Ranking observations or variables

exploratory statistics

slug: recipe-exploratory-statistics-ranking-observations-or-variables

Recipe: Ranking observations or variables

category: exploratory statistics

Problem

You need to identify the top or bottom performers in your dataset:

• rank customers by purchase amount
• rank products by sales or profitability
• detect trends or outliers based on relative position

Solution

Follow these steps to rank observations or variables:

• load the dataset
• select the variable(s) to rank
• apply ranking rules (ascending, descending, tied ranks)
• optionally filter or highlight top/bottom performers

Step Sequence

load step -> [step-rank] -> filter step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• Notes: focus on numeric fields such as amount, quantity, or total_sales

Output Dataset

• ranked_transactions — dataset with added rank fields
• Notes: ranks indicate relative position; can be used for reporting or further analysis

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load the dataset	Supports local file, database, or API sources
[step-rank]	Compute ranks for selected variables	Example: rank customers by total purchase amount descending
filter step	Optionally select top or bottom performers	Example: top 10 customers or products
chart step	Visualize ranked data	Optional bar chart, leaderboard, or table

Variations & Extensions

• Use multiple ranking criteria simultaneously (e.g., sales and frequency)
• Combine with calculate step to create derived metrics before ranking
• Integrate with dashboard step for dynamic reporting of top performers

Concepts Demonstrated

• Ranking observations or variables
• Identifying high- or low-performing entities
• Filtering and visualizing ranked results
• Sequencing analysis steps for insight

Related Recipes

• Univariate analysis of numeric variables
• Correlation analysis between numeric variables

Notes & Best Practices

• Clearly document ranking criteria and direction (ascending/descending)
• Handle ties consistently using a chosen method (average, min, max, sequential)
• Use visualizations to communicate rankings effectively

Metadata

title: "Ranking observations or variables"
category: "exploratory statistics"
difficulty: "Intermediate"
tags: [ranking, numeric, EDA, leaderboard]
inputs: [transactions_clean]
outputs: [ranked_transactions]
steps: [step-load, step-rank, step-filter, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"