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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, unionlibrary("tidyverse")## Warning: package 'tidyverse' was built under R version 4.1.2## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v stringr 1.4.0
## v tidyr 1.1.3 v forcats 0.5.1
## v readr 1.4.0## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# create the empty data frame
vaccination_df <- data.frame( age = character(),
unvaccinated_pop = double(),
unvaccinated_pop_per_100K = double(),
vaccinated_pop = double(),
vaccinated_pop_per_100K = double(),
unvaccinated_severe_cases = double(),
vaccinated_severe_cases = double()
)
vaccination_df## [1] age unvaccinated_pop
## [3] unvaccinated_pop_per_100K vaccinated_pop
## [5] vaccinated_pop_per_100K unvaccinated_severe_cases
## [7] vaccinated_severe_cases
## <0 rows> (or 0-length row.names)vaccination_df <- vaccination_df %>%
add_row(
age = "under 50",
unvaccinated_pop = 1116834,
unvaccinated_pop_per_100K = 0.233,
vaccinated_pop = 3501118,
vaccinated_pop_per_100K = 0.730,
unvaccinated_severe_cases = 43,
vaccinated_severe_cases = 11
) %>%
add_row(
age = "over 50",
unvaccinated_pop = 186078,
unvaccinated_pop_per_100K = 0.079,
vaccinated_pop = 2133516,
vaccinated_pop_per_100K = 0.904,
unvaccinated_severe_cases = 171,
vaccinated_severe_cases = 290
) %>%
add_row(
age = "total",
unvaccinated_pop = 1302912,
unvaccinated_pop_per_100K = 18.2,
vaccinated_pop = 5634634,
vaccinated_pop_per_100K = 78.7,
unvaccinated_severe_cases = 214,
vaccinated_severe_cases = 301
)
vaccination_df## age unvaccinated_pop unvaccinated_pop_per_100K vaccinated_pop
## 1 under 50 1116834 0.233 3501118
## 2 over 50 186078 0.079 2133516
## 3 total 1302912 18.200 5634634
## vaccinated_pop_per_100K unvaccinated_severe_cases vaccinated_severe_cases
## 1 0.730 43 11
## 2 0.904 171 290
## 3 78.700 214 301vaccination_df ## age unvaccinated_pop unvaccinated_pop_per_100K vaccinated_pop
## 1 under 50 1116834 0.233 3501118
## 2 over 50 186078 0.079 2133516
## 3 total 1302912 18.200 5634634
## vaccinated_pop_per_100K unvaccinated_severe_cases vaccinated_severe_cases
## 1 0.730 43 11
## 2 0.904 171 290
## 3 78.700 214 301write.csv(vaccination_df, "C:/Users/Uzma/CUNY-SPS-Assignments/data_607/week5/vaccination_data_analysis.csv", row.names=FALSE)vaccine_df <- read_csv( file = "C:/Users/Uzma/CUNY-SPS-Assignments/data_607/week5/vaccination_data_analysis.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## age = col_character(),
## unvaccinated_pop = col_double(),
## unvaccinated_pop_per_100K = col_double(),
## vaccinated_pop = col_double(),
## vaccinated_pop_per_100K = col_double(),
## unvaccinated_severe_cases = col_double(),
## vaccinated_severe_cases = col_double()
## )glimpse(vaccine_df)## Rows: 3
## Columns: 7
## $ age <chr> "under 50", "over 50", "total"
## $ unvaccinated_pop <dbl> 1116834, 186078, 1302912
## $ unvaccinated_pop_per_100K <dbl> 0.233, 0.079, 18.200
## $ vaccinated_pop <dbl> 3501118, 2133516, 5634634
## $ vaccinated_pop_per_100K <dbl> 0.730, 0.904, 78.700
## $ unvaccinated_severe_cases <dbl> 43, 171, 214
## $ vaccinated_severe_cases <dbl> 11, 290, 301vaccine_df## # A tibble: 3 x 7
## age unvaccinated_pop unvaccinated_pop_p~ vaccinated_pop vaccinated_pop_pe~
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 under ~ 1116834 0.233 3501118 0.73
## 2 over 50 186078 0.079 2133516 0.904
## 3 total 1302912 18.2 5634634 78.7
## # ... with 2 more variables: unvaccinated_severe_cases <dbl>,
## # vaccinated_severe_cases <dbl># afficacy vs. severe disease = 1 - (% fully vaxed severe cases per 100K / % not vaxed severe cases per 100K)
# (1) Do you have enough information to calculate the total population? What does this total population represent?
# (2) Calculate the Efficacy vs. Disease; Explain your results.
# (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?
# Population 7,155,090 Population includes Israelis age 12 and up; also includes partially vaxed individuals
# <50: 3.9 / 0.3 13.0 { How should these numbers be intepreted?
# >50: 91.9 / 13.6 6.8 total_population <- select(vaccine_df, "age", "unvaccinated_pop", "vaccinated_pop")
total_population## # A tibble: 3 x 3
## age unvaccinated_pop vaccinated_pop
## <chr> <dbl> <dbl>
## 1 under 50 1116834 3501118
## 2 over 50 186078 2133516
## 3 total 1302912 5634634# (1) Do you have enough information to calculate the total population? What does this total population represent?
# yes, the total population represents all vaccinated and unvaccinated, below the age 50 and above age 50.
total_population <-
mutate(total_population,
total_pop = (unvaccinated_pop + vaccinated_pop)
)
total_population## # A tibble: 3 x 4
## age unvaccinated_pop vaccinated_pop total_pop
## <chr> <dbl> <dbl> <dbl>
## 1 under 50 1116834 3501118 4617952
## 2 over 50 186078 2133516 2319594
## 3 total 1302912 5634634 6937546# (2) Calculate the Efficacy vs. Disease; Explain your results.
# (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?
vaccine_df <-
mutate(vaccine_df,
unvaccinated_severe_cases_per_100k = (unvaccinated_severe_cases*100000/unvaccinated_pop),
vaccinated_severe_cases_per_100k = (vaccinated_severe_cases*100000/vaccinated_pop),
efficacy_vs_diseases = (1-(vaccinated_severe_cases_per_100k/unvaccinated_severe_cases_per_100k))
)
vaccine_df## # A tibble: 3 x 10
## age unvaccinated_pop unvaccinated_pop_p~ vaccinated_pop vaccinated_pop_pe~
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 under ~ 1116834 0.233 3501118 0.73
## 2 over 50 186078 0.079 2133516 0.904
## 3 total 1302912 18.2 5634634 78.7
## # ... with 5 more variables: unvaccinated_severe_cases <dbl>,
## # vaccinated_severe_cases <dbl>, unvaccinated_severe_cases_per_100k <dbl>,
## # vaccinated_severe_cases_per_100k <dbl>, efficacy_vs_diseases <dbl># From the data provided in the efficacy vs severe diseases, which represent the ratio of the severe cases in vaccinated populationvs the unvaccinated population, we conclude that the fully vaccinated population is 98% less likely to contract severe deseases than the unvaccinated people below 50 age.
# For above 50 age group, afficacy fall down to 85.2%
# While for the total population, the efficacy drop down to 67.5%
# The reason is because the severe cases in the over 50 population are happening at a rate of 92% and this is causing overall average to go down.