The R code used is attached below for interested readers to verify.Īs a result of data cleaning, columns that are not needed in the dashboard (e.g. Such data cleaning was performed through R Studio. Since the focus of this project is on the statistics of the Netflix database, some variables in the dataset need to be modified in order to achieve the goal. This project uses Tableau Public to create and store graphs. In addition, Tableau Public allows users to upload their visualizations on the server work for presentation. Tableau’s intuitive interaction with data dramatically reduces the time and expertise threshold required to create data visualizations, enabling users with less statistical background to quickly and accurately understand data and create high-quality charts. With the incredible growth of data volumes, the need to process data has never been greater. Tableau Public is the free community version of Tableau, the world-renowned data visualization tool. This tool helped the design of this project by keeping the colors of the visual charts consistent and making the final result more readable. Ĭ is a web tool that allows users with no design background to generate perfect matching colors in seconds. Datasets cleaned with R Studio simplify the visualization process and make many complex presentations feasible. R is also very capable of data cleaning work. R Studio is an open-source integrated development environment (IDE) for R, which is an excellent programming language for statistical computing. These tools are R Studio, for data cleaning and exploratory data analysis, for palette selection and Tableau, for statistical analysis and data visualization. Three tools were used to complete this project, each focusing on specific aspects to optimize the final presentation. Although its python code is complex, the graph is well worth referring to. The color visually reminds the reader of the time’s order.In addition, author uses ascending order to arrange the rows makes difference noticable. The title and annotation clearly express the topic and the author’s findings. ![]() I think this graph is very well developed. In order to provide readers with the most accurate and comprehensive picture of the Netflix database, I chose the following topics for this dashboard: ![]() In order to save reader‘s time and effort in gathering information, I think a dashboard would be more intuitive to convey the analysis results. While Josh’s article is in a very readable structure, it is more suitable as a python visualization tutorial and not very friendly to readers with no programming experience. Josh also places the python code used before the graphical results and includes the findings of his analysis and reflections after each outcome, giving a traceable line of thought to the article and making it easy for the reader to follow his path as one reads. In Josh’s work, the visualization is used as a support tool for Exploratory Data Analysis (EDA), starting from a macro statistical overview of the entire database and gradually refining the analysis to find noteworthy points, eventually choosing the US and India as objectives for comparative analysis. This project was inspired by user Josh, who implemented visualization results through python. This dataset collects a total of 8,807 records, essentially covering Netflix’s full database. In addition, other variables, such as age rating, country of production, duration, and descriptions, give these titles a complete picture. ![]() ![]() The dataset contains metadata for Netflix’s movies and TV shows, including the original release date of each title and the date it was added to Netflix and director and cast information. The dataset used in this project comes from. This project aims to examine the basics of the Netflix database, analyze the complexity of the database, and describe the dataset in a visual language, in the hope of giving readers a grounded understanding of this popular service. Underlying Netflix’s commercial greatness is its vast database and detailed records that support its sophisticated recommendation algorithms. It is incredible to think that Netflix was once a small company whose main business was DVD rentals since 1997. As of mid-2021, they have over 8,000 movie and TV series stores on their platform for over 200 million paid subscribers. Since Netflix entered the streaming market in 2007, its expansion never seems to have slowed down. Click image to the dashboard on Tableau Background
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