Database description
The coronavirus package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
A csv format of the package dataset available here.
A summary dashboard is available here.
Functions
This package gives access a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The function below allows you to download the data.
- data(“coronavirus”)
Each of these functions are detailed in this course and some examples are provided.
data(“coronavirus”)
This is a basic example which shows you how to get the data:
library(coronavirus)
data("coronavirus")
This coronavirus dataset has the following fields:
head(coronavirus)
date province country lat long type cases
1 2020-01-22 Afghanistan 33.93911 67.70995 confirmed 0
2 2020-01-23 Afghanistan 33.93911 67.70995 confirmed 0
3 2020-01-24 Afghanistan 33.93911 67.70995 confirmed 0
4 2020-01-25 Afghanistan 33.93911 67.70995 confirmed 0
5 2020-01-26 Afghanistan 33.93911 67.70995 confirmed 0
6 2020-01-27 Afghanistan 33.93911 67.70995 confirmed 0tail(coronavirus)
date province country lat long type cases
150715 2020-07-26 Zhejiang China 29.1832 120.0934 recovered 0
150716 2020-07-27 Zhejiang China 29.1832 120.0934 recovered 0
150717 2020-07-28 Zhejiang China 29.1832 120.0934 recovered 0
150718 2020-07-29 Zhejiang China 29.1832 120.0934 recovered 0
150719 2020-07-30 Zhejiang China 29.1832 120.0934 recovered 0
150720 2020-07-31 Zhejiang China 29.1832 120.0934 recovered 0Here is an example of a summary total cases by region and type (top 20):
library(dplyr)
summary_df <- coronavirus %>% group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
summary_df %>% head(20)
# A tibble: 20 x 3
# Groups: country [12]
country type total_cases
<chr> <chr> <int>
1 US confirmed 4562038
2 Brazil confirmed 2662485
3 Brazil recovered 2008854
4 India confirmed 1695988
5 US recovered 1438160
6 India recovered 1094374
7 Russia confirmed 838461
8 Russia recovered 637217
9 South Africa confirmed 493183
10 Mexico confirmed 424637
11 Peru confirmed 407492
12 Chile confirmed 355667
13 Chile recovered 328327
14 Mexico recovered 327115
15 South Africa recovered 326171
16 United Kingdom confirmed 304793
17 Iran confirmed 304204
18 Colombia confirmed 295508
19 Spain confirmed 288522
20 Peru recovered 283915Summary of new cases during the past 24 hours by country and type (as of 2020-03-26):
library(tidyr)
coronavirus %>%
filter(date == max(date)) %>%
select(country = country, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
# A tibble: 188 x 4
# Groups: country [188]
country confirmed death recovered
<chr> <int> <int> <int>
1 US 67023 1259 24005
2 India 61242 793 39026
3 Brazil 52383 1212 52047
4 South Africa 11014 193 16570
5 Colombia 9488 295 5692
6 Mexico 8458 688 7015
7 Peru 6809 205 0
8 Argentina 5929 102 3184
9 Russia 5468 161 8735
10 Iraq 3346 70 1888
# … with 178 more rowstl;dr
library(coronavirus)
data("coronavirus")
head(coronavirus)
tail(coronavirus)
library(dplyr)
summary_df <- coronavirus %>% group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
summary_df %>% head(20)
library(tidyr)
coronavirus %>%
filter(date == max(date)) %>%
select(country = country, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
Code learned this week
| Command | Detail |
|---|---|
| data(“coronavirus”) | Get data for of all Corona Virus cases |
References
This tutorial uses the coronavirus package, created by Rami Krispin.