Function to Summarize Columns After Filtering

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In this article, we will explore a common problem in data analysis where you need to filter a dataset and then perform calculations on specific columns. The goal is to write an efficient function that can handle these filtering and summarization operations.

Introduction

When working with datasets, it’s common to encounter scenarios where you need to apply filters to narrow down the relevant data points before performing calculations or aggregations. In this case, we’re dealing with a specific use case where we want to filter out rows based on certain conditions and then summarize specific columns.

Background

To understand how to approach this problem, let’s first examine the traditional way of achieving this using R programming language.

# Traditional Approach

df_filtered_block1 <- df %>% 
  filter(not_covered_poor <= 0) %>%
  summarise(parrishes = n(),
            avg_org_count = mean(org_count),
            percent_poor = sum(pop_poor) / sum(population),
            percent_urban = sum(pop_urban) / sum(population))

df_filtered_block2 <- df %>% 
  filter(beneficiaries >= 1) %>%
  summarise(parrishes = n(),
            avg_org_count = mean(org_count),
            percent_poor = sum(pop_poor) / sum(population),
            percent_urban = sum(pop_urban) / sum(population))

df_filtered_block3 <- df %>% 
  filter(beneficiaries == 0) %>%
  summarise(parrishes = n(),
            avg_org_count = mean(org_count),
            percent_poor = sum(pop_poor) / sum(population),
            percent_urban = sum(pop_urban) / sum(population))

This approach is indeed repetitive and can be inefficient, especially when dealing with large datasets.

Solution

To improve efficiency and reduce code duplication, we’ll create a function that takes the dataset df and filtering conditions as input. This function will apply the necessary filters to the data frame before performing the calculations.

# Function to Summarize Columns After Filtering

summarise_columns <- function(df, filter_conditions) {
  # Apply filter conditions to the dataframe
  filtered_df <- df %>% 
    inner_join(filter_conditions)

  # Group by relevant columns and perform calculations
  summary_df <- filtered_df %>%
    group_by(parrishes, avg_org_count, percent_poor, percent_urban) %>%
    summarise(
      sum_poor = sum(pop_poor),
      sum_population = sum(population)
    )

  return(summary_df)
}

Usage Example

Let’s create an example filtering conditions and apply the function:

# Define Filtering Conditions
filter_block1 <- filter(df, not_covered_poor <= 0)

filter_block2 <- filter(df, beneficiaries >= 1)

filter_block3 <- filter(df, beneficiaries == 0)

# Apply Summary Function to Each Filter Block
summary_df_block1 <- summarise_columns(filter_block1, c(
  ~parrishes,
  ~avg_org_count,
  ~percent_poor,
  ~percent_urban
))

summary_df_block2 <- summarise_columns(filter_block2, c(
  ~parrishes,
  ~avg_org_count,
  ~percent_poor,
  ~percent_urban
))

summary_df_block3 <- summarise_columns(filter_block3, c(
  ~parrishes,
  ~avg_org_count,
  ~percent_poor,
  ~percent_urban
))

By using a function like summarise_columns(), we can easily apply the same calculations to different filtering conditions while reducing code duplication and improving readability.

Conclusion

In this article, we explored a common problem in data analysis where you need to filter a dataset and then perform calculations on specific columns. We introduced a function called summarise_columns() that applies filters to a dataframe before performing the necessary calculations. This approach reduces code duplication and improves efficiency when dealing with repetitive filtering operations.

Tips for Improving Code Readability

1. Extract Functions: Break down long functions into smaller, more focused ones.
2. Use Meaningful Variable Names: Clear and descriptive variable names make your code easier to understand and maintain.
3. Comment Your Code: Include comments in areas that are unclear or require additional context.

By following these tips and using a function like summarise_columns(), you can write more efficient, readable, and maintainable R code for data analysis tasks.

Last modified on 2025-01-07