Understanding Datasets

Overview

This analysis explores a synthetic medical dataset to understand its structure, contents, and data quality. The examination focuses on basic characteristics and missing data patterns.

Dataset Characteristics

Basic Structure

Initial steps observed the dataset dimensions: The dataset contains X rows (observations) and Y columns (variables)

Column names were obsered using a function to list main column categories

Variable types includes both numerical and categorical variables

Data Quality Assessment

The analysis later focused on missing Values. We examined missing data using multiple approaches:

1. Column-wise missing value counts

2. Summary statistics showing NA distributions

3. Comprehensive missing data profiling

Notes: This were done without using missing data packages. We will cover them in subsequent lessons

Statistical Summary

Key descriptive statistics for all variables including:

1. Central tendency measures (mean, median)

2. Dispersion metrics (standard deviation, range)

3. Distribution characteristics (skewness, kurtosis)

Analysis Components

Initial Data Inspection

-Verified successful data loading

-Checked variable names and types

-Missing Data Evaluation

-Quantified missing values per variable

-Assessed patterns of missingness

-Descriptive Statistics

-Generated comprehensive numerical summaries

-Calculated distribution metrics

-Data Cleaning(partial)

-Applied basic missing data handling

-Verified cleaning results

Usage Notes

The analysis uses standard R packages (tidyverse, skimr, psych)

All operations are reproducible with the provided code

Dataset should be placed in appropriate path before running