R_Covid-19_Data Analysis and Exploratory Data Analysis

Shoaib nadaf | Learner

What is Coronavirus

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19.

What are the symptoms of COVID-19?

The most common symptoms of COVID-19 are fever, tiredness, and dry cough. Some patients may have aches and pains, nasal congestion, runny nose, sore throat or diarrhea. These symptoms are usually mild and begin gradually. Some people become infected but don’t develop any symptoms and don’t feel unwell. Most people (about 80%) recover from the disease without needing special treatment. Around 1 out of every 6 people who gets COVID-19 becomes seriously ill and develops difficulty breathing. Older people, and those with underlying medical problems like high blood pressure, heart problems or diabetes, are more likely to develop serious illness. People with fever, cough and difficulty breathing should seek medical attention

Covid Data Import

This is where we get the Covid-19 Data : https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases

#install.packages(“httr”) #install.packages(“readxl”)

Install packages:

install.packages(“httr”) install.packages(“readxl”) install.packages(“kableExtra”) install.packages(“RCurl”) install.packages(“ggplot2”)

Loading the library and Download The Data from above mentioned website :

#rm(list=ls())
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#these libraries need to be loaded
library(utils)
library(httr)
#library(readxl)
covid_df <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", na.strings = "", fileEncoding = "UTF-8-BOM")
GET("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", authenticate(":",":",type = "ntlm")
    , write_disk(tf <- tempfile(fileext = ".csv")))

## Response [https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/]
##   Date: 2020-10-06 06:24
##   Status: 200
##   Content-Type: application/octet-stream
##   Size: 3.24 MB
## <ON DISK>  C:\Users\Irshad\AppData\Local\Temp\RtmpmOiJNP\file5d438c86e2c.csv

covid_df <- read.csv((tf))
head(covid_df ,3)

#write.csv(tf)

World Exploratory Data :

covid_world =covid_df
#india_cases
covid_world$dateRep<-as.Date.character(covid_world$dateRep,format = c("%d/%m/%y"))
covid_world

info_cov_world<-arrange(covid_world,dateRep) %>% group_by(dateRep) %>% summarize(cases=sum(cases),deaths=sum(deaths))
ggplot(info_cov_world,aes(x=dateRep))+geom_line(aes(y=cases,color="Cases"), size=1)+theme_bw() +ylab("Total Count")+xlab("Period")+ labs(title="Cummulative Count of Covid19 Cases",color = "Legend")+scale_color_manual(values = c("gold","red"))

ggplot(info_cov_world,aes(x=dateRep)) + geom_line(aes(y=deaths,color="Death"), size=1) +labs(title = "Cummulative Count of Covid19 Deaths")

#Covid 19 Data cleanUp

names(covid_df)

##  [1] "dateRep"                                                   
##  [2] "day"                                                       
##  [3] "month"                                                     
##  [4] "year"                                                      
##  [5] "cases"                                                     
##  [6] "deaths"                                                    
##  [7] "countriesAndTerritories"                                   
##  [8] "geoId"                                                     
##  [9] "countryterritoryCode"                                      
## [10] "popData2019"                                               
## [11] "continentExp"                                              
## [12] "Cumulative_number_for_14_days_of_COVID.19_cases_per_100000"

Covid Explore

Cases :

Covid_cases_total <- sum(covid_df$cases)
Covid_cases_total

## [1] 35247104

There have been 35247104 Cases in the world since 2020-10-06

Deaths :

Covid_deaths_total <- sum(covid_df$deaths)
Covid_deaths_total

## [1] 1038069

There have been 1038069 Deaths in the world since 2020-10-06

The features we have are : dateRep, day, month, year, cases, deaths, countriesAndTerritories, geoId, countryterritoryCode, popData2019, continentExp, Cumulative_number_for_14_days_of_COVID.19_cases_per_100000

Use this method (1)

library(dplyr) library(kableExtra) covid_df %>% group_by(countriesAndTerritories) %>% mutate(deathRate = sum (covid_df\(deaths)/sum(covid_df\)cases)) kable(covid_df) %>% kable_paper(bootstrap_options = “striped”, full_width = F)

# library(dplyr)
# library(kableExtra)
# covid_df %>% group_by(`countriesAndTerritories`) %>% mutate(deathRate = sum (covid_df$deaths)/sum(covid_df$cases))
# kbl(covid_df) %>%
#   kable_paper(bootstrap_options = "striped", full_width = F)

Use this method (2)

library(DT)

#r_covid_df <- covid_df %>% head(100)
datatable(
  covid_df ,extensions = "Buttons" ,options = list(dom = "Bfrtip" , buttons =  c('copy' ,'csv','excel','pdf','print'))
)

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

Graphs

Graph A: No Of Deaths In CountriesAndTerritories

No Of Deaths In CountriesAndTerritories

library(ggplot2)
library(dplyr)
#covid_df %>% filter(deaths > 0) %>% View()
covid_df %>% filter(deaths > 100) %>% 
  group_by(countriesAndTerritories) %>% summarise(Cases_Total = sum(cases) , deaths_Total = sum(deaths)) %>%
  ggplot(aes(countriesAndTerritories))+ #geom_point(aes(x = Cases_Total ) ,color = "yellow") +
  geom_point(aes(y = deaths_Total ), color = "red") + coord_flip()

Graph B : No Of Cases In CountriesAndTerritories:

No Of Cases In CountriesAndTerritories

library(ggplot2)
library(dplyr)
#covid_df %>% filter(deaths > 0) %>% View()
covid_df %>% filter(deaths > 100 ) %>% 
  group_by(countriesAndTerritories) %>% summarise(Cases_Total = sum(cases)) %>%
  ggplot(aes(countriesAndTerritories))+ #geom_point(aes(x = Cases_Total ) ,color = "yellow") +
  geom_point(aes(y = Cases_Total )) + coord_flip()

head(covid_df)

Graph C : Deaths VS Cases (Global)

Scatter Plot Deaths VS Cases (Global)

library(ggplot2)
deaths_ = covid_df$deaths
cases_ = covid_df$cases
scatter_cases_deaths <- ggplot(covid_df, aes(x = cases , y = deaths , color = month)) + 
  geom_point()

scatter_cases_deaths + labs(title = "Scatter Plot  Deaths VS Cases")

Graph D :

Cases VS continentExp

library(ggplot2)
scatter_cases_conti <-  ggplot(covid_df , aes(x = cases , y = continentExp , color = month))+ 
  geom_point() 

scatter_cases_conti +labs(title = "Cases VS continentExp")   

  #  ggplot(covid_df , aes(x = deaths , y = continentExp , color = month))+ 
  # geom_point() 

Deaths VS continentExp

library(ggplot2)

scatter_deaths_conti   <- ggplot(covid_df , aes(x = deaths , y = continentExp , color = month))+ 
  geom_point() 
scatter_deaths_conti +labs(title = "Deaths VS continentExp")

Plots :

plot(covid_df$cases,covid_df$deaths , xlab = "Cases", ylab = "Deaths" , 
        main = "Cases VS Deaths")

barplot(covid_df$cases,covid_df$deaths , xlab = "Cases", ylab = "Deaths" , 
        main = "Cases VS Deaths")

#plot(covid_df$cases, dnorm(covid_df$cases,covid_df$deaths),type="b", lwd=2, col="red")
#abline(h=0, col="green2")
#abline(v=0, col="blue")

Daily Report(India) :

#head(covid_df)
india_cases =covid_df[grep("India", covid_df$countriesAndTerritories),]
#india_cases
india_cases$dateRep<-as.Date.character(india_cases$dateRep,format = c("%d/%m/%y"))
india_cases

info_cov_india1<-arrange(india_cases,dateRep)%>%group_by(dateRep)%>% summarize(cured=sum(cases),deaths=sum(deaths))


ggplot(info_cov_india1,aes(x=dateRep))+ geom_line(aes(y=cured,color="Cases"), size=1.5)+theme_bw() +ylab("Total Count")+xlab("Period")+ labs(title="Cummulative Count of Covid19 Cases",color = "Legend")+scale_color_manual(values = c("gold","red"))

ggplot(info_cov_india1,aes(x=dateRep))+ geom_line(aes(y=deaths,color="Death"), size=1.5)+labs(title = "Cummulative Count of Covid19 Deaths")

#dev.off()

library(DT)

#r_covid_df <- covid_df %>% head(100)
datatable(
  india_cases,extensions = "Buttons" ,options = list(dom = "Bfrtip" , buttons =  c('copy' ,'csv','excel','pdf','print'))
)

daily= head(india_cases,1)
daily

#Daily active cases in India

cat(daily$day,"/",daily$month,"/",daily$year,"reported",daily$cases,"Active Cases in India","\n")

## 5 / 10 / 2020 reported 74442 Active Cases in India

#Daily Decreased in India

cat(daily$day,"/",daily$month,"/",daily$year,"reported",daily$deaths,"Deaths in India")

## 5 / 10 / 2020 reported 903 Deaths in India

Daily Cases in India 74442

Daily Deaths in India 903

Growth Factor of Daily New Cases

Growth factor is the factor by which a quantity multiplies itself over time. The formula used is every day’s new cases / new cases on the previous day. For example, a quantity growing by 7% every period (in this case daily) has a growth factor of 1.07.

A growth factor above 1 indicates an increase, whereas one which remains between 0 and 1 it is a sign of decline, with the quantity eventually becoming zero, whereas a growth factor constantly above 1 could signal exponential growth

yesterday_active_india =covid_df[grep("India", covid_df$countriesAndTerritories),]
yesterday= head(yesterday_active_india$cases,2)
#yesterday
growth =  yesterday[1] -yesterday[2]
growth #growth in numbers 

## [1] -1387

growth_rate = yesterday[1]/yesterday[2]
growth_rate

## [1] 0.9817088

Total growth in cases : -1387

Total growth_Rate in cases (%): 0.9817088

Cumulative No. for 14 days of COVID.19 cases per 100000 :

#head(india_cases)
#ggplot(india_cases,aes(x = month , y = Cumulative_number_for_14_days_of_COVID.19_cases_per_100000 , color = "red"), xlim = c(0,12))+geom_line()
head(india_cases,3)

plot(india_cases$month,india_cases$Cumulative_number_for_14_days_of_COVID.19_cases_per_100000,main = "Months Vs Comulative Number (India)" , xlab = "Months" ,ylab = "Comulative No. for 14 days", type = "l", col="red" , xlim = range(0:12))

scatterplot3d::scatterplot3d(india_cases$month,india_cases$Cumulative_number_for_14_days_of_COVID.19_cases_per_100000,main = "Months Vs Comulative Number" , xlab = "Months" ,ylab = "Comulative No. for 14 days"
                             
)

#dateRep < 2020-09-05

India State wise Data :

FROM 2020/01/30 TILL 2020/05/26

india_new_state <- read.csv("statewise_data_with_new_cases.csv")
head(india_new_state,3)

info_cov_india <- read.csv("covid_19_india.csv")
head(info_cov_india,3)

ggplot(india_new_state , aes(x = State.UT, y=Confirmed)) + geom_col(fill="orange")+ theme(axis.text.x=element_text(size=20, angle=90, hjust = 1, vjust = 0.5 ) , plot.background = element_rect( colour = NULL,  size = 20,  linetype = NULL,  color = NULL, fill = NULL, inherit.blank = FALSE)) +xlab("States/UTs")+ylab("Total Cases")+ labs(title="State/UT-wise Cases of COVID-19")+ theme_bw() + geom_text(aes(label=round(Confirmed) ), position=position_dodge(width=1.0),vjust=-0.25,angle=270)+coord_flip()

ggplot(india_new_state, aes(x=State.UT, y=Cured))+geom_col(fill="#00AFBB")+ theme(axis.text.x=element_text(size=10, angle=90, hjust = 1, vjust = 0.5 ) , plot.background = element_rect( colour = NULL,  size = 20,  linetype = NULL,  color = NULL, fill = NULL, inherit.blank = FALSE)) +xlab("States/UTs")+ylab("Recovered")+ labs(title="State/UT-wise Recovered from COVID-19")+ theme_bw()+  geom_text(aes(label=round(Cured) ), position=position_dodge(width=1.0),vjust=-0.25,angle=270)+coord_flip()

ggplot(india_new_state, aes(x=State.UT, y=Deaths))+geom_col(fill="red")+ theme(axis.text.x=element_text(size=10, angle=90, hjust = 1, vjust = 0.5 ) , plot.background = element_rect( colour = NULL,  size = 20,  linetype = NULL,  color = NULL, fill = NULL, inherit.blank = FALSE)) +xlab("States/UTs")+ylab("Deaths")+ labs(title="State/UT-wise Deaths from COVID-19")+ theme_bw()+  geom_text(aes(label=round(Deaths) ), position=position_dodge(width=1.0),vjust=-0.25,angle=270)+coord_flip()

Beds available in India :

bed_data <- read.csv("HospitalBedsIndia.csv",stringsAsFactors = F)
head(bed_data,3)

bed_data1 <- filter(bed_data,State.UT =="All India")
bed_data1

#names(bed_data1)

__________________________________________________________________________________________________________________________________

Number of Primary Health Center :29,899 Number of Communlity Health Centers :5568 Number of SubDistrict Hospitals :1255 Number of District Hospitals :1003 Total Public Health Facilities :37725 Number of Public Beds :739024 Number of Rural Hospitals :19810 Number of Urban Hospitals :431173 __________________________________________________________________________________________________________________________________

#plots
#bed_data #using bed_data 

ggplot(filter(bed_data,State.UT=="All India") )+
  geom_col(aes(x="Public",y=NumPublicBeds_HMIS,fill="Public")) + geom_col(aes(x="Rural",y=NumRuralBeds_NHP18,fill="Rural"))+ geom_col(aes(x="Urban",y=NumUrbanBeds_NHP18,fill="Urban"))+theme_bw() +ylab("Total Count")+xlab("Beds")+ labs(title="Category of beds available in India",fill = "Legen: Bed Type")+scale_fill_manual(values = c("gold","indianred3","springgreen3"))

State.UT-wise Beds to population ratio :

options(repr.plot.width = 15, repr.plot.height = 10)

#info_pop_india[ order(info_pop_india [,info_pop_india$State...Union.Territory]), ]
#info_pop_india<-info_pop_india[with(info_pop_india, order(info_pop_india$State...Union.Territory)),]
#head(info_pop_india,3)
#head(bed_data,3)
#Usage inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"),...)
#For example, by = c("a" = "b") will match x.a to y.b.


info_pop_india<-read.csv("population_india_census2011.csv",stringsAsFactors = F)

info_pop_india1<-inner_join(info_pop_india,bed_data,by=c("State...Union.Territory"="State.UT"))
info_pop_india1[,"beds_to_pop_ratio"]<-((info_pop_india1$NumPublicBeds_HMIS+info_pop_india1$NumRuralBeds_NHP18+info_pop_india1$NumUrbanBeds_NHP18)/info_pop_india1$Population)

#png(paste0("plot_",names(df)[x],".png"))
jpeg(file = "StateUT_wise_Beds_to_population_ratio.jpg")

plot_StateUT_wise_Beds_to_population_ratio <- ggplot(info_pop_india1, aes(x=State...Union.Territory, y=beds_to_pop_ratio , col = "red"))+geom_col()+xlab("States/UTs")+theme_bw()+ theme(axis.text.y=element_text(size=10),axis.text.x=element_text(size=10, angle=90, hjust = 1, vjust = 0.5 ),axis.title=element_text(size=21),plot.title = element_text(face = "bold",size=25))+ylab("Beds/Popolation")+ labs(title="State/UT-wise Beds to population ratio")+geom_abline()
plot_StateUT_wise_Beds_to_population_ratio

#dev.off()

plot_StateUT_wise_Beds_to_population_ratio