further analysis of age group for apps

app_data=read.csv('./data/appmap.csv', header=T, stringsAsFactors = F)
#str(app_data)

app_map=dplyr::filter(app_data, !is.na(longitude), !is.na(latitude), !is.na(group))%>%
  dplyr::filter(longitude>=73, longitude<136, latitude>=4, latitude<54)
#str(app_map)
N1=nrow(app_map)
agegroup1=rep(0, N1)
for (i in 1:N1){
  if (app_map$age[i]<=26){
    agegroup1[i]='post-90s'
  }
  else if (app_map$age[i]>26&app_map$age[i]<=36){
    agegroup1[i]='post-80s'
  }
  else if (app_map$age[i]>36&app_map$age[i]<=46){
    agegroup1[i]='post-70s'
  }
  else if (app_map$age[i]>46&app_map$age[i]<=56){
    agegroup1[i]='post-60s'
  }
  else if (app_map$age[i]>56){
    agegroup1[i]='post-50s'
  }
}
app_map=mutate(app_map, agegroup=agegroup1)
#unique(app_map$category)

agedis1=app_map%>%
  dplyr::filter(category %in% c("Property Industry 2.0", "Industry tag" , "video" , 
                                 "Services 1" ,"P2P net loan" ) ) %>%
  dplyr::group_by(agegroup, category)%>%
  summarise(n=n())%>%
  mutate(percent=n/sum(n))
ageplot1=ggplot(data=agedis1, aes(x=agegroup, y=percent,
                                fill=category))+
  geom_bar(stat = 'identity')+
  xlab('age group')+
  ylab('percent of number')+
  ggtitle('User Age Group Distribution of APP cateogoreis')+
  theme_bw()
ggplotly(ageplot1)