European Flights

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Last updated: 2022-07-13

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Knit directory: myTidyTuesday/

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In this weeks #TidyTuesday we take a look at the number of flights arriving and departing from several large European airports.

data is via Eurocontrol at https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20210914-1

suppressPackageStartupMessages({
library(tidyverse)
library(lubridate)
library(patchwork)
 })

source(here::here("code","_common.R"),
       verbose = FALSE,
       local = knitr::knit_global())

ggplot2::theme_set(grumanlib::theme_jim(base_size = 12))

Let’s see what we can do to improve BrendiA’s heatmap:

tweetrmd::include_tweet("https://twitter.com/brendialj/status/1547206511910600706")

For this week's #TidyTuesday, I examined the total commercial flight departures across the EU. The recent months show signs of recovery for the aviation industry after the pandemic hit.#RStats code: https://t.co/2sIpKRS1Ur#dataviz #ggplot2 pic.twitter.com/LJO5m4el58

— Brendi Ang (@brendialj) July 13, 2022

tt <- tidytuesdayR::tt_load("2022-07-12")

    Downloading file 1 of 1: `flights.csv`

flights <- tt$flights |>
  rename(airport_label = `Pivot Label`) |>
  janitor::clean_names() |>
  select(-year, -month_num, -month_mon)

total_flights <- flights |>
  group_by(flt_date) |>
  summarise(
    total_dep = sum(flt_dep_1, na.rm = TRUE),
    .groups = "drop"
  ) |>
  complete(flt_date = seq.Date(
    from = as_date("2022-01-01"),
    to = as_date("2022-12-31"),
    by = "day",
    fill = list(value = NA)
  )) |>
  mutate(
    year = year(flt_date),
    week_of_year = week(flt_date),
    month = month(flt_date, label = TRUE, abbr = TRUE),
    day = day(flt_date)
  )

# Function prepares data for plotting based on a given year by BrendiA at https://github.com/BrendiA/tidy-tuesday/blob/main/2022/week-28/week-28.R
create_df <- function(given_year) {

  # Extract first day of the week for the year
  first_day_of_year <-
    wday(as_date(paste(given_year, "01", "01", sep = "-")),
      week_start = 1
    )

  total_flights |>
    filter(year == given_year) |>
    mutate(day_of_week = wday(flt_date,
      label = TRUE,
      week_start = first_day_of_year
    )) |>
    #    mutate(day_of_week = forcats::fct_rev(day_of_week)) |>
    group_by(month) |>
    mutate(week_of_month = -1 * dense_rank(week_of_year)) |>
    ungroup()
}

best_contrast <- function(x, y = c("#010101", "#FFFFFF")) {
  contrasts <- prismatic::contrast_ratio(x, y)
  y[max(contrasts) == contrasts][1]
}

# Function plots heatmap based on the data generated by BrendiA at https://github.com/BrendiA/tidy-tuesday/blob/main/2022/week-28/week-28.R  with Emil Hvitfeldt's Prismatic text color adjustments
plot_heatmap <- function(df) {

  # Year list of the data frame
  given_year <- unique(df$year)

  ggplot(
    data = df,
    aes(
      y = week_of_month,
      x = day_of_week,
      fill = total_dep
    )
  ) +
    # Heat map
    geom_tile() +
    # Calendar days
    geom_text(aes(
      label = day,
      color = ggplot2::after_scale(purrr::map_chr(fill, best_contrast))
    ),
    size = 2
    ) +
    colorspace::scale_fill_continuous_sequential(
      name = "Number of departures",
      palette = "Viridis",
      na.value = "grey60", # Fill missing dates
      limits = c(0, 30000),
      breaks = seq(0, 30000, 10000),
      oob = scales::squish,
      label = scales::label_comma(accuracy = 1),
      c2 = 60, # increase
      l1 = 10
    ) + # decrease
    facet_wrap(~month,
      nrow = 1
    ) +
    theme_minimal() +
    labs(title = given_year) +
    theme(
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      axis.title = element_blank(),
      legend.direction = "horizontal",
      legend.key.width = unit(1.5, "cm"),
      legend.text = element_text(family = "Regular 400"),
      legend.title = element_text(vjust = 0.8, family = "Regular 400"),
      panel.spacing.x = unit(0, "lines"),
      panel.grid = element_blank(),
      plot.title = element_text(hjust = 0.5, family = "Black 900 Italic", size = 12, face = "bold"),
      strip.text = element_text(family = "Regular 400", size = 8)
    )
}

plot_list <-
  purrr::map(
    unique(sort(total_flights$year)),
    ~ create_df(.x) |>
      plot_heatmap()
  )

The functions above create a big list of heat maps, one for each year. I’ve added prismatic contrasts to the geom_text to make the dates just a bit more legible.

# Subtitle
sub <-
  str_wrap(
    "The aviation industry took a toll on the Covid-19 pandemic.
    There are signs of recovery, with commercial flights in the EU increasing from mid-June 2021.
    Will the rest of 2022 see a total number of departures close to pre-pandemic levels?",
    width = 90
  )

plot_list[[3]] +
  plot_list[[4]] +
  plot_list[[5]] +
  plot_list[[6]] +
  plot_list[[7]] +
  plot_layout(
    guides = "collect",
    ncol = 1,
    height = 0.5
  ) &
  plot_annotation(
    title = "Commercial flight departures in the EU",
    subtitle = sub,
    caption = "Source: Eurocontrol | #TidyTuesday Week 28 | @brendialj with the heatmap concept",
    theme = theme(
      legend.position = "bottom",
      plot.title = element_text(hjust = 0.5, size = 18, face = "bold"),
      plot.subtitle = element_text(hjust = 0.5, size = 14),
      plot.background = element_rect(fill = "#f0f8ff"),
      plot.caption = element_text(vjust = 1)
    )
  )

Session information

sessionInfo()

R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.1.1 lubridate_1.8.0 forcats_0.5.1   stringr_1.4.0  
 [5] dplyr_1.0.9     purrr_0.3.4     readr_2.1.2     tidyr_1.2.0    
 [9] tibble_3.1.7    ggplot2_3.3.6   tidyverse_1.3.1 workflowr_1.7.0

loaded via a namespace (and not attached):
  [1] readxl_1.4.0         backports_1.4.1      systemfonts_1.0.4   
  [4] workflows_1.0.0      selectr_0.4-2        tidytuesdayR_1.0.2  
  [7] splines_4.1.3        listenv_0.8.0        usethis_2.1.6       
 [10] digest_0.6.29        foreach_1.5.2        htmltools_0.5.2     
 [13] yardstick_1.0.0      viridis_0.6.2        parsnip_1.0.0       
 [16] fansi_1.0.3          magrittr_2.0.3       memoise_2.0.1       
 [19] tune_1.0.0           tzdb_0.3.0           recipes_1.0.1       
 [22] globals_0.15.1       modelr_0.1.8         gower_1.0.0         
 [25] vroom_1.5.7          R.utils_2.12.0       hardhat_1.2.0       
 [28] rsample_1.0.0        dials_1.0.0          colorspace_2.0-3    
 [31] rvest_1.0.2          textshaping_0.3.6    haven_2.5.0         
 [34] xfun_0.31            prismatic_1.1.0      callr_3.7.0         
 [37] crayon_1.5.1         jsonlite_1.8.0       survival_3.3-1      
 [40] iterators_1.0.14     glue_1.6.2           gtable_0.3.0        
 [43] ipred_0.9-13         R.cache_0.15.0       tweetrmd_0.0.9      
 [46] future.apply_1.9.0   scales_1.2.0         infer_1.0.2         
 [49] DBI_1.1.3            Rcpp_1.0.9           viridisLite_0.4.0   
 [52] bit_4.0.4            GPfit_1.0-8          lava_1.6.10         
 [55] prodlim_2019.11.13   httr_1.4.3           ellipsis_0.3.2      
 [58] farver_2.1.1         pkgconfig_2.0.3      R.methodsS3_1.8.2   
 [61] nnet_7.3-17          sass_0.4.1           dbplyr_2.2.1        
 [64] janitor_2.1.0        utf8_1.2.2           here_1.0.1          
 [67] labeling_0.4.2       tidyselect_1.1.2     rlang_1.0.3         
 [70] DiceDesign_1.9       later_1.3.0          munsell_0.5.0       
 [73] cellranger_1.1.0     tools_4.1.3          cachem_1.0.6        
 [76] cli_3.3.0            generics_0.1.3       broom_1.0.0         
 [79] evaluate_0.15        fastmap_1.1.0        yaml_2.3.5          
 [82] ragg_1.2.2           rematch2_2.1.2       bit64_4.0.5         
 [85] processx_3.7.0       knitr_1.39           fs_1.5.2            
 [88] workflowsets_0.2.1   future_1.26.1        whisker_0.4         
 [91] R.oo_1.25.0          xml2_1.3.3           compiler_4.1.3      
 [94] rstudioapi_0.13      curl_4.3.2           reprex_2.0.1        
 [97] lhs_1.1.5            bslib_0.3.1          stringi_1.7.8       
[100] highr_0.9            ps_1.7.1             lattice_0.20-45     
[103] Matrix_1.4-0         styler_1.7.0         conflicted_1.1.0    
[106] vctrs_0.4.1          tidymodels_0.2.0     pillar_1.7.0        
[109] lifecycle_1.0.1      furrr_0.3.0          jquerylib_0.1.4     
[112] httpuv_1.6.5         R6_2.5.1             promises_1.2.0.1    
[115] gridExtra_2.3        parallelly_1.32.0    codetools_0.2-18    
[118] MASS_7.3-55          assertthat_0.2.1     rprojroot_2.0.3     
[121] withr_2.5.0          parallel_4.1.3       hms_1.1.1           
[124] grid_4.1.3           rpart_4.1.16         timeDate_3043.102   
[127] class_7.3-20         snakecase_0.11.0     rmarkdown_2.14      
[130] grumanlib_0.1.0.9999 git2r_0.30.1         getPass_0.2-2