Use modern pipe in vignette

Author: arcresu

vignettes/ggautomap.Rmd

@@ -44,11 +44,15 @@ All of the summary geoms of `ggautomap` can then be used to draw your data.
 ## Scatter
 
 ```{r scatter, fig.height = 3, fig.width = 7}
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lga)) +
   geom_boundaries(feature_type = "nswgeo.lga") +
   geom_geoscatter(aes(colour = type), sample_type = "random", size = 0.5) +
-  coord_automap(feature_type = "nswgeo.lga", xlim = c(147, 153), ylim = c(-33.7, -29)) +
+  coord_automap(
+    feature_type = "nswgeo.lga",
+    xlim = c(147, 153),
+    ylim = c(-33.7, -29)
+  ) +
   guides(colour = guide_legend(override.aes = list(size = 1))) +
   theme_void()
 ```
@@ -63,15 +67,21 @@ provide it to each relevant geom. The geoms in this package are all inset-aware.
 See `{ggmapinset}` for details.
 
 ```{r inset, fig.height = 3, fig.width = 7}
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lga)) +
   geom_boundaries(feature_type = "nswgeo.lga") +
   geom_geoscatter(aes(colour = type), size = 0.5) +
   geom_inset_frame() +
-  coord_automap(feature_type = "nswgeo.lga", inset = configure_inset(
-    centre = "Blacktown", radius = 40, units = "km",
-    scale = 7, translation = c(400, -100)
-  )) +
+  coord_automap(
+    feature_type = "nswgeo.lga",
+    inset = configure_inset(
+      centre = "Blacktown",
+      radius = 40,
+      units = "km",
+      scale = 7,
+      translation = c(400, -100)
+    )
+  ) +
   theme_void()
 ```
 
@@ -82,8 +92,8 @@ packed circle in the centre of each feature. It also shows how you can
 fine-tune the plot with the usual `{ggplot2}` functions.
 
 ```{r packed, fig.height = 3, fig.width = 7}
-covid_cases_nsw %>%
-  dplyr::filter(year >= 2021) %>%
+covid_cases_nsw |>
+  dplyr::filter(year >= 2021) |>
   ggplot(aes(location = lhd)) +
   geom_boundaries(feature_type = "nswgeo.lhd") +
   geom_centroids(aes(colour = type), position = position_circle_repel_sf(scale = 35), size = 1) +
@@ -106,7 +116,7 @@ the rows are disease cases) then you can use `geom_choropleth()` to aggregate th
 into counts.
 
 ```{r choro-long, fig.height = 3, fig.width = 7}
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lhd)) +
   geom_choropleth() +
   geom_boundaries(
@@ -128,22 +138,36 @@ existing column that you'd like to map to the `fill` aesthetic, then instead use
 
 ```{r choro-wide, fig.height = 3, fig.width = 7}
 summarised_data <- data.frame(
-  lhd = c("Western Sydney", "Sydney", "Far West", "Mid North Coast", "South Western Sydney"),
+  lhd = c(
+    "Western Sydney",
+    "Sydney",
+    "Far West",
+    "Mid North Coast",
+    "South Western Sydney"
+  ),
   cases = c(250, 80, 20, NA, 100)
 )
 
-summarised_data %>%
+summarised_data |>
   ggplot(aes(location = lhd)) +
   geom_sf_inset(aes(fill = cases), stat = "automap", colour = NA) +
   geom_boundaries(
-    feature_type = "nswgeo.lhd", colour = "black", linewidth = 0.1,
+    feature_type = "nswgeo.lhd",
+    colour = "black",
+    linewidth = 0.1,
     outline.aes = list(colour = NA)
   ) +
   geom_inset_frame() +
-  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
-    centre = "Western Sydney", radius = 60, units = "km",
-    scale = 3.5, translation = c(350, 0)
-  )) +
+  coord_automap(
+    feature_type = "nswgeo.lhd",
+    inset = configure_inset(
+      centre = "Western Sydney",
+      radius = 60,
+      units = "km",
+      scale = 3.5,
+      translation = c(350, 0)
+    )
+  ) +
   scale_fill_gradient(low = "#e6f9ff", high = "#00394d", na.value = "grey90") +
   theme_void()
 ```
@@ -154,7 +178,7 @@ summarised_data %>%
 These examples give some different ways of placing text, accounting for possible insets.
 
 ```{r text-inset, fig.height = 3, fig.width = 7}
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lhd)) +
   geom_choropleth() +
   geom_boundaries(feature_type = "nswgeo.lhd") +
@@ -181,7 +205,7 @@ often doesn't make sense in mapping contexts.
 library(ggrepel)
 
 # label all features that have data
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lhd)) +
   geom_choropleth() +
   geom_boundaries(feature_type = "nswgeo.lhd") +
@@ -199,15 +223,21 @@ covid_cases_nsw %>%
     data = ~ dplyr::slice_head(.x, by = lhd)
   ) +
   scale_fill_distiller(direction = 1) +
-  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
-    centre = "Western Sydney", radius = 60, units = "km",
-    scale = 3.5, translation = c(350, 0)
-  )) +
+  coord_automap(
+    feature_type = "nswgeo.lhd",
+    inset = configure_inset(
+      centre = "Western Sydney",
+      radius = 60,
+      units = "km",
+      scale = 3.5,
+      translation = c(350, 0)
+    )
+  ) +
   labs(x = NULL, y = NULL) +
   theme_void()
 
 # label all features in the map regardless of data, hiding visually overlapping labels
-covid_cases_nsw %>%
+covid_cases_nsw |>
   ggplot(aes(location = lhd)) +
   geom_choropleth() +
   geom_boundaries(feature_type = "nswgeo.lhd") +
@@ -225,10 +255,16 @@ covid_cases_nsw %>%
     inherit.aes = FALSE
   ) +
   scale_fill_distiller(direction = 1, palette = 2) +
-  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
-    centre = "Western Sydney", radius = 60, units = "km",
-    scale = 4, translation = c(500, 0)
-  )) +
+  coord_automap(
+    feature_type = "nswgeo.lhd",
+    inset = configure_inset(
+      centre = "Western Sydney",
+      radius = 60,
+      units = "km",
+      scale = 4,
+      translation = c(500, 0)
+    )
+  ) +
   labs(x = NULL, y = NULL) +
   theme_void()
 ```