Story telling

From p1, we can see the red line, representing Asia, weaves its way through the graph, its peaks and troughs depicting the various waves of the pandemic, reflecting the effectiveness of containment measures, the emergence of new variants, and a host of other factors.

Parallelly, the blue line mirrors a similar, yet distinct journey for America. It may not follow the exact route as its Asian counterpart, underscoring the different ways in which the pandemic unfolded across these continents due to variances in population, healthcare infrastructure, policy responses, and societal behavior.

In p2 ,as we travel from one point to another, we see a convincing account of the severity of the epidemic on various continents. Some points on the Y-axis may be higher, indicating that although the number of cases is similar, the mortality rate is higher, which may indicate differences in the efficiency of healthcare systems, age demographics, or reporting systems.

On the other hand, while handling similar cases, points located near the X-axis indicate a lower mortality rate. These regions may demonstrate effective medical interventions, timely policies, or other socio-cultural factors that contribute to improving survival rates.

In p3,The longer bar chart shows the continents more seriously affected by the epidemic. Perhaps these areas are densely populated, have high travel rates, or face challenges in implementing extensive testing and containment measures. Each high standard contains countless personal stories, struggles, and recoveries.

In contrast, the shorter section tells a different story. These continents may have achieved success in early containment measures, or may benefit from lower population density or other geographical advantages. However, each case represented in these sections still implies destruction, loss, and adaptation.

As we browse through the entire plot, we begin to grasp the scale of the epidemic footprint. From the most severely affected regions to the relatively better performing regions, we all recall the global nature of this crisis and the interconnected destinies of these continents.

Reference

code

library(readr)
library(tidyr)
library(dplyr)
library(ggplot2)
library(readxl)
library(maps)
library(tidyverse)
data <- read_excel("C:/Users/user/Desktop/data visualisation assignment/data/COVID-19gdw.xlsx")

class(data$month)
## [1] "numeric"
# Check unique values and data type of the month variable
unique(data$month)
##  [1] 12 11 10  9  8  7  6  5  4  3  2  1
typeof(data$month)
## [1] "double"
p1 <- ggplot(data, aes(month, cases, fill = continentExp)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_fill_manual(values = c("Asia" = "red", "America" = "blue")) +
  labs(
    title = "P1-COVID-19 Cases by Month and Continent",
    x = "Month",
    y = "Number of Cases",
    fill = "Continent"
  ) +
  scale_x_continuous(breaks = 1:12, labels = 1:12)
p2 <- ggplot(data, aes(cases, deaths, color = continentExp)) +
  geom_point() +
  labs(
    title = "p2-COVID-19 Cases vs Deaths",
    x = "Number of Cases",
    y = "Number of Deaths",
    color = "Continent"
  )
p3 <- ggplot(data, aes(continentExp, cases)) +
  geom_bar(stat = "sum", fill = "steelblue") +
  labs(
    title = "P3-Total COVID-19 Cases by Continent",
    x = "Continent",
    y = "Total Cases"
  )

Reference

Plots