Covid-19 Analysis

library(FactoMineR)
library(PerformanceAnalytics)

## Loading required package: xts

## Loading required package: zoo

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## Attaching package: 'zoo'

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## Attaching package: 'PerformanceAnalytics'

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library(ggplot2)
library(plotly)

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library(rpart)
library(rpart.plot)
library(tidyverse)

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## v tidyr   1.0.2     v stringr 1.4.0
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## v purrr   0.3.3

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library(dplyr)
library(tidyr)
library(FactoMineR)
library(DT)
library(rpivotTable)
library(treemap)
library(treemapify)
library(tidyverse)
library(viridis)

## Loading required package: viridisLite

library(gridExtra)

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library(magrittr)

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library(ggforce) 
library(kableExtra)

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## Attaching package: 'kableExtra'

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library(PerformanceAnalytics)

data Editing

#Checking data
a<-read.csv("C://Users/md/Desktop/Research/Corona.csv")
a$Total.Cases<-as.numeric(a$Total.Cases)
a$New.Cases<-as.numeric(a$New.Cases)
a$Total.Deaths<-as.numeric(a$Total.Deaths)
a$Total.Recovered<-as.numeric(a$Total.Recovered)
a$Active.Cases<-as.numeric(a$Active.Cases)
a$Serious..Critical<-as.numeric(a$Serious..Critical)
a$Tot.Cases..1M.pop<-as.numeric(a$Tot.Cases..1M.pop)
datatable(a)

Correlation Matrix

chart.Correlation(a[,2:9], histogram=TRUE, pch=25)

Line chart

r<-ggplot(data = a,aes(x = Total.Cases, y = Total.Deaths,colour="Red"))+
  geom_line()
ggplotly(r)

n<-ggplot(data = a,aes(x = Total.Cases, y = Total.Recovered,colour="Red"))+
  geom_line()
ggplotly(n)

o<-ggplot(data = a,aes(x = Total.Deaths, y = Total.Recovered,colour="Red"))+
  geom_line()
ggplotly(o)

#Arrange line plot
grid.arrange(r,n,o,ncol=1)

Scatter Diagram

# Scatter Diagram og total cases and Total.Recovered

d4<-ggplot(
  data = a, 
  aes(x = Total.Cases, y = Total.Recovered)) +
  geom_point(aes(colour = Country..Other)) + 
  geom_smooth(method = "lm") +
  ggtitle("Total.Cases vs.  New.Cases") +
  xlab("Total.Cases") +
  ylab("New.Cases")
ggplotly(d4+scale_colour_viridis_d(option = "inferno"))

## `geom_smooth()` using formula 'y ~ x'

# Scatter Diagram Total Death and Tot.Cases..1M.pop
d5<-ggplot(
  data = a, 
  aes(x = New.Deaths, y =Tot.Cases..1M.pop)) +
  geom_point(aes(colour = Country..Other)) + 
  geom_smooth(method = "lm") +
  ggtitle("Total.Deaths vs.  Total.Recovered") +
  xlab("Total.Deaths") +
  ylab("Total.Recovered")
ggplotly(d5+scale_colour_viridis_d(option = "inferno"))

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

#Multiple Linear regression
fi<-ggplot(a)+
  geom_jitter(aes(Total.Cases,Total.Recovered,fill=Country..Other), colour="blue") + geom_smooth(aes(Total.Cases,Total.Recovered), method=lm, se=FALSE) +
  geom_jitter(aes(Total.Deaths,Serious..Critical,fill=Country..Other), colour="Yellow") + geom_smooth(aes(Total.Deaths,Serious..Critical), method=lm, se=FALSE) +
  geom_jitter(aes(Active.Cases,Serious..Critical,fill=Country..Other), colour="skyblue") + geom_smooth(aes(Active.Cases,Serious..Critical), method=lm, se=FALSE) +
  geom_jitter(aes(New.Cases,New.Deaths,fill=Country..Other), colour="red") + geom_smooth(aes(New.Cases,New.Deaths), method=lm, se=FALSE) +
  labs(x = "Percentage cover (%)", y = "Number of individuals (N)")
ggplotly(fi)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

pie chart

library(moonBook)
library(webr)
PieDonut(a,aes(Total.Cases,Total.Recovered),explode=1,explodeDonut=TRUE)

## Warning: Ignoring unknown aesthetics: explode

PieDonut(a,aes(Total.Cases,Total.Recovered),explode=3,r1=0.9,explodeDonut=TRUE,title="Total Case Vs New Case",star=3*pi/2,labelposition=0)

## Warning: Ignoring unknown aesthetics: explode

this Analysis is Prepared by Md. Mahdi hasan, B,Sc.(Hon’s) Department of Statistics, Dhaka College, Dhaka