library(plotly)
January 2nd, 2020
library(plotly)
This plot visualizes COVID-19 deaths in the U.S per state. Data is taken from:
https://coronavirus-resources.esri.com/datasets/628578697fb24d8ea4c32fa0c5ae1843_0
df <- read.csv("COVID-19_Cases_US.csv")
head(df)
## ï..X Y OBJECTID Province_State Country_Region ## 1 -86.64408 32.53953 1 Alabama US ## 2 -87.72207 30.72775 2 Alabama US ## 3 -85.38713 31.86826 3 Alabama US ## 4 -87.12511 32.99642 4 Alabama US ## 5 -86.56791 33.98211 5 Alabama US ## 6 -85.71266 32.10031 6 Alabama US ## Last_Update Lat Long_ Confirmed Recovered Deaths Active ## 1 2021/01/08 14:22:38+00 32.53953 -86.64408 4705 0 50 4655 ## 2 2021/01/08 14:22:38+00 30.72775 -87.72207 14845 0 171 14674 ## 3 2021/01/08 14:22:38+00 31.86826 -85.38713 1614 0 35 1579 ## 4 2021/01/08 14:22:38+00 32.99642 -87.12511 1981 0 48 1933 ## 5 2021/01/08 14:22:38+00 33.98211 -86.56791 4957 0 72 4885 ## 6 2021/01/08 14:22:38+00 32.10031 -85.71266 927 0 22 905 ## Admin2 FIPS Combined_Key Incident_Rate People_Tested ## 1 Autauga 1001 Autauga, Alabama, US 8421.486 NA ## 2 Baldwin 1003 Baldwin, Alabama, US 6649.973 NA ## 3 Barbour 1005 Barbour, Alabama, US 6538.119 NA ## 4 Bibb 1007 Bibb, Alabama, US 8846.119 NA ## 5 Blount 1009 Blount, Alabama, US 8572.269 NA ## 6 Bullock 1011 Bullock, Alabama, US 9177.309 NA ## People_Hospitalized UID ISO3 ## 1 NA 84001001 USA ## 2 NA 84001003 USA ## 3 NA 84001005 USA ## 4 NA 84001007 USA ## 5 NA 84001009 USA ## 6 NA 84001011 USA
plot_ly(data = df, type = 'bar', x = ~Province_State, y = ~Deaths )