.7  29.9  30.1 173242600.     30.1      32.1
#> # ... with 998 more rows, and 1 more variable: clo <dbl>

# ------------------------------------------------------------------------
# A more practical example
# Rolling correlations with the lhs being the x's and the rhs being corresponding y's

rolling_cor_pairs <- function(.data, x, window = 5) {

  roll_cor <- rollify(~cor(.x, .y), window = window)

  dot_equal <- enquo(x)
  dot_expr  <- quo_get_expr(dot_equal)
  dot_env   <- quo_get_env(dot_equal)
  lhs <- dot_expr[[2]]
  rhs <- dot_expr[[3]]

  lhs_quos <- lapply(lhs, function(x) new_quosure(x))[-1]
  rhs_quos <- lapply(rhs, function(x) new_quosure(x))[-1]

  dots_list <- map2(lhs_quos, rhs_quos,  ~quos(!! paste0(quo_name(.x), "_", quo_name(.y)) := roll_cor(!!quo_get_expr(.x), !!quo_get_expr(.y))))

  dots <- unlist(dots_list)

  mutate(.data, !!! dots)
}

rolling_cor_pairs(FB, .(adjusted, open, high) := .(open, high, adjusted), window = 5) %>%
  select(date, open, high, adjusted, adjusted_open, open_high, high_adjusted)
#> # A tibble: 1,008 x 7
#>    date        open  high adjusted adjusted_open open_high high_adjusted
#>    <date>     <dbl> <dbl>    <dbl>         <dbl>     <dbl>         <dbl>
#>  1 2013-01-02  27.4  28.2     28.0        NA        NA            NA
#>  2 2013-01-03  27.9  28.5     27.8        NA        NA            NA
#>  3 2013-01-04  28.0  28.9     28.8        NA        NA            NA
#>  4 2013-01-07  28.7  29.8     29.4        NA        NA            NA
#>  5 2013-01-08  29.5  29.6     29.1         0.749     0.879         0.946
#>  6 2013-01-09  29.7  30.6     30.6         0.805     0.882         0.980
#>  7 2013-01-10  30.6  31.5     31.3         0.859     0.919         0.985
#>  8 2013-01-11  31.3  32.0     31.7         0.884     0.927         0.990
#>  9 2013-01-14  32.1  32.2     31.0         0.667     0.931         0.887
#> 10 2013-01-15  30.6  31.7     30.1         0.379     0.948         0.316
#> # ... with 998 more rows