Visualization is an important tool for making data understandable. Visualization can be used for many different purposes, such as charts on the web used to visualize a dataset. Render time is important for websites since slow response times can cause users to leave the site. When creating a website with fast render times in mind, the selection of JavaScript library may be crucial. This work aims to determine if dataset size and/or chart type affects the render time of charts created by different JavaScript libraries. The comparison is done by a literature search to identify suitable libraries, followed by an experiment in which 40 websites are created to compare the performance of selected JavaScript libraries for rendering selected chart types. The results show that while both dataset size and chart type affect the render time in most cases, the libraries scale differently depending on the dataset size.

Visualizing data is important to make it easier to understand. Due to the common accessibility and the popularity of the web, a growth within web-based visualization is seen. One common and easy way to do this is by using an already implemented JavaScript library. When developing such a website some important properties to keep in mind are the render time and the memory usage. This study measured both the render time with different sizes of the dataset, and the render time when rendering different number of charts with the same dataset size. The memory usage was also measured with different sizes of the dataset. Six libraries were chosen to be included inthis study: D3, Echarts, CanvasJS, Chartist, Highcharts, and Plotly. An experiment has been performed to test the libraries render times and memory usage. The results of the experiment show that D3 has the overall lowest render times whilst CanvasJS had the lowest memory usage.