chapter 3: update fig label

03-traffic_characterisation.qmd

@@ -187,14 +187,10 @@ p_eur <- tfc_eur |> plot_timeline_per_year() + labs(subtitle = "Europe")
 p_bra / p_eur + plot_layout(guides = "collect") & theme(legend.position = "top")
 ```
 
-
-
 * For Brazil, traffic in the first half of 2023 exceeded the pre-pandemic traffic level
 * On a network level, the continual recovery of the traffic is on-going in Europe.
 * The first half of 2023 saw traffic levels at about 90-95% of the pre-pandemic network traffic and started following a classical *seasonal* pattern. However, traffic recovery in Europe is not at the same level for each pre-pandemic service/connection. The recovery also resulted in a light modification of network connectivity. The Russian invasion of Ukraine resulted in the closure of a significant portion of the airspace (about 20%). However, the overall impact of the closure on air traffic - on a network level - was relatively small.
 
-
-
 This high-level network perspective shows that traffic in both regions is comparable to pre-pandemic levels.
 It will be interesting to observe the further evolution and growth of air traffic.
 
@@ -216,8 +212,6 @@ p_share_of_network <- function(.annual_share_of_network_df){
   return(p_share)
 }
 
-
-
 # BRAZIL ========= Airport Level Study
 # Cleaning/prep process in Hugos-prep.qmd file(code lines 1826 to 1881)
 
@@ -268,6 +262,13 @@ study_apt_lvl <- study_apt_lvl |>
   left_join(bra_apts_names, by = join_by(ICAO))
 
  p_study1 <- study_apt_lvl  |>
+   mutate(
+     NAME = case_match(NAME
+                       , "Belo Horizonte" ~ "B.Horizonte"
+                       , "Rio Dumont"     ~ "R.Dumont"
+                       , "Porto Alegre"   ~ "P.Alegre"
+                       , .default = NAME)
+     ) |> 
    filter(!(YEAR == "2023")) |> 
    ggplot() +
    geom_col(aes(x = NAME, y = TOT_FLTS_YEAR, fill = YEAR)
@@ -288,7 +289,11 @@ study_apt_lvl <- study_apt_lvl |>
  #   labs(x = NULL, y = NULL,) 
  # 
  #  p_study1 / p_study2 
- 
+ ```
+ ```{r}
+ #| label: fig-bra-apt-tfc
+ #| fig.cap: Brazilian airport level traffic
+ #| 
  (p_share_of_network(tmp_annual_share) + 
     scale_x_discrete(guide = guide_axis(n.dodge = 2)) 
   ) + p_study1 + 
@@ -362,8 +367,11 @@ p1_eur <- annual_tfc_apt  |>   ggplot() +
 #    labs(x = NULL, y = NULL,) 
 
 # p1_eur / p2_eur
+```
 
-
+```{r}
+#| label: fig-eur-apt-level-tfc
+#| fig-cap: Europeaan airport level traffic
 
 # patch the 2 plots
 (p_share_of_network(tmp) + 
@@ -758,6 +766,7 @@ fm_apts_eur <- tfc_apts_eur |> fleet_mix_from_counts("EUR")
 ```{r}
 #| label: fig-fleet-mix
 #| fig-cap: Fleet mix observed at the study airports in 2023
+#| fig-height: 7
 
 key_year <- 2022
 fm_apts <- bind_rows(fm_apts_bra, fm_apts_eur) |> 

_scope-airports-map.R

@@ -25,9 +25,12 @@ this_airports <- readr::read_csv("./data/airport-LAT-LON-NAME.csv", show_col_typ
 worldmap <- ggplot2::borders("world2", colour="lightblue", fill="lightblue")
 ggplot2::ggplot() + worldmap + theme_void()
 
+library(ggplot2)
+library(ggrepel)
 library(rnaturalearth)
 library(rnaturalearthdata)
 library(patchwork)
+library(ggrepel)
 
 world   <- ne_countries(scale = "medium", returnclass = "sf")
 bra_map <- world |> dplyr::filter(admin == "Brazil")
@@ -38,16 +41,29 @@ eur_map <- ne_countries(
               ,"Germany","Belgium","Netherlands","Luxembourg"
               ,"Austria","Switzerland", "Italy"), scale = "medium")
 
+bra_apts_coord <- this_airports |> 
+  filter(grepl(pattern = "^SB", x = ICAO)) |> 
+  mutate(NUDGE_X = case_when(
+    ICAO %in% c("SBSV") ~ -20
+    ,.default = -10)
+  )
+
 bra_chart <- ggplot2::ggplot() +
   geom_sf(data = bra_map) +
   geom_point(data = this_airports |> filter(grepl(pattern = "^SB", x = ICAO))
              , aes(x = LON, y = LAT)) +
   geom_label_repel(data = this_airports |> filter(grepl(pattern = "^SB", x = ICAO))
                    , aes(x = LON, y = LAT
-                         , label = paste(ICAO, NAME)
+                         , label = stringr::str_wrap(paste(ICAO, NAME), 8)
                    )
+
+                   ,position = ggpp::position_nudge_center(x = -2, y = 2,
+                                                    center_x = 0, center_y = 0),
+                  # label.size = NA,
+                   label.padding = 0.2
+
                    , max.overlaps = Inf
-                   , force = 50
+                   # , force = 1
                    # , nudge_x = 5
   ) +
   theme_void()
@@ -62,11 +78,14 @@ eur_chart <- ggplot2::ggplot() +
                    , aes(x = LON, y = LAT
                          , label = paste(ICAO, NAME)
                    )
-                   , max.overlaps = Inf
+                   , max.overlaps = 0
                    , force = 50
                    # , nudge_x = 5
   ) +
   theme_void()
 
-bra_chart + eur_chart
+bra_chart
+
+  eur_chart
+