The v4 API supports Universal Analytics. For working with Google Analytics 4 (App+Web) use the new Data API.
The v4 API currently has these extras implemented over the v3 API:
- Cohorts
- Multiple date ranges
- Pivot tables
- Calculated metrics on the fly
- Much more powerful segments
- Quota system for GA360
- Caching
Check out more examples on using the API in actual use cases on the www.dartistics.com website.
Getting started
To get started refer to the tutorials linked on the homepage, consult
?google_analytics when within R to see the documentation,
and see these example queries:
## setup
library(googleAnalyticsR)
## authenticate
ga_auth()
## get your accounts
account_list <- ga_account_list()
## account_list will have a column called "viewId"
account_list$viewId
## View account_list and pick the viewId you want to extract data from
ga_id <- 123456
## simple query to test connection
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date")Jeff Swanson has made a video guide that goes into more detail about the above examples (Digital Marketing in R: How to Connect to Google Analytics API) and is embedded below, many thanks to Jeff.
Number of results
By default the function will return 1000 results. If you want to
fetch all rows, set max = -1
You don’t have to worry about paging through the API results, the library deals with that for you.
# 1000 rows only
thousand <- google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date")
# 2000 rows
twothousand <- google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = 2000)
# All rows
alldata <- google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = -1) If you are using anti-sampling, it will always fetch all rows. This is because it won’t make sense to fetch only the top results as the API splits up the calls over all days. If you want to limit it afterwards, use R by doing something like:
## anti_sample gets all results (max = -1)
gadata <- google_analytics(myID,
date_range = c(start_date, end_date),
metrics = "pageviews",
dimensions = "pageTitle",
segments = myseg,
anti_sample = TRUE)
## limit to top 25
top_25 <- head(gadata[order(gadata$pageviews, decreasing = TRUE), ] , 25)Date Ranges
You can send in dates in YYYY-MM-DD format:
google_analytics(868768, date_range = c("2016-12-31", "2017-02-01"), metrics = "sessions")Character strings are converted into Date class objects,
so you can also use R’s native Date object handling to
specify rolling dates:
yesterday <- Sys.Date() - 1
ThreedaysAgo <- Sys.Date() - 3
google_analytics(868768, date_range = c(ThreedaysAgo, yesterday), metrics = "sessions")Or use the v4 API shortcuts directly such as
"yesterday", "today" or
"XDaysAgo":
google_analytics(868768, date_range = c("5daysAgo", "yesterday"), metrics = "sessions")Compare date ranges
In v4 you can compare date ranges and return the two data sets in one
call. Send in a 4-length vector of dates in the form
(range1_start, range1_end, range2_start, range2_end)
google_analytics(868768,
date_range = c("16daysAgo", "9daysAgo", "8daysAgo", "yesterday"),
metrics = "sessions")When requesting multiple date ranges, you can use a new ordering feature of v4 to find the 10 most changed dimensions, rather than just say the top 10. An article about using this feature can be found here.
An example is shown below, using the order_type function
to sort by the change (DELTA) between dates:
delta_sess <- order_type("sessions","DESCENDING", "DELTA")
## find top 20 landing pages that changed most in sessions comparing this week and last week
gadata <- google_analytics(gaid,
date_range = c("16daysAgo", "9daysAgo", "8daysAgo", "yesterday"),
metrics = c("sessions"),
dimensions = c("landingPagePath"),
order = delta_sess,
max = 20)Anti-sampling
Anti-sampling is a common use case for using the API.
Sampling is due to your API request being over the session limits outlined in this Google article. This limit is higher for Google Analytics 360.
If you split up your API request so that the number of sessions falls under these limits, sampling will not occur.
Sampled data example
If you have sampling in your data request,
googleAnalyticsR reports it back to you in the console when
making the request.
library(googleAnalyticsR)
ga_auth()
sampled_data_fetch <- google_analytics(id,
date_range = c("2015-01-01","2015-06-21"),
metrics = c("users","sessions","bounceRate"),
dimensions = c("date","landingPagePath","source"))
#Calling APIv4....
#Data is sampled, based on 39.75% of visits.Unsampled data example
Setting the argument anti_sample=TRUE in a
google_analytics() request causes the calls to be split up
into small enough chunks to avoid sampling. This uses more API calls so
the data will reach you slower, but it should hold more detail.
library(googleAnalyticsR)
ga_auth()
unsampled_data_fetch <- google_analytics(id,
date_range = c("2015-01-01","2015-06-21"),
metrics = c("users","sessions","bounceRate"),
dimensions = c("date","landingPagePath","source"),
anti_sample = TRUE)
#anti_sample set to TRUE. Mitigating sampling via multiple API calls.
#Finding how much sampling in data request...
#Data is sampled, based on 39.75% of visits.
#Downloaded [10] rows from a total of [59235].
#Finding number of sessions for anti-sample calculations...
#Downloaded [172] rows from a total of [172].
#Calculated [3] batches are needed to download [59235] rows unsampled.
#Anti-sample call covering 128 days: 2015-01-01, 2015-05-08
#Downloaded [59235] rows from a total of [59235].
#Anti-sample call covering 23 days: 2015-05-09, 2015-05-31
#Downloaded [20239] rows from a total of [20239].
#Anti-sample call covering 21 days: 2015-06-01, 2015-06-21
#Downloaded [21340] rows from a total of [21340].
#Finished unsampled data request, total rows [100814]
#Successfully avoided samplingThe sequence involves a couple of exploratory API calls to determine the best split. This method will adjust the time periods to have the batch sizes large enough to not take too long, but small enough to have unsampled data.
Auto-anti sampling failure
In some cases the anti-sampling won’t work. This will mainly be due to filters for the View you are using meaning that the calculation for the sampling sessions are incorrect - Google Analytics vanilla samples on a property level (whilst GA360 samples on a View level).
Try using a raw profile if you can, otherwise you can set your own
sample period by using the anti_sample_batches flag to
indicate the sample size. Pick a number that is a little lower than the
smallest period you saw in the auto-sample batches. If in doubt, setting
anti_sample_batches to 1 will make a daily fetch.
## example setting your own anti_sample_batch to 5 days per batch
unsampled_data_fetch <- google_analytics(id,
date_range = c("2015-01-01","2015-06-21"),
metrics = c("users","sessions","bounceRate"),
dimensions = c("date","landingPagePath","source"),
anti_sample = TRUE,
anti_sample_batch = 5)Filters
Note these are different from account filters, which you can manipulate using the management API using the
ga_filterfamily of functions. See the Management API
Filters need to be built up from the met_filter and
dim_filter R objects.
These then need to be wrapped in filter_clause_ga4
function, which lets you specify the combination rules (AND
or OR). The met_filter and
dim_filter you pass in should be wrapped in a
list() - see examples:
One filter
campaign_filter <- dim_filter(dimension="campaign",operator="REGEXP",expressions="welcome")
my_filter_clause <- filter_clause_ga4(list(campaign_filter))
data_fetch <- google_analytics(ga_id,date_range = c("2016-01-01","2016-12-31"),
metrics = c("itemRevenue","itemQuantity"),
dimensions = c("campaign","transactionId","dateHour"),
dim_filters = my_filter_clause,
anti_sample = TRUE)Multiple filters
## create filters on metrics
mf <- met_filter("bounces", "GREATER_THAN", 0)
mf2 <- met_filter("sessions", "GREATER", 2)
## create filters on dimensions
df <- dim_filter("source","BEGINS_WITH","1",not = TRUE)
df2 <- dim_filter("source","BEGINS_WITH","a",not = TRUE)
## construct filter objects
fc2 <- filter_clause_ga4(list(df, df2), operator = "AND")
fc <- filter_clause_ga4(list(mf, mf2), operator = "AND")
## make v4 request
ga_data1 <- google_analytics(ga_id,
date_range = c("2015-07-30","2015-10-01"),
dimensions=c('source','medium'),
metrics = c('sessions','bounces'),
met_filters = fc,
dim_filters = fc2,
filtersExpression = "ga:source!=(direct)")
ga_data1
# source medium sessions bounces
# 1 baby.dk referral 3 2
# 2 bing organic 71 42
# 3 buttons-for-website.com referral 7 7
# 4 duckduckgo.com referral 5 3
# 5 google organic 642 520
# 6 google.se referral 3 2
# 7 izito.se referral 3 1
# 8 success-seo.com referral 35 35
# 9 video--production.com referral 11 11
# 10 yahoo organic 66 43
# 11 zapmeta.se referral 6 4Multiple reports
Using v4’s batching ability, you can send in multiple types of API requests at the same time (up to 5). The reports need to be for the same ViewID and date range.
To use, you need to create your API requests first using
make_ga_4_req(), then make the actual call using
fetch_google_analytics(). For the common use case of only
requesting one type of request, this is what
google_analytics() is doing behind the scenes.
Demo of querying two API requests
The below example requests with two date ranges and two reports.
When fetching multiple reports, you need to wrap the
make_ga_4_req() created objects in a
list()
## First request we make via make_ga_4_req()
multidate_test <- make_ga_4_req(ga_id,
date_range = c("2015-07-30",
"2015-10-01",
"2014-07-30",
"2014-10-01"),
dimensions = c('source','medium'),
metrics = c('sessions','bounces'),
order = order_type("sessions", "DESCENDING", "DELTA"))
## Second request - same date ranges and ID required, but different dimensions/metrics/order.
multi_test2 <- make_ga_4_req(ga_id,
date_range = c("2015-07-30",
"2015-10-01",
"2014-07-30",
"2014-10-01"),
dimensions=c('hour','medium'),
metrics = c('visitors','bounces'))
## Request the two calls by wrapping them in a list() and passing to fetch_google_analytics()
ga_data3 <- fetch_google_analytics(list(multidate_test, multi_test2))
ga_data3
# [[1]]
# source medium sessions.d1 bounces.d1 sessions.d2 bounces.d2
# 1 baby.dk referral 3 2 6 3
# 2 bing organic 71 42 217 126
# 3 buttons-for-website.com referral 7 7 0 0
# 4 duckduckgo.com referral 5 3 0 0
# 5 google organic 642 520 1286 920
# 6 google.se referral 3 2 12 9
# 7 izito.se referral 3 1 0 0
# 8 success-seo.com referral 35 35 0 0
# 9 video--production.com referral 11 11 0 0
# 10 yahoo organic 66 43 236 178
# 11 zapmeta.se referral 6 4 9 4
#
# [[2]]
# hour medium visitors.d1 bounces.d1 visitors.d2 bounces.d2
# 1 00 organic 28 16 85 59
# 2 00 referral 3 2 1 1
# 3 01 organic 43 28 93 66Metric expressions
Metric expressions are custom calculated metrics that can be calculated on the fly when you supply a named vector with the name of your created custom metric, and the calculation expression you are performing. These are different from the calculated metrics you can create in the web UI.
You need to use the
ga:prefix when creating custom metrics, unlike normal API requests
my_custom_metric <- c(visitPerVisitor = "ga:visits/ga:visitors")Use metric expressions as you would other metrics within the
metrics argument, but you also need to supply a
metricFormat (see docs)
vector the same length which specifies the type of the metric you have
calculated. These can be:
METRIC_TYPE_UNSPECIFIEDINTEGERFLOAT-
CURRENCY
PERCENTTIME
An example is shown below, where a calculated metric is combined with
a normal metric, bounces, which is type integer. You can
lookup what the default metrics types are in the meta
lookup table.
my_custom_metric <- c(visitPerVisitor = "ga:visits/ga:visitors")
ga_data4 <- google_analytics(ga_id,
date_range = c("2015-07-30",
"2015-10-01"),
dimensions=c('medium'),
metrics = c(my_custom_metric,
'bounces'),
metricFormat = c("FLOAT","INTEGER"))
ga_data4
# medium visitsPerVisitor bounces
# 1 (none) 1.000000 117
# 2 organic 1.075137 612
# 3 referral 1.012500 71Segments v4
Segments are more complex to configure that v3, but more powerful and in line to how you configure them in the UI.
A lot of feedback is about how to get the sample syntax right, so the examples below try to cover common scenarios.
v3 segments
You can choose to create segments via the v4 syntax or the v3 syntax for backward compatibility.
If you want to use v3 segments, then they can be used in the
segment_id argument of the segment_ga4()
function.
You can view the segment Ids by using
ga_segment_list()
## get list of segments
segs <- ga_segment_list()
## segment Ids and name:
segs[,c("name","id","definition")]
## example output
id name definition
1 -1 All Users
2 -2 New Users sessions::condition::ga:userType==New Visitor
3 -3 Returning Users sessions::condition::ga:userType==Returning Visitor
4 -4 Paid Traffic sessions::condition::ga:medium=~^(cpc|ppc|cpa|cpm|cpv|cpp)$
5 -5 Organic Traffic sessions::condition::ga:medium==organic
6 -6 Search Traffic sessions::condition::ga:medium=~^(cpc|ppc|cpa|cpm|cpv|cpp|organic)$
....The ID you require is in the $id column which you need
to prefix with “gaid::”
## choose the v3 segment
segment_for_call <- "gaid::-4"
## make the v3 segment object in the v4 segment object:
seg_obj <- segment_ga4("PaidTraffic", segment_id = segment_for_call)
## make the segment call
segmented_ga1 <- google_analytics(ga_id,
c("2015-07-30","2015-10-01"),
dimensions=c('source','medium','segment'),
segments = seg_obj,
metrics = c('sessions','bounces')
)
…or you can pass the v3 syntax for dynamic segments found from the
$definition column:
## or pass the segment v3 defintion in directly:
segment_def_for_call <- "sessions::condition::ga:medium=~^(cpc|ppc|cpa|cpm|cpv|cpp)$"
## make the v3 segment object in the v4 segment object:
seg_obj <- segment_ga4("PaidTraffic", segment_id = segment_def_for_call)
## make the segment call
segmented_ga1 <- google_analytics(ga_id,
c("2015-07-30","2015-10-01"),
dimensions=c('source','medium','segment'),
segments = seg_obj,
metrics = c('sessions','bounces')
)v4 segment syntax
Its recommended you embrace the new v4 syntax, as its more flexible and powerful in the long run.
The hierarachy of the segment elements you will need are:
-
segment_ga4()- this is the top of the segment tree. You can pass one or a list of these into agoogle_analytics()segment argument. Here you name the segment as it will appear in thesegmentdimension, and pass in segment definitions either via an existing segmentID or v3 definition; via a user scoped level; or via a session scoped level. The user and session scopes can have one or a list ofsegment_define()functions. -
segment_define()- this is where you define the types of segment filters that you are passing in - they are combined in a logical AND. The segment filters can be of asegment_vector_simpletype (where order doesn’t matter) or asegment_vector_sequencetype (where order does matter.) You can also pass in anot_vectorof the same length as the list of segment filters, which dictates if the segments you pass in are included (the default) or excluded. -
segment_vector_simple()andsegment_vector_sequence()- these are vectors ofsegment_element, and determine if the conditions are included in a logical OR fashion, or if the sequence of steps is important (for instance users who saw this page, then that page.)
-
segment_element- this is the lowest atom of segments, and lets you define on which metric or dimension you are segmenting on. You pass one or a list of these tosegment_vector_simple()orsegment_vector_sequence()
Demo: simple segment
se <- segment_element("sessions",
operator = "GREATER_THAN",
type = "METRIC",
comparisonValue = 1,
scope = "USER")
se2 <- segment_element("medium",
operator = "EXACT",
type = "DIMENSION",
expressions = "organic")
## choose between segment_vector_simple or segment_vector_sequence
## Elements can be combined into clauses, which can then be combined into OR filter clauses
sv_simple <- segment_vector_simple(list(list(se)))
sv_simple2 <- segment_vector_simple(list(list(se2)))
## Each segment vector can then be combined into a logical AND
seg_defined <- segment_define(list(sv_simple, sv_simple2))
## Each segement defintion can apply to users, sessions or both.
## You can pass a list of several segments
segment4 <- segment_ga4("simple", user_segment = seg_defined)
## Add the segments to the segments param
segment_example <- google_analytics(ga_id,
c("2015-07-30","2015-10-01"),
dimensions=c('source','medium','segment'),
segments = segment4,
metrics = c('sessions','bounces')
)
segment_example
# source medium segment sessions bounces
# 1 24.co.uk referral simple 1 1
# 2 aidsmap.com referral simple 1 0
# 3 aol organic simple 30 19
# 4 ask organic simple 32 17
## You can send in multiple segments at a time by adding them as a list
segment4_again <- segment_ga4("simple_2", user_segment = seg_defined)
## Add the segments to the segments param
segment_example2 <- google_analytics(ga_id,
c("2015-07-30","2015-10-01"),
dimensions=c('source','medium','segment'),
segments = list(segment4, segment4_again),
metrics = c('sessions','bounces')
)
# source medium segment sessions bounces
#1 (direct) (none) simple 30 22
#2 (direct) (none) simple_2 30 22
#3 127.0.0.1:7424 referral simple 1 1
#4 127.0.0.1:7424 referral simple_2 1 1
#5 analyticsforfun.com referral simple 5 1
#6 analyticsforfun.com referral simple_2 5 1
#...Demo: Sequence segment
se2 <- segment_element("medium",
operator = "EXACT",
type = "DIMENSION",
expressions = "organic")
se3 <- segment_element("medium",
operator = "EXACT",
type = "DIMENSION",
not = TRUE,
expressions = "organic")
## step sequence
## users who arrived via organic then via referral
sv_sequence <- segment_vector_sequence(list(list(se2),
list(se3)))
seq_defined2 <- segment_define(list(sv_sequence))
segment4_seq <- segment_ga4("sequence", user_segment = seq_defined2)
## Add the segments to the segments param
segment_seq_example <- google_analytics(ga_id,
c("2016-01-01","2016-03-01"),
dimensions=c('source','segment'),
segments = segment4_seq,
metrics = c('sessions','bounces')
)
segment_seq_example
# source segment sessions bounces
# 1 aol sequence 1 0
# 2 ask sequence 5 1
# 3 bestbackpackersinsurance.co.uk sequence 9 6
# 4 bing sequence 22 2Some more examples, using different match types, contributed by Pawel Kapuscinski:
con1 <-segment_vector_simple(list(list(segment_element("ga:dimension1",
operator = "REGEXP",
type = "DIMENSION",
expressions = ".*",
scope = "SESSION"))))
con2 <-segment_vector_simple(list(list(segment_element("ga:deviceCategory",
operator = "EXACT",
type = "DIMENSION",
expressions = "Desktop",
scope = "SESSION"))))
seq1 <- segment_element("ga:pagePath",
operator = "EXACT",
type = "DIMENSION",
expressions = "yourdomain.com/page-path",
scope = "SESSION")
seq2 <- segment_element("ga:eventAction",
operator = "REGEXP",
type = "DIMENSION",
expressions = "english",
scope = "SESSION",
matchType = "IMMEDIATELY_PRECEDES")
allSEQ <- segment_vector_sequence(list(list(seq1), list(seq2)))
results <- google_analytics(ga_id,
date_range = c("2016-08-08","2016-09-08"),
segments = segment_ga4("sequence+condition",
user_segment = segment_define(list(con1,con2,allSEQ))
),
metrics = c('ga:users'),
dimensions = c('ga:segment'))
# Users whose first session to website was social:
seg_social <- segment_element("channelGrouping",
operator = "EXACT",
type = "DIMENSION",
expressions = "Social")
seg_first_visit <- segment_element("sessionCount",
operator = "EXACT",
type = "DIMENSION",
expressions = "1")
# social referrral followed by first sessionCount
segment_social_first <- segment_vector_sequence(list(list(seg_social), list(seg_first_visit)))
sd_segment <- segment_define(list(segment_social_first))
segment_social <- segment_ga4("social_first", user_segment = sd_segment)
segment_data <- google_analytics_4(my_viewId,
date_range = c("8daysAgo", "yesterday"),
metrics = c("sessions", "users", sessions_per_user = "ga:sessions/ga:users"),
dimensions = c("date", "country", "channelGrouping", "sessionCount"),
segments = segment_social,
metricFormat = c("FLOAT", "INTEGER", "INTEGER"))If you are still struggling with segment syntax, you may want to try
the ganalytics
package by Johann de Boer which has developed a domain specific
language for making segments and may be easier to parse.
Cohort reports
Details on cohort reports and LTV can be found here and via the examples below.
## first make a cohort group
cohort4 <- make_cohort_group(list("Jan2016" = c("2016-01-01", "2016-01-31"),
"Feb2016" = c("2016-02-01","2016-02-28")))
## then call cohort report. No date_range and must include metrics and dimensions
## from the cohort list
cohort_example <- google_analytics(ga_id,
dimensions=c('cohort'),
cohort = cohort4,
metrics = c('cohortTotalUsers'))
cohort_example
# cohort cohortTotalUsers
# 1 Feb2016 19040
# 2 Jan2016 23378Pivot Requests
These change the shape of the returned data, which you could probably do in R anyway but it gives you another option when you make the request.
## filter pivot results to
pivot_dim_filter1 <- dim_filter("medium",
"REGEXP",
"organic|social|email|cpc")
pivot_dim_clause <- filter_clause_ga4(list(pivot_dim_filter1))
pivme <- pivot_ga4("medium",
metrics = c("sessions"),
maxGroupCount = 4,
dim_filter_clause = pivot_dim_clause)
pivtest1 <- google_analytics(ga_id,
c("2016-01-30","2016-10-01"),
dimensions=c('source'),
metrics = c('sessions'),
pivots = list(pivme))
names(pivtest1)
# [1] "source" "sessions" "medium.referral.sessions"
# [4] "medium..none..sessions" "medium.cpc.sessions" "medium.email.sessions"
# [7] "medium.social.sessions" "medium.twitter.sessions" "medium.socialMedia.sessions"
# [10] "medium.Social.sessions" "medium.linkedin.sessions" GA360 Quota System
If you have GA360, you have access to resource based quotas that increase the number of sessions before sampling kicks in from 1 to 100 million sessions.
To access this quota, set useResourceQuotas = TRUE in
the command.
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = -1,
useResourceQuotas = TRUE)Customising API fetches
googleAnalyticsR has some techniques implemented to get
the data as quickly as possible. This includes:
Batching large results
The v4 API allows you to send 5 batched calls at once in one API request, meaning by default 50,000 rows will be requested per API call. However, since this puts additional strain on the Google servers, for complicated queries (i.e. lots of segments or dimensions) this may fail with a 500 error. If that happens, the library will fall back to the slower fetching of 10,000 rows per API call.
If you want to default to the slower fetching, set
slow_fetch=TRUE in your request e.g.
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = -1,
slow_fetch = TRUE)Caching
Local caching is enabled that will mean if you make the exact same
API request with all arguments the same, the result will come from
memory rather than the API. By default this is within the RAM of the
same R session so will reset when you restart R, but if you want you can
set this to save the cache to disk using the
ga_cache_call() function. Use this to point to a folder to
store the cache files, and the cache will then be available inbetween R
sessions.
A demo on using caching is in the video here: googleAnalyticsR - caching and is embedded below:.
e.g.
ga_cache_call("my_cache_folder")
## will make the call and save files to my_cache_folder
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = -1)
## making the same exact call again will read from disk, and be much quicker
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
max = -1)This means that repeated long fetches can be much quicker as older requests will read from disk.
If you are running long repeated calls over time, this is a good strategy to only fetch the latest data from the API, relying on the cache for historic data - for instance, set a loop that requests each month’s worth of data - for the month’s you already have the requests will come from disk, but for the most recent month it will fetch from the API.
Rows Per call
The API lets you call up to 100,000 rows, which coupled with the
batching above means potentially 500,000 rows can be called per API
call. In reality, this many rows will probably be too heavy for the
Google servers, so you may want to experiment using increased row per
calls but with slow_fetch=TRUE to find the optimum.
e.g.
## making the same exact call again will read from disk, and be much quicker
google_analytics(ga_id,
date_range = c("2017-01-01", "2017-03-01"),
metrics = "sessions",
dimensions = "date",
rows_per_call = 40000,
slow_fetch = TRUE,
max = -1)The defaults are 10,000 rows per call (50,000 per batch) and
slow_fetch = FALSE which suit for most cases.
Fetching from multiple views
Whilst the v3 multi
account batching feature isn’t available for multi-accounts in v4,
by using future.apply
you can launch multiple R sessions that will fetch the API in parallel.
An example is shown below, that fetches from three View IDs at once.
The limiting factor will then be the v4 API limits, which are currently 1000 requests per 100 seconds per user, per Google API project (e.g. you will probably want to setup your own Google project if making a lot of requests)
library(googleAnalyticsR)
library(future.apply)
## setup multisession R for your parallel data fetches
plan(multisession)
## the ViewIds to fetch all at once
gaids <- c(12345634, 9888890,10624323)
my_fetch <- function(x) {
google_analytics(x,
date_range = c("2017-01-01","yesterday"),
metrics = "sessions",
dimensions = c("date","medium"))
}
## makes 3 API calls at once
all_data <- future_lapply(gaids, my_fetch)