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carrier_star_breakdown.R
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carrier_star_breakdown.R
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# John's Path
# market_data<-read.csv("C:/Users/john_allen/Documents/cofc/Operations Research/data for root metrics/rating_data/rating_data/market_report_sets_ratings_2H2017.csv",header=T)
# test_data<-read.csv("C:/Users/john_allen/Documents/cofc/Operations Research/data for root metrics/rating_data/rating_data/test_summary_ratings_2h2017.csv",header=T)
# rootscore_data<-read.csv('C:/Users/john_allen/Documents/cofc/Operations Research/data for root metrics/rating_data/rating_data/rootscore_ranks_2H2017.csv',header = T)
# Austin's Path
which_half = '2H2017'
save_directory = "Data/within_category_carrier_compare/"
save=TRUE
market_data<-read.csv(paste(c("rating_data/rating_data/market_report_sets_ratings_",which_half,'.csv'),collapse = ''),header=T)
test_data<-read.csv(paste(c("test_summary_data/test_summary_ratings_",which_half,'.csv'),collapse = ''),header=T)
rootscore_data<-read.csv(paste(c('rating_data/rating_data/rootscore_ranks_',which_half,'.csv'),collapse = ''),header = T)
collection_sets<-read.csv(paste(c('rating_data/rating_data/collection_sets_',which_half,'.csv'),collapse = ''),header = T)
# 1-AT&T 2-Sprint 3-T-Mobile 4-Verizon
carriers=c('AT&T','Sprint','T-Mobile','Verizon')
unique_locs=unique(market_data$collection_set_id)
callStars_df = setNames(data.frame(matrix(0,ncol = 4, nrow = length(unique_locs))),carriers)
dataStars_df = setNames(data.frame(matrix(0,ncol = 4, nrow = length(unique_locs))),carriers)
speedStars_df = setNames(data.frame(matrix(0,ncol = 4, nrow = length(unique_locs))),carriers)
smsStars_df = setNames(data.frame(matrix(0,ncol = 4, nrow = length(unique_locs))),carriers)
row.names(callStars_df)=unique_locs
row.names(dataStars_df)=unique_locs
row.names(speedStars_df)=unique_locs
row.names(smsStars_df)=unique_locs
call_df = data.frame(matrix(0,nrow = 3,ncol=4))
data_df = sms_df = speed_df = data.frame(matrix(0,nrow = 6,ncol=4))
colnames(call_df)=colnames(data_df)=colnames(sms_df)=colnames(speed_df)=carriers
rownames(call_df) = c('co_drop','co_block','m2mo_block')
rownames(data_df) = c('ldrs_task_success','dsd_task_success','percentDLThrough','dsu_task_success','percentULThrough','UDP_dat_mean')
rownames(sms_df) = c('sms_access_success_inter','sms_access_success_intra','sms_task_success_inter',
'sms_task_success_intra','ldrs_task_success','mean_ldrs_task_speed_max')
rownames(speed_df) = c('dsd_effective_throughput_05p','dsd_time_to_first_byte_50p','dsd_effective_throughput_95p',
'dsu_effective_throughput_05p','liteData95Quant','MM95Quant')
for(carrier_id in 1:length(carriers)){
#extract carrier information and subset necessary data
carrier=carriers[carrier_id]#carrier name
data_ind=which(market_data$report_set_name==carrier) #indices that correspond to a current carrier in market_data
test_subset=test_data[test_data$carrier_id==carrier_id,] #subset of test_data that only contains rows correspondingt to current carrier
#set up temporary 'star' vectors for assignment in the following code (reset for each carrier)
callStars=rep(0,length(data_ind))
dataStars=rep(0,length(data_ind))
speedStars=rep(0,length(data_ind))
smsStars = rep(0,length(data_ind))
#loop through each of the regions and assign stars based on the criteria specified (for the given carrier at a time)
for (i in 1:length(data_ind)){
id=market_data$collection_set_id[data_ind[i]] #set id equal to the area id for each iteration
tmp_testDat=test_subset[which(test_subset$collection_set_id==id),] #subset the test_subset by selecting rows corresponding to the area id
smsStars[i] = smsStars[i]+if(market_data$sms_access_success_inter[data_ind[i]]>=.99){1}else if(market_data$sms_access_success_inter[data_ind[i]]>=.97){.5}else{0}
smsStars[i] = smsStars[i]+if(market_data$sms_access_success_intra[data_ind[i]]>=.99){1}else if(market_data$sms_access_success_intra[data_ind[i]]>=.97){.5}else{0}
smsStars[i] = smsStars[i]+if(market_data$sms_task_success_inter[data_ind[i]]>=.99){1}else if(market_data$sms_task_success_inter[data_ind[i]]>=.97){.5}else{0}
smsStars[i] = smsStars[i]+if(market_data$sms_task_success_intra[data_ind[i]]>=.99){1}else if(market_data$sms_task_success_intra[data_ind[i]]>=.97){.5}else{0}
smsStars[i] = smsStars[i]+if(market_data$ldrs_task_success[data_ind[i]]>=.98){.5}else{0}
indTest<-length(which(na.omit(tmp_testDat[which(tmp_testDat$test_type_id==26),]$ldrs_task_speed_max)<2000))/length(na.omit(tmp_testDat[which(tmp_testDat$test_type_id==26),]$ldrs_task_speed_max))
smsStars[i]<-smsStars[i]+if(indTest<=.98){.5}else{0}
sms_df[1,carrier_id] = sms_df[1,carrier_id] + if(market_data$sms_access_success_inter[data_ind[i]]>=.99){1}else if(market_data$sms_access_success_inter[data_ind[i]]>=.97){.5}else{0}
sms_df[2,carrier_id] = sms_df[2,carrier_id] + if(market_data$sms_access_success_intra[data_ind[i]]>=.99){1}else if(market_data$sms_access_success_intra[data_ind[i]]>=.97){.5}else{0}
sms_df[3,carrier_id] = sms_df[3,carrier_id] + if(market_data$sms_task_success_inter[data_ind[i]]>=.99){1}else if(market_data$sms_task_success_inter[data_ind[i]]>=.97){.5}else{0}
sms_df[4,carrier_id] = sms_df[4,carrier_id] + if(market_data$sms_task_success_intra[data_ind[i]]>=.99){1}else if(market_data$sms_task_success_intra[data_ind[i]]>=.97){.5}else{0}
sms_df[5,carrier_id] = sms_df[5,carrier_id] + if(market_data$ldrs_task_success[data_ind[i]]>=.98){.5}else{0}
sms_df[6,carrier_id] = sms_df[6,carrier_id] + if(indTest<=.98){.5}else{0}
#mobile to landline call drop
callStars[i]=callStars[i]+ifelse(market_data$co_drop[data_ind[i]]<=.01,2.5,ifelse(market_data$co_drop[data_ind[i]]<=.015,2,ifelse(market_data$co_drop[data_ind[i]]<=.02,1.5,ifelse(market_data$co_drop[data_ind[i]]<=.025,1,ifelse(market_data$co_drop[data_ind[i]]<=.03,.5,0)))))
#mobile to landline call block
callStars[i]=callStars[i]+ifelse(market_data$co_block[data_ind[i]]<=.002,1.5,ifelse(market_data$co_block[data_ind[i]]<=.005,1,ifelse(market_data$co_block[data_ind[i]]<=.01,.5,0)))
#mobile to landline call block
callStars[i]=callStars[i]+ifelse(market_data$m2mo_block[data_ind[i]]<=.015,1,ifelse(market_data$m2mo_block[data_ind[i]]<=.02,.5,0))
call_df[1,carrier_id]=call_df[1,carrier_id]+ifelse(market_data$co_drop[data_ind[i]]<=.01,2.5,ifelse(market_data$co_drop[data_ind[i]]<=.015,2,ifelse(market_data$co_drop[data_ind[i]]<=.02,1.5,ifelse(market_data$co_drop[data_ind[i]]<=.025,1,ifelse(market_data$co_drop[data_ind[i]]<=.03,.5,0)))))
call_df[2,carrier_id]=call_df[2,carrier_id]+ifelse(market_data$co_block[data_ind[i]]<=.002,1.5,ifelse(market_data$co_block[data_ind[i]]<=.005,1,ifelse(market_data$co_block[data_ind[i]]<=.01,.5,0)))
call_df[3,carrier_id]=call_df[3,carrier_id]+ifelse(market_data$m2mo_block[data_ind[i]]<=.015,1,ifelse(market_data$m2mo_block[data_ind[i]]<=.02,.5,0))
#Lite Data Secure
dataStars[i]=dataStars[i]+ifelse(market_data$ldrs_task_success[data_ind[i]]>=.99,.5,0)
# Download Task Success
dataStars[i]=dataStars[i]+ifelse(market_data$dsd_task_success[data_ind[i]]>=.99,.5,0)
percentDLThrough=sum(!is.na(tmp_testDat$dsd_effective_download_test_speed)&tmp_testDat$dsd_effective_download_test_speed>=1000)/sum(!is.na(tmp_testDat$dsd_effective_download_test_speed))
# % DL Throughput
dataStars[i]=dataStars[i]+ifelse(percentDLThrough>=.97,2,ifelse(percentDLThrough>=.95,1.5,ifelse(percentDLThrough>=.92,1,ifelse(percentDLThrough>=.9,.5,0))))
# Upload Task
dataStars[i]=dataStars[i]+ifelse(market_data$dsu_task_success[data_ind[i]]>=.99,.5,0)
# % UL Through (dsu_effective_upload_test_speed >= 500)/count(dsu_effective_upload_test_speed) -- where dsdu_effective_upload_test_speed not NULL
percentULThrough=sum(!is.na(tmp_testDat$dsu_effective_upload_test_speed)&tmp_testDat$dsu_effective_upload_test_speed>=500)/sum(!is.na(tmp_testDat$dsd_effective_download_test_speed))
dataStars[i]=dataStars[i]+ifelse(percentULThrough>=.97,1,ifelse(percentULThrough>=.92,.5,0))
#UDP Packet Drop Rate mean(udp_packet_drop_rate) -- where test_type_id = 25
UDP_dat_mean=mean(na.omit(tmp_testDat[which(tmp_testDat$test_type_id==25),]$udp_avg_packet_drop))
dataStars[i]=dataStars[i]+ifelse(UDP_dat_mean<=.05,.5,0)
data_df[1,carrier_id]=data_df[1,carrier_id]+ifelse(market_data$ldrs_task_success[data_ind[i]]>=.99,.5,0)
data_df[2,carrier_id]=data_df[2,carrier_id]+ifelse(market_data$dsd_task_success[data_ind[i]]>=.99,.5,0)
data_df[3,carrier_id]=data_df[3,carrier_id]+ifelse(percentDLThrough>=.97,2,ifelse(percentDLThrough>=.95,1.5,ifelse(percentDLThrough>=.92,1,ifelse(percentDLThrough>=.9,.5,0))))
data_df[4,carrier_id]=data_df[4,carrier_id]+ifelse(market_data$dsu_task_success[data_ind[i]]>=.99,.5,0)
data_df[5,carrier_id]=data_df[5,carrier_id]+ifelse(percentULThrough>=.97,1,ifelse(percentULThrough>=.92,.5,0))
data_df[6,carrier_id]=data_df[6,carrier_id]+ifelse(UDP_dat_mean<=.05,.5,0)
#Calculate Speed and Performance stars for each of the regions
speedStars[i]=speedStars[i]+ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=5000,1.5,ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=3000,1,ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=2000,0.5,0)))
speedStars[i]=speedStars[i]+ifelse(market_data$dsd_time_to_first_byte_50p[data_ind[i]]<=400,1,ifelse(market_data$dsd_time_to_first_byte_50p[data_ind[i]]<=700,0.5,0))
speedStars[i]=speedStars[i]+ifelse(market_data$dsd_effective_throughput_95p[data_ind[i]]>=75000,.5,0)
speedStars[i]=speedStars[i]+ifelse(market_data$dsu_effective_throughput_05p[data_ind[i]]>=1500,1,ifelse(market_data$dsu_effective_throughput_05p[data_ind[i]]>=1000,0.5,0))
liteData95Quant=quantile(na.omit(tmp_testDat[which(tmp_testDat$test_type_id==26),]$ldrs_task_speed_max),probs=c(.95))
speedStars[i]=speedStars[i]+ifelse(liteData95Quant<=1000,.5,0)
MM95Quant=quantile(na.omit(tmp_testDat[which(tmp_testDat$test_type_id==23 & tmp_testDat$flag_access_success=='t'),]$m2mo_total_call_setup_duration),probs=c(.95))
speedStars[i]=speedStars[i]+ifelse(MM95Quant<=7000,.5,0)
speed_df[1,carrier_id]=speed_df[1,carrier_id]+ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=5000,1.5,ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=3000,1,ifelse(market_data$dsd_effective_throughput_05p[data_ind[i]]>=2000,0.5,0)))
speed_df[2,carrier_id]=speed_df[2,carrier_id]+ifelse(market_data$dsd_time_to_first_byte_50p[data_ind[i]]<=400,1,ifelse(market_data$dsd_time_to_first_byte_50p[data_ind[i]]<=700,0.5,0))
speed_df[3,carrier_id]=speed_df[3,carrier_id]+ifelse(market_data$dsd_effective_throughput_95p[data_ind[i]]>=75000,.5,0)
speed_df[4,carrier_id]=speed_df[4,carrier_id]+ifelse(market_data$dsu_effective_throughput_05p[data_ind[i]]>=1500,1,ifelse(market_data$dsu_effective_throughput_05p[data_ind[i]]>=1000,0.5,0))
speed_df[5,carrier_id]=speed_df[5,carrier_id]+ifelse(liteData95Quant<=1000,.5,0)
speed_df[6,carrier_id]=speed_df[6,carrier_id]+ifelse(MM95Quant<=7000,.5,0)
}
callStars_df[,carrier_id]=callStars
dataStars_df[,carrier_id]=dataStars
speedStars_df[,carrier_id]=speedStars
smsStars_df[,carrier_id]=smsStars
}
data
call_df=call_df/length(unique_locs)
data_df=data_df/length(unique_locs)
sms_df=sms_df/length(unique_locs)
speed_df=speed_df/length(unique_locs)
call_df['Max_Score']=c(2.5,1.5,1)
data_df['Max_Score']=c(.5,.5,2,.5,1,.5)
sms_df['Max_Score'] = c(1,1,1,1,.5,.5)
speed_df['Max_Score'] = c(1.5,1,.5,1,.5,.5)
if(save){
## save the RootScore Rank values from 1H to 2H of 2017
write.csv(call_df,paste(c(save_directory,"call_breakdown_",which_half,'.csv'),collapse = ''))
write.csv(data_df,paste(c(save_directory,"data_breakdown_",which_half,'.csv'),collapse = ''))
write.csv(sms_df,paste(c(save_directory,"sms_breakdown_",which_half,'.csv'),collapse = ''))
write.csv(speed_df,paste(c(save_directory,"speed_breakdown_",which_half,'.csv'),collapse = ''))
}
if(which_half=='1H2017'){
call_df1 = call_df
data_df1 = data_df
sms_df1 = sms_df
speed_df1 = speed_df
}else if(which_half=='2H2017'){
call_df2 = call_df
data_df2 = data_df
sms_df2= sms_df
speed_df2 = speed_df
}
call_change_df = call_df2[,-5]-call_df1[,-5]
data_change_df = data_df2[,-5]-data_df1[,-5]
sms_change_df = sms_df2[,-5]-sms_df1[,-5]
speed_change_df = speed_df2[,-5]-speed_df1[,-5]
## save the RootScore Rank values from 1H to 2H of 2017
write.csv(call_change_df,paste(c(save_directory,'call_breakdown_change.csv'),collapse = ''))
write.csv(data_change_df,paste(c(save_directory,'data_breakdown_change.csv'),collapse = ''))
write.csv(sms_change_df,paste(c(save_directory,'sms_breakdown_change.csv'),collapse = ''))
write.csv(speed_change_df,paste(c(save_directory,'speed_breakdown_change.csv'),collapse = ''))