diff --git a/README.Rmd b/README.Rmd
index 70d250b9..af3934c5 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -49,9 +49,11 @@ secondary <- "#f9c80e"
 tertiary <- "#177245"
 fourth_colour <- "#A393BF"
 fifth_colour <- "#2e8edd"
-colvec <- c(base = base, primary = primary, secondary = secondary,
-            tertiary = tertiary, fourth_colour = fourth_colour,
-            fifth_colour = fifth_colour)
+colvec <- c(
+  base = base, primary = primary, secondary = secondary,
+  tertiary = tertiary, fourth_colour = fourth_colour,
+  fifth_colour = fifth_colour
+)
 library(epiprocess)
 suppressMessages(library(tidyverse))
 theme_update(legend.position = "bottom", legend.title = element_blank())
@@ -61,7 +63,7 @@ delphi_pal <- function(n) {
 }
 scale_fill_delphi <- function(..., aesthetics = "fill") {
   discrete_scale(aesthetics = aesthetics, palette = delphi_pal, ...)
-} 
+}
 scale_color_delphi <- function(..., aesthetics = "color") {
   discrete_scale(aesthetics = aesthetics, palette = delphi_pal, ...)
 }
@@ -124,7 +126,8 @@ cases <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  geo_values = "*"
+) |>
   select(geo_value, time_value, case_rate = value)
 
 deaths <- pub_covidcast(
@@ -133,7 +136,8 @@ deaths <- pub_covidcast(
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20220101),
-  geo_values = "*") |>
+  geo_values = "*"
+) |>
   select(geo_value, time_value, death_rate = value)
 cases_deaths <-
   full_join(cases, deaths, by = c("time_value", "geo_value")) |>
@@ -156,7 +160,7 @@ First, to eliminate some of the noise coming from daily reporting, we do 7 day a
 
 ```{r smooth}
 cases_deaths <-
-  cases_deaths |> 
+  cases_deaths |>
   group_by(geo_value) |>
   epi_slide(
     cases_7dav = mean(case_rate, na.rm = TRUE),
@@ -181,7 +185,8 @@ cases_deaths <-
   ungroup() |>
   mutate(
     death_rate = outlr_death_rate_replacement,
-    case_rate = outlr_case_rate_replacement) |>
+    case_rate = outlr_case_rate_replacement
+  ) |>
   select(geo_value, time_value, case_rate, death_rate)
 cases_deaths
 ```
@@ -196,8 +201,8 @@ of the states, noting the actual forecast date:
 forecast_date_label <-
   tibble(
     geo_value = rep(plot_locations, 2),
-    source = c(rep("case_rate",4), rep("death_rate", 4)),
-    dates = rep(forecast_date - 7*2, 2 * length(plot_locations)),
+    source = c(rep("case_rate", 4), rep("death_rate", 4)),
+    dates = rep(forecast_date - 7 * 2, 2 * length(plot_locations)),
     heights = c(rep(150, 4), rep(1.0, 4))
   )
 processed_data_plot <-
@@ -209,7 +214,8 @@ processed_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -260,7 +266,8 @@ narrow_data_plot <-
   facet_grid(source ~ geo_value, scale = "free") +
   geom_vline(aes(xintercept = forecast_date)) +
   geom_text(
-    data = forecast_date_label, aes(x=dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right") +
+    data = forecast_date_label, aes(x = dates, label = "forecast\ndate", y = heights), size = 3, hjust = "right"
+  ) +
   scale_x_date(date_breaks = "3 months", date_labels = "%Y %b") +
   theme(axis.text.x = element_text(angle = 90, hjust = 1))
 ```
@@ -278,7 +285,8 @@ forecast_plot <-
   epipredict:::plot_bands(
     restricted_predictions,
     levels = 0.9,
-    fill = primary) +
+    fill = primary
+  ) +
   geom_point(data = restricted_predictions, aes(y = .data$value), color = secondary)
 ```
 </details>