doc: update from Rachel's comments

R/methods-epi_df.R

@@ -258,7 +258,7 @@ group_modify.epi_df <- function(.data, .f, ..., .keep = FALSE) {
 
 #' Complete epi_df
 #'
-#' A `tidyr::complete()` analogue for `epi_df`` objects. This function
+#' A `tidyr::complete()` analogue for `epi_df` objects. This function
 #' can be used, for example, to add rows for missing combinations
 #' of `geo_value` and `time_value`, filling other columns with `NA`s.
 #' See the examples for usage details.

vignettes/epiprocess.Rmd

@@ -86,7 +86,7 @@ edf %>%
 ```
 
 We can compute the 7 day moving average of the confirmed daily cases for each
-geo_value by using the `epi_slide_mean()` function. For a more in-depth guide to
+`geo_value` by using the `epi_slide_mean()` function. For a more in-depth guide to
 sliding, see `vignette("epi_df")`.
 
 ```{r}
@@ -96,15 +96,15 @@ edf %>%
 ```
 
 We can compute the growth rate of the confirmed cumulative cases for each
-geo_value. For a more in-depth guide to growth rates, see `vignette("growth_rate")`.
+`geo_value`. For a more in-depth guide to growth rates, see `vignette("growth_rate")`.
 
 ```{r}
 edf %>%
   group_by(geo_value) %>%
   mutate(cases_growth = growth_rate(x = time_value, y = cases_cumulative, method = "rel_change", h = 7))
 ```
 
-Detect outliers in daily reported cases for each geo_value. For a more in-depth
+Detect outliers in daily reported cases for each `geo_value`. For a more in-depth
 guide to outlier detection, see `vignette("outliers")`.
 
 ```{r message=FALSE}
@@ -114,8 +114,8 @@ edf %>%
   ungroup()
 ```
 
-Add a column to the epi_df object with the daily deaths for each geo_value and
-compute the correlations between cases and deaths for each geo_value. For a more
+Add a column to the epi_df object with the daily deaths for each `geo_value` and
+compute the correlations between cases and deaths for each `geo_value`. For a more
 in-depth guide to correlations, see `vignette("correlation")`.
 
 ```{r}