library(dplyr)# Vector with the names of the countries of interestcountries_of_interest <-c("Cambodia", "China","Fiji", "Guam", "Hong Kong SAR, China", "Indonesia", "Kiribati", "Korea", "Laos", "Malaysia", "Marshall Islands","Mongolia", "Myanmar", "NEW CALEDONIA","Philippines", "Samoa", "Singapore", "Solomon Islands", "Thailand","Tonga", "Taipei","Tuvalu", "Vanuatu", "Vietnam")# Filter out the APAC countriesfiltered_data <- Fossil_fuel_consumption_subsidies_2010_2022 %>%filter(Country %in% countries_of_interest)
# Summarize the subsidies for comparison#subsidies_summary <- filtered_data %>%# group_by(Country) %>%# summarise(# OilSubsidies = sum(Oil, na.rm = TRUE),# ElectricitySubsidies = sum(Electricity, na.rm = TRUE),# GasSubsidies = sum(Gas, na.rm = TRUE),# CoalSubsidies = sum(Coal, na.rm = TRUE),# TotalSubsidies = sum(Total, na.rm = TRUE))#I'm currently stuck here. I need to add several data cleaning steps before I can analyze the data.
#Objective: To prepare a tidy version of the "Fossil fuel consumption subsidies, 2010-2022" dataset for further analysis in the context of the Asia-Pacific climate finance landscape.#Step 1: Data Appraisal and Preliminary Cleaning#Review the structure of the "Fossil fuel consumption subsidies, 2010-2022" dataset to understand the variables and time span.#Remove any irrelevant data that does not correspond to the Asia-Pacific region or is outside the study period.#Standardize the country names for consistency across datasets.#Step 2: Data Transformation#Pivot the dataset from a wide format (with separate columns for each year's data) to a long format, where each row represents a single observation for a country-year combination.#Step 3: Data Integration#Prepare to merge the subsidies data with other relevant datasets, such as GDP figures and carbon market data.#Identify key joining variables, which will likely include 'Country' and 'Year'.#Step 4: Data Type Standardization#Convert all subsidy amounts to a consistent numeric format.#Ensure that country names are in a text format.#Format the 'Year' column to a date or integer format, as appropriate for temporal analysis.#Step 5: Data Validation#Perform spot checks on the merged data to ensure accuracy against the original datasets.#Address any inconsistencies or missing data through imputation or removal, as dictated by the research methodology.#Step 6: Analytical Variable Creation#Calculate any new variables needed for analysis, such as the proportion of subsidies to GDP.#Create indicators or flags that may be necessary for segmentation or stratification in the analysis (e.g., income level of countries, membership in regional agreements).#Step 7: Documentation and Metadata#Document each variable, its source, and any transformations or calculations performed.#Create a comprehensive codebook that details the tidy dataset structure and variable definitions.#Step 8: Finalization and Quality Assurance#Review the tidy dataset to ensure it meets the requirements for the intended analysis.#Save the dataset in a format conducive to the analysis tools that will be used (e.g., CSV, XLSX, RData).#Step 9: Data Visualization and Exploratory Analysis#Generate preliminary visualizations to understand the distribution and trends within the data.#Conduct exploratory analysis to identify any potential outliers or patterns of interest.