docs: remove duplicate word, mention time_type epi_df attr

Author: brookslogan

R/epi_df.R

@@ -6,18 +6,20 @@
 #' which can be seen as measured variables at each key. In brief, an `epi_df`
 #' represents a snapshot of an epidemiological data set at a point in time.
 #'
-#' @details An `epi_df` is a tibble with (at least) the following columns:
+#' @details An `epi_df` is a kind of tibble with (at least) the following
+#'   columns:
 #'
 #' - `geo_value`: A character vector representing the geographical unit of
 #'    observation. This could be a country code, a state name, a county code,
 #'    etc.
 #' - `time_value`: A date or integer vector representing the time of observation.
 #'
 #' Other columns can be considered as measured variables, which we also refer to
-#'   as signal variables. An `epi_df` object also has metadata with (at least)
-#'   the following fields:
+#'   as indicators or signals. An `epi_df` object also has metadata with (at
+#'   least) the following fields:
 #'
 #' * `geo_type`: the type for the geo values.
+#' * `time_type`: the type for the time values.
 #' * `as_of`: the time value at which the given data were available.
 #'
 #' Most users should use `as_epi_df`. The input tibble `x` to the constructor

R/slide.R

@@ -40,7 +40,7 @@
 #'
 #' You can specify the computation in one of the following ways:
 #'
-#' - Don't provide `.f`, and instead use use one or more
+#' - Don't provide `.f`, and instead use one or more
 #'   [`dplyr::summarize`]-esque ["data-masking"][rlang::args_data_masking]
 #'   expressions in `...`, e.g., `cfr_estimate_v0 =
 #'   death_rate_7d_av[[22]]/case_rate_7d_av[[1]]`. This way is sometimes more