1 Introduction

It was March 2020, still traumatized that Andrew Yang drop-out from the presidential race. The only candidate that is data informed and very original. The other headline news during this time frame were about Syrian Civil War, Coronavirus pandemic, European migrant crisis, and the presidential race in the United States (Wikipedia 2020).

I was very optimistic that the United States will not have a pandemic in any of its states or properties. At the same time, I was amazed at the many dashboards that were created to monitor the spread of Covid-19 globally. Below is an example code of how easy it is to pull information and create a visual map.

library(COVID19)
library(dplyr)
library(DT)
library(knitr)
library(kableExtra)

# grab data from library COVID19
X <- covid19(verbose = FALSE)

MarchCovid <- X %>% 
  filter(date == "2020-03-31") %>% 
  select(
    administrative_area_level_1,
    date,
    confirmed,
    recovered,
    deaths) %>% 
  arrange(desc(confirmed)) %>% 
    rename(country = 
             administrative_area_level_1)

## Turn to table
MarchCovid[1:10, ] %>% 
  kable(caption = 'March 2020 Covid19 Metrics') %>%   
  kable_styling()

Table 1.1: March 2020 Covid19 Metrics

id	country	date	confirmed	recovered	deaths
USA	United States	2020-03-31	188724	7024	5718
ITA	Italy	2020-03-31	105792	15729	12428
ESP	Spain	2020-03-31	95923	19259	8464
CHN	China	2020-03-31	82545	0	3314
DEU	Germany	2020-03-31	74132	71477	2653
FRA	France	2020-03-31	52128	9444	0
IRN	Iran	2020-03-31	44605	14656	2898
GBR	United Kingdom	2020-03-31	25521	135	2453
BEL	Belgium	2020-03-31	16981	1696	1377
CHE	Switzerland	2020-03-31	16605	1823	433

I started the Data Science Specialization when Covid-19 cases were below 200,000 in the US. These numbers affected people’s lives, their families, loved ones and neighbors. It changes the way we shop, travel, and live. It made us value our homes and the kitchen became the center of the house again.

Map visualization can be accomplish with many packages available in R. One of them is called leaflet package and below will show us how aggressive the spread of Covid19 throughout the globe during the end of March 2020.

library(leaflet)
library(viridisLite)
library(viridis)

# Create a map using Leaflet
mapVisData <- X %>% 
  filter(date == "2020-03-31") %>%    
  rename(country=
           administrative_area_level_1)  %>% 
  select(country,
          date,
          longitude,
          latitude,
          confirmed,
          recovered,
          deaths)

# Color code with viridislite and viridis
CountryColor <- colorFactor(
  viridis(76), 
  mapVisData$country)

Figure_1.1 <- leaflet(
  data = mapVisData) %>% 
  addTiles() %>% 
  setView(lng = 26.8206,
        lat = 30.8025,
        zoom = 2) %>% 
  addCircleMarkers(
     ~ longitude,
     ~ latitude,
     popup = paste(
     "Country: ",prettyNum(
       mapVisData$country, 
       big.mark = ","), 
     "<br>",
     "Confirmed: ",prettyNum(
       mapVisData$confirmed, 
       big.mark = ","), 
     "<br>",
     "Recovered: ", prettyNum(
       mapVisData$recovered, 
       big.mark = ","), 
     "<br>",
     "Deaths: ", prettyNum(
       mapVisData$deaths, 
       big.mark = ",")),
   weight = 1,
   radius = ~ sqrt(confirmed)*.1,
   stroke = FALSE,
   fillOpacity = .7,
   fillColor = ~CountryColor(country)
) 
Figure_1.1

Figure 1.1: End of March - Covid19 (RStudio Leaflet Team 2020)

At the beginning of the class, I was not able to code the way I code in R now. The progression and knowledge skills increase as I consume more of the information. Again, I had vivid dreams while taking these classes and if you decided to pursue the same route, may you have the sweetest of all dreams. I hope this book gets you through the night.

References

RStudio Leaflet Team. 2020. “Leaflet for R.” https://rstudio.github.io/leaflet/.

Wikipedia. 2020. “Portal:Current Events/March 2020.” Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/wiki/Portal:Current_events/March_2020.