This project is a data analysis task focusing on a computer specs dataset. The purpose is to provide a statistical and visual exploration of the data, to gain insights, and to answer some questions that could support decision-making processes.
The analysis is implemented in R, a popular language for statistical analysis and data science. This script uses several libraries, including tidyverse for data manipulation and visualization, ggplot2 for creating elegant data visualizations, and readr for data input/output.
The script, datasetAnalysis(italian).R, is divided into different parts:
The required R libraries for this project are loaded.
The dataset is loaded from a CSV file.
The dataset is cleaned by removing NA values.
Different functions are applied to the dataset to calculate statistical measures, such as mean, median, standard deviation, etc.
Several plots are created to visualize the data and the results of the analysis.
To run this script, you need to have R installed on your computer. If you don't have R installed, you can download it from The R Project for Statistical Computing.
Once you have R installed:
Clone or download this repository to your local machine.
Open the datasetAnalysis(italian).R script in an R environment (like RStudio).
Make sure to install any necessary packages. This can usually be done with the command install.packages("package-name") in the R console.
Run the script. You may need to set your working directory to the location where the script is saved. This can be done with the setwd("directory_path") command in R.
The results of the analysis are printed to the console and saved as visualizations. These visualizations provide a graphical representation of the findings, which can be easily interpreted and presented.
While this script provides a good starting point, there's always room for improvements and additions, such as:
- Adding more complex statistical analysis methods.
- Implementing machine learning models to make predictions based on the dataset.
- Creating interactive visualizations using libraries like plotly. Contributions Contributions, issues, and feature requests are welcome. Feel free to check issues page if you want to contribute.