The COVID-19 pandemic has affected countries and regions worldwide. In this report, we explore global trends by analyzing key COVID-19 metrics such as confirmed cases, deaths, recoveries, and active cases. The goal is to highlight insights about the most impacted countries, compare specific countries, and present a broader view of the global pandemic situation.
Data Overview
We used the COVID-19 Global Dataset to analyze the following key metrics: - Confirmed cases: Total number of confirmed cases of COVID-19. - Deaths: Total number of deaths due to COVID-19. - Recovered cases: Total number of recovered individuals. - Active cases: Number of currently active cases of COVID-19.
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library(tidyr)# Load the COVID-19 dataset (adjust the path if needed)covid_data <-read_csv("C:/Users/yashp/OneDrive/Documents/worldometer_coronavirus_summary_data.csv")
Rows: 226 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): country, continent
dbl (10): total_confirmed, total_deaths, total_recovered, active_cases, seri...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Preview the first few rows of the datasethead(covid_data)
# A tibble: 6 × 12
country continent total_confirmed total_deaths total_recovered active_cases
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanist… Asia 179267 7690 162202 9375
2 Albania Europe 275574 3497 271826 251
3 Algeria Africa 265816 6875 178371 80570
4 Andorra Europe 42156 153 41021 982
5 Angola Africa 99194 1900 97149 145
6 Anguilla North Am… 2984 9 2916 59
# ℹ 6 more variables: serious_or_critical <dbl>,
# total_cases_per_1m_population <dbl>, total_deaths_per_1m_population <dbl>,
# total_tests <dbl>, total_tests_per_1m_population <dbl>, population <dbl>
Top 10 Countries by Confirmed Cases
Let’s start by analyzing the top 10 countries by the total number of confirmed COVID-19 cases.
# Filter and arrange top 10 countries by confirmed casestop_countries <- covid_data %>%arrange(desc(total_confirmed)) %>%slice_head(n =10)# Plotting the top 10 countries by confirmed caseslibrary(ggplot2)ggplot(top_countries, aes(x =reorder(country, total_confirmed), y = total_confirmed)) +geom_bar(stat ="identity", fill ="steelblue") +coord_flip() +labs(title ="Top 10 Countries by Confirmed COVID-19 Cases",x ="Country",y ="Total Confirmed Cases" ) +theme_minimal()
Interpretation:
The plot above shows the top 10 countries with the highest number of confirmed COVID-19 cases. Countries like the USA, India, and Brazil are among the most impacted. This suggests that these countries have faced larger outbreaks due to factors such as population density, healthcare system capacity, and government response.
Comparison of Two Countries
We will now compare the COVID-19 metrics (confirmed cases, deaths, recoveries, and active cases) of two countries of your choice. Here, we use USA and India as examples.
# Filter data for USA and Indiacountry_comparison <- covid_data %>%filter(country %in%c("USA", "India")) %>%select(country, total_confirmed, total_deaths, total_recovered, active_cases) %>%pivot_longer(cols =-country, names_to ="metric", values_to ="value")# Plotting the comparison between USA and Indiaggplot(country_comparison, aes(x = metric, y = value, fill = country)) +geom_bar(stat ="identity", position ="dodge") +labs(title ="COVID-19 Comparison: USA vs India",x ="Metric",y ="Value" ) +theme_minimal()
Interpretation:
From this plot, we can observe how USA and India compare across key COVID-19 metrics:
USA has a significantly higher number of total confirmed cases, deaths, and active cases than India.
India shows a high number of recoveries, suggesting better management of the pandemic and higher recovery rates.
Despite the high number of confirmed cases in the USA, active cases remain a significant concern.
Insights
1. Most Impacted Countries
From the earlier analysis of the top 10 countries by confirmed cases, we can conclude that countries like the USA, India, and Brazil have been the hardest hit by COVID-19. This reflects larger outbreaks, more significant impacts on the healthcare system, and the need for continued interventions.
2. Recovery and Mortality
A closer look at countries with high confirmed cases reveals that India has shown strong recovery metrics compared to its high case numbers. On the other hand, countries like the USA have faced a much higher mortality rate, suggesting that there were significant challenges in managing the pandemic effectively despite the high number of recoveries.
3. Active Cases
Comparing active cases between countries shows that while India has a higher total confirmed case count, its active cases are considerably lower than the USA. This indicates better control over ongoing outbreaks in India as compared to the USA, which still has a significant number of active cases.
Conclusion
In conclusion, the global COVID-19 dataset provides valuable insights into the pandemic’s progress. Through the analysis of total confirmed cases, deaths, recoveries, and active cases, we can draw the following conclusions:
Countries like the USA and India have been the most impacted by COVID-19.
High recovery rates in some countries, such as India, suggest effective management of the virus, while others, like the USA, face challenges in controlling the virus despite high recovery rates.
Active cases in some regions, especially in the USA, remain a concern and continue to pose a threat to the healthcare system.
As the pandemic evolves, countries must continue to monitor these metrics closely to implement targeted responses, ensuring the continued safety and health of their populations.
