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Covid19_VS_VisualAnalytics_Timeseries_Data.Rmd
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Covid19_VS_VisualAnalytics_Timeseries_Data.Rmd
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---
title: "Covid19_VS_VisualAnalytics_Timeseries_Data"
author: "V.Srinivas"
date: "06/05/2020"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r }
# Load the required packages
library(ggplot2)
library(dplyr)
library(tidyverse)
library(caret)
library(scales)
```
```{r }
# Read datasets/confirmed_cases_worldwide.csv into confirmed_cases_worldwide
covid_ds <- data.table::fread('https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv', drop='province') %>%
mutate (date=as.Date(date)) %>%
select(-lat, -long)
```
```{r }
# View info about the dataset
glimpse(covid_ds)
str(covid_ds)
tail(covid_ds,20)
dim(covid_ds)
covid_ds$type[covid_ds$type == 'death'] <-'dead'
# Convert negative cases to positive assuming it is typo error
#covid_ds$cases = abs(covid_ds$cases)
#covid_ds %>% filter(cases < 0) %>% select(country,cases, type)
```
```{r }
# compute daily cases for all cuntries
daily_cases <- covid_ds %>%
group_by(date, country, type) %>%
summarize (daily_cases = sum(cases)) %>%
ungroup()
```
```{r }
# Totals so far worldwide
totals <- covid_ds %>%
group_by(type) %>%
summarize(total=sum(cases))
totals %>%
ggplot(aes(type, total, fill=type)) +
geom_col() +
scale_y_continuous(labels=comma)+
geom_text(aes(label=total), vjust=-.5) +
labs(subtitle="Total cases world wide")
```
```{r }
# Total daily cases across the globe
tot_daily_all_countries <- covid_ds %>%
group_by(date, type) %>%
mutate (total_daily_cases = sum(cases)) %>%
arrange(desc(total_daily_cases)) %>%
select(date, type, total_daily_cases)
tot_daily_all_countries %>%
filter(total_daily_cases == max(total_daily_cases)) %>%
head(1)
```
```{r }
tot_daily_all_countries %>% ggplot(aes(date,total_daily_cases, col=type)) +
geom_point()+
geom_line() +
facet_wrap(~type)+
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Total daily cases across all nations")
```
```{r }
# Confirmed cumulative cases country_wise
confirmed <- daily_cases %>%
filter(type=='confirmed') %>%
select(-type) %>%
group_by(country) %>%
mutate(cum_c_cases= cumsum(daily_cases)) %>%
select(-daily_cases) %>%
ungroup()
```
```{r }
# Recovered cumulative cases
recovered <- daily_cases %>%
filter(type=='recovered') %>%
select(-type) %>%
group_by(country) %>%
mutate(cum_r_cases= cumsum(daily_cases)) %>%
select(-daily_cases) %>%
ungroup()
```
```{r }
# dead cumulative cases
dead <- daily_cases %>%
filter(type=='dead') %>%
select(-type) %>%
group_by(country) %>%
mutate(cum_d_cases= cumsum(daily_cases)) %>%
select(-daily_cases) %>%
ungroup()
```
```{r }
# Combine cumulative cases of all 3 categories
all_cases <- confirmed %>%
inner_join(recovered, by=c('date', 'country')) %>%
inner_join(dead, by=c('date', 'country')) %>%
mutate(country=as.factor(country))
# All cumultaive cases, all countries on all dates with all 3 categories - Top 50
all_cases %>% arrange(desc(date,cum_c_cases)) %>% head(50)
```
```{r }
# All cumulative cases of all countries on all dates
countries_cases <- all_cases %>%
rename(confirmed=cum_c_cases, recovered=cum_r_cases, dead=cum_d_cases) %>%
gather(type, cum_cases, confirmed:dead) %>%
mutate(type=as.factor(type)) %>% arrange(desc(date))
```
```{r }
# Select, major hit nations (top 30)
top_20_countries <- covid_ds %>% group_by(country) %>% summarize(total_cases=sum(cases)) %>% arrange(desc(total_cases)) %>% head(30)
select_countries <- top_20_countries$country
```
```{r, fig.height=10 }
countries_cases %>%
filter(country %in% select_countries) %>%
ggplot(aes(country, cum_cases, fill=type)) +
geom_col( position = position_dodge(1)) +
scale_y_continuous(labels = comma)+
coord_flip() +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle="Total numbers across select nations")
```
```{r }
# Cumulative cases by date and type
countries_cases_all <- countries_cases %>%
group_by(date, type) %>%
mutate(tot_cum_cases = sum(cum_cases)) %>%
arrange(desc(tot_cum_cases))
# Cumulative cases by date and type - Top 50
cum_cases_all <- countries_cases_all %>%
group_by(date, type) %>%
top_n(1, wt = cum_cases) %>%
ungroup()
```
```{r }
cum_cases_all %>%
select(date, type, tot_cum_cases) %>%
arrange(desc(date, type)) %>% head(50)
```
```{r }
cum_cases_all %>%
ggplot(aes(date, tot_cum_cases, col=type)) +
geom_line(size=1) +
scale_y_continuous(label=comma) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Cumulative number progression across all nations")
```
```{r }
# Cases in select, major hot nations
select_cases <- countries_cases %>%
filter(country %in% select_countries)
```
```{r }
select_cases %>%
ggplot(aes(date, cum_cases, col=country)) +
geom_line(size=1) +
scale_y_continuous(label=comma) +
facet_grid(~type) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Cumulative number progression across major hit nations")
```
```{r }
# Compare China and USA
china_vs_usa <- countries_cases %>%
filter(country %in% c('China', 'US'))
china_vs_usa %>%
ggplot(aes(date, cum_cases, col=country)) +
geom_line(size=1) +
scale_y_continuous(label=comma) +
facet_grid(~type) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" China vs USA cumulative progression")
```
```{r }
# non-China and non_USA cases (non-extreme case countries)
non_china_usa_cases <- countries_cases %>%
filter(country %in% select_countries & country !='China' & country != 'US')
non_china_usa_cases %>%
ggplot(aes(date, cum_cases, col=country)) +
geom_line(size=1) +
facet_grid(~type) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle="Non_china, Non_USA cumulative number progression")
```
```{r }
# Cases in india
india_cases <- countries_cases %>%
filter(country %in% c('India')) %>%
select(-country)
india_cases %>% arrange(desc(date)) %>% head(50)
india_cases %>% group_by(type) %>% top_n(1) %>% rename(total_cases = cum_cases) %>%
ggplot(aes(type, total_cases, fill=type)) +
geom_col() +
geom_text(aes(label = total_cases), vjust = -0.5) +
labs(subtitle=" Total cases in India")
india_cases %>%
ggplot(aes(date, cum_cases, col=type)) +
geom_line(size=1) +
facet_grid(~ type) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Cumulative case progression in India")
```
```{r }
# Total daily cases in India
tot_d_india_cases <- covid_ds %>%
filter(country=='India') %>%
group_by(date, type) %>% mutate (total_daily_cases = sum(cases)) %>%
arrange(desc(total_daily_cases))%>%
select(date, type, total_daily_cases)
tot_d_india_cases %>% ggplot(aes(date,total_daily_cases, col=type)) +
geom_point()+
geom_line() +
facet_wrap(~type)+
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Total daily cases in India")
```
```{r }
# Cases in Oman & UAE
oman_vs_uae <- countries_cases %>%
filter(country %in% c('Oman', 'United Arab Emirates'))
oman_vs_uae %>% group_by(country,type) %>% top_n(1) %>% rename(total_cases = cum_cases) %>%
ggplot(aes(country, total_cases, fill=type)) +
geom_col(position = position_dodge(1)) +
geom_text(aes(label = total_cases),position = position_dodge(1), vjust = -0.5) +
labs(subtitle=" Total cases in Oman & UAE")
```
```{r }
oman_vs_uae %>% ggplot(aes(date, cum_cases, col=country)) +
geom_line(size=1) +
facet_grid(~type) +
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Oman vs UAE cumulative progression")
```
```{r }
# Total daily cases across the globe
oman_vs_uae_daily <- covid_ds %>%
filter(country=='Oman' | country == "United Arab Emirates") %>%
group_by(date,country, type) %>% mutate (total_daily_cases = sum(cases)) %>%
arrange(desc(total_daily_cases))%>%
select(date, country, type, total_daily_cases)
```
```{r }
oman_vs_uae_daily %>% ggplot(aes(date,total_daily_cases, col=country)) +
geom_point()+
geom_line() +
facet_wrap(~type)+
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "top") +
labs(subtitle=" Total daily cases in Oman & UAE")
```
```{r }
# Number of people dead for every 100 people recovered in the top 30 hits countries
percents <- covid_ds %>%
select(country, type, cases) %>%
group_by(country, type) %>%
summarize(tot_cases= sum(cases)) %>%
spread(type, tot_cases) %>%
summarize(recovery_percent= 100*sum(recovered)/sum(confirmed),
dead_percent= 100*sum(dead)/sum(confirmed),
dead_to_recovery= 100*sum(dead)/sum(recovered)) %>%
gather(type, percentage, recovery_percent:dead_to_recovery)
```
```{r }
# Plot Number of people dead for every 100 people recovered in select countries
percents %>%
filter(country %in% select_countries) %>%
ggplot(aes(country, percentage, fill=country)) +
geom_col()+
facet_grid(type~.)+
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "none") +
labs(subtitle="% of dead, recoveries and dead to recoveries")
```
```{r }
# Plot Number of people dead for every 100 people recovered in select countries, excluding UK and Netherlands
percents %>%
filter(country %in% select_countries & country != 'United Kingdom' & country != 'Netherlands') %>%
ggplot(aes(country, percentage, fill=country)) +
geom_col()+
facet_grid(type~.)+
theme(axis.text.x = element_text(
angle = 90,
size = 8,
hjust = 1
),
legend.position = "none") +
labs(subtitle="% of dead, recoveries and dead to recoveries")
```