Population base on max ozone New Updated Data

Dataset

#Load Ozone Dataset
load("Ozone Data with Region.RData")
combinedAir.final<-df4

#Load Population Dataset
m.pop<-read_xlsx("Population/Population County.xlsx")

Merge and Filter

#Merge two datset
combinedAir.final<-left_join(combinedAir.final,m.pop,by=c("Year","GEOID"="fips"))

#Filter base on category, on July 15, 2012
combine2015<-combinedAir.final%>%
  filter(Date..Local.=="2012-07-15")

combine2015<-combine2015%>%distinct(Date..Local.,GEOID,.keep_all = T)

## Check available Population
sum(combine2015$value,na.rm = T)*1000

## [1] 225487857

## Check Available Observation in 2012-07-15
nrow(combine2015)

## [1] 755

# Generating Class for favor category
## 1st category is Between 70-1.416
## 2nd Category is Between 70-3.065 
## 3rd Category is Between 70-1.416
## 4th Category is Between 70+3.065
## 5th Category is other.
combine2015$Class<-0
combine2015$Class<-ifelse(between(combine2015$Max.Ozone,70-1.416,70),yes = 1,
                          ifelse(between(combine2015$Max.Ozone,70-3.065,70),yes = 2,
                                 ifelse(between(combine2015$Max.Ozone,70,70+1.416),yes = 3,
                                        ifelse(between(combine2015$Max.Ozone,70,70+3.065),yes = 4,5))))

Summary Population Total

Here Total population Estimate base on last population cencus.

Total county is less than 1000 it is because effect of number of observation, some county may have no observation in that period of time.

sumed.pop<-combine2015%>%
  group_by(Class)%>%
  summarise(Total_pop=sum(value,na.rm = T)*1000,
            County=n(),
            list=paste(County.Name, collapse = ", "))

sumed.pop

## # A tibble: 5 × 4
##   Class Total_pop County list                                                   
##   <dbl>     <dbl>  <int> <chr>                                                  
## 1     1  11043705     18 El Dorado, Kern, Madera, San Bernardino, Tulare, Cook,…
## 2     2   4222414     15 Tuolumne, Douglas, Larimer, Porter, Harrison, Linn, Mo…
## 3     3   1083935      5 Fresno, Mariposa, Riley, Essex, Dewey                  
## 4     4    562782      3 Lake, Clinton, Clinton                                 
## 5     5 208575021    714 Houston, Elmore, Etowah, Baldwin, Colbert, DeKalb, Jef…