COVID-19 | Yuheng's Blog

Shiny App -- Covid-19 Viewer

Sat, 10 Oct 2020 00:00:00 +0000

Introduce `Covid-19 view`

This App is used to monitor global covid-19 cases and deaths, and it can compare the covid-19 cases in different countries.

There are four tabs: Map, Table, Graphs, and `About.

Map: This tab has two leaflet maps. The first one shows the global cumulative_cases. The second one shows the global cumulative_deaths. Because the dataset contains huge amount of values, the leaflet maps take much time to run. So, please be patient.
Table: This tab shows the latest covid-19 new_cases, cumulative_cases, new_deaths, and cumulative_deaths of selected countries. User can select multiple countries to compare them.
Graphs: There are one column plot and one line plot in this tab. The column plot help user compare the latest cumulative_cases and cumulative_deaths of selected countries. The line plot tells user the new_cases and new_deaths of selected countries. With these two plots, user can understand how successful selected countries control the coronavirus pandemic.
About: This tab has short introduction about Covid-19 viewer and its creator. It also contains the Acknowledgments and References sections.

You can download this App from Github.

This screenshot is an overview of Covid-19 Viewer.

Self Review

What did I do well

First, this is my first time to do shiny, I did a lot of self study and complete it without any bugs.

Second, it spends me 3 nights to deal with leaflet output for shiny app. During the process, I have trouble to create circle markers within leaflet maps. Thanks Stuart for helping me solve this problem!

What are the points I can improve on?

Loading my leaflet map in the first tab is time-consuming. Every time a user change the slide bar, it would take at least 10 seconds to show the results.

After completion of my About tab, I know a better way to write texts/paragraphs in shiny.

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

First, I want to make the line plot in Graph tab animated. The animated plot would look more entertaining.

Second, I want to make users be able to upload csv file in a tab called “Upload file”; after uploading the csv file, the App will draw maps and plots based on the latest dataset. This function can release the app maintainer from updating the covid-19 dataset every day.

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

In my plan, the leaflet map will look like the plot below, this plot is from WHO Coronavirus Disease (COVID-19) Dashboard. But after several-days tries, I still could not figure it out.

I also plan to make it available for users to upload csv files by themselves. But after two days, I still cannot solve it.

COVID-19 in Mexico

Wed, 09 Sep 2020 00:00:00 +0000

Data source

The dataset used in this post is from World Health Organization (WHO)(WHO 2020) on 15-09-2020.

Background story

Mexico and the U.S are neighbours. Because the U.S is much more developed than Mexico, there are many illegal immigrants from Mexico go across the border and live and/or work in the U.S.. The drug trafficking in Mexico is notorious. They commit crimes and the government can do little about them. However, surprisingly, during the coronavirus pandemic, criminal cartels provide food to poors and help hospitals procure the necessary equipment (Pazzona 2020). This strange story occurs mainly because of two reasons. First, the Mexico government does not have the capacity to organise effective operations to help poor people or hospitals. Second, the gangs’ income is decreasing due the coronavirus pandemic. So, they eagerly want the society back to normal.

Like the U.S, the transmission classification in Mexico is community transmission as well (fig 1 shows the location of Mexico). Community transmission means the government lacks capacity to control the coronavirus pandemic or the residents do not follow the rules.

Figure 1: Mexico and USA

What does the data tell us

Fig 2 shows the Date, New cases, Cumulative cases, New deaths, and Cumulative deaths of Mexico. Unlike Russia, the earliest date reported of Mexico was the end of February 2020. The highest daily new cases was 9556 on 3rd Aug 2020. The highest daily new deaths was 1091 on 5th Jun 2020.

Fig 3 shows the red line (New cases) increased dramatically after 1st Apr 2020. Daily new deaths is also surprisingly high; until now, the average of new deaths is 353.02. The high daily new cases will overwhelming Mexico’s hospitals and medical resources.

Fig 4 shows that Mexico’s cumulative cases was at 7th highest globally, but its cumulative deaths was at 4th highest globally. So far, Mexico’s death toll from the pandemic is the 13th highest on a per capita basis (Garcia and Gutierrez 2020). According to World Bank database, Mexico’s hospital beds (per 1,000 people) was only 1.5 in 2015. As we know, the availability of CCB-ICU beds, mechanical ventilation and ECMO devices are critical for saving lives in the coronavirus pandemic. But the treatment is expensive, as well as the test. According to the World Bank, the poverty headcount ratio at national poverty lines (% of population) of Mexico was 41.9%. This is crucial because even poor people know they are infected, they can’t afford the treatment. In addition, because of coronavirus pandemic, people can hardly find a job — they have no income.

Figure 2: COVID-19 cases reported in Mexico since 28-02-2020

Figure 3: Daily new cases and new deaths in Mexico

Figure 4: Cumulative cases vs. Cumulative deaths on 15-09-2020

My viewpoint

The global coronavirus pandemic slows down global ecnomy dramatically in 2020, and the situation may last for years. The control ability and the efficiency of governments and the industrial power of countries are tested in the circumstance. Latin American countries don’t have strong industrial base, so they can’t manufacture important medical devices by themselves. Most of the medical equipments are imported. The hopital beds per 1000 people is an indicator which represents the ability that a country dealing with the emergency situation like this time. Also, the cooperation of residents is important as well. In order to pass the difficult time, people must follow the instructions of national health department.

Acknowledgements

The packages used for producing this post are:

tidyverse (Wickham et al. 2019), janitor (Firke 2020), plotly (Sievert 2020), DT (Xie, Cheng, and Tan 2020), knitr (Xie 2014), readr (Wickham, Hester, and Francois 2018), ggmap (Kahle and Wickham 2013), ggthemes (Arnold 2019), gridExtra (Auguie 2017)

The poverty data and hospital bed data are gathered from the World Bank open data (Knippenberg et al. 2020)

References

Arnold, Jeffrey B. 2019. Ggthemes: Extra Themes, Scales and Geoms for ’Ggplot2’. https://CRAN.R-project.org/package=ggthemes.

Auguie, Baptiste. 2017. GridExtra: Miscellaneous Functions for "Grid" Graphics. https://CRAN.R-project.org/package=gridExtra.

Firke, Sam. 2020. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://CRAN.R-project.org/package=janitor.

Garcia, David Alire, and Miguel Angel Gutierrez. 2020. “Mexico’s Confirmed Coronavirus Deaths Rise to 70,604.” Msn. https://www.msn.com/en-au/lifestyle/wellbeing/mexico-s-confirmed-coronavirus-deaths-rise-to-70-604/ar-BB18YobE.

Kahle, David, and Hadley Wickham. 2013. “Ggmap: Spatial Visualization with Ggplot2.” The R Journal 5 (1): 144–61. https://journal.r-project.org/archive/2013-1/kahle-wickham.pdf.

Knippenberg, Erwin, Emi Suzuki, John Baffes, and E. Purdie. 2020. “World Bank Open Data.” Data. The World Bank. https://data.worldbank.org/.

Pazzona, Matteo. 2020. “Coronavirus: Crime Cartels Helping Communities Will Extract a High Price in Years to Come.” The Conversation. https://theconversation.com/coronavirus-crime-cartels-helping-communities-will-extract-a-high-price-in-years-to-come-138592.

Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.

WHO. 2020. “WHO Coronavirus Disease (Covid-19) Dashboard,” August. https://covid19.who.int/table.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain FranC’ois, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.

Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.

Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.

Xie, Yihui, Joe Cheng, and Xianying Tan. 2020. DT: A Wrapper of the Javascript Library ’Datatables’. https://CRAN.R-project.org/package=DT.

COVID-19 in Russia

Sun, 30 Aug 2020 00:00:00 +0000

The first post is about COVID19 in Russia.

Data source

The dataset used in this post is from World Health Organization (WHO)(WHO 2020) and the data source is Russian Federal State Statistics Service.

About Russia

WHO classifies COVID-19 transmission in Russia as clusters of cases. WHO defines clusters of cases as “cases that are clustered in time, geographic location and or by common exposures”.

Russia has 146,748,590 people. It has large country area but only a small proportion of the areas in which people are willing to live. This could be one of reasons of clusters of cases in Russia. Fig1 shows the COVID-19 cases reported in Russia since 31st, Jan 2020; we can see that the largest number of new cases was on 27th Apr (12559 new cases on this day).

Figure 1: COVID-19 cases reported in Russia since 2020-01-31

Table 1 shows the statistic summary of four variables — new cases, cumulative cases, new deaths, and cumulative deaths. The max of cumulative cases and cumulative deaths means the latest numbers, while the max of new cases and new deaths shows the peaks for these two variables. This table is not informative enough. So, I draw a boxplot (Fig2). The boxplot uses y-axis scales and transformations because when I put deaths and new cases in a same plot, the deaths can be hardly seen if they share same y-scale. The boxplot for new cases is left-skewed, which means newly reported cases are still high. In other words, Russia government is trying to control the virus spreading but does not succeed yet.

Table 1: Summary table
vairble	min	Q1	median	mean	Q3	max
new_cases	0.00	53.75	5224.00	4647.86	7743.50	12559.00
Cumulative cases	6	438	267443	355441	695871	985346
new_deaths	0.00	0.00	91.50	80.31	135.75	232.00
Cumulative deaths	0	0	2478	5310	10537	17025

Figure 2: Boxplot for four cumulative cases, cumulative deaths, new cases, and new deaths

Fig3 supports my conclusion above. Some pitfalls in the red line indicates the test results were not reported at these days.

Figure 3: Line plot illustrates the new cases and new death trend in Russia since 2020-01-31

My viewpoint

The bad situation in Russia will continue affecting its economy in at least next six months. Russia’s economy is vulnerable and Russia government wants eagerly to do their best for reopen their markets. Russia president, Putin, even allowed the coronavirus vaccine to skip crucial trials (i.e. testing in a larger number of humans to confirm its effectiveness) and claimed one of his daughters had been jabbed (Cockburn 2020). All these actions are becuase Russia cannot wait any longer. However, the bad news is covid-19 is mutated along time (Groch and Mannix 2020). We don’t know whether the vaccine will be still effective in future, even in next month.

Acknowledgements

The packages used for producing this post are:

tidyverse (Wickham et al. 2019), janitor (Firke 2020), plotly (Sievert 2020), DT (Xie, Cheng, and Tan 2020), knitr (Xie 2014), kableExtra (Zhu 2019), readr (Wickham, Hester, and Francois 2018)

References

Cockburn, Paige. 2020. “Why Development of Russia’s Covid-19 Vaccine Is ’Reckless’.” ABC News. ABC News. https://www.abc.net.au/news/2020-08-13/australian-experts-react-to-sputnik-vaccine-news-reckless/12552028.

Firke, Sam. 2020. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://CRAN.R-project.org/package=janitor.

Groch, Sherryn, and Liam Mannix. 2020. “The Same Coronavirus Strain Turned up in an Australian and a Tiger. How?” The Sydney Morning Herald. The Sydney Morning Herald. https://www.smh.com.au/national/the-same-coronavirus-strain-turned-up-in-an-australian-and-a-tiger-how-20200622-p554wq.html.

Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.

WHO. 2020. “WHO Coronavirus Disease (Covid-19) Dashboard,” August. https://covid19.who.int/table.

Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.

Xie, Yihui, Joe Cheng, and Xianying Tan. 2020. DT: A Wrapper of the Javascript Library ’Datatables’. https://CRAN.R-project.org/package=DT.

Zhu, Hao. 2019. KableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

COVID-19 | Yuheng's Blog

Shiny App -- Covid-19 Viewer

Introduce Covid-19 view

Self Review

What did I do well

What are the points I can improve on?

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

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

COVID-19 in Mexico

Data source

Background story

What does the data tell us

My viewpoint

Acknowledgements

References

COVID-19 in Russia

Data source

About Russia

My viewpoint

Acknowledgements

References

Introduce `Covid-19 view`