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ga cart aband.R
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## Cart Abandonment
library(tidyverse)
library(googleAnalyticsR)
library(janitor)
library(googleAuthR)
gar_auth("~/tu.httr-oauth")
account_list <- ga_account_list()
meta <- ga_meta()
# This Month
as.Date(start <- "2021-01-01")
as.Date(end <- "2021-06-30")
estee <- 160098300
mac <- 142253091
clinique <- 104892417
bobbi <- 171080712
#1.0 The Manual Way by date ----
df_ga <- google_analytics(mac, date_range = c(start, end),
dimensions = c("date"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
total_transactions <- sum(df_ga$transactions)
total_revenue <- sum(df_ga$transactionRevenue)
aov <- round(total_revenue/total_transactions)
df_ga_events <- google_analytics(mac, date_range = c(start, end),
dimensions = c("date"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
total_add_to_carts <- sum(df_ga_events$uniqueEvents)
aband_rate <- round(1-(total_transactions/total_add_to_carts),3)
lost_per_1000 <- 1000*aband_rate
lost_revenue_per1000 <- round(lost_per_1000*aov)
#2.0 The Manual Way - Source Medium ----
df_ga <- google_analytics(mac, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
organic <- df_ga %>% filter(str_detect(sourceMedium, "organic"))
total_transactions <- sum(organic$transactions)
total_revenue <- sum(organic$transactionRevenue)
aov <- round(total_revenue/total_transactions)
df_ga_events <- google_analytics(mac, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
organic_events <- df_ga_events %>% filter(str_detect(sourceMedium, "organic"))
total_add_to_carts <- sum(organic_events$uniqueEvents)
aband_rate <- round(1-(total_transactions/total_add_to_carts),3)
lost_per_1000 <- 1000*aband_rate
lost_revenue_per1000 <- round(lost_per_1000*aov)
#3.0 The Function Way - sourceMedium ----
aband_function_source_medium <- function(ga_id,start,end,dimension) {
df_ga <- google_analytics(ga_id, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
filtered <- df_ga %>% filter(str_detect(sourceMedium, dimension))
total_transactions <- sum(filtered$transactions)
total_revenue <- sum(filtered$transactionRevenue)
aov <- round(total_revenue/total_transactions)
df_ga_events <- google_analytics(ga_id, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
filtered_events <- df_ga_events %>% filter(str_detect(sourceMedium, dimension))
total_add_to_carts <<- sum(filtered_events$uniqueEvents)
aband_rate <<- round(1-(total_transactions/total_add_to_carts),3)
lost_per_1000 <<- 1000*aband_rate
lost_revenue_per1000 <<- round(lost_per_1000*aov)
}
aband_function_source_medium(mac,start,end,"organic")
#4.0 The Function Way - devices ----
aband_function_devices <- function(ga_id,start,end,dimension) {
df_ga <- google_analytics(ga_id, date_range = c(start, end),
dimensions = c("deviceCategory"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
filtered <- df_ga %>% filter(str_detect(deviceCategory, dimension))
total_transactions <- sum(filtered$transactions)
total_revenue <- sum(filtered$transactionRevenue)
aov <- round(total_revenue/total_transactions)
df_ga_events <- google_analytics(ga_id, date_range = c(start, end),
dimensions = c("deviceCategory"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
filtered_events <- df_ga_events %>% filter(str_detect(deviceCategory, dimension))
total_add_to_carts <<- sum(filtered_events$uniqueEvents)
aband_rate <<- round(1-(total_transactions/total_add_to_carts),3)
lost_per_1000 <<- 1000*aband_rate
lost_revenue_per1000 <<- round(lost_per_1000*aov)
}
aband_function_devices(mac,start,end,"mobile")
#5.0 The Generalized way with choose a dimensions ----
aband_function <- function(ga_id,start,end,ga_dim,dimension) {
df_ga <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
filtered <- df_ga %>% filter(str_detect(.[[1]], dimension))
total_transactions <- sum(filtered$transactions)
total_revenue <- sum(filtered$transactionRevenue)
aov <- round(total_revenue/total_transactions)
df_ga_events <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
filtered_events <- df_ga_events %>% filter(str_detect(.[[1]], dimension))
total_add_to_carts <<- sum(filtered_events$uniqueEvents)
aband_rate <<- round(1-(total_transactions/total_add_to_carts),3)
lost_per_1000 <<- 1000*aband_rate
lost_revenue_per1000 <<- round(lost_per_1000*aov)
}
aband_function(mac,start,end,"deviceCategory","mobile")
aband_function(mac,start,end,"deviceCategory","desktop")
aband_function(mac,start,end,"sourceMedium","organic")
aband_function(mac,start,end,"sourceMedium","cpc")
#6.0 Manual Data Frame deviceCategory ----
df_ga <- google_analytics(mac, date_range = c(start, end),
dimensions = c("deviceCategory"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
df_ga <- df_ga %>% mutate(aov = (transactionRevenue/transactions) )
df_ga_events <- google_analytics(mac, date_range = c(start, end),
dimensions = c("deviceCategory"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
df_ga_events <- df_ga_events %>% set_names(c("deviceCategory", "addtocart"))
joined_df <- df_ga %>% left_join(df_ga_events, by="deviceCategory")
joined_df_final <- joined_df %>% mutate(aband_rate = round(1-(transactions/addtocart),3),
lost_per_1000 = 1000*aband_rate,
lost_revenue_per1000 = round(lost_per_1000*aov)
)
head(joined_df_final)
#7.0 Manual Data Frame Source Medium ----
df_ga <- google_analytics(mac, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
df_ga <- df_ga %>% group_by(sourceMedium) %>% summarise(transactions = sum(transactions),
transactionRevenue = sum(transactionRevenue)) %>%
ungroup()
df_ga <- df_ga %>% mutate(aov = (transactionRevenue/transactions) )
df_ga_events <- google_analytics(mac, date_range = c(start, end),
dimensions = c("sourceMedium"),
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
df_ga_events <- df_ga_events%>% group_by(sourceMedium) %>% summarise(uniqueEvents = sum(uniqueEvents)) %>%
ungroup()
df_ga_events <- df_ga_events %>% set_names(c("sourceMedium", "addtocart"))
joined_df <- df_ga %>% left_join(df_ga_events, by="sourceMedium")
joined_df_final <- joined_df %>% mutate(aband_rate = round(1-(transactions/addtocart),3),
lost_per_1000 = 1000*aband_rate,
lost_revenue_per1000 = round(lost_per_1000*aov)
)
joined_df_final <- joined_df_final %>% arrange(desc(transactions))
head(joined_df_final)
#8.0 Generalize Data Frame Event Based----
aband_full_function <- function(ga_id,start,end,ga_dim) {
df_ga <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
df_ga <- df_ga %>% group_by_at(1) %>% summarise(transactions = sum(transactions),
transactionRevenue = round(sum(transactionRevenue))) %>%
ungroup()
df_ga
df_ga <- df_ga %>% mutate(aov = (transactionRevenue/transactions) )
df_ga$aov[is.nan(df_ga$aov)]<-0
df_ga
df_ga_events <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = c("uniqueEvents"),
filtersExpression = c("ga:eventCategory=~^(ecommerce|eCommerce)$;ga:eventAction=~^(add to bag|add to cart)$"),
max =-1,
anti_sample = TRUE)
df_ga_events <- df_ga_events%>% group_by_at(1) %>% summarise(uniqueEvents = sum(uniqueEvents)) %>%
ungroup()
df_ga_events
df_ga_events <- df_ga_events %>% set_names(c(names(df_ga_events[1]), "addtocart"))
joined_df <- df_ga %>% left_join(df_ga_events, by=names(df_ga_events[1]))
joined_df_final <- joined_df %>% mutate(aband_rate = round(1-(transactions/addtocart),3),
lost_per_1000 = 1000*aband_rate,
lost_revenue_per1000 = round(lost_per_1000*aov))
joined_df_final <- joined_df_final %>% arrange(desc(transactions))
joined_df_final <- joined_df_final %>% replace(is.na(.), 0)
}
mac_abnd_rate_sourcemedium <- aband_full_function(mac,start,end,"sourceMedium")
mac_abnd_rate_devices <- aband_full_function(mac,start,end,"deviceCategory")
mac_abnd_rate_countries <- aband_full_function(mac,start,end,"country")
estee_abnd_rate_sourcemedium <- aband_full_function(estee,start,end,"sourceMedium")
estee_abnd_rate_devices <- aband_full_function(estee,start,end,"deviceCategory")
estee_abnd_rate_countries <- aband_full_function(estee,start,end,"country")
clinique_abnd_rate_sourcemedium <- aband_full_function(clinique,start,end,"sourceMedium")
clinique_abnd_rate_devices <- aband_full_function(clinique,start,end,"deviceCategory")
clinique_abnd_rate_countries <- aband_full_function(clinique,start,end,"country")
bobbi_abnd_rate_sourcemedium <- aband_full_function(bobbi,start,end,"sourceMedium")
bobbi_abnd_rate_devices <- aband_full_function(bobbi,start,end,"deviceCategory")
bobbi_abnd_rate_countries <- aband_full_function(bobbi,start,end,"country")
#9.0 Uploading to GCS and BQ ----
#9.1 Setting up BigQuery and GCS ----
library(googleCloudStorageR)
library(bigQueryR)
#9.2 Setting Project --
bqr_global_project("erez-bigquery")
bqr_get_global_project()
#9.3 get project list --
projects <- bqr_list_projects()
head(projects)
my_project <- projects[2,1]
my_project
my_project_id <- projects[2,3]
my_project_id
#9.4 for first project, get datasets --
datasets <- bqr_list_datasets(my_project)
head(datasets)
my_dataset <- datasets[4,2]
my_dataset
#10.0 Upload Data to gcs----
#10.1 Get Storage Buckets --
gcs_list_buckets(my_project_id)
#gcs_list_buckets("erez-bigquery")
#10.2 Set bucket to store the data --
gcs_global_bucket("cart_abandonment") ## This will be the folder
#10.3 Upload function To Storage --
f <- function(input, output) {
write.table(input, sep = ",", col.names = FALSE, row.names = FALSE,
quote = FALSE, file = output, qmethod = "double",fileEncoding="UTF-8")}
filename=paste0("cart_abandonment.csv")
gcs_upload(mac_abnd_rate_sourcemedium, name = filename, object_function = f)
#gcs_upload(df, name = "easydf.csv", object_function = f)
#11.0 upload to BQ -----
path=paste0("gs://cart_abandonment/",filename) ##Make sure path is right
path
user_schema <- schema_fields(mac_abnd_rate_sourcemedium)
job <- bqr_upload_data(projectId = my_project_id,
datasetId = "elcalil_cart_abandonment", ## CREATE IN BQ IF NEEDED
tableId = "mac_by_source_medium",
upload_data = c(path),
create = c("CREATE_IF_NEEDED"), ##WILL CREATE TABLE ONLY
# writeDisposition = c("WRITE_TRUNCATE"),
schema = user_schema
# autodetect = FALSE
)
job
bqr_get_job(projectId = my_project, jobId = job$jobReference$jobId)
## 12.0 Generalize Data Frame Goals Based----
aband_full_goals_function <- function(ga_id,start,end,ga_dim,goalnumber) {
df_ga <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = c("transactions", "transactionRevenue"),
max =-1,
anti_sample = TRUE)
df_ga <- df_ga %>% group_by_at(1) %>% summarise(transactions = sum(transactions),
transactionRevenue = round(sum(transactionRevenue))) %>%
ungroup()
df_ga
df_ga <- df_ga %>% mutate(aov = (transactionRevenue/transactions) )
df_ga$aov[is.nan(df_ga$aov)]<-0
df_ga
df_ga_goals <- google_analytics(ga_id, date_range = c(start, end),
dimensions = ga_dim,
metrics = goalnumber,
max =-1,
anti_sample = TRUE)
df_ga_goals <- df_ga_goals%>% group_by_at(1) %>% summarize_if(is.numeric, sum, na.rm=TRUE) %>%
ungroup()
df_ga_goals
df_ga_goals <- df_ga_goals %>% set_names(c(names(df_ga_goals[1]), "addtocart"))
joined_df <- df_ga %>% left_join(df_ga_goals, by=names(df_ga_goals[1]))
joined_df_final <- joined_df %>% mutate(aband_rate = round(1-(transactions/addtocart),3),
lost_per_1000 = 1000*aband_rate,
lost_revenue_per1000 = round(lost_per_1000*aov))
joined_df_final <- joined_df_final %>% arrange(desc(transactions, transactionRevenue, addtocart, aov, aband_rate))
joined_df_final <- joined_df_final %>% replace(is.na(.), 0)
}
## EVENTS BASED
mac_abnd_rate_sourcemedium <- aband_full_function(mac,start,end,"sourceMedium")
mac_abnd_rate_devices <- aband_full_function(mac,start,end,"deviceCategory")
mac_abnd_rate_countries <- aband_full_function(mac,start,end,"country")
## Goals BASED
mac_abnd_rate_sourcemedium_goals <- aband_full_goals_function(mac,start,end,"sourceMedium", "goal10Completions")
mac_abnd_rate_devices_goals <- aband_full_goals_function(mac,start,end,"deviceCategory", "goal10Completions")
mac_abnd_rate_countries_goals <- aband_full_goals_function(mac,start,end,"country", "goal10Completions")
## EVENTS BASED
estee_abnd_rate_sourcemedium <- aband_full_function(estee,start,end,"sourceMedium")
estee_abnd_rate_devices <- aband_full_function(estee,start,end,"deviceCategory")
estee_abnd_rate_countries <- aband_full_function(estee,start,end,"country")
## Goals BASED
estee_abnd_rate_sourcemedium_goals <- aband_full_goals_function(estee,start,end,"sourceMedium", "goal1Completions")
estee_abnd_rate_devices_goals <- aband_full_goals_function(estee,start,end,"deviceCategory", "goal1Completions")
estee_abnd_rate_countries_goals <- aband_full_goals_function(estee,start,end,"country", "goal1Completions")
## EVENTS BASED
clinique_abnd_rate_sourcemedium <- aband_full_function(clinique,start,end,"sourceMedium")
clinique_abnd_rate_devices <- aband_full_function(clinique,start,end,"deviceCategory")
clinique_abnd_rate_countries <- aband_full_function(clinique,start,end,"country")
## Goals BASED
clinique_abnd_rate_sourcemedium_goals <- aband_full_goals_function(clinique,start,end,"sourceMedium", "goal1Completions")
clinique_abnd_rate_devices_goals <- aband_full_goals_function(clinique,start,end,"deviceCategory","goal1Completions")
clinique_abnd_rate_countries_goals <- aband_full_goals_function(clinique,start,end,"country", "goal1Completions")
## EVENTS BASED
bobbi_abnd_rate_sourcemedium <- aband_full_function(bobbi,start,end,"sourceMedium")
bobbi_abnd_rate_devices <- aband_full_function(bobbi,start,end,"deviceCategory")
bobbi_abnd_rate_countries <- aband_full_function(bobbi,start,end,"country")
## Goals BASED
bobbi_abnd_rate_sourcemedium_goals <- aband_full_goals_function(bobbi,start,end,"sourceMedium", "goal10Completions")
bobbi_abnd_rate_devices_goals <- aband_full_goals_function(bobbi,start,end,"deviceCategory", "goal10Completions")
bobbi_abnd_rate_countries_goals <- aband_full_goals_function(bobbi,start,end,"country", "goal10Completions")
## FUTURE IDEAS ----
## ADD DATES ?
## UPLOAD ALL TO BQ ??
## CHANGE TO GOALS ?