docs: fix typos in hop_index.Rd and slide_index2.Rd

R/hop-index.R

@@ -63,7 +63,7 @@
 #' hop_index(i, i, starts, stops, ~.x)
 #'
 #' # ---------------------------------------------------------------------------
-#' # Non-existant dates with `lubridate::months()`
+#' # Non-existent dates with `lubridate::months()`
 #'
 #' # Imagine you want to compute a 1 month rolling average on this
 #' # irregular daily data.
@@ -81,7 +81,7 @@
 #' )
 #'
 #' # This is because when you actually compute the `.i - .before` sequence,
-#' # you hit non-existant dates. i.e. `"2019-03-29" - months(1)` doesn't exist.
+#' # you hit non-existent dates. i.e. `"2019-03-29" - months(1)` doesn't exist.
 #' i - months(1)
 #'
 #' # To get around this, lubridate provides `add_with_rollback()`,

R/slide-index2.R

@@ -1,4 +1,4 @@
-#' Slide along multiples inputs simultaneously relative to an index
+#' Slide along multiple inputs simultaneously relative to an index
 #'
 #' `slide_index2()` and `pslide_index()` represent the combination
 #' of [slide2()] and [pslide()] with [slide_index()], allowing you to iterate

R/summary-index.R

@@ -63,6 +63,30 @@
 #'
 #' # Only evaluate the sum on windows that have the potential to be complete
 #' slide_index_sum(x, i, before = 2, after = 1, complete = TRUE)
+#'
+#' # ---------------------------------------------------------------------------
+#' # Panel data (grouped rolling operations)
+#'
+#' # In Stata, the equivalent of grouped rolling means is:
+#' #   rangestat (mean) roll_mean = sales, int(date -2 0) by(firm)
+#'
+#' # slider + dplyr replicates this pattern with group_by():
+#' df <- data.frame(
+#'   firm = rep(c("A", "B"), each = 4),
+#'   date = as.Date(c(
+#'     "2020-01-01", "2020-01-02", "2020-01-05", "2020-01-06",
+#'     "2020-01-01", "2020-01-03", "2020-01-04", "2020-01-07"
+#'   )),
+#'   sales = c(10, 12, 15, 13, 20, 22, 18, 25)
+#' )
+#'
+#' # 3-day rolling mean within each firm, respecting irregular date gaps
+#' df$roll_mean <- unlist(
+#'   lapply(split(df, df$firm), function(g) {
+#'     slide_index_mean(g$sales, g$date, before = 2)
+#'   })
+#' )
+#' df
 slide_index_sum <- function(
   x,
   i,