Time-Series Plot (2020-2022)
Abdulaziz Abdulrahman Alhammad / 3763447
The outbreak of the COVID-19 pandemic had adverse effects on countries economically and in other aspects. A large number of death cases were reported globally. Countries with higher population density faced health facility crises, as citizens affected by the COVID-19 virus were not hospitalized (Pinchoff et al., 2021).
As cases increased, countries implemented nationwide lockdowns and ‘stay at home’ orders to limit the spread of the COVID-19 outbreak. Businesses had to shut down as the pandemic shook the economies of many countries (Enane et al., 2021).
Over two years now, we look back to see the global pattern of COVID-19 spread. This paper implements time series analysis to visualize reported COVID-19 cases, recoveries, and deaths experienced worldwide to achieve the objective. For a global overview, top countries were chosen to highlight these COVID-19 factors further.
Methods
This paper used two types of visualizations to map the COVID-19 data.
• The first type of visualization is a time-series plot. The plot presents an overview of the number of confirmed cases, recoveries, and the number of deaths due to the COVID-19 outbreak.
• The other type of visualization is a bar plot that compares the top and bottom five countries based on the number of deaths, number of recoveries, and number of confirmed cases.
Evaluating these plots will further provide insights into the global trends and patterns of COVID-19 pandemic cases, recoveries, and deaths.
The study used data from a secondary source, i.e. a publicly hosted GitHub repository. The dataset contains information about the number of confirmed cases, recoveries, and deaths for each country. This kind of longitudinal and quantitative data is useful for global analysis. The original data source is from Johns Hopkins University Center for Systems Science and Engineering (Johns Hopkins University 2022).
First load up the required packages
Next, import the dataset into the R Environment.After loading the
dataset, the summary() function in R will be used to get an
overview summary about the dataset being used for the project.
Confirmed Recovered Deaths
Min. : 0 Min. : 0 Min. : 0
1st Qu.: 1220 1st Qu.: 0 1st Qu.: 17
Median : 23692 Median : 126 Median : 365
Mean : 736157 Mean : 145397 Mean : 13999
3rd Qu.: 255842 3rd Qu.: 17972 3rd Qu.: 4509
Max. :80625120 Max. :30974748 Max. :988609 The following Visualizations will be made;
Time-Series Line plot for;
Top and Bottom 5 Countries for Number of Cases.
Top and Bottom 5 Countries for Number of Recoveries.
Top and Bottom 5 Countries for Number of Deaths.
The dataset had the accumulated number of cases, deaths and recoveries. Time series plots provided a general overview which is not sufficient to understand the global distribution. Compared to that, the bar plots provided more comprehensive information on the number of cases, recoveries and death trends in different countries. A negative trend was observed in terms of recoveries as it started going down from 2021, July.
The US, India, Brazil, France, and the UK registered the highest number of confirmed cases. while countries like the Marshall Islands, Antarctica, and Micronesia had the lowest confirmed cases.
Brazil and India had one of the highest recoveries and deaths. Micronesia was among the worst recoveries as Antarctica and the Micronesia had the least cases due to the Covid-19 infections. The US also had the highest number of deaths in the world.
To conclude, data visualization is vital to uncover the trends in different factors caused by the COVID-19 outbreak. Findings from open-source data can help the public monitor the situation and take precautions based on the spread of the COVID-19 virus.
Enane, L. A., et al. (2021). “Social, economic, and health effects of the COVID-19 pandemic on adolescents retained in or recently disengaged from HIV care in Kenya, Plos one, 16(9), e0257210.
Johns Hopkins University 2022, Coronavirus Countries Aggregated, CSV file, GitHub, Viewed 10 May 2022, < https://github.com/datasets/covid-19>
Pinchoff, J., et al. (2021), Gendered economic, social and health effects of the COVID-19 pandemic and mitigation policies in Kenya, evidence from a prospective cohort survey in Nairobi informal settlements, BMJ open 11(3), e042749.