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https://github.com/rijul007/diamonds-analysis-using-r

Diamonds data analysis using R, exploring relationships between diamond attributes (such as carat, cut, color, and clarity) and price, with a focus on providing insights for engagement ring selection through various statistical techniques and data visualizations including histograms, boxplots, scatter plots, and bar charts.
https://github.com/rijul007/diamonds-analysis-using-r

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Diamonds data analysis using R, exploring relationships between diamond attributes (such as carat, cut, color, and clarity) and price, with a focus on providing insights for engagement ring selection through various statistical techniques and data visualizations including histograms, boxplots, scatter plots, and bar charts.

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README 

        
# Diamond Analysis Project



## Objective

To analyze the characteristics and pricing of diamonds using the built-in `diamonds` dataset from the `ggplot2` library in R. The project aims to answer specific questions about diamond attributes and their relationship to price, particularly focusing on engagement ring selection.



## Dataset Used

The analysis uses the `diamonds` dataset from the `ggplot2` library in R. This dataset contains information on 53,940 round-cut diamonds, including attributes such as carat weight, cut quality, color, clarity, and price.



## Analysis Techniques



The project employs various data analysis and visualization techniques using R:



1. **Data Loading and Exploration**: 

   - Utilized **`tidyverse`** and **`dplyr`** for data manipulation

   - Used **`glimpse()`** function to review data structure



2. **Data Visualization**:

   - Employed **`ggplot2`** for creating various types of plots:

     - Histograms and boxplots for price distribution

     - Boxplots for comparing table values across cut grades

     - Pie charts for clarity segment analysis

     - Scatter plots with trend lines for price-carat correlation

     - Bar charts for average price analysis

   - Used **`patchwork`** library for combining multiple plots



3. **Statistical Analysis**:

   - Calculated percentages and averages using **`dplyr`** functions like `group_by()`, `summarise()`, and `mutate()`

   - Applied sampling techniques using **`sample_n()`** function



4. **Data Transformation**:

   - Created new segments for clarity using **`mutate()`** and conditional statements

   - Filtered data for specific analyses using **`filter()`** function



5. **Advanced Visualization Techniques**:

   - Implemented faceting with **`facet_wrap()`** for multi-panel plots

   - Used **`geom_smooth()`** for adding trend lines to scatter plots

   - Applied custom color palettes and themes for enhanced aesthetics



6. **Scale and Axis Manipulation**:

   - Utilized **`scale_x_continuous()`** and **`scale_y_continuous()`** for customizing axis scales

   - Implemented **`scale_fill_manual()`** and **`scale_color_manual()`** for custom color schemes



## Results



1. The distribution of diamond prices is positively skewed with outliers.

2. Ideal-cut diamonds have a narrower range of table values compared to other cut grades.

3. Only 3.3% of the diamonds in the dataset are categorized as Internally Flawless (IF).

4. There's a positive correlation between carat weight and price for colorless diamonds (D, E, F colors).

5. For 1.00-1.25 carat Ideal-cut diamonds, prices increase significantly with higher clarity and color grades.



For a detailed view of the analysis and visualizations, you can access the [RPub Document here](https://rpubs.com/Rijul-Grover/1217027).



These insights provide valuable information for understanding diamond characteristics and pricing, particularly useful for engagement ring selection.