STAT40830 - Final Project
This final project is based on the material up to topic 9.
Instructions
Create a Shiny app to read and summarise the Human Development Indicators data.
The Shiny app must allow for the following:
1. Allow the user to select data for at least one country given the countries names.
2. Change the title depending on the country/countries selected.
3. Display a table with part of the data. The user must be able to select the number of rows and the columns to be displayed.
4. Produce two different plots showing different information each. The user must be able to change at least 3 input in each plot.
5. The shiny app must have built-in data for at least 5 different countries.
6. The shiny app must allow the user to upload any extra Human Development Indicators dataset
(hdro_indicators_COUNTRYNAME.csv) downloaded from data.humdata.org from any available county (You can assume that all files have the same structure).
Some notes:
Notice that you can re-use some code you created for Assignment 1, Assignment 2, or create it from scratch.
You can use functions from any R packages.
Remember that in your Shiny app you must never have any file path specific to your computer.
Submission
Submission: upload on the Final Project assessment on Brightspace a zip file containing:
1. all the files needed to build the Shiny app (either R script or qmd files and the data), (Submissions where we cannot reproduce the Shiny app will be capped at 30 points (i.e. 50% of the final grade for this final project))
Note that only your final submission will be marked.
How to get the data:
(Same as Assignments 1 and 2) For the final project you will have to use the dataset of the Human Development Indicators for at least different countries of your choice from data.humdata.org/dataset/?organization=undp-human-development-reportsoffice&q=Human+Development+Indicators:
Click over COUNTRYNAME - Human Development Indicators and download the dataset called Human Development Indicators for COUNTRYNAME it will give you a .csv file. (The dataset contains the following variables: country_code, country_name, indicator_id, indicator_name, index_id, index_name, value, year.) repeat this procedure for at least a second country.
