My Take On COVID-19

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Sun, 05 May 2019 00:00:00 +0100

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Tip 2

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Sun, 05 May 2019 00:00:00 +0100

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Sat, 01 Jun 2030 13:00:00 +0000

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My Shiny App

Wed, 02 Sep 2020 00:00:00 +0000

Introduction

My shiny app is wrapped around COVID-19 data, instead of targeting the whole world, it targets most of the Asian countries being the most densely populated continent, also being the birthplace of the pandemic, which is speculation at best according to some sources.

Instead of looking at the mainstream death rates recoveries and cases, My app is built around other factors and variables which might have an impact on the outcome of cases in each country. The app does not give a final say if a variable has a direct impact on covid-19 or cases. Everything in it is open to interpretation, and the user gets to drive their own conclusions.

For example, The plot Stringency Index section of the app, helps the user identify any Asian countries level of stringency. Which helps users understand which countries took the most measures against Covid-19. Not only that but the user can compare the level of Stringency between countries. Especially the ones you are interested in.

The next tab Interact takes the user to the plot where the user can input three variables of their choice from the drop-down menus, to interact with the plot. To determine which variables might be effecting the countries stringency index. Or other factors which may or may not play a role in the number of cases or deaths due to Covid-19.

The third and last tab Table, helps the user identify the trend in terms of COVID-19 cases to make sense of my there were so many different levels or why the stringency index changed over time.

To use the app click here.

Self Review

What did you do well?

I believe that I used other variables as opposed to only death rates and other factors. I tried distancing it from mainstream news and articles where you can easily get information on the topic.

– What are the points you can improve on?

The overall look of the app can certainly be improved, although visual appearance and beauty is subjective it can always be improved.

– What are some things that you’d like to add that’s plausible?

The app might be tricky to new users however, using the hints given throughout the app might prove helpful. And once the user learns by trial and error the user will be able to use the app as intended. Depending on the users drive.

– What are some things that you’d like to add that cannot be done yet due to limitation in data or technology?

I feel like there are limitations with data. I would like to have data:

According to hospitals and regions that is extensive.
The name and location of the hospitals.
The hospital’s capacity.
Total covid-19 patients that were admitted in a particular hospital.
How many patients survived and died in which hospitals?
Critical levels of patients diagnosed with covid-19.
How much did it cost the patient to get treatment for covid-19 at their hospital?

Also, most countries do not have a well-defined region, the outer borders are easy to get but, inner borders between states and provinces are a bit tricky. Especially on a global scale.

References

Shiny.rstudio.com. 2020. Shiny. [online] Available at: https://shiny.rstudio.com/ [Accessed 15 October 2020].

What is Stringency Index? - INSIGHTSIAS. (2020). Retrieved 18 September 2020, from https://www.insightsonindia.com/2020/05/09/what-is-stringency-index/#:~:text=The%20Stringency%20Index%20is%20a%20number%20from%200,used%20by%20the%20Oxford%20COVID-19%20Government%20Response%20Tracker.

Packages

v3.3.2, g. (2020). ggplot2 package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/ggplot2/versions/3.3.2

plotly/plotly.py. (2020). Retrieved 18 September 2020, from https://github.com/plotly/plotly.py

v0.7.8, d. (2020). dplyr package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/dplyr/versions/0.7.8

v0.1.0, r., 2020. Rnaturalearth Package | R Documentation. [online] Rdocumentation.org. Available at: https://www.rdocumentation.org/packages/rnaturalearth/versions/0.1.0 [Accessed 15 October 2020].

Cran.r-project.org. 2020. [online] Available at: https://cran.r-project.org/web/packages/blogdown/blogdown.pdf [Accessed 15 October 2020].

Pakistan & CoVID-19

Wed, 02 Sep 2020 00:00:00 +0000

DATA Description

The data set is for the Covid-19 virus affecting all the countries, categorising the cases into confirmed cases, recovered cases and cases that resulted in death. The data set has a lot of limited variables. The data is collected from this link here. Our primary focus for the effected country will be Pakistan. @RamiKrispin

Why Pakistan?

Pakistan, officially the Islamic Republic of Pakistan, is a country in South Asia. It is the world’s fifth-most populous country with a population exceeding 212.2 million. It is the 33rd-largest country by area, spanning 881,913 square kilometres (340,509 square miles).

As the worlds 5th largest populated country, the government of Pakistan had a very different approach in how to handle, and contain the virus.

First the government was hesitent in imposing the lockdown. As reported by this article here.

Then the government with pressure from the opposition international community and the people imposed the lockdown as described in this article.

Here are some important dates according this source.

Methodology

We will start by filtering the loaded data, the country we want is Pakistan, so we will be filtering that. The first case in Pakistan was reported in February, so let’s get rid of additional dates. Some variables will be taken out, the province variable is empty, coordinates like latitude and longitude make no sense as all the recorded cases are from the same coordinates.

With the lockdown lifted during important events there were a lot of reservations. Some important dates are

13th of March (lockdown imposed)

27th of March (Eid-ul-Fitr, a one day religious festival)

24th of May (Smart lockdown imposed, only affected areas under lockdown)

31st of July (Eid-ul-Adha, a three day religions festival)

8th of August (lockdown lifted)

14th of August (Independence Day)

The table is interactive, for users to do their own research with respect to day, month, week of the year, number of cases reported, deaths reported, patients recovered. To identify the surge in numbers, especially after the above mentioned dates.

Confirmed	Death	Recovered
Min. : 1	Min. : 0.00	Min. : -8
1st Qu.: 340	1st Qu.: 7.00	1st Qu.: 90
Median : 1013	Median : 23.00	Median : 676
Mean : 1730	Mean : 36.81	Mean : 1641
3rd Qu.: 2721	3rd Qu.: 60.00	3rd Qu.: 2060
Max. :12073	Max. :178.00	Max. :19772

Confirmed Summary Summarising Pakistan’s COVID19 cases, in confirmed cases, in a day the least number of cases that were reported is 1 and the maximum number of cases reported in a day are 12,073. The average number of confirmed cases throughout the period have been 1,730. From a range perspective, 1,013 has been the middle for reported cases in a day.

The first quartile (Q1) tells us the middle value from the smallest value which in this case is 1 and the median 1,013. In other words, the first 25% of the cases are reported lower than 340.

The third Quartile (Q3) tells us the middle value from the largest value which in this case is 12,073 and the median 1,013. In other words, the last 25% of the cases are reported higher than 2,721.

Death Summary

Summarising Pakistan’s deaths reported due to COVID19, in deaths reported the least number of deaths reported is 0 on a day, and the maximum number of deaths reported on a day is 178. The average deaths or the mean of deaths reported throughout the period is 36. The mid-range on the number of deaths is at 23.

The first quartile (Q1) tells us the middle value from the smallest value which in this case is 0 and the median 23. In other words, the first 25% of the deaths reported are lower than 7.

The third Quartile (Q3) tells us the middle value from the largest value which in this case is 178 and the median 23. In other words, the last 25% of the cases are reported higher than 60.

Recovered Summary

Summarizing the recovered COVID19 cases in Pakistan. The least number of recovered cases are a bit different, the least number of patients that recovered in a day are -8 which means that the cases reported were untrue, follow this link to get a better understand of the interpretation. The maximum number of recovered cases reported in a day are 19,772. The average recovery of covid19 patients recovered throughout the period is 1,641.

The first quartile (Q1) tells us the middle value from the smallest value which in this case is -8 and the median 676. In other words, the first 25% of the deaths reported are lower than 90.

The third Quartile (Q3) tells us the middle value from the largest value which in this case is 19,772 and the median 676. In other words, the last 25% of the cases are reported higher than 2,060.

Romania & CoVID-19

Wed, 02 Sep 2020 00:00:00 +0000

DATA Description

The data sets used are of the COVID-19 virus, containing data on all countries. The first data set contains the total cases, new cases, cases per million of the countries population, total deaths, new deaths, new tests, total tests and stingency index. The data set has extensive variables, however, not all are used in this blog. The data set was obtained from here. The first set although extensive, did not contain the variable of cases recovered. A second data set was used to obtain the cases recovered from here.

Key Variables

variables	class	description
Date	Date	Date for the cases recorded
New_cases	Numeric	new cases recorded on a particular day
Cases_per_million	Numeric	cases recorded per million of the country’s population
Total_deaths	Numeric	total deaths recorded
New_deaths	Numeric	new deaths recorded
New_tests	Numeric	new tests conducted each day
Total_tests	Numeric	number of tests performed
Stringency Index	Numeric	governments response to the pandemic
recovered	Numeric	number of covid-19 cases recovered

Why Romania?

Romania is part of the European Union, according to Wikipedia, Romania is the largest country in Southeastern Europe and the twelfth-largest in Europe, having an area of 238,397 square kilometres. According to the 2011 census, Romania’s population is 20,121,641. As part of the European Union, Romania attracts a lot of tourism; the number of tourists has been rising steadily, reaching 9.33 million foreign tourists in 2016, according to the World Bank.

We are going to be looking into at what scale the Covid-19 affected Romania and how did Romania try to contain it.

Methodology

The data sets contain information on Covid-19 cases on all countries of the world, hence, we will filter it to give us details only on Romania. Only variables that are needed will be selected for exploration. The first covid-19 case was reported in Romania on 2020-02-27.

First, identify the ratio of tests and positive tested for covid-19.

The plot shows how many tests were performed and how many people tested positive for covid-19, color coded by the month. Interacting with the graph shows a growth in tested-positive cases. Try selecting the month by double clicking.

With an increasing trend in the number of patients diagnosed with covid-19, we need to look at the recovery-to-death ratio.

The data shows that the death ratio has been low in comparison to recovery. The highest death count was recorded in the month of August at 58. Meanwhile, in the month of July, the highest number of patients recovered were recorded at 1,707. There is no particular trend in the recovered to death ratio, however, death count increased in August. You can look at it yourself by interacting with the Month legend; just double click on the month you want to select.

Romanain Governments’ Response

How did the government handle this in Romania? For this, we will be looking into the stringency index, which in layman terms translates into the level of emergency imposed or the governments’ response to the pandemic spread.

The higher the stringency the more the measures taken to get the situation under control. With an increase in the number of cases, the Romanian Government implemented plans to get control on the situation as mentioned in this article. The stringency rate jumped from 16.67% to 58.33% in March. Travel restrictions were imposed, as mentioned here.

In April, fines and penalties were imposed to discourage citizens to break the lockdown regulations, as mentioned in this article. The stringency index jumped from 58.33% to 87.04%.

In May, some measures were relaxed on the previous lockdown policy, as mentioned here, dropping the stringency index from 87.045 to 75%.

In June, the Romanian Government decided to relax the restrictions and resume social and economic activity. More details are provided here, further dropping the stringency index from 75% to 54.41% and towards the end of June, the index dropped to 41.67%.

After a surge in cases, the Romanian Government announced new bylaws to be followed to get the cases under control without restricting the social and economic health of the country. All the measures are mentioned here. With these new regulations, the stringency index increased from 41.67% to 45.37% and has been stable ever since, from July to August.

Was the Stringency working?

The figure below shows the stringency index with the new cases reported, which lets us know the effectiveness of the Romanian Government. The figure speaks for itself; during the initial spike the increase in the stringency level did control the new cases reported. However, cases spiked as soon as the regulations were relaxed which is to be expected. Overall having a direct relation between the level of stringency and new cases reported.

References

Coronavirus Pandemic (COVID-19) – the data. (2020). Retrieved 18 September 2020, from https://ourworldindata.org/coronavirus-data

RamiKrispin/coronavirus. (2020). Retrieved 18 September 2020, from https://github.com/RamiKrispin/coronavirus

Romania. (2020). Retrieved 18 September 2020, from https://en.wikipedia.org/wiki/Romania

Continuarea măsurile de prevenire și limitare a eventualelor îmbolnăviri cu noul coronavirus. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/continuarea-masurile-de-prevenire-si-limitare-a-eventualelor-imbolnaviri-cu-noul-coronavirus

Comunicat de presă 20 martie, ora 9:36am. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/comunicat-de-presa-20-martie-ora-9-36am

BULETIN DE PRESĂ 2 Aprilie 2020, ora 13.00. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/buletin-de-presa-2-aprilie-2020-ora-13-00

Informare de presă 9 mai 2020, ora 1:23 pm. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/informare-de-presa-9-mai-2020-ora-1-23-pm

Precizări de presă 15 iunie 2020. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/precizari-de-presa-15-iunie-2020

INFORMARE DE PRESĂ - 21 iulie 2020, ora 11.37. (2020). Retrieved 18 September 2020, from https://stirioficiale.ro/informatii/informare-de-presa-21-iulie-2020-ora-11-37

Packages

v1.3.1, r. (2020). readr package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/readr/versions/1.3.1

v3.3.2, g. (2020). ggplot2 package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/ggplot2/versions/3.3.2

v0.7.8, d. (2020). dplyr package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/dplyr/versions/0.7.8

Wickham, H. (2020). Easily Install and Load the ‘Tidyverse’ [R package tidyverse version 1.3.0]. Retrieved 18 September 2020, from https://cran.r-project.org/web/packages/tidyverse/index.html

v0.8.3, t. (2020). tidyr package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/tidyr/versions/0.8.3

plotly/plotly.py. (2020). Retrieved 18 September 2020, from https://github.com/plotly/plotly.py

RamiKrispin/coronavirus. (2020). Retrieved 18 September 2020, from https://github.com/RamiKrispin/coronavirus

Auguie, B. (2020). Miscellaneous Functions for “Grid” Graphics [R package gridExtra version 2.3]. Retrieved 18 September 2020, from https://cran.r-project.org/web/packages/gridExtra/index.html

v1.7.9, l. (2020). lubridate package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/lubridate/versions/1.7.9

v1.2.1, k. (2020). kableExtra package | R Documentation. Retrieved 18 September 2020, from https://www.rdocumentation.org/packages/kableExtra/versions/1.2.1

An example preprint / working paper

Sun, 07 Apr 2019 00:00:00 +0000

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Tue, 05 Feb 2019 00:00:00 +0000

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CoronaShiny Package

Mon, 01 Jan 0001 00:00:00 +0000

Package

In this blog I will be talking about packages, what do they entail and what is a good package like and then we will be reviewing a package made by a colleague of mine. The name of the package is CoronaShiny. The author of the Package is Vinny Vu. You can check it out on github. The package is wrapped around the coronavirus pandemic and has an inbuilt shiny-app inside the package. We will discuss what the package entails a bit later on.

What did I think this package would do? Did it do it?

The idea I got for what the package contains and what I can use from it, is that the package contains an app helping users interact and get visualizations on the cumulative cases of corona-virus in each continent. The package has been designed to launch the app with the launch_app() function. Also, the package has an inbuilt function of selectInput01, the function helps the user to update the app inputs directly from the function instead of indulging in a lengthy process of customized coding for each input. This is what I understood from reading the README file on the git repository.

Using the package CoronaShiny, provided as promised. It is simple to use with no complications which make any package desirable to use.

About the APP

The shiny application helps users explore summary figures of confirmed cases, confirmed deaths, and recovered cases across different continents with the use of line-graphs and summary data tables. The application further, explores six countries with the highest cumulative coronavirus confirmed cases using line-graphs. Finally, a data table showing the total case numbers, to compare case numbers across all countries.

Are there improvements that could be made to the code?

Improving code is subjective, everyone has their own style of coding, for a greater audience and for greater understanding simple code is better. However, more functions can be made to draw out parts of the shiny app instead of launching the whole app. They can be easily called out and played around with, by the user to suit their needs.

Are there improvements that could be made to the shiny app inside the package?

Although the shiny application is wonderful the application only offers mainstream plots, the app does not have a niche factor. Other variables can be introduced to the application or different type of helping visualizations to make the app stand out.

Is the documentation sufficient to get started?

The documentation is accurate and to the point helping you navigate through the application and package effectively. I found the pkgdown website the most helpful it had everything I needed to know about the package. Here is the link to the pkgdown website.

What did I learn from reviewing the package?

A lot of effort and work goes into making a package, you can not just make a package and make it available for others to use. You also have to document everything that what you did, not only for first-time users to understand what the package is all about but also for your future self.

Package Review

Checking off boxes as applicable, and elaborate in comments below.

Documentation

The package includes all the following forms of documentation:

Installation instructions: for the package is found in README
Vignette(s) demonstrating major functionality that runs successfully locally
Function Documentation: for all exported functions in R help
Examples for all exported functions in R Help that run successfully locally

Functionality

Installation: Installation succeeds as documented.
Functionality: Any functional claims of the software been confirmed.
Automated tests: Unit tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.