Google analytics organic search loop problems - loops

Question

The loop functions completely different and I'm not sure if it is because of the google analytics package because very little is different between the code.
Doesn't WORK difference in print statement output shows results are not coming out correctly.
library(googleAnalyticsR)
library(tidyverse)
#settings
start_dat <- as.character(Sys.Date()-31)
end_dat <- as.character(Sys.Date()-1)
#Authorize Google Analytics R- this will open a webpage
#You must be logged into your Google Analytics account on your web browser
ga_auth()
account_sum <- ga_account_list()
#Add the start and end date to the date frame, as well as some columns to use to populate the metrics
account_sum$start_dat <- start_dat
account_sum$end_dat <- end_dat
## choose the v3 segment
segment_for_call <- "gaid::-5"
## make the v3 segment object in the v4 segment object:
seg_ob <- segment_ga4("OrganicTraffic", segment_id = segment_for_call)
# cycle through the list of views, pull the data, and add it to the
#account_summary
for (i in 1:5){
view_id <- (Book1CSV[[1]][i])
views=view_id
ga_dat <- google_analytics_4(views,
date_range = c(start_dat, end_dat),
segments = seg_ob,
metrics = c("sessions", "pageviews"),
dimensions = c("year","segment"))
ga_dat <- summarise(ga_dat,
sessions = sum(sessions),
pageviews = sum(pageviews))
account_sum$sessions[i] <- ga_data$sessions
account_sum$pageviews[i] <- ga_data$pageviews
print(account_summary)
}
clean_sum <- select(account_sum,
ID = webPropertyId ,
Account = accountName,
Views = views,
Type = type,
Level = level,
'Start Date' = start_dat,
'End Date' = end_dat,
Sessions = sessions,
Pageviews = pageviews)
write.csv (ga_dat, "doesntwork.csv", row.names = TRUE)
THIS WORKS!!!!!!!!!!!!!!! Print statement prints code
library(googleAnalyticsR)
library(tidyverse)
#settings
start_date <- as.character(Sys.Date()-31)
end_date <- as.character(Sys.Date()-1)
metrics <- c("sessions", "pageviews")
dimensions <- "year"
#Authorize Google Analytics R- this will open a webpage
#You must be logged into your Google Analytics account on your web browser
ga_auth()
account_summary <- ga_account_list()
#Add the start and end date to the date frame, as well as some columns to use to populate the metrics
account_summary$start_date <- start_date
account_summary$end_date <- end_date
# cycle through the list of views, pull the data, and add it to the
#account_summary
for (i in 1:6){
view_id <- (Book1CSV[[1]][i])
ga_data <- google_analytics_4(viewId = view_id,
date_range = c(start_date,end_date),
metrics = metrics,
dimensions = dimensions)
# This query might return multiple rows (if it spans a year boundary), so
#collapse and clean up
ga_data <- summarise(ga_data,
sessions = sum(sessions),
pageviews = sum(pageviews))
#add the totals to the account summary
account_summary$sessions[i] <- ga_data$sessions
account_summary$pageviews[i] <- ga_data$pageviews
print(account_summary)
}
# Make a more compact set of data
clean_summary <- select(account_summary,
Account = accountName,
View = viewId,
Type = type,
Level = level,
'Start Date' = start_date,
'End Date' = end_date,
Sessions = sessions,
Pageviews = pageviews,
ID = webPropertyId)
select
write.csv (clean_summary, "worksfine.csv", row.names = FALSE)
Can't really understand whats going wrong here. Would like some detailed help. I have uploaded a file called Book1CSV as I couldn't get a try-catch statement that would work to catch the error. Google merchant store beta account was causing the crash.

Answer 1

This is an anti-R pattern, modifying an object in a loop. Its better to return a list of data.frames, then merge then at the end and create your summary.
So something like:
my_view_ids <- c(12345,233445,232434)
## fetch data, create a list of
all_data <- lapply(my_view_ids, function(id){
one_data <- tryCatch(google_analytics_4(viewId = id,
date_range = c(start_date,end_date),
metrics = metrics,
dimensions = dimensions),
error = function(ex){message(ex); NULL})
one_data$viewId <- id
one_data
})
## Reduce the list of data.frames to one data.frame
all_dataframe <- Reduce(rbind, all_data)
### make your summary etc.