A new Google Analytics R library using the new v4 of the Google Analytics Reporting API. Built using googleAuthR. The successor to shinyga it allows online OAuth2 authentication within Shiny apps, along with new features such as batching and compatibility with other Google APIs.
These are all great libraries, that I have taken inspiration from.
googleAuthR apps like searchConsoleRgoogleAuthR batch, meaning 10 calls at once to GA - great for big fetches. For big data calls this is implemented automatically so could be 10x quicker than normal GA fetchingYou need the version > 0.3.0 of googleAuthR too:
install.packages("googleAuthR")
install.packages("googleAnalyticsR")devtools::install_github("MarkEdmondson1234/googleAnalyticsR")Todo. But check out ?google_analytics_4 and these example queries:
## setup
library(googleAnalyticsR)
## authenticate, or use the RStudio Addin "Google API Auth" with analytics scopes set
ga_auth()
## get your accounts
account_list <- google_analytics_account_list()
## pick a profile with data to query
ga_id <- account_list[23,'viewId']## 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
## demo showing how the new filters work
ga_data1 <- google_analytics_4(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 4Example with two date ranges and two reports.
## demo of querying two date ranges at a time
## we make the request via make_ga_4_req() to use in next demo
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'))
ga_data2 <- fetch_google_analytics_4(multidate_test)
ga_data2
# 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
## Demo querying two reports at the same time
## Use make_ga_4_req() to make multiple requests and then send
## them as a list to fetch_google_analytics_4()
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'))
## all requests must have same viewID and dateRange
ga_data3 <- fetch_google_analytics_4(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 66
ga_data4 <- google_analytics_4(ga_id,
date_range = c("2015-07-30",
"2015-10-01"),
dimensions=c('medium'),
metrics = c(visitsPerVisitor = "ga:visits/ga:visitors",
'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 are more complex to configure that v3, but more powerful and in line to how you configure them in the UI
## make a segment element
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))
## if only one AND definition, you can leave out wrapper list()
seg_defined_one <- segment_define(sv_simple)
## 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_4(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
## 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_4(ga_id,
c("2016-04-01","2016-05-01"),
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 2Details on cohort reports and LTV can be found here.
## first make a cohort group
cohort4 <- make_cohort_group(list("cohort 1" = c("2015-08-01", "2015-08-01"),
"cohort 2" = c("2015-07-01","2015-07-01")))
## then call cohort report. No date_range and must include metrics and dimensions
## from the cohort list
cohort_example <- google_analytics_4(ga_id,
dimensions=c('cohort'),
cohort = cohort4,
metrics = c('cohortTotalUsers'))
cohort_example
# cohort cohortTotalUsers
# 1 cohort 1 14
# 2 cohort 2 20
## 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_4(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" v3 API calls are also tried and tested.
For syntax of filters and dimensions, this library parses in exactly as specified in the Google Analytics v3 API docs, so check those out. Note you do not need to encode symbols, but may have to encode URLs if you are filtering for those in say ga:pagePath
library(googleAnalyticsR)
## Authenticate in Google OAuth2
## this also sets options
ga_auth()
## if you need to re-authenticate use ga_auth(new_user=TRUE)
## if you have your own Google Dev console project keys,
## then don't run ga_auth() as that will set the authentication project to the defaults.
## instead put your options here, and run googleAuthR::gar_auth()
## get account info, including View Ids
account_list <- google_analytics_account_list()
ga_id <- account_list$viewId[1]
## get a list of what metrics and dimensions you can use
meta <- google_analytics_meta()
head(meta)
## pick the account_list$viewId you want to see data for.
## metrics and dimensions can have or have not "ga:" prefix
gadata <- google_analytics(id = ga_id,
start="2015-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium"))
## multi accounts, pass character vector of viewIds
## outputs a list of data.frames, named after the viewId
multi_gadata <- google_analytics(id = c("123456","9876545","765432"),
start="2015-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium"))
## if more than 10000 rows in results, auto batching
## example is setting lots of dimensions to try and create big sampled data
batch_gadata <- google_analytics(id = account_list$viewId[1],
start="2014-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium", "landingPagePath","hour","minute"),
max=99999999)
## mitigate sampling by setting samplingLevel="WALK"
## this will send lots and lots of calls to the Google API limits, beware
walk_gadata <- google_analytics(id = account_list$viewId[1],
start="2014-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium", "landingPagePath"),
max=99999999, samplingLevel="WALK")
## multi-channel funnels set type="mcf"
mcf_gadata <- google_analytics(id = account_list$viewId[1],
start="2015-08-01", end="2015-08-02",
metrics = c("totalConversions"),
dimensions = c("sourcePath"),
type="mcf")
## google_analytics dataframes include these meta data attributes:
- attr(*, "containsSampledData")= logi FALSE
- attr(*, "samplePercent")= num 100
- attr(*, "samplingLevel")= chr "DEFAULT"
- attr(*, "profileInfo")=List of 6
..$ profileId : chr "535656"
..$ accountId : chr "2748374"
..$ webPropertyId : chr "UA-278374-1"
..$ internalWebPropertyId: chr "5279208"
..$ profileName : chr "XXXXX"
..$ tableId : chr "mcf:539656"
- attr(*, "dateRange")=List of 2
..$ startDate: chr "2015-08-01"
..$ endDate : chr "2015-08-02"
- attr(*, "totalResults")= int 4
## reach meta-data via attr()
attr(gadata, "profileInfo")
attr(gadata, "dateRange")With the amount of API calls possible with this library via batching and walking, its more likely the shared Google API project will hit the 50,000 calls per day limit.
To mitigate that, use your own Google Developer Console Project key, so it is not shared across all users of this library.
Get your own OAuth2 clientID and client secret and add to the options like so:
TODO: guide on how you get your own client Id.
Below a new client Id is set for both offline and Shiny use. Put the lines at the top of your R script with your own credentials.
In this example, the scopes are also set so a user can authenticate with searchConsoleR at the same time, which is possible as they both use googleAuthR as a backend.
options("googleAuthR.client_id" = "994xxx55706-afliqtuxxxxxxx2fd4kesu6.apps.googleusercontent.com")
options("googleAuthR.client_secret" = "3JhLa_GxxxxxCQYLe31c64")
options("googleAuthR.scopes.selected" = c("https://www.googleapis.com/auth/analytics",
"https://www.googleapis.com/auth/analytics.readonly",
"https://www.googleapis.com/auth/webmasters"))
options("googleAnalyticsR.webapp.client_id" = "994xxxx55706-e9qompxxxxx9pcab1u005cv.apps.googleusercontent.com")
options("googleAnalyticsR.webapp.client_secret" = "zcofxxxxxxElemXN5sf")To use in Shiny, use the googleAuth with_shiny
## in server.R
library(googleAuthR)
library(googleAnalyticsR)
library(shiny)
shinyServer(function(input, output, session){
## Get auth code from return URL
access_token <- reactiveAccessToken(session)
## Make a loginButton to display using loginOutput
output$loginButton <- renderLogin(session, access_token(),
logout_class = "btn btn-danger")
gadata <- reactive({
gadata <- with_shiny(google_analytics,
id = "222222",
start="2015-08-01", end="2015-08-02",
metrics = c("sessions", "bounceRate"),
dimensions = c("source", "medium"),
shiny_access_token = access_token())
})Limited support for more user friendly calls to BigQuery Google Analytics 360 exports is implemented from version 0.2.0.
This is intended for users who may be familar with Google Analytics but not with SQL necessary to extract equivalent data from a BigQuery export of Google Analytics 360 data.
google_analytics_bq parses your metrics and dimensions into BigQuery queries.
This is under active development, and features will expand in the future. Eventually the BigQuery reports can do a lot more than the normal Google Analytics API as it allows for statistics, grouping, timestamps and unique visitor and visit Ids.
To use, you need to also install the bigQueryR library.
Stable version is on CRAN:
install.packages("bigQueryR")Development version on devtools:
devtools::install_github("MarkEdmondson1234/bigQueryR")Once installed, authenticate to BigQuery:
library(bigQueryR)
## go through Google oAuth2 flow
## needs email that has access to the BigQuery dataset
bqr_auth()
## get lists of your project and datasets
bqr_list_projects()
bqr_list_datasets("project-id")If you want to authenticate with Google Analytics and BigQuery in the same session (or others) then its best to authenticate with googleAuthR::gar_auth() with the appropriate scopes set. The below lets you authenticate with Google Analytics, Google Cloud Storage and BigQuery:
options(googleAuthR.scopes.selected = c("https://www.googleapis.com/auth/analytics",
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/bigquery"))
googleAuthR::gar_auth()You may also want to use a JSON file to authenticate with BigQuery. Make sure to add the service email to the users of the Google project, and then download the JSON file and authenitcate via:
googleAuthR::gar_auth_service("gwt-download-XXXX.json")See googleAuthR help for more details.
For BigQuery Google Analytics 360 exports, the dataset is the same as the GA View ID you are exporting.
You can then export BigQuery data via:
bq <- google_analytics_bq("project-id", "dataset-id-ga-viewid",
start = "2016-01-01", end = "2016-02-01",
metrics = "users",
dimensions = c("source","medium"))
head(bq)
source medium users
1 mavas|blahbs.ru display 47837
2 examp.ae referral 6090
3 lm.facebook.com referral 335
4 ghtie_yahoo display 133900
5 fjsfs_inmobi display 19887
6 tripadvisor.co.dk referral 307 At the moment the metrics/dimensions are limited as they need to added manually to a lookup table.
Segments/filters are not supported. The data is unsampled though.
bq2 <- google_analytics_bq("project-id", "dataset-id-ga-viewid",
start = "2016-01-01", end = "2016-02-01",
metrics = "users",
dimensions = c("source","medium","landingPagePath"))
> Error in google_analytics_bq("project-id", "dataset-id-ga-viewid", start = "2016-01-01", :
dimension not yet supported. Must be one of referralPath, campaign, source, medium, keyword, adContent, adwordsCampaignID, adwordsAdGroupID, transactionId, date, visitorId, visitId, visitStartTime, visitNumber You can also query the BigQuery table directly using the query parameter.
This is just as you would in the BigQuery interface, and allows queries you can also do there.
q <- "SELECT
date,
SUM (totals.visits) visits,
SUM (totals.pageviews) pageviews,
SUM (totals.transactions) transactions,
SUM (totals.transactionRevenue)/1000000 revenue
FROM [87010628.ga_sessions_20160327],[87010628.ga_sessions_20160328],[87010628.ga_sessions_20160329]
GROUP BY date
ORDER BY date ASC "
bq3 <- google_analytics_bq("project-id", "dataset-id-ga-viewid",
query = q)You can also just output the query for use within the interface, if you pass in the parameter return_query_only
just_query <- google_analytics_bq("project-id", "dataset-id-ga-viewid",
start = "2016-01-01", end = "2016-02-01",
metrics = "users",
dimensions = c("source","medium"),
return_query_only = TRUE)
just_query
# [1] "SELECT trafficSource.source as source, trafficSource.medium as medium, COUNT(fullVisitorId) as users
# FROM (TABLE_DATE_RANGE([dataset-id-ga-viewid.ga_sessions_], TIMESTAMP('2016-01-01'),
# TIMESTAMP('2016-02-01'))) GROUP BY source, medium LIMIT 100"You could then paste this query into the BigQuery interface, or modify it and send it back in via google_analytics_q.
The metrics and dimensions implemented so far are in the two lookups below.
They include the BigQuery exclusive hitTimestamp, fullVisitorId, visitId etc.
Read the full Google Analytics 360 BigQuery data schema for an idea on what can be queried.
lookup_bq_query_m <- c(visits = "SUM(totals.visits) as sessions",
sessions = "SUM(totals.visits) as sessions",
pageviews = "SUM(totals.pageviews) as pageviews",
timeOnSite = "SUM(totals.timeOnSite) as timeOnSite",
bounces = "SUM(totals.bounces) as bounces",
transactions = "SUM(totals.transactions) as transactions",
transactionRevenue = "SUM(totals.transactionRevenue)/1000000 as transactionRevenue",
newVisits = "SUM(totals.newVisits) as newVisits",
screenviews = "SUM(totals.screenviews) as screenviews",
uniqueScreenviews = "SUM(totals.uniqueScreenviews) as uniqueScreenviews",
timeOnScreen = "SUM(totals.timeOnScreen) as timeOnScreen",
users = "COUNT(fullVisitorId) as users",
exits = "COUNT(hits.isExit) as exits",
entrances = "COUNT(hits.isEntrance) as entrances",
eventValue = "SUM(hits.eventinfo.eventValue) as eventValue",
metricXX = {a function to output hit level custom metrics})
lookup_bq_query_d <- c(referralPath = "trafficSource.referralPath as referralPath",
hitTimestamp = "(visitStartTime + (hits.time/1000)) as hitTimestamp",
campaign = "trafficSource.campaign as campaign",
source = "trafficSource.source as source",
medium = "trafficSource.medium as medium",
keyword = "trafficSource.keyword as keyword",
adContent = "trafficSource.adContent as adContent",
adwordsCampaignID = "trafficSource.adwordsClickInfo.campaignId as adwordsCampaignId",
adwordsAdGroupID = "trafficSource.adwordsClickInfo.adGroupId as adwordsAdGroupId",
# adwords...etc...
transactionId = "hits.transaction.transactionId as transactionId",
date = "date",
fullVisitorId = "fullVisitorId",
userId = "userId",
visitorId = "visitorId",
visitId = "visitId",
visitStartTime = "visitStartTime",
visitNumber = "visitNumber",
browser = "device.browser as browser",
browserVersion = "device.browserVersion as browserVersion",
operatingSystem = "device.operatingSystem as operatingSystem",
operatingSystemVersion = "device.operatingSystemVersion as operatingSystemVersion",
mobileDeviceBranding = "device.mobileDeviceBranding as mobileDeviceBranding",
flashVersion = "device.flashVersion as flashVersion",
language = "device.language as language",
screenColors = "device.screenColors as screenColors",
screenResolution = "device.screenResolution as screenResolution",
deviceCategory = "device.deviceCategory as deviceCategory",
continent = "geoNetwork.continent as continent",
subContinent = "geoNetwork.subContinent as subContinent",
country = "geoNetwork.country as country",
region = "geoNetwork.region as region",
metro = "geoNetwork.region as metro",
pagePath = "hits.page.pagePath as pagePath",
eventCategory = "hits.eventInfo.eventCategory as eventCategory",
eventAction = "hits.eventInfo.eventAction as eventAction",
eventLabel = "hits.eventInfo.eventLabel as eventLabel",
dimensionXX = {a function to output hit level custom dimensions})This will increase as I get time to work on it, but feel free to submit a pull request with more.