# install the ncov2019 package
remotes::install_github("GuangchuangYu/nCov2019")

# open it in the library
library(nCov2019)

# get ncov data
x <- get_nCov2019(lang='en')

# check the total confirmed cases and update date
x

# see a table of global values
x['global', ]

# Check values within china
x[]

# check table of values in decending order
x[1,]


# Check data from one province
x['Hubei', ] # replace Hubei with any province

install.packages("forcats")
library(forcats)
install.packages("ggplot2")
library(ggplot2)

# make your first plot of the data
d = x['Anhui',] # replace Anhui with any province
d$confirm=as.numeric(d$confirm)
d$name = fct_reorder(d$name, d$confirm)
ggplot(d, aes(name, confirm)) + 
  geom_col(fill='firebrick', alpha = 05) + coord_flip() +
  geom_text(aes(y = confirm+2, label=confirm), hjust=0) +
  theme_minimal(base_size=14) + 
  scale_y_continuous(expand=c(0,10)) +
  xlab(NULL) + ylab(NULL)


# check values for the current day
head(x[by='today'], 10)

# get a total summary 
summary(x)

# summarize by the day
summary(x, by="today")


library(ggplot2)
ggplot(summary(x), aes(as.Date(date, "%m.%d"), as.numeric(confirm))) +
  geom_col(fill='firebrick') + theme_minimal(base_size = 14) +
  xlab(NULL) + ylab(NULL) + 
  labs(caption = paste("accessed date:", time(x)))



x <- load_nCov2019(lang='en')
x


# only check the first 6 rows
head(x[][c(1:6, 9:11)])


# check the first 6 rows for Wuhan
head(subset(x['Hubei',], city == "Wuhan"))[c(1:6, 9:11)]



library(ggplot2)
install.packages("ggrepel")
require(ggrepel)

# See the cumulative rate of confirmed cases over time
d <- x['Anhui',] # replace Anhui with any province
ggplot(d, aes(time, as.numeric(cum_confirm), group=city, color=city)) +
  geom_point() + geom_line() +
  geom_text_repel(aes(label=city), data=d[d$time == time(x), ], hjust=1) +
  theme_minimal(base_size = 14) + theme(legend.position='none') +
  scale_color_viridis_d()+
  xlab(NULL) + ylab(NULL)


# read the summary of cumulative cases
head(summary(x)[,1:5])


# summarize from a specific province
summary(x, 'Hubei')[,1:5]




# city-specific cumulative summary
ggplot(subset(x['Hubei',], city == "Huanggang"), 
       aes(time, as.numeric(cum_confirm))) + geom_col(fill = "firebrick")+
  theme_classic()


require(nCov2019)
x = get_nCov2019(lang='en')

# plot affected areas on a world map
plot(x)



# install a china map
remotes::install_github("GuangchuangYu/chinamap", force = TRUE)



require(chinamap)
cn = get_map_china()
## translate province 
cn$province <- trans_province(cn$province)

# plot a chloropleth map of affected areas
plot(x, chinamap=cn)


# change the color
plot(x, chinamap=cn, palette="Purples")