ForAbby.R

library(tidyverse)

N = 1000

#So imagine we have all the data we need for people with low birth weights

race = sample(c('Caucasian','AfricanAmerican'), size = N, replace = T) #Race of people who have low birth weight
birthweight = sample(c('Low','Normal'), size = N, replace = T)
social_class = sample(c('Upper','Middle','Lower'), size = N, replace = T) #Social class of people with low birth weight.  These are the strata

mydata = data_frame(race = race, social_class = social_class, birthweight = birthweight) #Imagine these are all the people with low birth weights


normal.bw = mydata %>% 
            group_by(race,social_class,birthweight) %>% 
            summarise(N = n()) %>%  
            ungroup %>% 
            filter(birthweight == 'Normal') %>% 
            spread(race,N) %>% 
            mutate(Ratio_low.nw = AfricanAmerican/Caucasian) %>% 
            select(social_class, Ratio_low.nw)


low.bw = mydata %>% 
          group_by(race,social_class,birthweight) %>%
          summarise(N = n()) %>% 
          ungroup %>% 
          filter(birthweight == 'Low') %>% 
          spread(race,N) %>% 
          mutate(Ratio_low.bw = AfricanAmerican/Caucasian) %>% 
          select(social_class,Ratio_low.bw)


normal.bw %>% 
  left_join(low.bw) %>% 
  mutate(Ratio.of.Ratio = Ratio_low.bw/Ratio_low.nw)