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Tourism data

Source: vignettes/tourism.qmd

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()