In GeeHLee/GoogleR: Google Analytics API into R
v4 features
The v4 API is where new features will appear in the future.
It currently has these extras implemented over the v3 API:
- Cohorts
- Multiple date ranges
- Pivot tables
- Calculated metrics on the fly
- Much more powerful segments
v4 API Explorer Shiny app
A demo Shiny app where you can explore the new v4 features is available.
To use - v4 API calls
Consult ?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']
Anti-sampling v4
A new feature from version 0.3.0 is anti-sampling via batching.
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_4(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 new argument anti_sample=TRUE in a google_analytics_4() request now 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_4(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
Looping over [2] batches.
Fetching data...
Fetching data...
Downloaded [59235] rows from a total of [59235].
Anti-sample call covering 23 days: 2015-05-09, 2015-05-31
Looping over [2] batches.
Fetching data...
Fetching data...
Downloaded [20239] rows from a total of [20239].
Anti-sample call covering 21 days: 2015-06-01, 2015-06-21
Looping over [2] batches.
Fetching data...
Fetching data...
Downloaded [21340] rows from a total of [21340].
Finished unsampled data request, total rows [100814]
Successfully avoided sampling
The 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.
Hourly anti-sampling calls
For large traffic websites, sometimes even if the batches are down to one API call per day, it isn't enough to remove sampling. In those cases, googleAnalyticsR will split the calls further into hourly batches.
If you are still experiencing sampling with hourly batches, then its time to use the google_analytics_bq function :)
New Filter Syntax
## 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 4
Querying multiple report types at a time
Example with two date ranges and two reports. When querying two date ranges, you can sort the metrics by how much they have changed by using orderType = DELTA e.g. order_type("sessions", "DESCENDING", "DELTA")
## 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'),
order = order_type("sessions", "DESCENDING", "DELTA"))
ga_data2 <- fetch_google_analytics_4(multidate_test)
ga_data2
# source medium sessions.d1 bounces.d1 sessions.d2 bounces.d2
# 1 (direct) (none) 5923 3328 0 0
# 2 example_mem.co.uk referral 5846 1100 0 0
# 3 google cpc 5476 2669 0 0
# 4 examp-link.net referral 2241 653 0 0
# 5 moneysavingexampl.com referral 1869 549 0 0
# 6 emailCampaign email 1268 772 0 0
## 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
On-the-fly calculated metrics
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 71
Segments 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 for the library is on 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() and the data.frame in $items
## get list of segments
my_segments <- ga_segment_list()
## just the segment items
segs <- my_segments$items
## segment Ids and name:
segs[,c("name","id","defintion")]
## 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_4(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_4(ga_id,
c("2015-07-30","2015-10-01"),
dimensions=c('source','medium','segment'),
segments = seg_obj,
metrics = c('sessions','bounces')
)
v4 segment syntax
However, 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 a google_analytics_4() segment argument. Here you name the segment as it will appear in the segment dimension, 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 of segment_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 a segment_vector_simple type (where order doesn't matter) or a segment_vector_sequence type (where order does matter.) You can also pass in a not_vector of 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() and segment_vector_sequence() - these are vectors of segment_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 to segment_vector_simple() or segment_vector_sequence()
## make two segment elements
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_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("2016-01-01","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 2
Some more examples, using some of the 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_4(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'))
RStudio Addin: Segment helper
New in version 0.2.0 is an RStudio Addin to help create segments via a UI rather than the lists above.
You can call it via googleAnalyticsR:::gadget_GASegment() or within the RStudio interface like displayed below:
Cohort reports
Details on cohort reports and LTV can be found here.
## 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_4(ga_id,
dimensions=c('cohort'),
cohort = cohort4,
metrics = c('cohortTotalUsers'))
cohort_example
# cohort cohortTotalUsers
# 1 Feb2016 19040
# 2 Jan2016 23378
Pivot Requests
## 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"
GeeHLee/GoogleR documentation built on May 6, 2019, 5:34 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
v4 features
The v4 API is where new features will appear in the future.
It currently has these extras implemented over the v3 API:
- Cohorts
- Multiple date ranges
- Pivot tables
- Calculated metrics on the fly
- Much more powerful segments
v4 API Explorer Shiny app
A demo Shiny app where you can explore the new v4 features is available.
To use - v4 API calls
Consult ?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']
Anti-sampling v4
A new feature from version 0.3.0 is anti-sampling via batching.
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_4(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 new argument anti_sample=TRUE in a google_analytics_4() request now 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_4(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 Looping over [2] batches. Fetching data... Fetching data... Downloaded [59235] rows from a total of [59235]. Anti-sample call covering 23 days: 2015-05-09, 2015-05-31 Looping over [2] batches. Fetching data... Fetching data... Downloaded [20239] rows from a total of [20239]. Anti-sample call covering 21 days: 2015-06-01, 2015-06-21 Looping over [2] batches. Fetching data... Fetching data... Downloaded [21340] rows from a total of [21340]. Finished unsampled data request, total rows [100814] Successfully avoided sampling
The 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.
Hourly anti-sampling calls
For large traffic websites, sometimes even if the batches are down to one API call per day, it isn't enough to remove sampling. In those cases, googleAnalyticsR will split the calls further into hourly batches.
If you are still experiencing sampling with hourly batches, then its time to use the google_analytics_bq function :)
New Filter Syntax
## 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 4
Querying multiple report types at a time
Example with two date ranges and two reports. When querying two date ranges, you can sort the metrics by how much they have changed by using orderType = DELTA e.g. order_type("sessions", "DESCENDING", "DELTA")
## 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'), order = order_type("sessions", "DESCENDING", "DELTA")) ga_data2 <- fetch_google_analytics_4(multidate_test) ga_data2 # source medium sessions.d1 bounces.d1 sessions.d2 bounces.d2 # 1 (direct) (none) 5923 3328 0 0 # 2 example_mem.co.uk referral 5846 1100 0 0 # 3 google cpc 5476 2669 0 0 # 4 examp-link.net referral 2241 653 0 0 # 5 moneysavingexampl.com referral 1869 549 0 0 # 6 emailCampaign email 1268 772 0 0 ## 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
On-the-fly calculated metrics
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 71
Segments 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 for the library is on 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() and the data.frame in $items
## get list of segments my_segments <- ga_segment_list() ## just the segment items segs <- my_segments$items ## segment Ids and name: segs[,c("name","id","defintion")] ## 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_4(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_4(ga_id, c("2015-07-30","2015-10-01"), dimensions=c('source','medium','segment'), segments = seg_obj, metrics = c('sessions','bounces') )
v4 segment syntax
However, 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_4()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()
## make two segment elements 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_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("2016-01-01","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 2
Some more examples, using some of the 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_4(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'))
RStudio Addin: Segment helper
New in version 0.2.0 is an RStudio Addin to help create segments via a UI rather than the lists above.
You can call it via googleAnalyticsR:::gadget_GASegment() or within the RStudio interface like displayed below:
Cohort reports
Details on cohort reports and LTV can be found here.
## 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_4(ga_id, dimensions=c('cohort'), cohort = cohort4, metrics = c('cohortTotalUsers')) cohort_example # cohort cohortTotalUsers # 1 Feb2016 19040 # 2 Jan2016 23378
Pivot Requests
## 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"
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.