Scenario ABC

Dataset

load("Ozone Data with Region.RData")
pop <- read_excel("Population/Population County.xlsx")
pop12<-pop%>%filter(Year==2012)

Scenario A

## A1
A<-df4%>%
  filter(Year==2012,between(Max.Ozone,75,75+0.905))

unique(A$GEOID)%>%length() 

## [1] 349

unique(A$Date..Local.)%>%length() 

## [1] 160

A<-left_join(A,pop12,by=c("GEOID"="fips"))

A.pop<-A%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) # ALL OZONE LEVEL IS 75, So i think it have same kind of drought

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

A.pop<-A.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

A.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01049  70.9 132814089
## 2 01073 658.  132814089
## 3 01089 343.  132814089

### A2
A2<-df4%>%
  filter(Year==2012,between(Max.Ozone,70,70+0.905))

unique(A2$GEOID)%>%length()

## [1] 517

unique(A2$Date..Local.)%>%length() 

## [1] 196

A2<-left_join(A2,pop12,by=c("GEOID"="fips"))

A2.pop<-A2%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) # ALL OZONE LEVEL IS 70

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

A2.pop<-A2.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

A2.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01033  54.6 178745925
## 2 01051  80.2 178745925
## 3 01073 658.  178745925

###A3

A3<-df4%>%
  filter(Year==2012,between(Max.Ozone,51,51+0.905))

unique(A3$GEOID)%>%length() 

## [1] 772

unique(A3$Date..Local.)%>%length() 

## [1] 320

A3<-left_join(A3,pop12,by=c("GEOID"="fips"))

A3.pop<-A3%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) # ALL OZONE LEVEL IS 51

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

A3.pop<-A3.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

A3.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01003 190.  225735053
## 2 01033  54.6 225735053
## 3 01049  70.9 225735053

Scenario B

## Scenario B
B<-df4%>%
  filter(Year==2012,between(Max.Ozone,75,75+0.905)|between(Max.Ozone,75,75+1.693))

unique(B$GEOID)%>%length() 

## [1] 442

unique(B$Date..Local.)%>%length() 

## [1] 172

B<-left_join(B,pop12,by=c("GEOID"="fips"))

B.pop<-B%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) # ALL OZONE LEVEL IS 75, So i think it have same kind of drought

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

B.pop<-B.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 
B.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01049  70.9 158680441
## 2 01073 658.  158680441
## 3 01089 343.  158680441

### B2
B2<-df4%>%
  filter(Year==2012,between(Max.Ozone,70,70+0.905)|between(Max.Ozone,70,70+1.693))

unique(B2$GEOID)%>%length() 

## [1] 606

unique(B2$Date..Local.)%>%length() 

## [1] 204

B2<-left_join(B2,pop12,by=c("GEOID"="fips"))

B2.pop<-B2%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) 

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

B2.pop<-B2.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

B2.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01033  54.6 198796234
## 2 01049  70.9 198796234
## 3 01051  80.2 198796234

###B3

B3<-df4%>%
  filter(Year==2012,between(Max.Ozone,51,51+0.905)|between(Max.Ozone,51,51+1.693))

unique(B3$GEOID)%>%length()

## [1] 775

unique(B3$Date..Local.)%>%length() 

## [1] 337

B3<-left_join(B3,pop12,by=c("GEOID"="fips"))

B3.pop<-B3%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) 

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

B3.pop<-B3.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 
B3.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01003 190.  225894339
## 2 01033  54.6 225894339
## 3 01049  70.9 225894339

Scenario C

### Scenario C
C<-df4%>%
  filter(Year==2012,between(Max.Ozone,75,75+2.598)|between(Max.Ozone,75,75+1.693))

unique(C$GEOID)%>%length() 

## [1] 488

unique(C$Date..Local.)%>%length() 

## [1] 179

C<-left_join(C,pop12,by=c("GEOID"="fips"))

C.pop<-C%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) # ALL OZONE LEVEL IS 75, So i think it have same kind of drought

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

C.pop<-C.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 
C.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01049  70.9 172255515
## 2 01073 658.  172255515
## 3 01089 343.  172255515

### C2
C2<-df4%>%
  filter(Year==2012,between(Max.Ozone,70,70+2.598)|between(Max.Ozone,70,70+1.693))

unique(C2$GEOID)%>%length() 

## [1] 637

unique(C2$Date..Local.)%>%length() 

## [1] 205

C2<-left_join(C2,pop12,by=c("GEOID"="fips"))

C2.pop<-C2%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) 

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

C2.pop<-C2.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

C2.pop%>%head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01003 190.  202848364
## 2 01033  54.6 202848364
## 3 01049  70.9 202848364

###C3

C3<-df4%>%
  filter(Year==2012,between(Max.Ozone,51,51+2.598)|between(Max.Ozone,51,51+1.693))

unique(C3$GEOID)%>%length()

## [1] 776

unique(C3$Date..Local.)%>%length() 

## [1] 340

C3<-left_join(C3,pop12,by=c("GEOID"="fips"))

C3.pop<-C3%>%
  distinct(GEOID,MonitorID,.keep_all = T)%>%
  group_by(GEOID,MonitorID,Max.Ozone)%>%
  summarise(pop=value) 

## `summarise()` has grouped output by 'GEOID', 'MonitorID'. You can override
## using the `.groups` argument.

C3.pop<-C3.pop%>%
  group_by(GEOID)%>%
  summarise(Pop=mean(pop))%>%
  mutate(Sumpop=sum(Pop,na.rm = T)*1000) 

C3.pop %>% head(3)

## # A tibble: 3 × 3
##   GEOID   Pop    Sumpop
##   <chr> <dbl>     <dbl>
## 1 01003 190.  226634039
## 2 01033  54.6 226634039
## 3 01049  70.9 226634039