jasonrwang / oecd_insights Goto Github PK

We want to see some preliminary correlations between the data to overall Age Dependency!

• GDP
• Cost of healthcare
• Cost of living
• Population
• Life expectancy
• GINI

Later, we can analyze the young and old data.

• Show head of each data set (or at least one for each data set)
• Describe how we did our cleaning
• Explain why we picked ~50 countries
• Explain why we picked the countries that we did (relevance to OECD)

In OECD_insights.RMD, we need to continue documenting and explaining our project:

• Introduce age dependency
• Show age dependency trends
• Link to news articles/government press releases
• Discuss the stress criteria (hypotheses related to age dependency)
• Relate each criteria to an indicator

To-Do

• Philip: Have a great day
• Mees: Make fork / limitations / further research
• Jason: Create influence diagram + help Siemon
• Siemon: Explain Philip figures + code to words + convert to 1 document

• Download datasets (.csv) and organize into folders
• Read files into working environment (use read.csv)
• Make selection of 50 countries that best fit all datasets (Phillip) - use a function?

Datasets:

• Age dependency ratio (old) (J)
• Age dependency ratio (young) (J)
• GDP (S)
• Cost of healthcare (S)
• Cost of living (S)
• Population (P)
• Life expectancy (P)
• GINI (P)

In OECD_insights.RMD, we need to continue documenting and explaining our project:

• Collection
• Description
• Quality
• Exploratory Analysis

We need to do this analysis for each of our datasets. It might be best to do so in tables. For example, each table can have the datasets as rows and the columns can be used to describe each, or some, of the four above points.