The spread of COVID-19 across countries visualization with R
This article is originally published at https://analyzecore.com
COVID-19 or Coronavirus pandemic has a huge and unpredictable effect on our lives. I wanted to see the speed and spreading of the virus across countries. And the following is what and how I’ve seen:
COVID-19 or Coronavirus pandemic has a huge and unpredictable effect on our lives. I wanted to see the speed and spreading of the virus across countries. And the following is what and how I’ve seen:
The animated visualization focuses on the chronology of virus distribution that started in China and spread globally. For strengthening a visual effect I placed countries (top 90 of all) in two semidiagonals, based on the date when each country reached the peak daily cases of the disease (dark red grid).
For a more detailed analysis, I’ve created two stationary charts. The first is the same as the animated one but countries are ordered from bottom to top.
The second centered on a day of maximum amount cases and shows how long and intensive were previous and next stages. It gives an opportunity to compare the effectiveness of different countries.
All values of new cases for each country were normalized via min/max normalization and ranged from 0 to 1. You can use the following R code with comments to play with the public dataset:
library(tidyverse)
library(reshape2)
library(purrrlyr)
# download dataset
df <- read_csv(url('https://covid.ourworldindata.org/data/ecdc/full_data.csv'))
# normalization function
fun_normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
# preprocess data
df_prep <- df %>%
filter(location != 'World') %>%
group_by(location) %>%
# remove earlier dates
filter(date > as.Date('2020-01-15', format = '%Y-%m-%d')) %>%
# remove coutries with less than 1000 total cases
filter(max(total_cases) > 1000) %>%
# replace negative values with the mean
mutate(new_cases = ifelse(new_cases < 0,
round((lag(new_cases, default = 0) + lead(new_cases, default = 0)) / 2),
new_cases)) %>%
ungroup() %>%
select(location, date, new_cases) %>%
# prepare data for normalization
dcast(., date ~ location, value.var = 'new_cases') %>%
# replace NAs with 0
dmap_at(c(2:ncol(.)), function(x) ifelse(is.na(x), 0, x)) %>%
# normalization
dmap_at(c(2:ncol(.)), function(x) fun_normalize(x)) %>%
melt(., id.vars = c('date'), variable.name = 'country') %>%
mutate(value = round(value, 6))
# define countries order for plots
country_ord_1 <- df_prep %>%
group_by(country) %>%
filter(value == 1) %>%
ungroup() %>%
arrange(date, country) %>%
distinct(country) %>%
mutate(is_odd = ifelse((row_number() - 1) %% 2 == 0, TRUE, FALSE))
country_ord_anim <- bind_rows(country_ord_1 %>%
filter(is_odd == TRUE) %>%
arrange(desc(row_number())),
country_ord_1 %>%
filter(is_odd == FALSE))
# data for animated plot
df_plot_anim <- df_prep %>%
mutate(country = factor(country, levels = c(as.character(country_ord_anim$country)))) %>%
group_by(country) %>%
mutate(first_date = min(date[value >= 0.03])) %>%
mutate(cust_label = ifelse(date >= first_date, as.character(country), '')) %>%
ungroup()
# color palette
cols <- c('#e7f0fa','#c9e2f6', '#95cbee', '#0099dc', '#4ab04a', '#ffd73e', '#eec73a', '#e29421', '#e29421', '#f05336', '#ce472e')
# Animated Heatmap plot
p <- ggplot(df_plot_anim, aes(y = country, x = date, fill = value)) +
theme_minimal() +
geom_tile(color = 'white', width = .9, height = .9) +
scale_fill_gradientn(colours = cols, limits = c(0, 1),
breaks = c(0, 1),
labels = c('0', 'max'),
guide = guide_colourbar(ticks = T, nbin = 50, barheight = .5, label = T, barwidth = 10)) +
geom_text(aes(x = first_date, label = cust_label), size = 3, color = '#797D7F') +
scale_y_discrete(position = 'right') +
coord_equal() +
theme(legend.position = 'bottom',
legend.direction = 'horizontal',
plot.title = element_text(size = 20, face = 'bold', vjust = 2, hjust = 0.5),
axis.text.x = element_text(size = 8, hjust = .5, vjust = .5, face = 'plain'),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle('The spread of COVID-19 across countries: new daily cases normalized to location maximum')
# animated chart
library(gganimate)
library(gifski)
anim <- p +
transition_components(date) +
ggtitle('The spread of COVID-19 across countries: new daily cases normalized to location maximum',
subtitle = 'Date {frame_time}') +
shadow_mark()
animate(anim,
nframes = as.numeric(difftime(max(df_plot_anim$date), min(df_plot_anim$date), units = 'days')) + 1,
duration = 12,
fps = 12,
width = 1000,
height = 840,
start_pause = 5,
end_pause = 25,
renderer = gifski_renderer())
anim_save('covid-19.gif')
# Heatmap plot 1
df_plot_1 <- df_prep %>%
mutate(country = factor(country, levels = c(as.character(country_ord_1$country)))) %>%
group_by(country) %>%
mutate(first_date = min(date[value >= 0.03])) %>%
ungroup()
ggplot(df_plot_1, aes(y = country, x = date, fill = value)) +
theme_minimal() +
geom_tile(color = 'white', width = .9, height = .9) +
scale_fill_gradientn(colours = cols, limits = c(0, 1),
breaks = c(0, 1),
labels = c('0', 'max'),
guide = guide_colourbar(ticks = T, nbin = 50, barheight = .5, label = T, barwidth = 10)) +
geom_text(aes(x = first_date, label = country), size = 3, color = '#797D7F') +
scale_y_discrete(position = 'right') +
coord_equal() +
theme(legend.position = 'bottom',
legend.direction = 'horizontal',
plot.title = element_text(size = 20, face = 'bold', vjust = 2, hjust = 0.5),
axis.text.x = element_text(size = 8, hjust = .5, vjust = .5, face = 'plain'),
axis.text.y = element_text(size = 6, hjust = .5, vjust = .5, face = 'plain'),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle('The spread of COVID-19 across countries: new daily cases normalized to location maximum')
# Heatmap plot 2
df_plot_2 <- df_prep %>%
group_by(country) %>%
filter(date >= min(date[value > 0])) %>%
arrange(date, .by_group = TRUE) %>%
mutate(centr_day = min(row_number()[value == 1]),
n_day = row_number() - centr_day) %>%
ungroup()
country_ord_2 <- df_plot_2 %>%
group_by(country) %>%
filter(date >= min(date[value == 1])) %>%
summarise(value = sum(value)) %>%
ungroup() %>%
arrange(value, country) %>%
distinct(country)
df_plot_2 <- df_plot_2 %>%
mutate(country = factor(country, levels = c(as.character(country_ord_2$country)))) %>%
group_by(country) %>%
mutate(first_date = min(n_day[value >= 0.01])) %>%
ungroup()
# Heatmap plot 2
ggplot(df_plot_2, aes(y = country, x = n_day, fill = value)) +
theme_minimal() +
geom_tile(color = 'white', width = .9, height = .9) +
scale_fill_gradientn(colours = cols, limits = c(0, 1),
breaks = c(0, 1),
labels = c('0', 'max'),
guide = guide_colourbar(ticks = T, nbin = 50, barheight = .5, label = T, barwidth = 10)) +
geom_text(aes(x = first_date, label = country), size = 3, color = '#797D7F') +
coord_equal() +
theme(legend.position = 'bottom',
legend.direction = 'horizontal',
plot.title = element_text(size = 20, face = 'bold', vjust = 2, hjust = 0.5),
axis.text.x = element_text(size = 8, hjust = .5, vjust = .5, face = 'plain'),
#axis.text.y = element_text(size = 6, hjust = .5, vjust = .5, face = 'plain'),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
ggtitle('Comparison of different countries effectiveness against COVID-19
(new daily cases normalized to location maximum and data centered on a day with maximum new cases)')
The post The spread of COVID-19 across countries visualization with R appeared first on AnalyzeCore by Sergey Bryl' - data is beautiful, data is a story.
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This article is originally published at https://analyzecore.com
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