Welcome to the presentation on Household Expenditure Statistics. In this project, we analyze and visualize household expenditure data from New Zealand to understand distribution patterns across different tables and HEC codes.
August 14, 2024
Welcome to the presentation on Household Expenditure Statistics. In this project, we analyze and visualize household expenditure data from New Zealand to understand distribution patterns across different tables and HEC codes.
Before creating the plot, we performed data preprocessing, including filtering, removing NA values, and ensuring that relevant columns are correctly formatted. Here’s a brief overview of the data:
## 'data.frame': 41 obs. of 10 variables: ## $ Table : Factor w/ 4 levels "T1","T2","T3",..: 1 1 1 2 2 2 2 2 2 3 ... ## $ Year : chr "2019" "2023" "D001" "2023" ... ## $ MsCode : chr "M001" "M001" "M001" "M001" ... ## $ CatCode : chr "C000A" "C000A" "C000A" "C001A" ... ## $ HECCode : chr "04" "04" "04" "04" ... ## $ Estimate: num 344.5 398 15.5 467.8 457 ... ## $ RSE : num 4.8 4.3 8 7.1 13.6 6.4 16.2 13.2 4.3 60.7 ... ## $ LowerCIB: num 327.9 380.8 7.5 434.5 394.7 ... ## $ UpperCIB: num 361.2 415.2 23.5 501.2 519.3 ... ## $ Flag : chr " " " " " " " " ...
Here we visualize the distribution of household expenditure using a boxplot. The plot below shows the expenditure distribution across different tables and HEC codes.
plotly_plot <- ggplotly(plot) plotly_plot
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