Tourism data

What this vignette covers

This vignette provides a quick tour of the `ecotourism` package’s tourism datasets. You’ll learn how to join quarterly counts with regional metadata, and optionally how to enrich the data by linking each region to its nearest weather station.

- Because of its size, the tourism dataset was split into two separate datasets, with a shared key provided for joining them.

## 1 Data overview

This is the glimpse of your data :

    library(dplyr)
    library(ecotourism)

    data("tourism_region")
    tourism_region |> glimpse()

    Rows: 2,353
    Columns: 5
    $ region    <chr> "ACT - South West", "APY Lands", "Abbotsford", "Aberfoyle Pa…
    $ lon       <dbl> 148.9773, 131.3201, 145.0000, 138.6094, 150.9138, 144.8245, …
    $ lat       <dbl> -35.37625, -27.05758, -37.80368, -35.07996, -33.73492, -37.8…
    $ region_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 2…
    $ ws_id     <chr> "949250-99999", "944740-99999", "959360-99999", "946830-9999…

    data("tourism_quarterly")
    tourism_quarterly |> glimpse()

    Rows: 50,314
    Columns: 6
    $ year      <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, …
    $ quarter   <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
    $ purpose   <chr> "Business", "Business", "Business", "Holiday", "Holiday", "B…
    $ trips     <dbl> 3.2724, 1.7111, 104.5548, 96.3376, 37.1830, 11.2575, 67.2443…
    $ region_id <int> 4, 6, 9, 9, 14, 16, 16, 20, 20, 23, 29, 29, 30, 30, 32, 33, …
    $ ws_id     <chr> "946830-99999", "948650-99999", "946750-99999", "946750-9999…

## 2 Join regional metadata to quarterly counts

We keep only rows with a valid region name after the join.

    Rows: 49,145
    Columns: 9
    $ year      <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, …
    $ quarter   <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
    $ purpose   <chr> "Business", "Business", "Business", "Holiday", "Holiday", "B…
    $ trips     <dbl> 3.2724, 1.7111, 104.5548, 96.3376, 37.1830, 11.2575, 67.2443…
    $ region_id <int> 4, 6, 9, 9, 14, 16, 16, 20, 20, 23, 29, 29, 30, 30, 32, 33, …
    $ ws_id     <chr> "946830-99999", "948650-99999", "946750-99999", "946750-9999…
    $ region    <chr> "Aberfoyle Park", "Acton", "Adelaide", "Adelaide", "Agnes Wa…
    $ lon       <dbl> 138.6094, 144.8245, 138.6005, 138.6005, 151.7012, 148.9133, …
    $ lat       <dbl> -35.07996, -37.86191, -34.92611, -34.92611, -24.38361, -20.1…

If you only need a subset of columns, select them first to keep memory small, for example: `select(region_id, quarter, visitors, lon, lat)`.

## 3 Optional: add nearest weather station metadata

If your quarterly table contains a `ws_id`, you can join station attributes:

## 4 A quick map

We’ll plot tourism site coordinates and, when available, their weather stations.

    library(ggplot2)
    library(ggthemes)

    ggplot() +
      geom_sf(data = oz_lga) +
      geom_point(data = tourism, aes(x = lon, y = lat),
                   alpha = 0.8, size = 0.5) +
      theme_map()

If station coordinates `stn_lon` and `stn_lat` are present, overlay them too:

    library(ggplot2)
    library(ggthemes)

    ggplot() +
        geom_sf(data = oz_lga) +
        geom_point(data = tourism, aes(x = lon, y = lat),
                   alpha = 0.5, size = 0.4, color = "blue") +
        geom_point(data = tourism, aes(x = stn_lon, y = stn_lat),
                   shape = 17, size = 0.5, alpha = 0.5, color = "red") +
      theme_map()