R Notebook

Author

Pinandito Wisambudi

Final Paper Plan

Starts with these datasets:

  • Carbon Price: Data on carbon pricing mechanisms

  • Emissions Data: Data on annual CO2 emissions per country.

Joining Datasets

  • Join Key: The datasets will be joined using the country and the year as key variables. This allows for a comprehensive analysis across different times and geographies.

  • Preparation: Ensure that country names and year data are consistent across datasets to avoid mismatches during the join operation.

Pivoting Data

  • Pivot Requirement: For datasets that are in wide format (e.g., years as columns), pivot them to long format (e.g., a single column for the year and another for the value) to facilitate analysis.

  • Tools: Use the pivot_longer() function from the tidyr package in R for pivoting operations.

Data Types

  • Dates: Ensure year data is in a date format or as a numeric year to facilitate time series analysis.

  • Numeric: GDP, inflation rates, employment rates, CO2 emissions, and population figures should be numeric.

  • Character: Country names should be in character format.

  • Factors: Consider converting categorical variables (e.g., types of carbon pricing mechanisms) into factors for analysis.

Step by Step Plan

Step 1: Understand Your Data

1.1 Review Data Sources: Examine the datasets. Understand the variables and time periods covered.

1.2 Identify Key Variables: Pinpoint the key variables, including country names, years, carbon pricing, GDP, inflation rates, employment rates, CO2 emissions, and population figures.

Step 2: Prepare Your Data

2.1 Consolidate Data Files: Because the data is spread across multiple files, I will them into a single dataset.

2.2 Check for Consistency: Ensure that country names and year formats are consistent across all datasets.

Step 3: Clean Your Data

3.1 Handle Missing Data: Identify missing data, deciding whether to impute missing values or remove records with missing data.

3.2 Remove Duplicates: Check for and remove any duplicate records to ensure the integrity of your analysis.

3.3 Correct Data Errors: Look for obvious errors in the data (e.g., impossible values) and correct them based on context or external references.

Step 4: Transform Your Data

4.1 Convert Data Types: Make sure each variable is of the correct data type (e.g., numeric, character, date).

4.2 Pivot Data: Pivot wide-format datasets to long format where necessary, especially for time series analysis.

Step 5: Merge Datasets

5.1 Identify Merge Keys: Use country and year as keys for merging datasets. Ensure these keys match perfectly across datasets to avoid merge errors.

5.2 Merge Datasets: Use R functions to merge the datasets.

Step 6: Create Analytical Base Table (ABT)

6.1 Structure ABT: Design ABT to include all necessary variables for analysis, ensuring it is structured for easy access and manipulation.

6.2 Final Checks: Perform final checks for anomalies or inconsistencies in the merged dataset.

Step 7: Document Process

7.1 Code Comments: Throughout R script, I will include comments explaining the purpose of each significant block of code.

7.2 Methodology Documentation: Document the methodology.