1.0 Introduction
The 2019–20 coronavirus pandemic is a pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in Wuhan, Hubei, China in December 2019.
The pandemic has spread like forest fire, infecting around 14.3m as of date and more than 600K fatalities. A graphical dashboard enables an eagle’s eye view of the status as of date. The goal of this report is to understand trajectory of confirmed cases, deaths and recovered cases in the United States, which is the worst affected nation.
2.0 Major data and design challenges
The data downloaded from the reference mentioned below has time series data. Existing structure of the data is not helpful in creating a visualization as for each country, across a single column, #cases for each date starting Jan 22 till date is mentioned across the row
For the graph to be leggible and self explanatory, extra effort is required to clearly portray the intended information
Per the instructions for the assignment, we have to only build static visualizations due to which deep dive across dimensions is not possible
3.0 Solutionto data and design challenges listed above
I have manipulated the original dataset to combine information from 3 files into a single dataframe specific to US specific details
Below is my thought process around how i would like to show case data in the form of infographs
COVID spread in US.
4.0 Step-by-step description on how the data visualization was prepared
- loading the data
Global_confirmed<-read.csv("time_series_covid19_confirmed_global.csv")
Global_deaths<-read.csv("time_series_covid19_deaths_global.csv")
Global_recovered<-read.csv("time_series_covid19_recovered_global.csv")
library(readxl)
US<-read_excel("US.xlsx")- The raw data files for confirmed cases, deaths, and recovered cases globally are as shown below
Global_confirmed[1:5,1:4]## Province.State Country.Region Lat Long
## 1 Afghanistan 33.93911 67.70995
## 2 Albania 41.15330 20.16830
## 3 Algeria 28.03390 1.65960
## 4 Andorra 42.50630 1.52180
## 5 Angola -11.20270 17.87390Global_confirmed[1:5,179:183]## X7.14.20 X7.15.20 X7.16.20 X7.17.20 X7.18.20
## 1 34740 34994 35070 35229 35301
## 2 3667 3752 3851 3906 4008
## 3 20216 20770 21355 21948 22549
## 4 861 862 877 880 880
## 5 541 576 607 638 687Global_deaths[1:5,1:4]## Province.State Country.Region Lat Long
## 1 Afghanistan 33.93911 67.70995
## 2 Albania 41.15330 20.16830
## 3 Algeria 28.03390 1.65960
## 4 Andorra 42.50630 1.52180
## 5 Angola -11.20270 17.87390Global_deaths[1:5,179:183]## X7.14.20 X7.15.20 X7.16.20 X7.17.20 X7.18.20
## 1 1048 1094 1113 1147 1164
## 2 97 101 104 107 111
## 3 1028 1040 1052 1057 1068
## 4 52 52 52 52 52
## 5 26 27 28 29 29Global_recovered[1:5,1:4]## Province.State Country.Region Lat Long
## 1 Afghanistan 33.93911 67.70995
## 2 Albania 41.15330 20.16830
## 3 Algeria 28.03390 1.65960
## 4 Andorra 42.50630 1.52180
## 5 Angola -11.20270 17.87390Global_recovered[1:5,179:183]## X7.14.20 X7.15.20 X7.16.20 X7.17.20 X7.18.20
## 1 21454 22456 22824 23151 23273
## 2 2062 2091 2137 2214 2264
## 3 14295 14792 15107 15430 15744
## 4 803 803 803 803 803
## 5 118 124 124 199 210Dataset for visualization
- I have created single dataset for just US cases over the entire time period available
US[1:5,]## # A tibble: 5 x 4
## Date `Confirmed Cases` Deaths Recovered
## <dttm> <dbl> <dbl> <dbl>
## 1 2020-01-22 00:00:00 1 0 0
## 2 2020-01-23 00:00:00 1 0 0
## 3 2020-01-24 00:00:00 2 0 0
## 4 2020-01-25 00:00:00 2 0 0
## 5 2020-01-26 00:00:00 5 0 0- Using the data for each of the categories, i plotted individual charts to understand the scale and other important
Global Confirmed cases
library(ggplot2)
p=ggplot(US,aes(x=Date))+geom_line(aes(y=`Confirmed Cases`),colour='orange',size=1.5)+ggtitle("Spread of COVID - Confirmed Cases")+xlab("Date")+ylab("#Cases")+scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
p
Global Deaths
q=ggplot(US,aes(x=Date))+geom_line(aes(y=Deaths),colour='red',size=1.5)+ggtitle("Spread of COVID - Deaths")+xlab("Date")+ylab("#Cases")+scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
q
Global Recovered cases
r=ggplot(US,aes(x=Date))+geom_line(aes(y=US$Recovered),colour='green',size=1.5)+ggtitle("Spread of COVID - Recovered Cases")+xlab("Date")+ylab("#Cases")+scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
r
- However, to understand the complete picture of patients affected vis a vis mortality rate and #cases recovered, it is important to combine the charts into one graph as below
Final Visualization
Global Confirmed cases, Deaths and Recoveries
s=ggplot(US,aes(x=Date))+geom_line(aes(y=`Confirmed Cases`),colour='orange',size=1.5)+geom_line(aes(y=Deaths),colour='red',size=1.5)+geom_line(aes(y=US$Recovered),colour='green',size=1.5)+ggtitle("Spread of COVID Confirmed, Deaths and Recovered Cases")+xlab("Date")+ylab("#Cases")+scale_y_continuous(labels = function(x) format(x, scientific = FALSE))
s
5.0 Insights
It is quite evident that #confirmed cases started to rise significantly from mid March, where #deaths started to increase in the last week of March, and lastly the recovery numbers started to pick up 2 weeks from being detected (as is the lag in a person acquiring COVID versus starting to show the symptoms).
From the last chart, it is easier to interpret that the recovery rate is around 33% of the confirmed cases
Also, it is worth noticing that unlike the pandemics in the past, though the pandemic has sustained for a very long time and is unclear when will we emerge out of it, still the mortality rate in this case is very less around 2-3%
6.0 References
Data - “COVID”