## # A tibble: 5 x 2
##   county      newCases
##   <chr>          <dbl>
## 1 Los Angeles     1042
## 2 Sacramento       394
## 3 San Diego        361
## 4 Santa Clara      289
## 5 Riverside        205
Most New Cases California Counties
County New Cases
Los Angeles 1,042
Sacramento 394
San Diego 361
Santa Clara 289
Riverside 205 
## # A tibble: 5 x 2
##   county          cases
##   <chr>           <dbl>
## 1 Los Angeles    252066
## 2 Riverside       55073
## 3 Orange          51758
## 4 San Bernardino  50385
## 5 San Diego       42032
Most Cumulative Cases California Counties
County Cum Cases
Los Angeles 252,066
Riverside 55,073
Orange 51,758
San Bernardino 50,385
San Diego 42,032

Attributes of Population Estimates

names of columns dimension: 3275 x 165 number of rows: 3275 structure: tibble/data frame

## # A tibble: 5 x 2
##   county     newCasesCapita
##   <chr>               <dbl>
## 1 Kings            0.000791
## 2 Madera           0.000451
## 3 Calaveras        0.000261
## 4 Sacramento       0.000254
## 5 Sutter           0.000227
Most New Cases per capita California Counties
County New Cases per Capita
Kings 0.0007912
Madera 0.0004513
Calaveras 0.0002614
Sacramento 0.0002539
Sutter 0.0002269 
## # A tibble: 5 x 2
##   county   casePerCapita
##   <chr>            <dbl>
## 1 Imperial        0.0619
## 2 Kings           0.0460
## 3 Kern            0.0340
## 4 Tulare          0.0324
## 5 Merced          0.0308
Most cumulative per capita California Counties
County Cumulative Cases per Capita
Imperial 0.0619044
Kings 0.0460246
Kern 0.0339513
Tulare 0.0324199
Merced 0.0307584 
## # A tibble: 17 x 2
##    county    totCases
##    <chr>        <dbl>
##  1 Alpine        0   
##  2 Del Norte    25.2 
##  3 El Dorado    42.0 
##  4 Humboldt     58.3 
##  5 Lake         97.8 
##  6 Lassen       39.3 
##  7 Mariposa     17.4 
##  8 Mono          6.92
##  9 Nevada       47.1 
## 10 Placer       94.6 
## 11 Plumas       16.0 
## 12 Shasta       27.8 
## 13 Sierra        0   
## 14 Siskiyou     62.0 
## 15 Solano       96.5 
## 16 Trinity      32.6 
## 17 Tuolumne     58.7
dat %>%
  summarize(totCases = sum(cases))
## # A tibble: 1 x 1
##   totCases
##      <dbl>
## 1 47000535
dat %>%
  summarize(totnewCases = sum(newCases, na.rm = TRUE))
## # A tibble: 1 x 1
##   totnewCases
##         <dbl>
## 1      757052

The total number of cases is 29381287 and total number of new cases is 634918 and the total number of safe counties is 12.

I think the scaling made some states worse and some states better. For example, for California the slope looked a lot more prevalent in New Cases but after being scaled by its huge population, it seemed less severe represented by a smaller looking slope. Louisiana had the oppoisite effect. The slope appeared bigger after being scaled with population. I think that is because its population is small.