further analysis of age group

device_data=read.csv('./data/phonedata.csv', header=T, stringsAsFactors = F)
device_map=dplyr::filter(device_data, !is.na(longitude), !is.na(latitude), !is.na(group))%>%
  dplyr::filter(longitude>=73, longitude<136, latitude>=4, latitude<54)
  N=nrow(device_map)
agegroup=rep(0, N)
for (i in 1:N){
  if (device_map$age[i]<=26){
    agegroup[i]='post-90s'
  }
  else if (device_map$age[i]>26&device_map$age[i]<=36){
    agegroup[i]='post-80s'
  }
  else if (device_map$age[i]>36&device_map$age[i]<=46){
    agegroup[i]='post-70s'
  }
  else if (device_map$age[i]>46&device_map$age[i]<=56){
    agegroup[i]='post-60s'
  }
  else if (device_map$age[i]>56){
    agegroup[i]='post-50s'
  }
}
device_map=mutate(device_map, agegroup=agegroup)

agedis=device_map%>%
  dplyr::filter(phone_brand_English %in% c('Xiaomi', 'Huawei', 'OPPO', 
                                           'vivo', 'samsung')) %>%
  dplyr::group_by(agegroup, phone_brand_English)%>%
  summarise(n=n())%>%
  mutate(percent=n/sum(n))
ageplot=ggplot(data=agedis, aes(x=agegroup, y=percent,
                        fill=phone_brand_English))+
  geom_bar(stat = 'identity')+
  xlab('age group')+
  ylab('percent of number')+
  ggtitle('User Age Group Distribution of Top 5 Phone Brands')+
  theme_bw()
ggplotly(ageplot)