Exploratory Data Analysis: much more than data cleaning

In the rush to get a paper done, the exploratory data analysis (EDA) is often skipped or ignored. But much more than data cleaning and assumption checking, this step is essential to get you “used to” your data and turn raw tables into insights. It also helps you spot problems before they become bad decisions and guides you to the statistical test or model that actually makes sense.

In this post, we’ll walk you through why EDA matters, a compact checklist of what to inspect before running tests, and two R tools that make EDA fast and easy.

If you want help running your EDA or having an expert team do it for you, check our Services page to learn more about our Data Analysis packages and how Outtadesk can help you turn data into publishable results.

Why EDA matters

EDA is a diagnostic toolbox that protects your results. Good EDA will:

• Reveal data quality issues (e.g., missing values) that bias analyses.

• Explore the data distributions and relationships so you can choose appropriate tests and models.

• Identify outliers and heterogeneity that deserve special treatment or explanation.

• Decide on transformations and model selection so your inference is valid and interpretable.

  
  

Skipping EDA is like driving blindfolded after tuning the engine: you might go fast, but you won’t know if you’re headed in the right direction.

Quick checklist: what to check before running statistical tests

Use this checklist as a minimal EDA routine before you run any inferential statistics or models:

• Understand variable types: knowing your variable types (numeric, categorical, date/time) is essential since tests depend on variable type.

• Missing values: check the percentage of missing values per variable, consider imputation or explicit modeling.

• Basic summaries & distributions: Mean, median, standard deviation, IQR, skewness/kurtosis; histogram/density and boxplots for continuous variables; barplots for categorical.

• Outliers & influential points: Visualize (boxplots, scatterplots) and compute leverage/influence measures if modeling.

• Relationships & correlation: Pairwise scatterplots, correlation matrices (and categorical associations). Watch for non-linear patterns.

• Sample size & balance: Are group sizes sufficient for planned tests? Unequal groups affect power and test choice.

• Assumptions for planned test: Normality, homoscedasticity, independence test, and visualization. If violated, consider non-parametric or robust alternatives.

• Data inspection: Inspect factor levels, date parsing, units, duplicated rows, and unexpected values (e.g., -999).

• Reproducible snapshot: Save a reproducible report or script that documents the EDA steps (so analyses can be repeated).

Two R packages that speed EDA: radiant and explore

Radiant is a platform-independent, browser-based interface built on Shiny that exposes a wide set of data, descriptive, inferential, and modeling tools through menus and interactive panels. It bundles functionality across data management, basic stats, modeling, and multivariate analyses, and it is designed so users can recreate results or export R Markdown reports and state files for reproducibility. Radiant is especially handy when you want a point-and-click interface that still produces code and reports you can save.

Strengths:

• Menu-driven EDA: quick summaries, tables, and plots without writing code.

• Basics menu: probability sims, CLT demos, t-tests, chi-sq, correlations.

• Modeling and multivariate modules: regression, classification, PCA, clustering.

• Reproducibility: export state files and R Markdown to capture and share analysis steps.

• Integration: Use radiant outputs and functions inside your own R scripts when needed. 

If you prefer low code, one-line EDA, explore is for you. The explore package makes common EDA tasks radically simple due to its interactive exploration and the ability to generate automated, shareable reports with a single call. It fits well into tidy workflows and supports one-line generation of comprehensive EDA reports (including univariate, bivariate, and multivariate overviews and handling of a target variable). Its compact EDA with neat reports and tidy outputs makes Explore a great pick.

Strengths:

• One-line reports: automated HTML/markdown summaries for quick sharing.

• Interactive Shiny-driven views when you want to dig visually into variables and relations.

• Target-aware summaries that speed feature inspection for modeling tasks.

Download the code and example of the dataset.

If you’d like a ready-to-use, commented R script that shows examples of radiant and explore use, use the button below to receive the code and a dataset example in your email. The file includes annotated code snippets you can drop into your project and adapt.

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