library(magrittr) # pipe operations
library(lubridate) # date operations
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(tidyverse) # ggplot2, tidyr, dplyr...
## -- Attaching packages ------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ---------------------------------- tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()
## x lubridate::setdiff() masks base::setdiff()
## x lubridate::union() masks base::union()
library(gridExtra) # multiple grid-based plots on a page
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
library(ggforce) # accelerating ggplot2
library(kableExtra) # complex tables
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
library(leaflet) ## map
## load data into R
data.confirmed <- read.csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv')
data.deaths <- read.csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv')
data.recovered <- read.csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')
dim(data.confirmed)
## [1] 477 63
data.confirmed[1:10, 1:10] %>%
kable('pandoc', booktabs=T, caption='Raw Data (Confirmed, First 10 Columns only)') %>%
kable_styling(font_size=6, latex_options = c('striped', 'hold_position', 'repeat_header'))
## Warning in kable_styling(., font_size = 6, latex_options = c("striped", : Please
## specify format in kable. kableExtra can customize either HTML or LaTeX outputs.
## See https://haozhu233.github.io/kableExtra/ for details.
Raw Data (Confirmed, First 10 Columns only)
|
Thailand |
15.0000 |
101.0000 |
2 |
3 |
5 |
7 |
8 |
8 |
|
Japan |
36.0000 |
138.0000 |
2 |
1 |
2 |
2 |
4 |
4 |
|
Singapore |
1.2833 |
103.8333 |
0 |
1 |
3 |
3 |
4 |
5 |
|
Nepal |
28.1667 |
84.2500 |
0 |
0 |
0 |
1 |
1 |
1 |
|
Malaysia |
2.5000 |
112.5000 |
0 |
0 |
0 |
3 |
4 |
4 |
| British Columbia |
Canada |
49.2827 |
-123.1207 |
0 |
0 |
0 |
0 |
0 |
0 |
| New South Wales |
Australia |
-33.8688 |
151.2093 |
0 |
0 |
0 |
0 |
3 |
4 |
| Victoria |
Australia |
-37.8136 |
144.9631 |
0 |
0 |
0 |
0 |
1 |
1 |
| Queensland |
Australia |
-28.0167 |
153.4000 |
0 |
0 |
0 |
0 |
0 |
0 |
|
Cambodia |
11.5500 |
104.9167 |
0 |
0 |
0 |
0 |
0 |
1 |
n.col <- ncol(data.confirmed)
## get dates from column names
dates <- names(data.confirmed)[5:n.col] %>% substr(2,8) %>% mdy()
range(dates)
## [1] "2020-01-22" "2020-03-20"
min.date <- min(dates)
max.date <- max(dates)
max.date.txt <- max.date %>% format('%d %B %Y')
## select last column, which is the number of latest confirmed cases
x <- data.confirmed
x$confirmed <- x[, ncol(x)]
x %<>% select(c(Country.Region, Province.State, Lat, Long, confirmed)) %>%
mutate(txt=paste0(Country.Region, ' - ', Province.State, ': ', confirmed))
m <- leaflet(width=1200, height=800) %>% addTiles()
# circle marker (units in pixels)
m %<>% addCircleMarkers(x$Long, x$Lat,
radius=2+log2(x$confirmed), stroke=F,
color='red', fillOpacity=0.5,
popup=x$txt)
# world
m
## China
m %>% setView(95, 35, zoom=4)
## Australia and New Zealand
m %>% setView(135, -27, zoom=4)
## US and Canada
m %>% setView(-105, 40, zoom=4)
## Europe
m %>% setView(10, 50, zoom=4)
## Brazil
m %>% setView(-39, -25, zoom=4)
## data cleaning and transformation
cleanData <- function(data) {
## remove some columns
data %<>% select(-c(Province.State, Lat, Long)) %>% rename(country=Country.Region)
## convert from wide to long format
data %<>% gather(key=date, value=count, -country)
data %<>% mutate(date = date %>% substr(2,8) %>% mdy())
## aggregate by country
data %<>% group_by(country, date) %>% summarise(count=sum(count, na.rm=T)) %>% as.data.frame()
return(data)
}
## clean the three datasets
data.confirmed <- data.confirmed %>% cleanData() %>% rename(confirmed=count)
data.deaths <- data.deaths %>% cleanData() %>% rename(deaths=count)
data.recovered <- data.recovered %>% cleanData() %>% rename(recovered=count)
## merge above 3 datasets into one, by country and date
data <- data.confirmed %>% merge(data.deaths) %>% merge(data.recovered)
## countries/regions with confirmed cases, excl. cruise ships
countries <- data %>% pull(country) %>% setdiff('Cruise Ship')
## first 10 records when it first broke out in China
data %>% filter(country=='Brazil') %>% tail(25) %>%
kable('pandoc', booktabs=T, caption='Raw Data (with last 10 Columns Only)',
format.args=list(big.mark=',')) %>%
kable_styling(latex_options = c('striped', 'hold_position', 'repeat_header'))
## Warning in kable_styling(., latex_options = c("striped", "hold_position", :
## Please specify format in kable. kableExtra can customize either HTML or LaTeX
## outputs. See https://haozhu233.github.io/kableExtra/ for details.
Raw Data (with last 10 Columns Only)
| 35 |
Brazil |
2020-02-25 |
0 |
0 |
0 |
| 36 |
Brazil |
2020-02-26 |
1 |
0 |
0 |
| 37 |
Brazil |
2020-02-27 |
1 |
0 |
0 |
| 38 |
Brazil |
2020-02-28 |
1 |
0 |
0 |
| 39 |
Brazil |
2020-02-29 |
2 |
0 |
0 |
| 40 |
Brazil |
2020-03-01 |
2 |
0 |
0 |
| 41 |
Brazil |
2020-03-02 |
2 |
0 |
0 |
| 42 |
Brazil |
2020-03-03 |
2 |
0 |
0 |
| 43 |
Brazil |
2020-03-04 |
4 |
0 |
0 |
| 44 |
Brazil |
2020-03-05 |
4 |
0 |
0 |
| 45 |
Brazil |
2020-03-06 |
13 |
0 |
0 |
| 46 |
Brazil |
2020-03-07 |
13 |
0 |
0 |
| 47 |
Brazil |
2020-03-08 |
20 |
0 |
0 |
| 48 |
Brazil |
2020-03-09 |
25 |
0 |
0 |
| 49 |
Brazil |
2020-03-10 |
31 |
0 |
0 |
| 50 |
Brazil |
2020-03-11 |
38 |
0 |
0 |
| 51 |
Brazil |
2020-03-12 |
52 |
0 |
0 |
| 52 |
Brazil |
2020-03-13 |
151 |
0 |
0 |
| 53 |
Brazil |
2020-03-14 |
151 |
0 |
0 |
| 54 |
Brazil |
2020-03-15 |
162 |
0 |
0 |
| 55 |
Brazil |
2020-03-16 |
200 |
0 |
1 |
| 56 |
Brazil |
2020-03-17 |
321 |
1 |
2 |
| 57 |
Brazil |
2020-03-18 |
372 |
3 |
2 |
| 58 |
Brazil |
2020-03-19 |
621 |
6 |
2 |
| 59 |
Brazil |
2020-03-20 |
793 |
11 |
2 |
## counts for the whole world
data.world <- data %>% group_by(date) %>%
summarise(country='World',
confirmed = sum(confirmed),
deaths = sum(deaths),
recovered = sum(recovered))
data %<>% rbind(data.world)
data %<>% mutate(remaining.confirmed = confirmed - deaths - recovered)
## sort by country and date
data %<>% arrange(country, date)
## daily increases of deaths and recovered cases
## set NA to the increases on day1
n <- nrow(data)
day1 <- min(data$date)
data %<>% mutate(new.confirmed = ifelse(date == day1, NA, confirmed - lag(confirmed, n=1)),
new.deaths = ifelse(date == day1, NA, deaths - lag(deaths, n=1)),
new.recovered = ifelse(date == day1, NA, recovered - lag(recovered, n=1)))
## check for decreases in confirmed, recovered and deaths
# idx <- which(data$new.confirmed < 0)
# idx2 <- idx - 1
# data[c(idx, idx2), ] %>% arrange(country, date)
# idx <- which(data$new.recovered < 0)
# idx2 <- idx - 1
# data[c(idx, idx2), ] %>% arrange(country, date)
# idx <- which(data$new.deaths < 0)
# idx2 <- idx - 1
# data[c(idx, idx2), ] %>% arrange(country, date)
## change negative number of new cases to zero
data %<>% mutate(new.confirmed = ifelse(new.confirmed < 0, 0, new.confirmed),
new.deaths = ifelse(new.deaths < 0, 0, new.deaths),
new.recovered = ifelse(new.recovered < 0, 0, new.recovered))
## death rate based on total deaths and recovered cases
data %<>% mutate(rate.upper = (100 * deaths / (deaths + recovered)) %>% round(1))
## lower bound: death rate based on total confirmed cases
data %<>% mutate(rate.lower = (100 * deaths / confirmed) %>% round(1))
## death rate based on the number of death/recovered on every single day
data %<>% mutate(rate.daily = (100 * new.deaths / (new.deaths + new.recovered)) %>% round(1))
## ranking by confirmed cases
data.latest <- data %>% filter(date == max(date)) %>%
select(country, date,
confirmed, new.confirmed, remaining.confirmed,
recovered, deaths, new.deaths) %>%
mutate(ranking = dense_rank(desc(confirmed)))
k.top <- 25
## top 20 countries: 21 incl. 'World'
top.countries <- data.latest %>% filter(ranking <= k.top + 1) %>%
arrange(ranking) %>% pull(country) %>% as.character()
top.countries %>% setdiff('World') %>% print()
## [1] "China" "Italy" "Spain" "Germany"
## [5] "Iran" "US" "France" "Korea, South"
## [9] "Switzerland" "United Kingdom" "Netherlands" "Austria"
## [13] "Belgium" "Norway" "Sweden" "Denmark"
## [17] "Malaysia" "Portugal" "Japan" "Canada"
## [21] "Czechia" "Brazil" "Australia" "Cruise Ship"
## [25] "Israel"
## add 'Others'
top.countries %<>% c('Others')
## put all others in a single group of 'Others'
df <- data.latest %>% filter(!is.na(country)) %>%
mutate(country=ifelse(ranking <= k.top + 1, as.character(country), 'Others')) %>%
mutate(country=country %>% factor(levels=c(top.countries)))
df %<>% group_by(country) %>%
summarise(confirmed=sum(confirmed), new.confirmed=sum(new.confirmed),
remaining.confirmed=sum(remaining.confirmed),
recovered=sum(recovered), deaths=sum(deaths), new.deaths=sum(new.deaths)) %>%
mutate(death.rate=(100 * deaths/confirmed) %>% round(1))
df %<>% select(c(country, confirmed, deaths, death.rate,
new.confirmed, new.deaths, remaining.confirmed))
## convert from wide to long format, for drawing area plots
df.long <- df %>% filter(country!='World') %>%
gather(key=type, value=count, -country)
## set factor levels to show them with proper text and in a desirable order
df.long %<>% mutate(type=recode_factor(type,
confirmed='Total Confirmed',
deaths='Total Deaths',
death.rate='Death Rate (%)',
new.confirmed='New Confirmed (compared with one day before)',
new.deaths='New Deaths (compared with one day before)',
remaining.confirmed='Remaining Confirmed'))
## bar chart
df.long %>% ggplot(aes(x=country, y=count, fill=country, group=country)) +
geom_bar(stat='identity') +
geom_text(aes(label=count, y=count), size=2, vjust=0) +
xlab('') + ylab('') +
labs(title=paste0('Top 20 Countries with Most Confirmed Cases - ', max.date.txt)) +
scale_fill_discrete(name='Country', labels=df$txt) +
theme(legend.title=element_blank(),
legend.position='none',
plot.title=element_text(size=11),
axis.text=element_text(size=7),
axis.text.x=element_text(angle=45, hjust=1)) +
facet_wrap(~type, ncol=1, scales='free_y')
## Warning: Unknown or uninitialised column: 'txt'.
df %>% mutate(death.rate=death.rate %>% format(nsmall=1) %>% paste0('%')) %>%
kable('pandoc', booktabs=T, row.names=T, align=c('l', rep('r', 6)),
caption=paste0('Cases in Top 20 Countries - ', max.date.txt,
'. See a complete list of all infected countries at the end of this report.'),
format.args=list(big.mark=',')) %>%
kable_styling(font_size=7, latex_options=c('striped', 'hold_position', 'repeat_header'))
## Warning in kable_styling(., font_size = 7, latex_options = c("striped", : Please
## specify format in kable. kableExtra can customize either HTML or LaTeX outputs.
## See https://haozhu233.github.io/kableExtra/ for details.
Cases in Top 20 Countries - 20 março 2020. See a complete list of all infected countries at the end of this report.
| 1 |
World |
272,166 |
11,299 |
4.2% |
29,458 |
1,432 |
173,611 |
| 2 |
China |
81,250 |
3,253 |
4.0% |
94 |
4 |
6,731 |
| 3 |
Italy |
47,021 |
4,032 |
8.6% |
5,986 |
627 |
38,549 |
| 4 |
Spain |
20,410 |
1,043 |
5.1% |
2,447 |
213 |
17,779 |
| 5 |
Germany |
19,848 |
67 |
0.3% |
4,528 |
23 |
19,601 |
| 6 |
Iran |
19,644 |
1,433 |
7.3% |
1,237 |
149 |
11,466 |
| 7 |
US |
19,100 |
244 |
1.3% |
5,423 |
44 |
18,856 |
| 8 |
France |
12,726 |
450 |
3.5% |
1,779 |
207 |
12,264 |
| 9 |
Korea, South |
8,652 |
94 |
1.1% |
87 |
3 |
7,018 |
| 10 |
Switzerland |
5,294 |
54 |
1.0% |
1,219 |
13 |
5,225 |
| 11 |
United Kingdom |
4,014 |
178 |
4.4% |
1,298 |
40 |
3,769 |
| 12 |
Netherlands |
3,003 |
107 |
3.6% |
536 |
30 |
2,894 |
| 13 |
Austria |
2,388 |
6 |
0.3% |
375 |
0 |
2,373 |
| 14 |
Belgium |
2,257 |
37 |
1.6% |
462 |
16 |
2,219 |
| 15 |
Norway |
1,914 |
7 |
0.4% |
168 |
0 |
1,906 |
| 16 |
Sweden |
1,639 |
16 |
1.0% |
200 |
5 |
1,607 |
| 17 |
Denmark |
1,337 |
9 |
0.7% |
112 |
3 |
1,327 |
| 18 |
Malaysia |
1,030 |
3 |
0.3% |
130 |
1 |
940 |
| 19 |
Portugal |
1,020 |
6 |
0.6% |
235 |
3 |
1,009 |
| 20 |
Japan |
963 |
33 |
3.4% |
39 |
4 |
739 |
| 21 |
Canada |
943 |
12 |
1.3% |
143 |
3 |
922 |
| 22 |
Czechia |
833 |
0 |
0.0% |
139 |
0 |
829 |
| 23 |
Brazil |
793 |
11 |
1.4% |
172 |
5 |
780 |
| 24 |
Australia |
791 |
7 |
0.9% |
110 |
1 |
758 |
| 25 |
Cruise Ship |
712 |
7 |
1.0% |
0 |
0 |
380 |
| 26 |
Israel |
705 |
0 |
0.0% |
28 |
0 |
691 |
| 27 |
Others |
13,879 |
190 |
1.4% |
2,511 |
39 |
12,979 |
data.long <- data %>% filter(country %in% top.countries) %>%
select(c(country, date, rate.upper, rate.lower, rate.daily)) %>%
mutate(country=factor(country, levels=top.countries)) %>%
gather(key=type, value=count, -c(country, date))
# set factor levels to show them in a desirable order
data.long %<>% mutate(type=recode_factor(type, rate.daily='Daily',
rate.lower='Lower bound',
rate.upper='Upper bound'))
## three death rates
rate.max <- data.long$count %>% max(na.rm=T)
ggplot(data.long, aes(x=date, y=count, color=type)) +
geom_line() +
xlab('') + ylab('Death Rate (%)') +
theme(legend.position='bottom', legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
axis.text.x=element_text(angle=45, hjust=1)) +
ylim(c(0, 100)) +
facet_wrap(~country, ncol=4)
## Warning: Removed 1 row(s) containing missing values (geom_path).
plot1 <- df %>% filter(country!='World') %>%
ggplot(aes(x=confirmed, y=deaths, col=death.rate)) +
geom_text(aes(label=country), size=2.5, check_overlap=T, vjust=-0.8) +
geom_point() +
xlab('Total Confirmed') + ylab('Total Deaths') +
labs(col="Death Rate (%)") +
scale_color_gradient(low='#56B1F7', high='#132B43') +
scale_x_log10() + scale_y_log10()
plot2 <- df %>% filter(country!='World') %>%
ggplot(aes(x=new.confirmed, y=new.deaths, col=death.rate)) +
geom_text(aes(label=country), size=2.5, check_overlap=T, vjust=-0.7) +
geom_point() +
xlab('New Confirmed') + ylab('New Deaths') +
labs(col="Death Rate (%)") +
scale_color_gradient(low='#56B1F7', high='#132B43') +
scale_x_log10() + scale_y_log10()
grid.arrange(plot1, plot2, ncol=1)
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## convert from wide to long format, for drawing area plots
data.long <- data %>%
select(c(country, date, confirmed, remaining.confirmed, recovered, deaths)) %>%
gather(key=type, value=count, -c(country, date))
## set factor levels to show them in a desirable order
data.long %<>% mutate(type=recode_factor(type, confirmed='Confirmed',
remaining.confirmed='Remaining Confirmed',
recovered='Recovered',
deaths='Deaths'))
## plot: cases by type
df <- data.long %>% filter(country %in% top.countries) %<>%
mutate(country=country %>% factor(levels=c(top.countries)))
p <- df %>% filter(country != 'World') %>%
ggplot(aes(x=date, y=count)) + xlab('') + ylab('Count') +
theme(legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
plot.title=element_text(size=11),
axis.text.x=element_text(angle=45, hjust=1)) +
facet_wrap(~type, ncol=2, scales='free_y')
## area plot
plot1 <- p + geom_area(aes(fill=country)) +
labs(title=paste0('Cases around the World - ', max.date.txt))
## line plot and in log scale
linetypes <- rep(c("solid", "dashed", "dotted"), each=9)
colors <- rep(c('black', 'blue', 'red', 'green', 'orange', 'purple', 'yellow', 'grey'), 4)
plot2 <- p + geom_line(aes(color=country, linetype=country)) +
scale_linetype_manual(values=linetypes) +
scale_color_manual(values=colors) +
labs(title=paste0('Cases around the World - Log Scale - ', max.date.txt)) +
scale_y_continuous(trans='log10')
## show 2 plots together
grid.arrange(plot1, plot2, ncol=1)
## Warning: Transformation introduced infinite values in continuous y-axis
## plot: excluding China
p <- df %>% filter(!(country %in% c('World', 'China'))) %>%
ggplot(aes(x=date, y=count)) + xlab('') + ylab('Count') +
theme(legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
plot.title=element_text(size=11),
axis.text.x=element_text(angle=45, hjust=1)) +
facet_wrap(~type, ncol=2, scales='free_y')
p + geom_area(aes(fill=country)) +
labs(title=paste0('Cases around the World (excl. China) - ', max.date.txt))
## area plot
# plot1 <- p + geom_area(aes(fill=country)) +
# labs(title=paste0('Cases around the World (excl. China) - ', max.date.txt))
## line plot in log scale
# plot2 <- p + geom_line(aes(color=country, linetype=country)) +
# scale_linetype_manual(values=linetypes) +
# scale_color_manual(values=colors) +
# labs(title=paste0('Cases around the World (excl. China) - Log Scale - ', max.date.txt)) +
# scale_y_continuous(trans='log10')
# ## show 2 plots together
# grid.arrange(plot1, plot2, ncol=1)
## if Australia in not in top 20, add it in and remove 'Others'
if(!('Australia' %in% top.countries)) {
top.countries %<>% setdiff('Others') %>% c('Australia')
df <- data.long %>% filter(country %in% top.countries) %<>%
mutate(country=country %>% factor(levels=c(top.countries)))
}
## cases by country - area plot
df %>% filter(type != 'Confirmed') %>%
ggplot(aes(x=date, y=count, fill=type)) +
geom_area(alpha=0.5) +
# xlab('') + ylab('') +
labs(title=paste0('Numbers of COVID-19 Cases in Top 20 Countries (plus World and Australia) - ',
max.date.txt)) +
scale_fill_manual(values=c('red', 'green', 'black')) +
theme(legend.title=element_blank(), legend.position='bottom',
plot.title = element_text(size=9),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.key.size=unit(0.4, 'cm'),
# legend.text=element_text(size=7),
strip.text.x=element_text(size=7),
axis.text=element_text(size=7),
axis.text.x=element_text(angle=45, hjust=1)) +
facet_wrap(~country, ncol=4, scales='free_y')
## cases by country - line plot - log scale
p <- df %>% ggplot(aes(x=date, y=count, color=type)) +
geom_line() +
labs(title=paste0('Numbers of COVID-19 Cases in Top 20 Countries (plus World and Australia) - ',
max.date.txt)) +
scale_color_manual(values=c('purple', 'red', 'green', 'black')) +
theme(legend.title=element_blank(), legend.position='bottom',
plot.title = element_text(size=9),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.key.size=unit(0.4, 'cm'),
# legend.text=element_text(size=7),
strip.text.x=element_text(size=7),
axis.text=element_text(size=7),
axis.text.x=element_text(angle=45, hjust=1)) +
scale_y_continuous(trans='log10')
p + facet_wrap(~country, ncol=4, scales='free_y')
## Warning: Transformation introduced infinite values in continuous y-axis
## plot over multiple pages
# p + facet_wrap_paginate(~country, nrow=4, ncol=3, page=1, scales='free_y')
# p + facet_wrap_paginate(~country, nrow=4, ncol=3, page=2, scales='free_y')
# data %<>% filter(country=='China')
# data %<>% filter(country=='Australia')
data %<>% filter(country=='Brazil')
n <- nrow(data)
## remaining confirmed and its increase
plot1 <- ggplot(data, aes(x=date, y=remaining.confirmed)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='Remaining Confirmed Cases') +
theme(axis.text.x=element_text(angle=45, hjust=1))
plot2 <- ggplot(data, aes(x=date, y=new.confirmed)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='New Confirmed Cases') +
theme(axis.text.x=element_text(angle=45, hjust=1))
## show two plots side by side
grid.arrange(plot1, plot2, ncol=2)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## a scatter plot with a smoothed line and vertical x-axis labels
plot1 <- ggplot(data, aes(x=date, y=deaths)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='Accumulative Deaths') +
theme(axis.text.x=element_text(angle=45, hjust=1))
plot2 <- ggplot(data, aes(x=date, y=recovered)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='Accumulative Recovered Cases') +
theme(axis.text.x=element_text(angle=45, hjust=1))
plot3 <- ggplot(data, aes(x=date, y=new.deaths)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='New Deaths') +
theme(axis.text.x=element_text(angle=45, hjust=1))
plot4 <- ggplot(data, aes(x=date, y=new.recovered)) +
geom_point() + geom_smooth() +
xlab('') + ylab('Count') + labs(title='New Recovered Cases') +
theme(axis.text.x=element_text(angle=45, hjust=1))
## show four plots together, with 2 plots in each row
grid.arrange(plot1, plot2, plot3, plot4, nrow=2)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## three death rates
plot1 <- ggplot(data, aes(x=date)) +
geom_line(aes(y=rate.upper, colour='Upper bound')) +
geom_line(aes(y=rate.lower, colour='Lower bound')) +
geom_line(aes(y=rate.daily, colour='Daily')) +
xlab('') + ylab('Death Rate (%)') + labs(title='Overall') +
theme(legend.position='bottom', legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
axis.text.x=element_text(angle=45, hjust=1)) +
ylim(0, 90)
## focusing on last 2 weeks
plot2 <- ggplot(data[n-(14:0),], aes(x=date)) +
geom_line(aes(y=rate.upper, colour='Upper bound')) +
geom_line(aes(y=rate.lower, colour='Lower bound')) +
geom_line(aes(y=rate.daily, colour='Daily')) +
xlab('') + ylab('Death Rate (%)') + labs(title='Last two weeks') +
theme(legend.position='bottom', legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
axis.text.x=element_text(angle=45, hjust=1))
# + ylim(0, 10)
grid.arrange(plot1, plot2, ncol=2)
## Warning: Removed 54 row(s) containing missing values (geom_path).
## Warning: Removed 35 row(s) containing missing values (geom_path).
## Warning: Removed 57 row(s) containing missing values (geom_path).
## Warning: Removed 10 row(s) containing missing values (geom_path).
## Warning: Removed 10 row(s) containing missing values (geom_path).
## sort by date descendingly and re-order columns
data %<>% arrange(desc(date)) %>%
select(c(date, confirmed, deaths, recovered, remaining.confirmed,
new.confirmed, new.deaths, new.recovered, rate.lower, rate.upper, rate.daily))
## output as a table
data %>% kable('pandoc', booktabs=T, longtable=T, caption='Cases in the Whole World',
format.args=list(big.mark=',')) %>%
kable_styling(font_size=5, latex_options=c('striped', 'hold_position', 'repeat_header'))
## Warning in kable_styling(., font_size = 5, latex_options = c("striped", : Please
## specify format in kable. kableExtra can customize either HTML or LaTeX outputs.
## See https://haozhu233.github.io/kableExtra/ for details.
Cases in the Whole World
| 2020-03-20 |
793 |
11 |
2 |
780 |
172 |
5 |
0 |
1.4 |
84.6 |
100 |
| 2020-03-19 |
621 |
6 |
2 |
613 |
249 |
3 |
0 |
1.0 |
75.0 |
100 |
| 2020-03-18 |
372 |
3 |
2 |
367 |
51 |
2 |
0 |
0.8 |
60.0 |
100 |
| 2020-03-17 |
321 |
1 |
2 |
318 |
121 |
1 |
1 |
0.3 |
33.3 |
50 |
| 2020-03-16 |
200 |
0 |
1 |
199 |
38 |
0 |
1 |
0.0 |
0.0 |
0 |
| 2020-03-15 |
162 |
0 |
0 |
162 |
11 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-14 |
151 |
0 |
0 |
151 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-13 |
151 |
0 |
0 |
151 |
99 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-12 |
52 |
0 |
0 |
52 |
14 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-11 |
38 |
0 |
0 |
38 |
7 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-10 |
31 |
0 |
0 |
31 |
6 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-09 |
25 |
0 |
0 |
25 |
5 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-08 |
20 |
0 |
0 |
20 |
7 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-07 |
13 |
0 |
0 |
13 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-06 |
13 |
0 |
0 |
13 |
9 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-05 |
4 |
0 |
0 |
4 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-04 |
4 |
0 |
0 |
4 |
2 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-03 |
2 |
0 |
0 |
2 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-02 |
2 |
0 |
0 |
2 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-03-01 |
2 |
0 |
0 |
2 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-02-29 |
2 |
0 |
0 |
2 |
1 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-02-28 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-02-27 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-02-26 |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
0.0 |
NaN |
NaN |
| 2020-02-25 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-24 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-23 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-22 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-21 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-20 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-19 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-18 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-17 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-16 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-15 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-14 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-13 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-12 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-11 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-10 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-09 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-08 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-07 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-06 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-05 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-04 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-03 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-02 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-02-01 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-31 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-30 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-29 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-28 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-26 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-25 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-24 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-23 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
NaN |
NaN |
NaN |
| 2020-01-22 |
0 |
0 |
0 |
0 |
NA |
NA |
NA |
NaN |
NaN |
NA |
data.latest %>% arrange(desc(confirmed)) %>% select(-c(date, ranking)) %>%
kable('pandoc', booktabs=T, longtable=T, row.names=T,
caption=paste0('Cases by Country (', max.date.txt, ')'),
format.args=list(big.mark=',')) %>%
kable_styling(font_size=7, latex_options=c('striped', 'hold_position', 'repeat_header'))
## Warning in kable_styling(., font_size = 7, latex_options = c("striped", : Please
## specify format in kable. kableExtra can customize either HTML or LaTeX outputs.
## See https://haozhu233.github.io/kableExtra/ for details.
Cases by Country (20 março 2020)
| 1 |
World |
272,166 |
29,458 |
173,611 |
87,256 |
11,299 |
1,432 |
| 2 |
China |
81,250 |
94 |
6,731 |
71,266 |
3,253 |
4 |
| 3 |
Italy |
47,021 |
5,986 |
38,549 |
4,440 |
4,032 |
627 |
| 4 |
Spain |
20,410 |
2,447 |
17,779 |
1,588 |
1,043 |
213 |
| 5 |
Germany |
19,848 |
4,528 |
19,601 |
180 |
67 |
23 |
| 6 |
Iran |
19,644 |
1,237 |
11,466 |
6,745 |
1,433 |
149 |
| 7 |
US |
19,100 |
5,423 |
18,856 |
0 |
244 |
44 |
| 8 |
France |
12,726 |
1,779 |
12,264 |
12 |
450 |
207 |
| 9 |
Korea, South |
8,652 |
87 |
7,018 |
1,540 |
94 |
3 |
| 10 |
Switzerland |
5,294 |
1,219 |
5,225 |
15 |
54 |
13 |
| 11 |
United Kingdom |
4,014 |
1,298 |
3,769 |
67 |
178 |
40 |
| 12 |
Netherlands |
3,003 |
536 |
2,894 |
2 |
107 |
30 |
| 13 |
Austria |
2,388 |
375 |
2,373 |
9 |
6 |
0 |
| 14 |
Belgium |
2,257 |
462 |
2,219 |
1 |
37 |
16 |
| 15 |
Norway |
1,914 |
168 |
1,906 |
1 |
7 |
0 |
| 16 |
Sweden |
1,639 |
200 |
1,607 |
16 |
16 |
5 |
| 17 |
Denmark |
1,337 |
112 |
1,327 |
1 |
9 |
3 |
| 18 |
Malaysia |
1,030 |
130 |
940 |
87 |
3 |
1 |
| 19 |
Portugal |
1,020 |
235 |
1,009 |
5 |
6 |
3 |
| 20 |
Japan |
963 |
39 |
739 |
191 |
33 |
4 |
| 21 |
Canada |
943 |
143 |
922 |
9 |
12 |
3 |
| 22 |
Czechia |
833 |
139 |
829 |
4 |
0 |
0 |
| 23 |
Brazil |
793 |
172 |
780 |
2 |
11 |
5 |
| 24 |
Australia |
791 |
110 |
758 |
26 |
7 |
1 |
| 25 |
Cruise Ship |
712 |
0 |
380 |
325 |
7 |
0 |
| 26 |
Israel |
705 |
28 |
691 |
14 |
0 |
0 |
| 27 |
Ireland |
683 |
126 |
675 |
5 |
3 |
0 |
| 28 |
Pakistan |
501 |
47 |
485 |
13 |
3 |
1 |
| 29 |
Greece |
495 |
77 |
470 |
19 |
6 |
0 |
| 30 |
Luxembourg |
484 |
149 |
480 |
0 |
4 |
0 |
| 31 |
Qatar |
470 |
10 |
460 |
10 |
0 |
0 |
| 32 |
Finland |
450 |
50 |
440 |
10 |
0 |
0 |
| 33 |
Chile |
434 |
196 |
428 |
6 |
0 |
0 |
| 34 |
Poland |
425 |
70 |
419 |
1 |
5 |
0 |
| 35 |
Iceland |
409 |
79 |
404 |
5 |
0 |
0 |
| 36 |
Singapore |
385 |
40 |
261 |
124 |
0 |
0 |
| 37 |
Indonesia |
369 |
58 |
322 |
15 |
32 |
7 |
| 38 |
Ecuador |
367 |
168 |
362 |
0 |
5 |
2 |
| 39 |
Turkey |
359 |
167 |
355 |
0 |
4 |
1 |
| 40 |
Saudi Arabia |
344 |
70 |
336 |
8 |
0 |
0 |
| 41 |
Slovenia |
341 |
55 |
340 |
0 |
1 |
0 |
| 42 |
Thailand |
322 |
50 |
279 |
42 |
1 |
0 |
| 43 |
Romania |
308 |
31 |
283 |
25 |
0 |
0 |
| 44 |
Bahrain |
285 |
7 |
184 |
100 |
1 |
0 |
| 45 |
Egypt |
285 |
29 |
238 |
39 |
8 |
2 |
| 46 |
Estonia |
283 |
16 |
282 |
1 |
0 |
0 |
| 47 |
Russia |
253 |
54 |
243 |
9 |
1 |
0 |
| 48 |
India |
244 |
50 |
219 |
20 |
5 |
1 |
| 49 |
Peru |
234 |
0 |
230 |
1 |
3 |
3 |
| 50 |
Philippines |
230 |
13 |
204 |
8 |
18 |
1 |
| 51 |
Iraq |
208 |
16 |
142 |
49 |
17 |
4 |
| 52 |
South Africa |
202 |
52 |
202 |
0 |
0 |
0 |
| 53 |
Mexico |
164 |
46 |
159 |
4 |
1 |
0 |
| 54 |
Lebanon |
163 |
6 |
155 |
4 |
4 |
0 |
| 55 |
Kuwait |
159 |
11 |
141 |
18 |
0 |
0 |
| 56 |
San Marino |
144 |
25 |
126 |
4 |
14 |
3 |
| 57 |
United Arab Emirates |
140 |
0 |
107 |
31 |
2 |
2 |
| 58 |
Panama |
137 |
28 |
136 |
0 |
1 |
0 |
| 59 |
Slovakia |
137 |
14 |
136 |
0 |
1 |
0 |
| 60 |
Armenia |
136 |
21 |
135 |
1 |
0 |
0 |
| 61 |
Serbia |
135 |
32 |
133 |
1 |
1 |
1 |
| 62 |
Taiwan* |
135 |
27 |
107 |
26 |
2 |
1 |
| 63 |
Argentina |
128 |
31 |
122 |
3 |
3 |
0 |
| 64 |
Colombia |
128 |
26 |
127 |
1 |
0 |
0 |
| 65 |
Croatia |
128 |
23 |
122 |
5 |
1 |
0 |
| 66 |
Bulgaria |
127 |
33 |
124 |
0 |
3 |
0 |
| 67 |
Latvia |
111 |
25 |
110 |
1 |
0 |
0 |
| 68 |
Uruguay |
94 |
15 |
94 |
0 |
0 |
0 |
| 69 |
Vietnam |
91 |
6 |
75 |
16 |
0 |
0 |
| 70 |
Algeria |
90 |
3 |
47 |
32 |
11 |
2 |
| 71 |
Bosnia and Herzegovina |
89 |
26 |
87 |
2 |
0 |
0 |
| 72 |
Costa Rica |
89 |
20 |
88 |
0 |
1 |
0 |
| 73 |
Hungary |
85 |
12 |
80 |
2 |
3 |
2 |
| 74 |
Jordan |
85 |
16 |
84 |
1 |
0 |
0 |
| 75 |
Brunei |
78 |
3 |
77 |
1 |
0 |
0 |
| 76 |
Morocco |
77 |
14 |
73 |
1 |
3 |
1 |
| 77 |
Andorra |
75 |
22 |
74 |
1 |
0 |
0 |
| 78 |
Sri Lanka |
73 |
13 |
70 |
3 |
0 |
0 |
| 79 |
Dominican Republic |
72 |
38 |
70 |
0 |
2 |
0 |
| 80 |
Albania |
70 |
6 |
68 |
0 |
2 |
0 |
| 81 |
Belarus |
69 |
18 |
64 |
5 |
0 |
0 |
| 82 |
Cyprus |
67 |
0 |
67 |
0 |
0 |
0 |
| 83 |
North Macedonia |
67 |
19 |
66 |
1 |
0 |
0 |
| 84 |
Moldova |
66 |
17 |
64 |
1 |
1 |
0 |
| 85 |
Malta |
64 |
11 |
62 |
2 |
0 |
0 |
| 86 |
Tunisia |
54 |
15 |
53 |
0 |
1 |
0 |
| 87 |
Cambodia |
51 |
14 |
50 |
1 |
0 |
0 |
| 88 |
Kazakhstan |
49 |
5 |
46 |
0 |
3 |
3 |
| 89 |
Lithuania |
49 |
13 |
48 |
1 |
0 |
0 |
| 90 |
Oman |
48 |
0 |
36 |
12 |
0 |
0 |
| 91 |
Azerbaijan |
44 |
0 |
37 |
6 |
1 |
0 |
| 92 |
Georgia |
43 |
3 |
42 |
1 |
0 |
0 |
| 93 |
Venezuela |
42 |
0 |
42 |
0 |
0 |
0 |
| 94 |
Burkina Faso |
40 |
7 |
39 |
0 |
1 |
0 |
| 95 |
New Zealand |
39 |
11 |
39 |
0 |
0 |
0 |
| 96 |
Senegal |
38 |
7 |
36 |
2 |
0 |
0 |
| 97 |
Uzbekistan |
33 |
10 |
33 |
0 |
0 |
0 |
| 98 |
Martinique |
32 |
9 |
31 |
0 |
1 |
0 |
| 99 |
Ukraine |
29 |
13 |
26 |
0 |
3 |
1 |
| 100 |
Liechtenstein |
28 |
0 |
28 |
0 |
0 |
0 |
| 101 |
Afghanistan |
24 |
2 |
23 |
1 |
0 |
0 |
| 102 |
Honduras |
24 |
12 |
24 |
0 |
0 |
0 |
| 103 |
Bangladesh |
20 |
3 |
16 |
3 |
1 |
0 |
| 104 |
Cameroon |
20 |
7 |
20 |
0 |
0 |
0 |
| 105 |
Congo (Kinshasa) |
18 |
4 |
18 |
0 |
0 |
0 |
| 106 |
Rwanda |
17 |
9 |
17 |
0 |
0 |
0 |
| 107 |
Cuba |
16 |
5 |
15 |
0 |
1 |
0 |
| 108 |
Ghana |
16 |
5 |
16 |
0 |
0 |
0 |
| 109 |
Jamaica |
16 |
1 |
13 |
2 |
1 |
0 |
| 110 |
Bolivia |
15 |
3 |
15 |
0 |
0 |
0 |
| 111 |
Montenegro |
14 |
11 |
14 |
0 |
0 |
0 |
| 112 |
Maldives |
13 |
0 |
13 |
0 |
0 |
0 |
| 113 |
Paraguay |
13 |
2 |
13 |
0 |
0 |
0 |
| 114 |
Guatemala |
12 |
3 |
11 |
0 |
1 |
0 |
| 115 |
Mauritius |
12 |
9 |
12 |
0 |
0 |
0 |
| 116 |
Nigeria |
12 |
4 |
11 |
1 |
0 |
0 |
| 117 |
Monaco |
11 |
4 |
11 |
0 |
0 |
0 |
| 118 |
Cote d’Ivoire |
9 |
0 |
8 |
1 |
0 |
0 |
| 119 |
Ethiopia |
9 |
3 |
9 |
0 |
0 |
0 |
| 120 |
Togo |
9 |
8 |
8 |
1 |
0 |
0 |
| 121 |
Trinidad and Tobago |
9 |
0 |
9 |
0 |
0 |
0 |
| 122 |
Guyana |
7 |
0 |
6 |
0 |
1 |
0 |
| 123 |
Kenya |
7 |
0 |
7 |
0 |
0 |
0 |
| 124 |
Seychelles |
7 |
1 |
7 |
0 |
0 |
0 |
| 125 |
Equatorial Guinea |
6 |
0 |
6 |
0 |
0 |
0 |
| 126 |
Kyrgyzstan |
6 |
3 |
6 |
0 |
0 |
0 |
| 127 |
Mongolia |
6 |
0 |
6 |
0 |
0 |
0 |
| 128 |
Tanzania |
6 |
0 |
6 |
0 |
0 |
0 |
| 129 |
Barbados |
5 |
0 |
5 |
0 |
0 |
0 |
| 130 |
Suriname |
4 |
3 |
4 |
0 |
0 |
0 |
| 131 |
Bahamas, The |
3 |
0 |
3 |
0 |
0 |
0 |
| 132 |
Central African Republic |
3 |
2 |
3 |
0 |
0 |
0 |
| 133 |
Congo (Brazzaville) |
3 |
0 |
3 |
0 |
0 |
0 |
| 134 |
Gabon |
3 |
2 |
2 |
0 |
1 |
1 |
| 135 |
Madagascar |
3 |
3 |
3 |
0 |
0 |
0 |
| 136 |
Namibia |
3 |
0 |
3 |
0 |
0 |
0 |
| 137 |
Benin |
2 |
0 |
2 |
0 |
0 |
0 |
| 138 |
Bhutan |
2 |
1 |
2 |
0 |
0 |
0 |
| 139 |
Haiti |
2 |
2 |
2 |
0 |
0 |
0 |
| 140 |
Kosovo |
2 |
0 |
2 |
0 |
0 |
0 |
| 141 |
Liberia |
2 |
0 |
2 |
0 |
0 |
0 |
| 142 |
Mauritania |
2 |
0 |
2 |
0 |
0 |
0 |
| 143 |
Saint Lucia |
2 |
0 |
2 |
0 |
0 |
0 |
| 144 |
Sudan |
2 |
0 |
1 |
0 |
1 |
0 |
| 145 |
Zambia |
2 |
0 |
2 |
0 |
0 |
0 |
| 146 |
Angola |
1 |
1 |
1 |
0 |
0 |
0 |
| 147 |
Antigua and Barbuda |
1 |
0 |
1 |
0 |
0 |
0 |
| 148 |
Cabo Verde |
1 |
1 |
1 |
0 |
0 |
0 |
| 149 |
Chad |
1 |
0 |
1 |
0 |
0 |
0 |
| 150 |
Djibouti |
1 |
0 |
1 |
0 |
0 |
0 |
| 151 |
El Salvador |
1 |
0 |
1 |
0 |
0 |
0 |
| 152 |
Eswatini |
1 |
0 |
1 |
0 |
0 |
0 |
| 153 |
Fiji |
1 |
0 |
1 |
0 |
0 |
0 |
| 154 |
Gambia, The |
1 |
0 |
1 |
0 |
0 |
0 |
| 155 |
Guinea |
1 |
0 |
1 |
0 |
0 |
0 |
| 156 |
Holy See |
1 |
0 |
1 |
0 |
0 |
0 |
| 157 |
Nepal |
1 |
0 |
0 |
1 |
0 |
0 |
| 158 |
Nicaragua |
1 |
0 |
1 |
0 |
0 |
0 |
| 159 |
Niger |
1 |
1 |
1 |
0 |
0 |
0 |
| 160 |
Papua New Guinea |
1 |
1 |
1 |
0 |
0 |
0 |
| 161 |
Saint Vincent and the Grenadines |
1 |
0 |
1 |
0 |
0 |
0 |
| 162 |
Somalia |
1 |
0 |
1 |
0 |
0 |
0 |
| 163 |
Zimbabwe |
1 |
1 |
1 |
0 |
0 |
0 |