update of figure labels

03-traffic_characterisation.qmd

@@ -259,16 +259,16 @@ tmp_annual_share <- annual_tfc_bra |> mutate(YEAR = as.factor(YEAR)) |>
 
 #append airport names
 study_apt_lvl <- study_apt_lvl |> 
-  left_join(bra_apts_names, by = join_by(ICAO))
-
- p_study1 <- study_apt_lvl  |>
-   mutate(
+  left_join(bra_apts_names, by = join_by(ICAO)) |> 
+  mutate(
      NAME = case_match(NAME
                        , "Belo Horizonte" ~ "B.Horizonte"
                        , "Rio Dumont"     ~ "R.Dumont"
                        , "Porto Alegre"   ~ "P.Alegre"
                        , .default = NAME)
-     ) |> 
+     ) 
+
+ p_study1 <- study_apt_lvl  |>
    filter(!(YEAR == "2023")) |> 
    ggplot() +
    geom_col(aes(x = NAME, y = TOT_FLTS_YEAR, fill = YEAR)
@@ -289,16 +289,19 @@ study_apt_lvl <- study_apt_lvl |>
  #   labs(x = NULL, y = NULL,) 
  # 
  #  p_study1 / p_study2 
- ```
- ```{r}
+``` 
+
+```{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 + 
-  plot_layout(widths = c(1, 4)) + 
-  plot_annotation("Brazil Study Airports Annual Movements")
+  plot_layout(widths = c(1, 4)) 
+
+#+ 
+#  plot_annotation("Brazil Study Airports Annual Movements")
 ```
 
 ```{r}
@@ -377,8 +380,11 @@ p1_eur <- annual_tfc_apt  |>   ggplot() +
 (p_share_of_network(tmp) + 
     scale_x_discrete(guide = guide_axis(n.dodge = 2)) 
   ) + p1_eur + 
-  plot_layout(widths = c(1, 4)) + 
-  plot_annotation("European Study Airports Annual Movements")
+  plot_layout(widths = c(1, 4)) 
+
+# remove title of joint plot - info in caption
+#+ 
+#  plot_annotation("European Study Airports Annual Movements")
 ```
 
 ```{=html}
@@ -568,7 +574,7 @@ peak_day_comb <- bind_rows(peak_day_bra, peak_day_eur)
 
 ```{r}
 #| label: fig-peak-day
-#| fig-cap: Airport peak daily traffic 
+#| fig-cap: Airport peak daily traffic (2023)
 
 plot_peak_day_tfc <- function(.df, .year, ...){
   viz <- ggplot( data = .df %>% filter(YEAR == .year)
@@ -584,7 +590,8 @@ plot_peak_day_tfc <- function(.df, .year, ...){
      scale_fill_manual(values = bra_eur_colours) + 
      facet_grid(RWY ~., as.table = FALSE, switch = "y", scales = "free", space = "free") +
     # my_own_theme_bw +
-     labs(x = paste0("peak day traffic (", .year,")"), y = NULL, fill = "Region") +
+     labs(# x = paste0("peak day traffic (", .year,")") ,  # blank out xlabel == caption
+          y = NULL, fill = "Region") +
      theme(legend.position = c(0.9, 0.15)
           ,axis.ticks = element_blank()
           )
@@ -766,7 +773,7 @@ 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
+#| fig-height: 5
 
 key_year <- 2022
 fm_apts <- bind_rows(fm_apts_bra, fm_apts_eur) |> 

04-predictability.qmd

@@ -168,11 +168,9 @@ Evolution of arrival punctuality at study airports (2019 vs 2023)
 
 :::
 
-
 ```{r}
 #| label: fig-evolution-ARR-punc
-#| fig-caption: Evolution of arrival punctuality window
-
+#| fig-cap: Evolution of arrival punctuality window
 
 tmp <-  punc_bra |> bind_rows(punc_eur) |> filter(PHASE == "ARR")
 
@@ -228,8 +226,6 @@ tmp2 |>
     theme(legend.position = "none")
 ```
 
-
-
 On average, the share of flights arriving within -/+ 15 minutes of their scheduled time varies wider amongst the European study airports (c.f @fig-evolution-ARR-punc). 
 The observed punctuality (and associated predictability) within the Brazilian system shows a more homogenuous pattern with a general trend towards 60% or more over the past two years.
 
@@ -330,14 +326,14 @@ p2023 <- p3_bra_earlylate + p3_eur_earlylate
 
 ```{r}
 #| label: fig-early-vs-late-arrivals
-#| fig-cap: Change of share of early and late arrivals (2019 vs 2022)
+#| fig-cap: Change of share of early and late arrivals (2019 vs 2023)
 #| fig-height: 6
 
-(p2019 / p2022 / p2023) + plot_layout(guides = "collect") & 
+(p2019 / p2023) + plot_layout(guides = "collect") & 
   theme(legend.position = 'top')
 ```
 
-@fig-early-vs-late-arrivals compares the share of early and late arrivals at each study airport in 2019 and 2022.
+@fig-early-vs-late-arrivals compares the share of early and late arrivals at each study airport in 2019 and 2023.
 From a high-level perspective, air traffic tends to arrive well ahead of schedule in Brazil, while Europe observes a higher share of delayed arrivals.
 Guarulhos (SBGR) remained the Brazilian airport with the highest share of early flights in 2022 (i.e. 33%), followed by Campinas (SBKP) with 30%.
 Both airports are essential hubs in the country, and anticipation can be a consequence sought by air operators for better accommodation of the flight network.
@@ -482,7 +478,7 @@ lbl_df <- tmp2 |>
 
 ```{r}
 #| label: fig-evolution-DEP-punc
-#| fig-caption: Evolution of arrival punctuality window
+#| fig-cap: Evolution of arrival punctuality window
 
  tmp2 |> select(REGION, APT, YEAR, PHASE, WITHIN_M15P15, COL_SORT ) |> 
     ggplot() + 
@@ -575,6 +571,8 @@ p2023 <- p3_bra_earlylate + p3_eur_earlylate
 ```{r}
 #| label: fig-early-vs-late-departures
 #| fig-cap: Change of share of early and late departures (2019 vs 2023)
+#| fig-height: 6
+
 (p2019 / p2023) + plot_layout(guides = "collect") & 
   theme(legend.position = 'top')
 ```