Introduction

In this data dive, we explore the significance of documenting models and referencing data documentation. This analysis is based on the dataset `diabetes_binary_5050split_health_indicators_BRFSS2015_1.csv`

library(tidyverse)
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# Load necessary libraries
library(dplyr)
library(ggplot2)

Data Preparation

First, we load the dataset.

dataset <-read_delim("C:/Users/Akshay Dembra/Downloads/Stats_Selected_Dataset/diabetes_binary_5050split_health_indicators_BRFSS2015_1.csv" , delim = ",")
## Rows: 70692 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (22): Diabetes_binary, HighBP, HighChol, CholCheck, BMI, Smoker, Stroke,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dataset

Unclear Columns or Values

Here are three columns from the dataset that were unclear until the documentation was consulted:

  1. Diabetes_binary: Without documentation, it’s unclear whether this column represents a diagnosis of diabetes or a risk factor.
  2. GenHlth: The values range from 1 to 5, but without documentation, it’s uncertain what each value signifies regarding general health status.
  3. PhysHlth: This column contains numerical values that could represent days of poor physical health, but this is not obvious without documentation.

Reason for Encoding

The encoding choices likely aim to simplify data processing and analysis by using numerical representations:

  1. Diabetes_binary: A binary encoding (0 or 1) is efficient for classification tasks.
  2. GenHlth: Using a scale allows for easy comparison and statistical analysis.
  3. PhysHlth: Numerical values facilitate quantitative analysis.

Without reading the documentation, misinterpretations could lead to incorrect analyses and conclusions.

Unclear Element After Documentation

Even after consulting the documentation, the column NoDocbcCost remains unclear. It appears to relate to healthcare access issues due to cost, but the exact criteria for this variable are not fully explained.

Visualization Highlighting Issues Below is a visualization using the NoDocbcCost column, which was initially unclear regarding its scale:

Explore the NoDocbcCost Column

Before creating the visualization, it’s helpful to understand the distribution of the NoDocbcCost column:

Creating a bar plot for NoDocbcCost

ggplot(dataset, aes(x = factor(NoDocbcCost))) +
  geom_bar(fill = "skyblue") +
  labs(title = "Distribution of NoDocbcCost",
       x = "Could Not See Doctor Due to Cost",
       y = "Count",
       caption = "0 = No, 1 = Yes") +
  theme_minimal()

Explanation

The bar plot of the NoDocbcCost column reveals that a significant portion of individuals reported not being able to see a doctor due to cost, highlighting cost as a major barrier to healthcare access. This insight emphasizes the need for policies aimed at improving healthcare affordability and accessibility.

Risks and Mitigationtigationtigation

Significant risks include:

Misinterpretation: Incorrect analyses due to misunderstood data encoding.

Data Quality Issues: Incomplete documentation can lead to assumptions that affect model accuracy.

To mitigate these risks:

Always consult comprehensive documentation before analysis.

If documentation is lacking, seek clarification from data providers or domain experts.

Conclusion

This data dive emphasizes the critical role of thorough documentation in understanding datasets and ensuring accurate analyses. Further investigation might include seeking additional information on unclear elements like NoDocbcCost.