Useful Packages
library(FactoMineR)
library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
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library(ggplot2)
library(plotly)
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library(rpart)
library(rpart.plot)
library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------------- tidyverse 1.3.0 --
## 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
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library(dplyr)
library(tidyr)
library(FactoMineR)
library(DT)
library(rpivotTable)
library(treemap)
library(treemapify)
library(tidyverse)
library(viridis)
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library(gridExtra)
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library(ggcorrplot)
library(moonBook)
library(webr)
library(webreadr)
library(magrittr)
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library(lubridate)
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library(ggforce)
library(kableExtra)
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library(PerformanceAnalytics)
library(tseries)
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library(forecast)
Checking Data
#Checking data
co<-read.csv("C://Users/md/Desktop/Research/10 countries cocona Update.csv")
as.Date(co$DateRep)
## [1] "0008-03-20" "0014-03-20" "0016-03-20" "0017-03-20" "0018-03-20"
## [6] "0019-03-20" "0021-03-20" "0022-03-20" "0023-03-20" "0024-03-20"
## [11] "0001-03-20" "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20"
## [16] "0006-03-20" "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20"
## [21] "0011-03-20" "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20"
## [26] "0016-03-20" "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20"
## [31] "0021-03-20" "0022-03-20" "0023-03-20" "0020-02-20" "0021-02-20"
## [36] "0022-02-20" "0023-02-20" "0024-02-20" "0025-02-20" "0026-02-20"
## [41] "0027-02-20" "0028-02-20" "0029-02-20" "0001-03-20" "0002-03-20"
## [46] "0003-03-20" "0004-03-20" "0005-03-20" "0006-03-20" "0007-03-20"
## [51] "0008-03-20" "0009-03-20" "0010-03-20" "0011-03-20" "0012-03-20"
## [56] "0013-03-20" "0014-03-20" "0015-03-20" "0016-03-20" "0017-03-20"
## [61] "0018-03-20" "0019-03-20" "0020-03-20" "0021-03-20" "0022-03-20"
## [66] "0023-03-20" "0031-01-20" "0022-02-20" "0023-02-20" "0024-02-20"
## [71] "0025-02-20" "0026-02-20" "0027-02-20" "0028-02-20" "0029-02-20"
## [76] "0001-03-20" "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20"
## [81] "0006-03-20" "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20"
## [86] "0011-03-20" "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20"
## [91] "0016-03-20" "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20"
## [96] "0021-03-20" "0022-03-20" "0023-03-20" "0001-02-20" "0025-02-20"
## [101] "0026-02-20" "0027-02-20" "0028-02-20" "0029-02-20" "0001-03-20"
## [106] "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20" "0006-03-20"
## [111] "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20" "0011-03-20"
## [116] "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20" "0016-03-20"
## [121] "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20" "0021-03-20"
## [126] "0022-03-20" "0023-03-20" "0021-01-20" "0025-01-20" "0027-01-20"
## [131] "0031-01-20" "0001-02-20" "0002-02-20" "0003-02-20" "0006-02-20"
## [136] "0011-02-20" "0013-02-20" "0014-02-20" "0021-02-20" "0022-02-20"
## [141] "0025-02-20" "0027-02-20" "0028-02-20" "0029-02-20" "0001-03-20"
## [146] "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20" "0006-03-20"
## [151] "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20" "0011-03-20"
## [156] "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20" "0016-03-20"
## [161] "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20" "0021-03-20"
## [166] "0022-03-20" "0023-03-20" "0028-01-20" "0029-01-20" "0031-01-20"
## [171] "0001-02-20" "0002-02-20" "0003-02-20" "0004-02-20" "0007-02-20"
## [176] "0008-02-20" "0012-02-20" "0026-02-20" "0027-02-20" "0028-02-20"
## [181] "0029-02-20" "0001-03-20" "0002-03-20" "0003-03-20" "0004-03-20"
## [186] "0005-03-20" "0006-03-20" "0007-03-20" "0008-03-20" "0009-03-20"
## [191] "0010-03-20" "0011-03-20" "0012-03-20" "0013-03-20" "0014-03-20"
## [196] "0015-03-20" "0016-03-20" "0017-03-20" "0018-03-20" "0019-03-20"
## [201] "0020-03-20" "0021-03-20" "0022-03-20" "0023-03-20" "0029-01-20"
## [206] "0030-01-20" "0031-01-20" "0008-02-20" "0017-02-20" "0026-02-20"
## [211] "0027-02-20" "0028-02-20" "0029-02-20" "0001-03-20" "0002-03-20"
## [216] "0003-03-20" "0004-03-20" "0005-03-20" "0006-03-20" "0007-03-20"
## [221] "0008-03-20" "0009-03-20" "0010-03-20" "0011-03-20" "0012-03-20"
## [226] "0013-03-20" "0014-03-20" "0015-03-20" "0016-03-20" "0017-03-20"
## [231] "0018-03-20" "0019-03-20" "0020-03-20" "0021-03-20" "0022-03-20"
## [236] "0023-03-20" "0020-01-20" "0024-01-20" "0026-01-20" "0027-01-20"
## [241] "0031-01-20" "0001-02-20" "0002-02-20" "0004-02-20" "0005-02-20"
## [246] "0006-02-20" "0007-02-20" "0009-02-20" "0010-02-20" "0011-02-20"
## [251] "0016-02-20" "0017-02-20" "0018-02-20" "0019-02-20" "0020-02-20"
## [256] "0021-02-20" "0022-02-20" "0023-02-20" "0024-02-20" "0025-02-20"
## [261] "0026-02-20" "0027-02-20" "0028-02-20" "0029-02-20" "0001-03-20"
## [266] "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20" "0006-03-20"
## [271] "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20" "0011-03-20"
## [276] "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20" "0016-03-20"
## [281] "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20" "0021-03-20"
## [286] "0022-03-20" "0023-03-20" "0026-02-20" "0028-02-20" "0029-02-20"
## [291] "0001-03-20" "0002-03-20" "0003-03-20" "0004-03-20" "0005-03-20"
## [296] "0006-03-20" "0007-03-20" "0008-03-20" "0009-03-20" "0010-03-20"
## [301] "0011-03-20" "0012-03-20" "0013-03-20" "0014-03-20" "0015-03-20"
## [306] "0016-03-20" "0017-03-20" "0018-03-20" "0019-03-20" "0020-03-20"
## [311] "0021-03-20" "0022-03-20" "0023-03-20"
Correlation Matrix
corr <- round(cor(co[,2:3]), 1)
corr
## Cases Deaths
## Cases 1.0 0.7
## Deaths 0.7 1.0
# Compute a matrix of correlation p-values
p.mat <- cor_pmat(co[,2:3])
p.mat
## Cases Deaths
## Cases 0.000000e+00 3.232661e-55
## Deaths 3.232661e-55 0.000000e+00
# Visualize the correlation matrix
# method = "square" (default)
ggcorrplot(corr)
# method = "circle"
ggcorrplot(corr, method = "circle")
# Add correlation coefficients
# argument lab = TRUE
ggcorrplot(corr, hc.order = TRUE, type = "lower",
lab = TRUE)
Treemap Analysis
#Treemap Analysis of Top Countries Cases and Death
ggplot(co, aes(area = Cases, fill = Deaths, label = GeoId,
subgroup = Countries.and.territories)) +
geom_treemap() +
geom_treemap_subgroup_border() +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = .9, colour =
"White", fontface = "italic", min.size = 0) +
geom_treemap_text(colour = "Red", place = "topleft", reflow = T)
Linear regression plot
# Linear regression Analysis
lm<-lm(formula = Cases~Deaths,data = co)
lm
##
## Call:
## lm(formula = Cases ~ Deaths, data = co)
##
## Coefficients:
## (Intercept) Deaths
## 305.13 10.07
summary(lm)
##
## Call:
## lm(formula = Cases ~ Deaths, data = co)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1958.0 -304.1 -262.5 36.6 6834.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 305.1344 52.4189 5.821 1.46e-08 ***
## Deaths 10.0743 0.5213 19.326 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 871.7 on 311 degrees of freedom
## Multiple R-squared: 0.5457, Adjusted R-squared: 0.5442
## F-statistic: 373.5 on 1 and 311 DF, p-value: < 2.2e-16
# Linear regression plot
a<-ggplot(data = co,aes(Cases,Deaths,fill=Countries.and.territories))+
geom_point()+
geom_smooth(method = "lm")
a
## `geom_smooth()` using formula 'y ~ x'
Bangladeshi covid-19 Outbreak 2020
# country Analysis
#Bangladesh
t1<-co%>%filter(Countries.and.territories=="Bangladesh")%>%
group_by(DateRep,Cases,Deaths,Countries.and.territories,GeoId)
datatable(t1,caption = 't1')
#Pie Chart about total case and Death
PieDonut(t1,aes(Cases,Deaths),explode=1,explodeDonut=TRUE,title = "Bangladesh covid-19 Total Cases and Death")
## Warning: Ignoring unknown aesthetics: explode
PieDonut(t1,aes(Cases,Deaths),explode=3,r1=0.9,explodeDonut=TRUE,title="Bangladesh covid-19 Total Cases and Death",star=3*pi/2,labelposition=0)
## Warning: Ignoring unknown aesthetics: explode
#Correlation Matrix
corr2 <- round(cor(t1[,2:3]), 1)
corr2
## Cases Deaths
## Cases 1.0 0.9
## Deaths 0.9 1.0
# Compute a matrix of correlation p-values
p.mat2 <- cor_pmat(t1[,2:3])
p.mat2
## Cases Deaths
## Cases 0.000000e+00 5.325786e-05
## Deaths 5.325786e-05 0.000000e+00
# Visualize the correlation matrix
# method = "square" (default)
ggcorrplot(corr2)
# method = "circle"
ggcorrplot(corr2, method = "circle")
# Add correlation coefficients
# argument lab = TRUE
ggcorrplot(corr2, hc.order = TRUE, type = "lower",
lab = TRUE)
# Treemap Analysis
tr1<-ggplot(t1, aes(area = Cases, fill = Deaths,label = GeoId)) +
geom_treemap() +
geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
grow = TRUE)
tr1
# Line Diagram
l1<-ggplot(data = t1, aes(Cases,Deaths,fill= Deaths))+
geom_line()
l1
# Regression plot
reg1<-ggplot(data = t1,aes(Cases,Deaths),colour=Deaths)+
geom_point()+
geom_smooth(method = "lm")
reg1
## `geom_smooth()` using formula 'y ~ x'
# covid-19 Death Outbreak in Bangladesh
death1<-ggplot(data = t1, aes(x = Cases, y = Deaths, color = Deaths, size = Deaths)) +
geom_path() +
geom_point() +
facet_wrap(~ DateRep)+
ggtitle("Covid-19 Death Outbreak in Banglaesh")
death1
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
# covid-19 Case Outbreak in Bangladesh
Case1<-ggplot(data = t1, aes(x = Cases, y = Deaths, color = Cases, size = Cases)) +
geom_path() +
geom_point() +
facet_wrap(~ DateRep)+
ggtitle("Covid-19 Case Outbreak in Banglaesh")
Case1
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
# forecasting of Death
Death.arima<-auto.arima(t1$Deaths)
Death.arima
## Series: t1$Deaths
## ARIMA(0,1,0)
##
## sigma^2 estimated as 0.8889: log likelihood=-12.24
## AIC=26.48 AICc=27.05 BIC=26.68
#Forecasting
Death_model_forecast_Bangladesh<-forecast(Death.arima,h=11)
Death_model_forecast_Bangladesh
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 11 4 2.791741597 5.208258 2.1521282 5.847872
## 12 4 2.291264579 5.708735 1.3867147 6.613285
## 13 4 1.907235057 6.092765 0.7993922 7.200608
## 14 4 1.583483194 6.416517 0.3042565 7.695744
## 15 4 1.298252076 6.701748 -0.1319669 8.131967
## 16 4 1.040383435 6.959617 -0.5263429 8.526343
## 17 4 0.803248745 7.196751 -0.8890091 8.889009
## 18 4 0.582529159 7.417471 -1.2265706 9.226571
## 19 4 0.375224790 7.624775 -1.5436153 9.543615
## 20 4 0.179151444 7.820849 -1.8434836 9.843484
## 21 4 -0.007339773 8.007340 -2.1286973 10.128697
#Forecast plot
plot(Death_model_forecast_Bangladesh,col = "orange")
# forecasting of Case
case.arima_Bangladesh<-auto.arima(t1$Cases)
case.arima_Bangladesh
## Series: t1$Cases
## ARIMA(0,1,0) with drift
##
## Coefficients:
## drift
## 4.0000
## s.e. 0.8012
##
## sigma^2 estimated as 6.5: log likelihood=-20.66
## AIC=45.33 AICc=47.33 BIC=45.72
#Forecasting
Case_model_forecast_Bangladesh<-forecast(case.arima_Bangladesh,h=11)
Case_model_forecast_Bangladesh
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 11 43 39.73267 46.26733 38.00305 47.99695
## 12 47 42.37930 51.62070 39.93325 54.06675
## 13 51 45.34082 56.65918 42.34503 59.65497
## 14 55 48.46534 61.53466 45.00611 64.99389
## 15 59 51.69403 66.30597 47.82649 70.17351
## 16 63 54.99671 71.00329 50.76003 75.23997
## 17 67 58.35546 75.64454 53.77932 80.22068
## 18 71 61.75860 80.24140 56.86650 85.13350
## 19 75 65.19802 84.80198 60.00916 89.99084
## 20 79 68.66780 89.33220 63.19827 94.80173
## 21 83 72.16350 93.83650 66.42700 99.57300
#Forecast plot
plot2<-plot(Case_model_forecast_Bangladesh,col = "orange")
plot2
## $mean
## Time Series:
## Start = 11
## End = 21
## Frequency = 1
## [1] 43 47 51 55 59 63 67 71 75 79 83
##
## $lower
## Time Series:
## Start = 11
## End = 21
## Frequency = 1
## 80% 95%
## 11 39.73267 38.00305
## 12 42.37930 39.93325
## 13 45.34082 42.34503
## 14 48.46534 45.00611
## 15 51.69403 47.82649
## 16 54.99671 50.76003
## 17 58.35546 53.77932
## 18 61.75860 56.86650
## 19 65.19802 60.00916
## 20 68.66780 63.19827
## 21 72.16350 66.42700
##
## $upper
## Time Series:
## Start = 11
## End = 21
## Frequency = 1
## 80% 95%
## 11 46.26733 47.99695
## 12 51.62070 54.06675
## 13 56.65918 59.65497
## 14 61.53466 64.99389
## 15 66.30597 70.17351
## 16 71.00329 75.23997
## 17 75.64454 80.22068
## 18 80.24140 85.13350
## 19 84.80198 89.99084
## 20 89.33220 94.80173
## 21 93.83650 99.57300