Introduction

The topic I chose is video game sales, which I have always been interested in and curious about. Through visualizing sales data, we can gain valuable insights into the most popular genres, platforms, and regions for gaming, as well as emerging trends. Understanding trends and patterns in video game sales is important for developers, publishers, and gamers. In this article, I’ll explore different visualization techniques for analyzing video game sales data, using ggplot in R.

Inspiration and Dataset

The R Graph Gallery website has been an invaluable resource for me in my data visualization projects. I was particularly impressed by the variety of visualizations showcased on the website, such as pie charts, lollipop charts, and line graphs (R Graph Gallery, 2023). The examples provided not only helped me improve my coding skills in R, but also gave me ideas for creating effective data visualizations in my own projects.

The video game sales dataset used in my analysis was sourced from Kaggle (Ibrii, 2022). The dataset includes updated sales data that provide comprehensive information on the performance of different video games across various regions. This data allowed me to conduct detailed analysis and visualization of the gaming industry.

Visualizations

First, I started with a pie chart, displaying the total sales distribution by region. The data shows that North America (NA) has the highest sales among all regions, indicating its significant contribution to the global sales of video games. In addition, a stacked bar chart of video game sales was implemented to display a clear visual representation of the sales data across different gaming genres and regions. From this chart, it is evident that the action genre is the best-selling across all regions. The chart allows for easy comparison of sales across different genres and regions, highlighting the dominance of action games in the market. 

Then I created a bar chart to display the highest scoring genres in order and discovered that role playing game is the highest scored genre. I first cleaned the dataset in R since there were many null values in User_Score column. When creating the visualization, I also noticed the similarity of score data for each genre. In such cases, adjusting the x-axis range can help to better distinguish between the genres and highlight any significant differences in their scores. In my case, I adjusted the x-axis to range from 6.5 to 8, and ordered them based on their score, to provide better differentiation between the genres. This adjustment helped to create a clearer visualization that allowed for easy comparison of scores across the top 12 genres.

The next pie chart shows the best-selling publishers by global sales, with Nintendo being the most successful publisher, holding a significant share of the market.

I also created a lollipop chart to visualize the performance of different games in the market in a timeline. From the graph, it is clear to see that one game that stands out in the report is Wii Sports, which was released in 2006 and has the highest sales among all games. Initially, I used the game name as the x-axis, but it resulted in a cluttered and chaotic visualization. After several iterations, I realized that it would be more effective to use a timeline as the x-axis to show the chronological order of the games.

Reflection

In the context of this report, I intend to create a complete infographic poster of video game sales for further exploration in the future. While the current work demonstrates significant progress in exploring different visualization graphs and analyzing data from various perspectives, there are still some technique issues that require attention. For example, one of the areas that I need to improve is the effective use of different fonts in R. Despite attempting to download the necessary font and utilizing the “extrafont” function in R, I was still unable to modify the font as desired. Further effort is necessary in order to resolve this issue , which would help to create a more engaging and informative design. In conclusion, through further experimentation and skill development, I aim to create a more refined infographic with more perspective, effectively communicating the key data and insights related to video game sales.

Reference

R Graph Gallery. (2023). Retrieved March 21, 2023, from https://r-graph-gallery.com/

Ibrahim Muhammad Naeem. (2023). <i>Video Game Sales Dataset Updated -Extra Feat</i> [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4984906

Image credit: Game controller image from indivisablegame.com