2020年03月21日
English | 简体中文
knitr::opts_chunk$set(
fig.path = "man/figures/",
message = FALSE
)- raw data from DXY-COVID-19-Data
- preprocessed data from nCoV-2019-Data
if (!require(remotes)) install.packages("remotes")
if (!require(ncovmap)) remotes::install_github("yiluheihei/ncovmap")Feature:
get_ncov2(): retrieve latest or time-series data of covid-2019plot_china_map(): plot on china mapplot_province_map(): plot on province map of chinaplot_world_map(): plot on world mapplot_foreign_map(): plot on japan, korea, italy or iran map
library(ncovmap)
library(leafletCN)
library(magrittr) # for pipe# latest data
ncov <- get_ncov2(method = "api")
# ncov class inherit from data.frame
ncov## All COVID 2019 Data
## Updated at 2020-03-21 02:59:14
## From https://github.com/BlankerL/DXY-COVID-19-Data
data.frame(ncov) %>%
head()## continentName continentEnglishName countryName countryEnglishName
## 1 欧洲 Europe 爱尔兰 Ireland
## 2 大洋洲 Oceania 新喀里多尼亚 <NA>
## 3 亚洲 Asia 斯里兰卡 SriLanka
## 4 非洲 Africa 突尼斯 Tunisia
## 5 北美洲 North America 阿鲁巴 <NA>
## 6 欧洲 Europe 立陶宛 Lithuania
## provinceName provinceEnglishName province_zipCode province_confirmedCount
## 1 爱尔兰 Ireland 961003 683
## 2 新喀里多尼亚 <NA> 0 2
## 3 斯里兰卡 SriLanka 953007 71
## 4 突尼斯 Tunisia 981006 54
## 5 阿鲁巴 <NA> 0 5
## 6 立陶宛 Lithuania 964004 63
## province_suspectedCount province_curedCount province_deadCount cityName
## 1 0 0 3 <NA>
## 2 0 0 0 <NA>
## 3 0 1 0 <NA>
## 4 0 0 0 <NA>
## 5 0 0 0 <NA>
## 6 0 0 0 <NA>
## cityEnglishName city_confirmedCount city_suspectedCount city_curedCount
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## 6 <NA> NA NA NA
## city_deadCount city_zipCode updateTime
## 1 NA NA 2020-03-21 02:59:14
## 2 NA NA 2020-03-21 02:59:14
## 3 NA NA 2020-03-21 02:59:14
## 4 NA NA 2020-03-21 02:59:14
## 5 NA NA 2020-03-21 02:59:14
## 6 NA NA 2020-03-21 02:59:14
# china data
china <- ncov['china']
china## China COVID 2019 Data
## Updated at 2020-03-21 02:59:14
## From https://github.com/BlankerL/DXY-COVID-19-Data
# Hubei province of china
hubei <- ncov['Hubei']
hubei## Hubei COVID 2019 Data
## Updated at 2020-03-21 02:03:01
## From https://github.com/BlankerL/DXY-COVID-19-Data
# Beijing
beijing <- ncov['Beijing']
# world data
world <- ncov['world']
world## World COVID 2019 Data
## Updated at 2020-03-21 02:59:14
## From https://github.com/BlankerL/DXY-COVID-19-Data
data.frame(world) %>%
head()## countryEnglishName provinceName continentName continentEnglishName
## 1 Albania 阿尔巴尼亚 欧洲 Europe
## 2 Algeria 阿尔及利亚 非洲 Africa
## 3 Andorra 安道尔 欧洲 Europe
## 4 Antigua and Barbuda 安提瓜和巴布达 北美洲 North America
## 5 Argentina 阿根廷 南美洲 South America
## 6 Armenia 亚美尼亚 亚洲 Asia
## countryName provinceEnglishName province_zipCode province_confirmedCount
## 1 阿尔巴尼亚 Albania 965001 70
## 2 阿尔及利亚 Algeria 981001 90
## 3 安道尔 Andorra 965002 75
## 4 安提瓜和巴布达 Antigua and Barbuda 974001 1
## 5 阿根廷 Argentina 973001 158
## 6 亚美尼亚 Armenia 955002 136
## province_suspectedCount province_curedCount province_deadCount cityName
## 1 0 0 2 <NA>
## 2 0 12 7 <NA>
## 3 0 1 0 <NA>
## 4 0 0 0 <NA>
## 5 0 1 3 <NA>
## 6 0 1 0 <NA>
## cityEnglishName city_confirmedCount city_suspectedCount city_curedCount
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## 6 <NA> NA NA NA
## city_deadCount city_zipCode updateTime
## 1 NA NA 2020-03-21 02:59:14
## 2 NA NA 2020-03-21 02:59:14
## 3 NA NA 2020-03-21 02:03:01
## 4 NA NA 2020-03-21 02:03:01
## 5 NA NA 2020-03-21 02:59:14
## 6 NA NA 2020-03-21 02:59:14
plot_china_map(china, legend_position = "bottomleft")
Hubei province
plot_province_map(
hubei,
"Hubei",
bins = c(0, 100, 200, 500, 1000, 10000)
)
Beijing
plot_province_map(
beijing,
"Beijing",
bins = c(0, 10, 50, 100)
)
plot_world_map(world, legend_position = "bottomleft")
korea_ncov <- get_foreign_ncov("韩国")
plot_foreign_map(korea_ncov, "korea")
jp_ncov <- get_foreign_ncov("日本")
plot_foreign_map(jp_ncov, "japan")
iran_ncov <- get_foreign_ncov("伊朗")
plot_foreign_map(iran_ncov, "iran")
italy_ncov <- get_foreign_ncov("意大利")
plot_foreign_map(italy_ncov, "italy")
## not run
foreign_countries <- c("韩国", "伊朗", "日本", "意大利")
names(foreign_countries) <- c("korea", "iran", "japan", "italy")
htmltools::tagList(purrr::imap(
foreign_countries,
~ get_foreign_ncov(.x) %>%
plot_foreign_map(.y)
))