Github-Archive Event Analysis

This research project fetches event-data from githubarchives.org, filters the data to extract the information of interest, generates basic statistics and plots regarding to these statistics. The experiment is deployed to gain general knowledge on basic github-usage. Therefore, the following questions were followed:<br> 1) How are GitHub-events distributed? - This can be derived by quantitative analysis of the distribution of different Event-Types.<br> 2) What is the common ratio of commits per push, what are extremes? - again quantitative analysis of push-events is used. To visualize the results of this analysis, two plots are created. Each plot addresses one of the research-questions described above. Additionally, textual output is written to the terminal containing the precise numbers of the analysis and can be captured via native terminal functions.<br> The data-files created by downloading and unzipping are just used as input for analysis and do not depict "final output". The given results were collected/created for the default time-period: 01.01.2015 00:00 to 01:00. The python3-Scripts need python version 3 and were executed on Linux! Additional libraries are required: matplotlib for python3本研究项目从githubarchives.org获取事件数据，经筛选提取目标信息后，生成基础统计指标及对应可视化图表。 本实验旨在获取GitHub基础使用的通用知识，因此围绕以下研究问题展开： 1）GitHub事件的分布规律如何？——可通过对不同事件类型（Event-Types）的分布开展定量分析得出结论。 2）每一次推送（push）的平均提交（commit）次数通常为多少？极端值又如何？——同样可通过对推送事件的定量分析实现。 为直观呈现本次分析的结果，本研究共生成两幅可视化图表，每幅图表分别对应前文所述的一个研究问题。此外，分析所得的精确数值将以文本形式输出至终端，可通过原生终端功能捕获该输出内容。 通过下载和解压缩生成的数据文件仅用作分析输入，并非本项目的最终输出成果。 本次研究所得结果采集自默认时间段：2015年1月1日00:00至当日01:00。 本项目所使用的Python3脚本需依赖Python 3版本，且已在Linux系统上运行。此外，还需安装Python3版本的额外依赖库：matplotlib。