---
title: "Covid-19 Analysis Dashboard"
author: "Dharshini Balasundaram"
date: "2025-01-02"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r setup, include=FALSE}
setwd("C:/Users/dhars/Downloads/Dhass/codeing/PowerBI/PowerBi Main Course/Step 6/1. DataSets/R Programming Datasets")
getwd()
covid<-read.csv("WHO-COVID-19-global-data.csv")
covid
attach(covid)
library(dplyr)
library(plotly)
library("ggplot2")
library(corrplot)
library(scatterplot3d)
library(carData)
library(plotrix)
palette()
```
Countries ~ By WHO Region
=======================================================================
Row
-----------------------------------------------------------------------
### Countries has WHO_region:
```{r}
#How many Countries has WHO_region:
Count_WHO_regions_countriesOnly = covid%>%
select(WHO_region,Country)%>%
group_by(WHO_region)%>%
summarize(No.Of.Countries = n_distinct(Country))%>%
filter(WHO_region != "")
plot_ly(data = Count_WHO_regions_countriesOnly,
x = ~WHO_region, y = ~No.Of.Countries,
color = ~No.Of.Countries, type = "scatter",
mode = "markers+text",
text = ~No.Of.Countries,
textposition = "top middle") %>%
layout(title = "WHO Regions vs. Number of Countries",
xaxis = list(title = "WHO Region"),
yaxis = list(title = "Number of Countries"))
```
### Country doesnot have WHO_region
```{r}
#How many Country doesnot have WHO_region:
Count_Of_Countries_Doesnot_have_WHO_regions = covid%>%
select(WHO_region,Country)%>%
group_by(WHO_region)%>%
summarize(No.Of.Countries = n_distinct(Country))%>%
filter(WHO_region == "")
plott = plot_ly(data = Count_Of_Countries_Doesnot_have_WHO_regions,
x = ~WHO_region,
y = ~No.Of.Countries,
color = ~No.Of.Countries, type = "bar",
text = ~No.Of.Countries,
textposition = "inside",
textfont = list(color = "black")) %>%
layout(title = "Countries doesnot have WHO_region",
xaxis = list(title = "WHO Region"),
yaxis = list(title = "Number of Countries"))
plott
```
Covid19 statistics
=======================================================================
Row
-----------------------------------------------------------------------
### Covid statistics Results:
```{r}
#Total New Cases:
Total_New_Cases = sum(covid$New_cases, na.rm = TRUE)
#Total New Deaths:
Total_New_Deaths = sum(covid$New_deaths, na.rm = TRUE)
#Total Covid Cases:
Total_Covid_Cases = Total_New_Cases + Total_New_Deaths
#Total combined results:
Total_Results = c(Total.Covid.Cases = Total_Covid_Cases,Total.New.Deaths = Total_New_Deaths,Total.New.Cases = Total_New_Cases)
sorted_Total_Results <- Total_Results[order(Total_Results, decreasing = TRUE)]
barplot(sorted_Total_Results, col = heat.colors(3),main = "COVID-19 statistics"
, xlab = "Covid Statistics", ylab = "Values")
legend(x = "topright", legend = c(sorted_Total_Results) ,
col = heat.colors(3), pch = 16, bty = "o", title = "Total_Results")
```
Covid19 Total Cases
=======================================================================
Row
-----------------------------------------------------------------------
### Country with respect to maximum & minimum[Excluding 0] Total Covid Cases:
```{r}
covid <- covid %>% mutate(Total_Covid_Cases = New_cases + New_deaths)
#Which country gives maximum & minimum Total Cases:
#Which county gives maximum Total Cases:
Maximum_Total_Cases = covid %>% select(Country,Total_Covid_Cases)%>% group_by(Country) %>%
summarise(Total_Cases = sum(Total_Covid_Cases, na.rm = TRUE))%>%
filter(Total_Cases == max(Total_Cases, na.rm = TRUE))
#Which county gives minimum Total Cases[Excluding 0]:
Minimum_Total_Cases = covid %>% select(Country,Total_Covid_Cases)%>% group_by(Country) %>%
summarise(Total_Cases = sum(Total_Covid_Cases, na.rm = TRUE))%>%
filter(Total_Cases > 0) %>% filter(Total_Cases == min(Total_Cases, na.rm = TRUE))
Countries_with_MaxMin_TotalCases_Occured = rbind(Maximum_Total_Cases,Minimum_Total_Cases)
bar_heights10 = barplot(Countries_with_MaxMin_TotalCases_Occured$Total_Cases,
names.arg = Countries_with_MaxMin_TotalCases_Occured$Country,
main = "Max/Min Overall Cases Occurred by Country",
xlab = "Country",
ylab = "Over All Cases",
col = terrain.colors(length(Country)),
las = 1/2)
text(x = bar_heights10, y = Countries_with_MaxMin_TotalCases_Occured$Total_Cases/2,
labels = Countries_with_MaxMin_TotalCases_Occured$Total_Cases,
pos = 2, cex = 0.5, col = "black")
```
### Top 10 Countries gives maximum Total Covid Cases:
```{r}
#Top 10 Countries gives maximum Total Cases:
Top10_Countries_Total_Cases = covid %>% select(Country,Total_Covid_Cases)%>% group_by(Country) %>%
summarise(Total_Cases = sum(Total_Covid_Cases, na.rm = TRUE))%>%
arrange(desc(Total_Cases)) %>% slice(1:10)
countries4 <- Top10_Countries_Total_Cases$Country
new_cases4 <- Top10_Countries_Total_Cases$Total_Cases
bar_heights11 <- barplot(new_cases4, names.arg = countries4, col = terrain.colors(length(new_cases4)),
las = 3, cex.names = 0.8, main = "Top 10 Countries by New COVID-19 Over All Cases",
xlab = "Countries", ylab = "Total Cases")
text(x = bar_heights11, y = new_cases4/2, labels = new_cases4, pos = 1, cex = 0.5, col = "black")
```
### Top 10 Countries gives minimum Total Covid Cases- Excluding 0:
```{r}
#Bottom 10 Countries gives minimum Total Cases:
Bottom10_Countries_Total_Cases = covid %>% select(Country,Total_Covid_Cases)%>% group_by(Country) %>%
summarise(Total_Cases = sum(Total_Covid_Cases, na.rm = TRUE))%>%
arrange(Total_Cases) %>% slice(1:10)%>%
filter(Total_Cases > 0)
countries5 <- Bottom10_Countries_Total_Cases$Country
new_cases5 <- Bottom10_Countries_Total_Cases$Total_Cases
bar_heights12 <- barplot(new_cases5, names.arg = countries5, col = terrain.colors(length(new_cases5)),
las = 3, cex.names = 0.8, main = "Bottom 10 Countries by New COVID-19 Over All Cases",
xlab = "Countries", ylab = "Total Cases")
text(x = bar_heights12, y = new_cases5/2, labels = new_cases5, pos = 2, cex = 0.5, col = "black")
```
Covid19 New Cases
=======================================================================
Row
-----------------------------------------------------------------------
### Country with respect to maximum & minimum[Excluding 0] New Cases:
```{r}
#Which county gives maximum & minimum New Cases:
#Which county gives maximum New Cases:
Maximum_New_Cases = covid %>% select(Country, New_cases) %>% group_by(Country) %>%
summarise(Total_New_Cases = sum(New_cases, na.rm = TRUE)) %>% filter(Total_New_Cases == max(Total_New_Cases, na.rm = TRUE))
#Which county gives minimum New Cases:
Minimum_New_Cases = covid %>% select(Country, New_cases) %>% group_by(Country) %>%
summarise(Total_New_Cases = sum(New_cases, na.rm = TRUE)) %>% filter(Total_New_Cases > 0) %>% filter(Total_New_Cases == min(Total_New_Cases, na.rm = TRUE))
Countries_with_MaxMin_NewCases_Occured = rbind(Maximum_New_Cases,Minimum_New_Cases)
bar_heights10 = barplot(Countries_with_MaxMin_NewCases_Occured$Total_New_Cases,
names.arg = Countries_with_MaxMin_NewCases_Occured$Country,
main = "Max/Min Cases Occurred by Country",
xlab = "Country",
ylab = "Total New Cases",
col = terrain.colors(length(Country)),
las = 1/2)
text(x = bar_heights10, y = Countries_with_MaxMin_NewCases_Occured$Total_New_Cases/2,
labels = Countries_with_MaxMin_NewCases_Occured$Total_New_Cases,
pos = 2, cex = 0.7, col = "black")
```
### Top 10 Countries gives maximum New Cases:
```{r}
#Top 10 Countries gives maximum New Cases:
Top10_Countries_New_Cases = covid %>% select(Country, New_cases) %>% group_by(Country) %>% summarise(Total_New_Cases = sum(New_cases, na.rm = TRUE)) %>% arrange(desc(Total_New_Cases)) %>% slice(1:10)
# Separate country names and new case counts
countries <- Top10_Countries_New_Cases$Country
new_cases <- Top10_Countries_New_Cases$Total_New_Cases
# Create the bar plot and store the bar heights
bar_heights <- barplot(new_cases, names.arg = countries, col = terrain.colors(length(new_cases)),
las = 3, cex.names = 0.8, main = "Top 10 Countries by New COVID-19 Cases",
xlab = "Countries", ylab = "Total New Cases")
# Add the new cases values on top of each bar
text(x = bar_heights, y = new_cases/2, labels = new_cases, pos = 1, cex = 0.5, col = "black")
```
### Top 10 Countries gives minimum New Cases- Excluding 0:
```{r}
#Bottom 10 Countries gives minimum New Cases:
Bottom10_Countries_New_Cases = covid %>% select(Country, New_cases) %>% group_by(Country) %>%
summarise(Total_New_Cases = sum(New_cases, na.rm = TRUE)) %>% arrange((Total_New_Cases)) %>% slice(1:10)%>%
filter(Total_New_Cases > 0)
countries1 <- Bottom10_Countries_New_Cases$Country
new_cases1 <- Bottom10_Countries_New_Cases$Total_New_Cases
# Create the bar plot and store the bar heights
bar_heights1 <- barplot(new_cases1, names.arg = countries1, col = terrain.colors(length(new_cases1)),
las = 3, cex.names = 0.8, main = "Bottom 10 Countries by New COVID-19 Cases",
xlab = "Countries", ylab = "Total New Cases")
# Add the new cases values on top of each bar
text(x = bar_heights1, y = new_cases1/2, labels = new_cases1, pos = 3, cex = 0.5, col = "black")
```
Covid19 Death Cases
=======================================================================
Row
-----------------------------------------------------------------------
### Country with respect to maximum & minimum[Excluding 0] New Deaths:
```{r}
#Which county gives maximum and minimum New Deaths:
#Which county gives maximum New Deaths:
Maximum_New_deaths = covid %>% select(Country, New_deaths) %>% group_by(Country) %>%
summarise(Total_New_Deaths = sum(New_deaths, na.rm = TRUE)) %>% filter(Total_New_Deaths == max(Total_New_Deaths, na.rm = TRUE))
#Which county gives minimum New Deaths:
Minimum_New_deaths = covid %>% select(Country, New_deaths) %>% group_by(Country) %>%
summarise(Total_New_Deaths = sum(New_deaths, na.rm = TRUE)) %>% filter(Total_New_Deaths > 0) %>% filter(Total_New_Deaths == min(Total_New_Deaths, na.rm = TRUE))
Countries_with_MaxMin_Deaths_Occured = rbind(Maximum_New_deaths,Minimum_New_deaths)
bar_heights10 = barplot(Countries_with_MaxMin_Deaths_Occured$Total_New_Deaths,
names.arg = Countries_with_MaxMin_Deaths_Occured$Country,
main = "Max/Min Deaths Occurred by Country",
xlab = "Country",
ylab = "Total New Deaths",
col = terrain.colors(length(Country)),
las = 1/2)
text(x = bar_heights10, y = Countries_with_MaxMin_Deaths_Occured$Total_New_Deaths/2,
labels = Countries_with_MaxMin_Deaths_Occured$Total_New_Deaths,
pos = 2, cex = 0.7, col = "black")
```
### Top 10 Countries gives maximum New Deaths:
```{r}
#Top 10 Countries gives maximum New Deaths:
Top10_Countries_New_Deaths = covid %>% select(Country, New_deaths) %>% group_by(Country) %>% summarise(Total_New_Deaths = sum(New_deaths, na.rm = TRUE)) %>% arrange(desc(Total_New_Deaths)) %>% slice(1:10)
countries2 <- Top10_Countries_New_Deaths$Country
new_cases2 <- Top10_Countries_New_Deaths$Total_New_Deaths
bar_heights2 <- barplot(new_cases2, names.arg = countries2, col = terrain.colors(length(new_cases2)),
las = 3, cex.names = 0.8, main = "Top 10 Countries by New COVID-19 Death Cases",
xlab = "Countries", ylab = "Total New Deaths")
text(x = bar_heights2, y = new_cases2/2, labels = new_cases2, pos = 3, cex = 0.5, col = "black")
```
### Top 10 Countries gives minimum New Deaths - Excluding 0:
```{r}
#Bottom 10 Countries gives minimum New Deaths - Excluding 0:
Bottom10_Countries_New_Deaths = covid %>% select(Country, New_deaths) %>% group_by(Country) %>%
summarise(Total_New_Deaths = sum(New_deaths, na.rm = TRUE))%>% filter(Total_New_Deaths >= 1) %>% arrange((Total_New_Deaths)) %>% slice(1:10)
countries3 <- Bottom10_Countries_New_Deaths$Country
new_cases3 <- Bottom10_Countries_New_Deaths$Total_New_Deaths
bar_heights3 <- barplot(new_cases3, names.arg = countries3, col = terrain.colors(length(new_cases3)),
las = 3, cex.names = 0.8, main = "Bottom 10 Countries by New COVID-19 Death Cases",
xlab = "Countries", ylab = "Total New Deaths")
text(x = bar_heights3, y = new_cases3/2, labels = new_cases3, pos = 3, cex = 0.5, col = "black")
```
COVID-19 Cases ~ By Years
=======================================================================
Row
-----------------------------------------------------------------------
### Total Deaths Over the Years
```{r}
#Which Year has maximum, minimum new Deaths occurred:
Date_Formatt = as.Date(covid$Date_reported, format = "%d-%m-%Y")
Fetching_Only_Year = format(Date_Formatt, "%Y")
covid$Year <- Fetching_Only_Year
Total_NewDeath_By_Years = summarise(group_by(covid, Year), Total_New_Deaths = sum(New_deaths, na.rm = TRUE))
plot(Total_NewDeath_By_Years$Year, Total_NewDeath_By_Years$Total_New_Deaths,
col = c("red","blue","green","aquamarine2","gold2"),
pch = 18,
cex = 0.9,
xlab = "Year",
ylab = "Maximum Deaths",
main = "Total Deaths Over the Years",
col.lab = "navyblue", col.main = "springgreen4")
legend(x = "topright", legend = c(Total_NewDeath_By_Years$Total_New_Deaths) ,
col = c("red","blue","green","aquamarine2","gold2"), pch = 16, bty = "o", title = "Total New Deaths")
```
### Total New Cases Over the Years
```{r}
#Which Year has maximum, minimum new Cases:
Total_NewCases_By_Years = summarise(group_by(covid, Year), Total_New_Cases = sum(New_cases, na.rm = TRUE))
plot(Total_NewCases_By_Years$Year, Total_NewCases_By_Years$Total_New_Cases,
col = c("red","blue","green","aquamarine2","gold2"),
pch = 18,
cex = 0.9,
ylab = "Year",
xlab = "Maximum Cases",
main = "New Cases Over the Years",
col.lab = "navyblue", col.main = "springgreen4")
legend(x = "topright", legend = c(Total_NewCases_By_Years$Total_New_Cases) ,
col = c("red","blue","green","aquamarine2","gold2"), pch = 16, bty = "o", title = "Total New Cases")
```
### Overall Cases Over the Years
```{r}
#Total Cases Over the years:
Total_Cases_By_Years = summarise(group_by(covid, Year), Total_Cases = sum(New_cases+New_deaths, na.rm = TRUE))
plot(Total_Cases_By_Years$Year, Total_Cases_By_Years$Total_Cases,
col = c("red","blue","green","aquamarine2","gold2"),
pch = 18,
cex = 0.9,
xlab = "Year",
ylab = "Total Cases",
main = "Total Cases Over the Years",
col.lab = "navyblue", col.main = "springgreen4")
legend(x = "topright", legend = c(Total_Cases_By_Years$Total_Cases) ,
col = c("red","blue","green","aquamarine2","gold2"), pch = 16, bty = "o", title = "Total Cases")
```
COVID-19 Max/Min Cases ~ By Years
=======================================================================
Row
-----------------------------------------------------------------------
### Max/Min Deaths Occurred by Year
```{r}
#Which Year has maximum new Deaths occurred:
year_with_the_maximum_new_deaths = filter(Total_NewDeath_By_Years, Total_New_Deaths == max(Total_New_Deaths))
#Which Year has minimum new Deaths occurred:
year_with_the_minimum_new_deaths = filter(Total_NewDeath_By_Years, Total_New_Deaths == min(Total_New_Deaths))
Years_with_MaxMin_Deaths_Occured = rbind(year_with_the_maximum_new_deaths,year_with_the_minimum_new_deaths)
bar_heights10 = barplot(Years_with_MaxMin_Deaths_Occured$Total_New_Deaths,
names.arg = Years_with_MaxMin_Deaths_Occured$Year,
main = "COVID-19's Deadliest and Least Deadly Years",
xlab = "Year",
ylab = "Total New Deaths",
col = terrain.colors(length(Years_with_MaxMin_Deaths_Occured$Year)),
las = 1/2)
text(x = bar_heights10, y = Years_with_MaxMin_Deaths_Occured$Total_New_Deaths/2,
labels = Years_with_MaxMin_Deaths_Occured$Total_New_Deaths,
pos = 2, cex = 0.7, col = "black")
```
### Max/Min New Cases Occurred by Years
```{r}
#Which Year has maximum new Cases:
year_with_the_maximum_new_Cases = filter(Total_NewCases_By_Years, Total_New_Cases == max(Total_New_Cases))
#Which Year has minimum new Cases:
year_with_the_minimum_new_cases = filter(Total_NewCases_By_Years, Total_New_Cases == min(Total_New_Cases))
Years_with_MaxMin_Cases_Occured = rbind(year_with_the_maximum_new_Cases,year_with_the_minimum_new_cases)
bar_heights10 = barplot(Years_with_MaxMin_Cases_Occured$Total_New_Cases,
names.arg = Years_with_MaxMin_Cases_Occured$Year,
main = "Highest and Lowest New COVID-19 Cases by Year",
xlab = "Year",
ylab = "Total New Cases",
col = terrain.colors(length(Years_with_MaxMin_Cases_Occured$Year)),
las = 1/2)
text(x = bar_heights10, y = Years_with_MaxMin_Cases_Occured$Total_New_Cases/2,
labels = Years_with_MaxMin_Cases_Occured$Total_New_Cases,
pos = 2, cex = 0.7, col = "black")
```
### Max/Min Overall Cases Occurred by Year
```{r}
#Which Year we had a minimum covid cases:
year_with_min_TotalCases = filter(Total_Cases_By_Years, Total_Cases == min(Total_Cases))
#Which Year we had a maximum covid cases:
year_with_max_TotalCases = filter(Total_Cases_By_Years, Total_Cases == max(Total_Cases))
years_with_maxandmin_cases = rbind(year_with_min_TotalCases,year_with_max_TotalCases)
bar_heights9 = barplot(years_with_maxandmin_cases$Total_Cases,
names.arg = years_with_maxandmin_cases$Year,
main = "COVID-19 Cases Highest and Lowest Years",
xlab = "Date Reported",
ylab = "Total New Cases",
col = terrain.colors(length(years_with_maxandmin_cases$Year)),
las = 2)
text(x = bar_heights9, y = years_with_maxandmin_cases$Total_Cases/2,
labels = years_with_maxandmin_cases$Total_Cases,
pos = 2, cex = 0.7, col = "black")
```
COVID-19 Deaths Cases ~ By Date
=======================================================================
Row
-----------------------------------------------------------------------
### Max/Min Deaths Occurred by Dates
```{r}
Total_NewDeath_ByDate = summarise(group_by(covid, Date_reported), Total_New_Deaths = sum(New_deaths, na.rm = TRUE))
# maximum new deaths occurred
Datewith_max_new_deaths <- filter(Total_NewDeath_ByDate, Total_New_Deaths == max(Total_New_Deaths, na.rm = TRUE))
# minimum new deaths occurred
Datewith_min_new_deaths <- filter(Total_NewDeath_ByDate, Total_New_Deaths == min(Total_New_Deaths, na.rm = TRUE))
# bind the two data frames
Death_Date_Occured <- rbind(Datewith_max_new_deaths, Datewith_min_new_deaths)
# create bar plot
bar_heights4 <- barplot(Death_Date_Occured$Total_New_Deaths,
names.arg = Death_Date_Occured$Date_reported,
main = "Highest and Lowest Death Toll Days",
xlab = "Date Reported",
ylab = "Total New Deaths",
col = terrain.colors(length(Death_Date_Occured$Date_reported)),
las = 2)
# add text labels
text(x = bar_heights4, y = Death_Date_Occured$Total_New_Deaths/2,
labels = Death_Date_Occured$Total_New_Deaths,
pos = 2, cex = 0.7, col = "black")
```
### Tracking the Peak COVID-19 Death Rates
```{r}
#Fetching the Total New Deaths by date:
Total_NewDeath_ByDate = summarise(group_by(covid, Date_reported), Total_New_Deaths = sum(New_deaths, na.rm = TRUE))
#Top 10 New Deaths occured by Date:
Top10_NewDeaths_By_Date = Total_NewDeath_ByDate %>% arrange(desc(Total_NewDeath_ByDate$Total_New_Deaths))%>% slice(1:10)
date_repoted3 = Top10_NewDeaths_By_Date$Date_reported
total_new_Deaths2 = Top10_NewDeaths_By_Date$Total_New_Deaths
bar_heights7 <- barplot(total_new_Deaths2, names.arg = date_repoted3, col = terrain.colors(length(total_new_Deaths2)),
las = 3, cex.names = 0.8, main = "Dates with Highest COVID-19 Deaths",
xlab = "Date Reported", ylab = "Total New Deaths")
text(x = bar_heights7, y = total_new_Deaths2/2, labels = total_new_Deaths2, pos = 3, cex = 0.5, col = "black")
```
### Tracking the Bottom COVID-19 Death Rates
```{r}
#Bottom 10 New Deaths occured by Date:
Bottom10_NewDeaths_By_Date = Total_NewDeath_ByDate %>% arrange(Total_NewDeath_ByDate$Total_New_Deaths)%>% slice(1:10)
date_repoted4 = Bottom10_NewDeaths_By_Date$Date_reported
total_new_Deaths3 = Bottom10_NewDeaths_By_Date$Total_New_Deaths
bar_heights8 <- barplot(total_new_Deaths3, names.arg = date_repoted4, col = terrain.colors(length(total_new_Deaths3)),
las = 3, cex.names = 0.8, main = "Dates with Minimal COVID-19 Deaths",
xlab = "Date Reported", ylab = "Total New Deaths")
text(x = bar_heights8, y = total_new_Deaths3/2, labels = total_new_Deaths3, pos = 3, cex = 0.5, col = "black")
```
COVID-19 New Cases ~ By Date
=======================================================================
Row
-----------------------------------------------------------------------
### Max/Min New Cases Occurred by Dates
```{r}
#On which date has maximum, minimum new cases occurred:
Total_NewCases_ByDate = summarise(group_by(covid, Date_reported), Total_New_Cases = sum(New_cases, na.rm = TRUE))
Total_NewCases_ByDates = arrange(Total_NewCases_ByDate, desc(Total_New_Cases))
#maximum new cases occurred:
Datewith_max_new_cases = filter(Total_NewCases_ByDate, Total_New_Cases == max(Total_New_Cases))
#minimum new cases occurred:
Datewith_min_new_cases = filter(Total_NewCases_ByDate, Total_New_Cases == min(Total_New_Cases))
NewCases_Date_Occured = rbind(Datewith_max_new_cases,Datewith_min_new_cases)
bar_heights4 = barplot(NewCases_Date_Occured$Total_New_Cases,
names.arg = NewCases_Date_Occured$Date_reported,
main = "Highest and Lowest Cases Toll Days",
xlab = "Date Reported",
ylab = "Total New Cases",
col = c("aquamarine2", "gold"),
las = 2)
text(x = bar_heights4, y = NewCases_Date_Occured$Total_New_Cases/2,
labels = NewCases_Date_Occured$Total_New_Cases,
pos = 2, cex = 0.7, col = "black")
```
### Tracking the Peak COVID-19 New Cases Rates
```{r}
#Fetching the Total New Cases by date:
Total_NewCases_ByDate = summarise(group_by(covid, Date_reported), Total_New_Cases = sum(New_cases, na.rm = TRUE))
Total_NewCases_ByDates = arrange(Total_NewCases_ByDate, desc(Total_New_Cases))
#Top 10 New cases occured by Date:
Top10_NewCases_By_Date = Total_NewCases_ByDates %>% arrange(desc(Total_NewCases_ByDates$Total_New_Cases))%>% slice(1:10)
date_repoted1 = Top10_NewCases_By_Date$Date_reported
total_new_cases1 = Top10_NewCases_By_Date$Total_New_Cases
bar_heights5 <- barplot(total_new_cases1, names.arg = date_repoted1, col = terrain.colors(length(total_new_cases1)),
las = 3, cex.names = 0.8, main = "Dates with Highest COVID-19 New Cases",
xlab = "Date Reported", ylab = "Total New Cases")
text(x = bar_heights5, y = total_new_cases1/2, labels = total_new_cases1, pos = 3, cex = 0.5, col = "black")
```
### Tracking the Bottom COVID-19 New Case Rates
```{r}
Total_NewCases_ByDate = summarise(group_by(covid, Date_reported), Total_New_Cases = sum(New_cases, na.rm = TRUE))
Total_NewCases_ByDates = arrange(Total_NewCases_ByDate, desc(Total_New_Cases))
#Bottom 10 New cases occurred by Date:
Bottom10_NewCases_By_Date = Total_NewCases_ByDates %>% arrange(Total_NewCases_ByDates$Total_New_Cases)%>% slice(1:10)
date_repoted2 = Bottom10_NewCases_By_Date$Date_reported
total_new_cases2 = Bottom10_NewCases_By_Date$Total_New_Cases
bar_heights6 <- barplot(total_new_cases2, names.arg = date_repoted2, col = terrain.colors(length(total_new_cases2)),
las = 3, cex.names = 0.8, main = "Dates with Minimal COVID-19 Cases",
xlab = "Date Reported", ylab = "Total New Cases")
text(x = bar_heights6, y = total_new_cases2/2, labels = total_new_cases2, pos = 3, cex = 0.5, col = "black")
```
Max cumulative cases/deaths ~ By country, date-reported
=======================================================================
Row
-----------------------------------------------------------------------
### Maximum cumulative deaths by each country by date-reported
```{r}
#calculating maximum cumulative deaths by each country:
cummlative_deaths = covid %>%
select(Country,Date_reported, Cumulative_deaths) %>%
group_by(Country) %>%
filter(Cumulative_deaths == max(Cumulative_deaths))%>%
slice_max(order_by = Cumulative_deaths, n =1, with_ties = FALSE) %>% # Ensures only the max value is taken, no ties
ungroup() %>%
arrange(desc(Cumulative_deaths))
plot_ly(data = cummlative_deaths, x = ~cummlative_deaths$Country, y = cummlative_deaths$Cumulative_deaths,color = cummlative_deaths$Cumulative_deaths, type = "bar")
```
### Maximum cumulative cases by each country by date-reported
```{r}
#calculating maximum cumulative cases by each country:
cummlative_cases = covid %>%
select(Country,Date_reported, Cumulative_cases) %>%
group_by(Country) %>%
filter(Cumulative_cases == max(Cumulative_cases))%>%
slice_max(order_by = Cumulative_cases, n =1, with_ties = FALSE) %>% # Ensures only the max value is taken, no ties
ungroup() %>%
arrange(desc(Cumulative_cases))
plot_ly(data = cummlative_cases, x = ~cummlative_cases$Country, y = cummlative_cases$Cumulative_cases,color = cummlative_cases$Cumulative_cases, type = "bar")
```
