Import data

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
vaccine<-read.csv("https://raw.githubusercontent.com/nancunjie4560/Data607/master/israeli_vaccination_data_analysis_start%20(1)%20(1).csv",header=TRUE, sep=",")

#  select the rows for the data
vaccine<-data.frame(vaccine,stringsAsFactors = FALSE)
head(vaccine)
##   Age Population..            X             Severe.Cases                 X.1
## 1       Not Vax\n% Fully Vax\n% Not Vax\nper 100K\n\n\np Fully Vax\nper 100K
## 2 <50    1,116,834    3,501,118                       43                  11
## 3            23.3%        73.0%                                             
## 4 >50      186,078    2,133,516                      171                 290
## 5             7.9%        90.4%                                             
## 6                                                                           
##             Efficacy
## 1 vs. severe disease
## 2                   
## 3                   
## 4                   
## 5                   
## 6
vaccine<-vaccine[1:5,]

# rename the variables
vaccine<-rename(vaccine,'Age Range'='Age', 'Unvaccinated Population' = Population.. , 'Vaccinated Population' = X, 'Unvaccinated Hospitalize per 100k'= Severe.Cases, 'Vaccinated Hospitalize per 100k' = X.1, 'Efficacy vs Severe disease' = Efficacy)

Tidy data

vaccine<-vaccine[-1,]
vaccine$`Percentage of Unvaccinated`[1]=vaccine$`Unvaccinated Population`[2]
vaccine$`Percentage of Unvaccinated`[3]=vaccine$`Unvaccinated Population`[4]
vaccine$`Percentage of Vaccinated`[1]=vaccine$`Vaccinated Population`[2]
vaccine$`Percentage of Vaccinated`[3]=vaccine$`Vaccinated Population`[4]
vaccine<-vaccine[-c(2,4),]
vaccine[] <- lapply(vaccine, gsub, pattern =",", replacement = "")
vaccine[] <- lapply(vaccine, gsub, pattern ="%", replacement = "")


data <- as.data.frame(sapply(vaccine, as.numeric),na.omit=T)
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
data$`Age Range`=vaccine$`Age Range`
str(data)
## 'data.frame':    2 obs. of  8 variables:
##  $ Age Range                        : chr  "<50" ">50"
##  $ Unvaccinated Population          : num  1116834 186078
##  $ Vaccinated Population            : num  3501118 2133516
##  $ Unvaccinated Hospitalize per 100k: num  43 171
##  $ Vaccinated Hospitalize per 100k  : num  11 290
##  $ Efficacy vs Severe disease       : num  NA NA
##  $ Percentage of Unvaccinated       : num  23.3 7.9
##  $ Percentage of Vaccinated         : num  73 90.4

Caculation

(1) Do you have enough information to calculate the total population? What does this total population represent?

population<-data%>%
  group_by(data[,c(1:3)])%>%
  summarise(total = sum(`Unvaccinated Population`+`Vaccinated Population`))
## `summarise()` has grouped output by 'Age Range', 'Unvaccinated Population'. You can override using the `.groups` argument.
pop_lower50<-(population$total[1])/0.963
pop_lower50
## [1] 4795381
pop_50above<-(population$total[2])/0.983
pop_50above
## [1] 2359709
total_population<-sum(pop_lower50,pop_50above)
total_population
## [1] 7155090

Yes, I believe there is enough information to calculate the total Israel population, and the total population is 7155090. first, I found that the population of age less than 50. The given information shows that 96.3% of Israel’s people are either get fully vaccinated or not vaccinated at all. I figure out that 3.7% of the population of whose age is less than 50 may get one vaccine shot. From this info, I can calculate the total population of whose age is less than 50. The same work has been done to calculate the population whose age is more than 50. sum up these to the range of age, I can calculate the total population of Israel.

(2) Calculate the Efficacy vs. Disease; Explain your results.

Efficacy vs. severe disease = 1 - (% fully vaxed severe cases per 100K / % not vaxed severe cases per 100K)

# Efficacy age less than 50
vac_hos_rate_less50<-data$`Vaccinated Hospitalize per 100k`[1]/data$`Vaccinated Population`[1] *100000
vac_hos_rate_less50
## [1] 0.3141854
unvac_hos_rate_less50<-data$`Unvaccinated Hospitalize per 100k`[1]/data$`Unvaccinated Population`[1] *100000
unvac_hos_rate_less50
## [1] 3.850169
data$`Efficacy vs Severe disease`[1] = 1-vac_hos_rate_less50/unvac_hos_rate_less50
data$`Efficacy vs Severe disease`[1] 
## [1] 0.918397
# Efficacy age 50 above
vac_hos_rate_50above<-data$`Vaccinated Hospitalize per 100k`[2]/data$`Vaccinated Population`[2] *100000
vac_hos_rate_50above
## [1] 13.59259
unvac_hos_rate_50above<-data$`Unvaccinated Hospitalize per 100k`[2]/data$`Unvaccinated Population`[2] *100000
unvac_hos_rate_50above
## [1] 91.89695
data$`Efficacy vs Severe disease`[2] = 1-vac_hos_rate_50above/unvac_hos_rate_50above
data$`Efficacy vs Severe disease`[2] 
## [1] 0.8520888

Use the above Formula: Efficacy vs. severe disease = 1 - (% fully vaxed severe cases per 100K / % not vaxed severe cases per 100K) Found that the Efficacy for age less than 50 is 91.837%, and the Efficacy for age more than 50 is 85.208%. These two numbers are pretty high that shows the vaccinations are very effective according to the Efficacy vs. severe disease rate calculations.

(3) From your calculation of efficacy vs. disease, are you able to compare the rate of severe cases in unvaccinated individuals to that in vaccinated individuals?

Yes, from the Efficacy calculation, I can see the following hospitalized patterns.

for those whose age is less than 50, 0.3% of them who have been vaccinated are hospitalized. for those whose age is less than 50, 3.8% of them who haven’t been vaccinated are hospitalized. for those whose age is more than 50, 13.59% of them who have been vaccinated are hospitalized. for those whose age is more than 50, 91.89% of them who haven’t been vaccinated are hospitalized.

barplot(c('age > 50'=unvac_hos_rate_50above, 'age < 50'=unvac_hos_rate_less50),main='Unvaccinated Hospital Rate',ylab = 'Hospital Rate',ylim=range(pretty(c(0, 100))))

The vaccination works very effectively for those aged more than 50 since the hospitalization rate is dramatically decreased for those who are vaccinated. However, the vaccination shows less difference of hospitalization for those whose age is less than 50. Even though the vaccination shows less difference for those whose age is less than 50, I believe everyone should get the vaccination to avoid spread to the people whose age is more than 50 because it is a huge risk for older than 50-year-old people.