Recreating Table 1
In dbglm: Generalised Linear Models by Subsampling and One-Step Polishing

library(dbglm)
library(DBI)
library(MonetDBLite)
library(dplyr)
library(dbplyr)
library(purrr)
library(tidyr) # Eventually just library(tidyverse)
library(rlang)
library(tibble)
library(vctrs)
library(tidypredict)
library(RSQLite)
library(knitr)
library(duckdb)
library(readr)
library(bigrquery)
library(stringr)

ms <- MonetDBLite::src_monetdblite("~/VEHICLE")
monetdb.read.csv(ms$con, "Fleet30Nov2017a.csv",tablename="vehicles",quote="",nrow.check=10000,best.effort=TRUE,lower.case.names=TRUE)
vehicles<-tbl(ms,"vehicles")
cars <- filter(vehicles, vehicle_type == "PASSENGER CAR/VAN") %>% 
    mutate(isred=ifelse(basic_colour=="RED",1,0)) %>% 
    filter(number_of_seats >1 & number_of_seats < 7) %>% filter(number_of_axles==2) %>%
    compute()

# Full data
t1<- system.time({
  m1<-glm(isred~power_rating+number_of_seats+gross_vehicle_mass, data=cars, family = "binomial")
})
t1<- unname(t1[3])
row.1<- c(unname(m1$coefficients), t1)
m1<- summary(m1)
row.2<- unname(m1$coefficients[,2])

# Replicate 1 - monet
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.3<- c(aa, mean(a1))
row.4<- bb

# Replicate 2 - monet
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.5<- c(aa, mean(a1))
row.6<- bb

vehicles<-read_csv("Fleet30Nov2017a.csv", quote = "")
names(vehicles)<-tolower(names(vehicles))
vehicles$power_rating<-as.numeric(as.character(vehicles$power_rating))
vehicles$number_of_seats<-as.numeric(as.character(vehicles$number_of_seats))
vehicles$number_of_axles<-as.numeric(as.character(vehicles$number_of_axles))

sqlite<-dbDriver("SQLite")
con<-dbConnect(sqlite,"nzcars.db")
RSQLite:::initExtension(con)
dbWriteTable(con,"vehicles",vehicles, overwrite = T)
dbDisconnect(con)

sqlite<-dbDriver("SQLite")
con<-dbConnect(sqlite,"nzcars.db")
RSQLite:::initExtension(con)
sqlitevehicles<-tbl(con,"vehicles")

cars0 <- filter(sqlitevehicles, vehicle_type == "PASSENGER CAR/VAN") %>%
    mutate(isred=ifelse(basic_colour=="RED",1,0)) %>% 
    filter(number_of_seats >1 & number_of_seats < 7) %>% filter(number_of_axles==2) %>%
    compute()

#sqlite replicate 1
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars0)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.7<- c(aa, mean(a1))
row.8<- bb

#sqlite replicate 2
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars0)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.9<- c(aa, mean(a1))
row.10<- bb

con_duck<- dbConnect(duckdb::duckdb())
duckdb_read_csv(con_duck, "Fleet30Nov2017a.csv", "Fleet30Nov2017a.csv", quote = "", lower.case.names=TRUE, check.names = T)
cars<- dbReadTable(con_duck, "Fleet30Nov2017a.csv")
cars1 <- filter(cars, vehicle_type == "PASSENGER CAR/VAN") %>% 
  mutate(isred=ifelse(basic_colour=="RED",1,0)) %>% 
  filter(number_of_seats >1 & number_of_seats < 7) %>% filter(number_of_axles==2) %>%
  compute()

#duckDB replicate 1
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars1)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.11<- c(aa, mean(a1))
row.12<- bb

#duckDB replicate 2
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~power_rating+number_of_seats+gross_vehicle_mass,tbl=cars1)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.13<- c(aa, mean(a1))
row.14<- bb

# dq_auth
con<- dbConnect(bigrquery::bigquery(), project = "dbglm-305201", dataset = "asd")
bigq<- tbl(con, "dbglm")
cars11 <- filter(bigq, vehicle_type == "PASSENGER CAR/VAN") %>% 
  mutate(isred=ifelse(basic_colour=="RED",1,0)) %>% 
  filter(number_of_seats >1 & number_of_seats < 7) %>% filter(number_of_axles==2) %>%
  compute()

#Bigrquery Replicate 1
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~POWER_RATING+NUMBER_OF_SEATS+GROSS_VEHICLE_MASS,tbl=cars11)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.15<- c(aa, mean(a1))
row.16<- bb

#Bigrquery Replicate 2
a1<- numeric(0)
aa<- rep(0, 4)
bb<- rep(0, 4)
for(i in 1:10){
  t2<- system.time({
  model<-dbglm(isred~POWER_RATING+NUMBER_OF_SEATS+GROSS_VEHICLE_MASS,tbl=cars11)
  })
  t2<- unname(t2[3])
  a1<- c(a1, t2)
  aa<- aa+0.1 * unname(model$hatbeta[,1])
  bb<- bb + 0.1 * unname(sqrt(diag(model$hatV)))
}
row.17<- c(aa, mean(a1))
row.18<- bb

output<- rbind(row.1, row.2, row.3, row.4, row.5, row.6, row.7, row.8, row.9, row.10, row.11, row.12, row.13, row.14, row.15, row.16, row.17, row.18)
output[c(2,4,6,8,10,12,14,16,18),]<- output[c(2,4,6,8,10,12,14,16,18),]*100
output[,2]<- round(output[,2]*1000,2)
output[,4]<- round(output[,4]*1000,2)
output[,1]<- round(output[,1],2)
output[,3]<- round(output[,3],2)
output[,5]<- round(output[,5],2)
output[c(2,4,6,8,10,12,14,16,18),5]<- rep("",9)
output<- cbind(rep(c("\U03B2", "SEx100"),9), output)
output<- cbind(c("Full Data", "", "Replicate 1", "", "Replicate 2", "", "Replicate 3","", "Replicate 4","","Replicate 5", "", "Replicate 6","", "Replicate 7", "", "Replicate 8", ""), output)
output<- as.data.frame(output)
colnames(output)<- c("", "", "Intercept", "Power/1000", "Seats", "Mass/1000", "Processing Time (s)")
output$Method<- c("","","MonetDB", "", "MonetDB", "", "SQLite", "", "SQLite","","duckDB","","duckDB","", "Big Query", "", "Big Query", "")
kable(output, row.names = F)