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)
  
library(geofacet)
 })

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 Covid-19 pandemic took a toll on the aviation industry .
    There are signs of recovery, with EU commercial flight departures increasing from mid-June 2021.
    Will the rest of 2022 see 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)
    )
  )

Cameron Kay posted a noteworthy animation. I’ll skip it this time go another direction.

tweetrmd::include_tweet("https://twitter.com/cameronskay/status/1547291272183832577")

My first #TidyTuesday contribution (now with legible text and more Norway!) #ICPSR #sumprog22 pic.twitter.com/efky0OQfLD

— Cameron Kay (@cameronskay) July 13, 2022

flights |>
  group_by(year = year(flt_date), month = month(flt_date), state_name) |>
  summarise(
    total_dep = sum(flt_dep_1, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(color = case_when(
    year < 2020 ~ "#b2e2e2",
    year == 2020 ~ "#66c2a4",
    year == 2021 ~ "#238b45",
    year == 2022 ~ "#006d2c"
  )) |>
  ggplot(aes(
    x = month,
    total_dep
  )) +
  geom_line(aes(
    group = factor(year),
    color = color
  ),
  show.legend = TRUE
  ) +
  scale_color_identity(
    guide = "legend",
    labels = c("2022", "2021", "2020", "Prior Yrs")
  ) +
  scale_x_continuous(
    breaks = c(3, 6, 9),
    labels = c("Mar", "Jun", "Sep"),
    minor_breaks = NULL
  ) +
  scale_y_continuous(
    limits = c(0, NA),
    n.breaks = 3,
    labels = scales::comma
  ) +
  facet_geo(~state_name,
    grid = "eu_grid1",
    label = "name",
    scales = "free_y"
  ) +
  labs(
    y = "Monthly Departures",
    title = "Commercial flight departures in select EU Countries",
    subtitle = sub,
    color = NULL,
    caption = "Source: Eurocontrol | #TidyTuesday Week 28"
  ) +
  theme(
    legend.position = c(0.1, 0.1),
    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 = "#edf8fb"),
    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] geofacet_0.2.0  patchwork_1.1.1 lubridate_1.8.0 forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.9     purrr_0.3.4     readr_2.1.2    
 [9] tidyr_1.2.0     tibble_3.1.7    ggplot2_3.3.6   tidyverse_1.3.1
[13] 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] sp_1.5-0             splines_4.1.3        listenv_0.8.0       
 [10] usethis_2.1.6        digest_0.6.29        foreach_1.5.2       
 [13] htmltools_0.5.2      yardstick_1.0.0      viridis_0.6.2       
 [16] parsnip_1.0.0        fansi_1.0.3          magrittr_2.0.3      
 [19] memoise_2.0.1        tune_1.0.0           tzdb_0.3.0          
 [22] recipes_1.0.1        globals_0.15.1       modelr_0.1.8        
 [25] gower_1.0.0          vroom_1.5.7          R.utils_2.12.0      
 [28] hardhat_1.2.0        rsample_1.0.0        jpeg_0.1-9          
 [31] dials_1.0.0          colorspace_2.0-3     rvest_1.0.2         
 [34] ggrepel_0.9.1        textshaping_0.3.6    haven_2.5.0         
 [37] xfun_0.31            prismatic_1.1.0      callr_3.7.0         
 [40] crayon_1.5.1         jsonlite_1.8.0       survival_3.3-1      
 [43] iterators_1.0.14     glue_1.6.2           gtable_0.3.0        
 [46] ipred_0.9-13         R.cache_0.15.0       tweetrmd_0.0.9      
 [49] future.apply_1.9.0   imguR_1.0.3          scales_1.2.0        
 [52] infer_1.0.2          DBI_1.1.3            Rcpp_1.0.9          
 [55] viridisLite_0.4.0    units_0.8-0          bit_4.0.4           
 [58] GPfit_1.0-8          proxy_0.4-27         lava_1.6.10         
 [61] prodlim_2019.11.13   geogrid_0.1.1        httr_1.4.3          
 [64] ellipsis_0.3.2       farver_2.1.1         R.methodsS3_1.8.2   
 [67] pkgconfig_2.0.3      nnet_7.3-17          sass_0.4.1          
 [70] dbplyr_2.2.1         janitor_2.1.0        utf8_1.2.2          
 [73] here_1.0.1           labeling_0.4.2       tidyselect_1.1.2    
 [76] rlang_1.0.3          DiceDesign_1.9       later_1.3.0         
 [79] munsell_0.5.0        cellranger_1.1.0     tools_4.1.3         
 [82] cachem_1.0.6         cli_3.3.0            generics_0.1.3      
 [85] broom_1.0.0          evaluate_0.15        fastmap_1.1.0       
 [88] yaml_2.3.5           ragg_1.2.2           rematch2_2.1.2      
 [91] bit64_4.0.5          processx_3.7.0       knitr_1.39          
 [94] fs_1.5.2             workflowsets_0.2.1   future_1.26.1       
 [97] whisker_0.4          R.oo_1.25.0          xml2_1.3.3          
[100] compiler_4.1.3       rstudioapi_0.13      curl_4.3.2          
[103] png_0.1-7            e1071_1.7-11         reprex_2.0.1        
[106] lhs_1.1.5            bslib_0.3.1          stringi_1.7.8       
[109] highr_0.9            ps_1.7.1             rgeos_0.5-9         
[112] lattice_0.20-45      Matrix_1.4-0         styler_1.7.0        
[115] classInt_0.4-7       conflicted_1.1.0     vctrs_0.4.1         
[118] tidymodels_0.2.0     pillar_1.7.0         lifecycle_1.0.1     
[121] furrr_0.3.0          jquerylib_0.1.4      httpuv_1.6.5        
[124] R6_2.5.1             promises_1.2.0.1     KernSmooth_2.23-20  
[127] gridExtra_2.3        parallelly_1.32.0    codetools_0.2-18    
[130] MASS_7.3-55          assertthat_0.2.1     rprojroot_2.0.3     
[133] withr_2.5.0          rnaturalearth_0.1.0  parallel_4.1.3      
[136] hms_1.1.1            grid_4.1.3           rpart_4.1.16        
[139] timeDate_3043.102    class_7.3-20         snakecase_0.11.0    
[142] rmarkdown_2.14       grumanlib_0.1.0.9999 git2r_0.30.1        
[145] sf_1.0-7             getPass_0.2-2