New Helper Functions to Write about modeler HOT 4 OPEN

roll_mean <- function(n, vec) seq(from = 0, to = n) %>% map(~ lag(vec, n = .x)) %>% do.call(cbind, .) %>% rowMeans(na.rm = T)

safe_ifelse <- function(cond, yes, no) structure(ifelse(cond, yes, no), class = class(yes))

acf_by_group <- function(.df, .group_var, .value_var, ...){
  group_var <- enquo(.group_var)
  value_var <- deparse(substitute(.value_var))
  nest_cols <- names(.df)
  group_col <- deparse(substitute(.group_var))
  nest_cols <- nest_cols[nest_cols != group_col]
  
  .df %>%
    tidyr::nest_(key_col = "data", nest_cols = nest_cols) %>%
    dplyr::mutate(acf_results = purrr::map(data, ~ acf(.x[[value_var]], plot = F, ...)),
                  acf_values = purrr::map(acf_results, ~ drop(.x$acf))) %>%
    tidyr::unnest(acf_values) %>%
    dplyr::group_by(!!group_var) %>%
    dplyr::mutate(lag = seq(0, n() - 1)) %>%
    dplyr::ungroup()
}

roll_numeric <- function(x, n, func, ...){
  stopifnot(length(x) > 0)
  positions <- seq_along(x)
  vapply(positions, function(i, .x = x, .n = n){
    start <- max(i - .n, 0) + 1
    func(.x[seq(start, i)], ...)
  }, numeric(1))
}

roll_mean <- function(...) roll_numeric(func = mean, ...)
roll_sum <- function(...) roll_numeric(func = sum, ...)