- COVID19country_0402.csv
- COVID19_0402.csv
- COVID19_TS.xls
- COVID19city_0402.csv
- COVID19country_0331.csv
- COVID19_0331.csv
- COVID19city_0331.csv
- COVID19country_0330.csv
- COVID19_0330.csv
- COVID19city_0330.csv
- COVID19country_0329.csv
- COVID19_0329.csv
- COVID19city_0329.csv
- COVID19country_0321.csv
- COVID19country_0322.csv
- COVID19country_0323.csv
- COVID19country_0324.csv
- COVID19country_0325.csv
- COVID19country_0326.csv
- COVID19country_0327.csv
- COVID19country_0316.csv
- COVID19country_0317.csv
- COVID19country_0318.csv
- COVID19country_0319.csv
- COVID19country_0320.csv
- COVID19city_0323.csv
- COVID19city_0324.csv
- COVID19city_0325.csv
- COVID19city_0326.csv
- COVID19city_0327.csv
- COVID19city_0217.csv
- COVID19city_0218.csv
- COVID19city_0219.csv
- COVID19city_0220.csv
- COVID19city_0221.csv
- COVID19city_0222.csv
- COVID19city_0223.csv
- COVID19city_0224.csv
- COVID19city_0225.csv
- COVID19city_0226.csv
- COVID19city_0227.csv
- COVID19city_0228.csv
- COVID19city_0229.csv
- COVID19city_0301.csv
- COVID19city_0302.csv
- COVID19city_0303.csv
- COVID19city_0304.csv
- COVID19city_0305.csv
- COVID19city_0306.csv
- COVID19city_0307.csv
- COVID19city_0308.csv
- COVID19city_0309.csv
- COVID19city_0310.csv
- COVID19city_0311.csv
- COVID19city_0312.csv
- COVID19city_0313.csv
- COVID19city_0314.csv
- COVID19city_0315.csv
- COVID19city_0316.csv
- COVID19city_0317.csv
- COVID19city_0318.csv
- COVID19city_0319.csv
- COVID19city_0320.csv
- COVID19city_0321.csv
- COVID19city_0322.csv
- COVID19_0324.csv
- COVID19_0325.csv
- COVID19_0326.csv
- COVID19_0327.csv
- COVID19_0217.csv
- COVID19_0218.csv
- COVID19_0219.csv
- COVID19_0220.csv
- COVID19_0221.csv
- COVID19_0222.csv
- COVID19_0223.csv
- COVID19_0224.csv
- COVID19_0225.csv
- COVID19_0226.csv
- COVID19_0227.csv
- COVID19_0228.csv
- COVID19_0229.csv
- COVID19_0301.csv
- COVID19_0302.csv
- COVID19_0303.csv
- COVID19_0304.csv
- COVID19_0305.csv
- COVID19_0306.csv
- COVID19_0307.csv
- COVID19_0308.csv
- COVID19_0309.csv
- COVID19_0310.csv
- COVID19_0311.csv
- COVID19_0312.csv
- COVID19_0313.csv
- COVID19_0314.csv
- COVID19_0315.csv
- COVID19_0316.csv
- COVID19_0317.csv
- COVID19_0318.csv
- COVID19_0319.csv
- COVID19_0320.csv
- COVID19_0321.csv
- COVID19_0322.csv
- COVID19_0323.csv
- COVID19country_0328.csv
- COVID19_0328.csv
- COVID19city_0328.csv
说明:
1、数据来源:国家卫健委公告、丁香园每日数据、163疫情地图每日数据。
2、数据部分为手工整理,部分为R语言网络抓取。
3、原始数据,可以改为 time series类,用于时间序列分析,另外可用于估算SEIR模型的相关参数。
4、分享数据为excel表格,未针对R应用进行数据清洗整理。R、PYTHON等应用需作数据格式调整。
5、数据包括:一个主文件,三个打包文件(分国内分省、国内分城市、国家三个部分)。
6、个别数据可能有口径差异,比如浙江省数据存在12例的差异,官方有说明,本数据未作调整。
7、本数据为本人辛苦整理,仅作学习参考之用,请勿以此作为论文数据引用。
8、主表后续数据不再补充。
9、4月2日起,每日数据在回复中(大概在9楼位置)隔天补充,以免给版主添加审核麻烦。
10、最新的国内汇总表在回复中,国外数据请需要的同学自行汇总,用R语言编个循环语句就可以,很容易的。