diff --git a/R/slide.R b/R/slide.R
index 4b3f73c49..fafa264c6 100644
--- a/R/slide.R
+++ b/R/slide.R
@@ -627,7 +627,7 @@ epi_slide_mean <- function(x, col_names, ..., before, after, ref_time_values,
       .data_group,
       tibble(time_value = c(missing_times, pad_early_dates, pad_late_dates), .real = FALSE)
     ) %>%
-      arrange(time_value)
+      arrange(.data$time_value)
 
     # If a group contains duplicate time values, `frollmean` will still only
     # use the last `k` obs. It isn't looking at dates, it just goes in row
@@ -721,7 +721,7 @@ full_date_seq <- function(x, before, after, time_step) {
   # `tsibble` classes apparently can't be added to in different units, so even
   # if `time_step` is provided by the user, use a unit step.
   if (inherits(x$time_value, c("yearquarter", "yearweek", "yearmonth")) ||
-    is.numeric(x$time_value)) {
+        is.numeric(x$time_value)) {
     all_dates <- seq(min(x$time_value), max(x$time_value), by = 1L)
 
     if (before != 0) {
diff --git a/tests/testthat/test-epi_slide.R b/tests/testthat/test-epi_slide.R
index 0833b71c3..8b5094522 100644
--- a/tests/testthat/test-epi_slide.R
+++ b/tests/testthat/test-epi_slide.R
@@ -1037,17 +1037,17 @@ test_that("results for different time_types match between epi_slide and epi_slid
     epiprocess::as_epi_df(rbind(tibble(
       geo_value = "al",
       time_value = date_seq,
-      a = 1:length(date_seq),
+      a = seq_along(date_seq),
       b = rand_vals
     ), tibble(
       geo_value = "ca",
       time_value = date_seq,
-      a = length(date_seq):1,
+      a = rev(seq_along(date_seq)),
       b = rand_vals + 10
     ), tibble(
       geo_value = "fl",
       time_value = date_seq,
-      a = length(date_seq):1,
+      a = rev(seq_along(date_seq)),
       b = rand_vals * 2
     )), ...)
   }
@@ -1175,17 +1175,17 @@ test_that("helper `full_date_seq` returns expected date values", {
     epiprocess::as_epi_df(rbind(tibble(
       geo_value = "al",
       time_value = date_seq,
-      a = 1:length(date_seq),
+      a = seq_along(date_seq),
       b = rand_vals
     ), tibble(
       geo_value = "ca",
       time_value = date_seq,
-      a = length(date_seq):1,
+      a = rev(seq_along(date_seq)),
       b = rand_vals + 10
     ), tibble(
       geo_value = "fl",
       time_value = date_seq,
-      a = length(date_seq):1,
+      a = rev(seq_along(date_seq)),
       b = rand_vals * 2
     )), ...)
   }