#########################################################################################
# Run this after the Table 4 lines in the code for Huber and Gunderson 2022
# Political Research Quarterly "Putting a fresh face forward: 
# Does the gender of a police chief affect public perceptions?".
#########################################################################################

female_HI <- clean %>% filter(sexismscalescaled>=0.35) %>% filter(candidate=="female")
  male_HI <- clean %>% filter(sexismscalescaled>=0.35) %>% filter(candidate=="male")

t.test(female_HI$domviolence , male_HI$domviolence)
t.test(female_HI$sexassault  , male_HI$sexassault)
t.test(female_HI$violcrime   , male_HI$violcrime)
t.test(female_HI$corruption  , male_HI$corruption)
t.test(female_HI$polbrutality, male_HI$polbrutality)
t.test(female_HI$commleaders , male_HI$commleaders)
t.test(female_HI$support     , male_HI$support)

female_LO <- clean %>% filter(sexismscalescaled<=0.35) %>% filter(candidate=="female")
  male_LO <- clean %>% filter(sexismscalescaled<=0.35) %>% filter(candidate=="male")

t.test(female_LO$domviolence , male_LO$domviolence)
t.test(female_LO$sexassault  , male_LO$sexassault)
t.test(female_LO$violcrime   , male_LO$violcrime)
t.test(female_LO$corruption  , male_LO$corruption)
t.test(female_LO$polbrutality, male_LO$polbrutality)
t.test(female_LO$commleaders , male_LO$commleaders)
t.test(female_LO$support     , male_LO$support)

#########################################################################################
# Run the following to output to Stata
#########################################################################################

cleanSTATA = subset(clean, select=-c(rid, Q28))

library(foreign)
write.dta(cleanSTATA, "G:/PRQ FF.dta") 

# Sample Stata command:

ttest support if sexismscalescaled>0.35 & sexismscalescaled<., by(candidate)