Explore New Zealand’s Tourist Industry with R and Shiny

February 24, 2016

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

New Zealand's Ministry of Business, Innovation and Employment has just released the New Zealand Tourism Dashboard, an interactive application which allows NZ residents (and curious onlookers everywhere) to explore the economic impact of tourism in the far-flung nation. The dashboard is implemented using Shiny, and all of the graphics and analyses were created using the R language 

NZ dashboard

Each tab at the top of the window leads to a different Shiny application (or a collection of applications) to explore data related to New Zealand tourism. You can explore the types of attractions visited (museums, nature, or Maori activities?), which nationalities visit the most often (Australia, closely followed by China), the types of accommodations preferred by different nationalities (residents of Oceania and Africa tend to stay with friends) and what tourists spend money on (the Chinese spend more of their money on accommodations), and explore the performance of regions within New Zealand (here, Wellington):


You can explore all of the data and reports at the link below. By the way, it's only appropriate that New Zealand use the R language for an application like this: R was created in New Zealand!

New Zealand Ministry of Business, Innovation and Employment: The New Zealand Tourism Dashboard (via Peter Ellis)

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

plotly webpage

dominolab webpage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training





CRC R books series

Six Sigma Online Training

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)