Package: collective.googleanalytics
Introductioncollective.googleanalytics is a product that integrates Plone with Google
Analytics. It includes both tracking and reporting functionality. The product
enables easy tracking of the standard Google statistics as well as external
links, e-mail address clicks and file downloads. It also defines Analytics
reports that are used to query Google and display the results using Google
Visualizations. Reports are Zope objects that can be imported and exported
using GenericSetup XML and modified on a site-by-site basis. The product
currently provides a portlet that can display results of reports as well
as a control panel for authorizing your site and configuring settings.ContentsIntroductionInstallationOn Plone 3ConfigurationRegistering a DomainTracking PluginsUsing ReportsManaging ReportsSite-Wide ReportsPage-Specific ReportsThe Report Rendering ProcessReport PropertiesReport settingsQuery criteriaTable builderVisualization settingsReport bodyUsing TAL and TALES in ReportsContextual Results PluginVariable Date Range PluginCreating a New ReportDefining Reports in a Filesystem ProductWhere to Learn MoreGoogle Analytics APIGoogle Visualizations APITAL and TALESCreditsDevelopmentCode ReviewUser DocumentationOtherChangelog1.6.1 (2017-08-22)1.6.0 (2017-02-21)1.5.0 (2016-07-27)1.4.4 (2014-10-15)1.4.3 (2013-05-28)1.4.2 (2013-05-28)1.4.1 (2013-05-28)1.4.1 - 2012-08-291.4 - 2012-08-271.3 - 2012-04-091.2 - 2011-05-311.1 - 2011-03-241.0 - 2010-12-071.0b3 - 2010-06-301.0b2 - 2010-06-181.0b1 - 2010-05-051.0a4 - 2010-03-241.0a3 - 2010-03-051.0a2 - 2010-02-101.0a1 - 2009-12-23InstallationTo install collective.googleanalytics, add it to the eggs section of your buildout. Then
re-run buildout and restart Zope.On Plone 3On Plone 3 it is recommended to use the following version pins:[versions]
collective.googleanalytics = 1.4.1
gdata = 2.0.17
simplejson = 2.3.3ConfigurationWhen you install the product from the Plone Add-ons control panel, a new
control panel called Google Analytics will be added to your Plone site.
In this control panel, you can authorize the site to access your Google
Analytics data. If you receive an error when you attempt to authorize, see
the section onRegistering a Domainbelow.After authorizing your site, you can select the profile where you want to
track analytics for this site. Choose a profile from the dropdown menu. If you
do not see any profiles listed, make sure that your Google Analytics account
has access to at least one profile. Also, be sure to remove any Google
Analytics tracking code that you may have pasted in the Site control panel.Once you have selected a profile for tracking, you can choose which tracking
plugins to enable. Tracking plugins allow you to track events on your site
in Google Analytics in addition to the normal data on page views and visits.
Which plugins you have available to you depends on which products you have
installed. For information on the default tracking plugins, see the
section onTracking Pluginsbelow.Finally, you can choose roles to exclude from tracking. Google Analytics
data will not be recorded for users with the selected roles. Note that this
setting does not affect the inclusion of scripts specified in the Site
control panel.On the settings tab, you can also configure the amount of time, in minutes,
that account information and report results will be cached, reducing the need
to query Google. Sixty minutes is the default caching interval.Registering a DomainWhen you authorize your site to access your Google Analytics data, you may
receive an error:The site "http://example.com" has not been registered.When this happens, you need to register your domain with Google before you
can authorize your site. Follow steps 1-3 of Google’sdirections for
registering a new domain. In step two, you can upload the file provided
by Google to your Plone site root. Be sure to use the File content type, and
make sure that the short name matches the filename specified by Google.Tracking Pluginscollective.googleanalytics ships with four tracking plugins:CommentsThis plugin records a tracking event in Google Analytics for each comment
created on your site.Email linksThis plugin records a tracking event in Google Analytics each time a
visitor clicks on a “mailto:” link.External linksThis plugin records a tracking event in Google Analytics each at time a
visitor clicks on a link with a domain name different from the domain
name of your site.File downloadsThis plugin records a tracking event in Google Analytics each time a
visitor clicks on a link to a file with one of these extensions: avi, css,
doc, eps, exe, jpg, js, mov, mp3, pdf, png, ppt, rar, svg, txt, vsd, vxd,
wma, wmv, xls, zip.Using ReportsAfter you have authorized your site in the control panel, you can begin using
Analytics reports. First navigate to the page where you would like to display
the report results. Where you place the portlet depends on your goals and the
type of reports you are displaying. Even if you place a portlet on a public
page, the portlet will only be visible to users who have the “View Google
Analytics Reports” permission, which, by default is assigned to Managers.Next, place the portlet as you normally would, using the manage portlets page
and selecting Google Analytics from the list of available portlets. In the
portlet add form, you can set the title of the portlet, the profile the portlet
will use and the reports it will display. Note that the title of the portlet is
not the same as the title of the report, which is displayed inside the portlet.
The profile is the Google property where the statistics that you want to access
live. Which profiles you can select within the portlet depends on which
profiles the Analytics account you provide to Plone can access. If you do not
see any profiles in the dropdown menu, check to be sure that you have
authorized your site in the Google Analytics control panel.You can select more than one report to display within a single portlet. Each
report performs its own query to Google, however, so including many reports
on a single page may cause a decrease in performance. To change the order of
the reports within a portlet, see the section onManaging Reportsbelow.Once you have set the portlet title and profile and selected one or more
reports, click save to add the portlet. When you navigate to the page where
you assigned the portlet, you should see the results of your report.Managing Reportscollective.googleanalytics ships with twelve default reports:Site-Wide ReportsSite Visits: Line ChartTop 5 Pages: TableTop 5 Sources: TableSite Page Views: Line ChartTime on Site: Line ChartSite Unique Visitors: Line ChartPage-Specific ReportsPage Views: SparklineTime on Page: SparklineTop 10 Page Sources: TableTop 10 Keywords: TableTop 5 Previous Pages: TableTop 5 Next Pages: TableReports live in a Plone tool called portal_analytics. To view and modify
reports, navigate to the root of the site in the ZMI and click on the
portal_analytics utility. Since they are standard Zope objects, reports can
be copied, pasted, renamed, deleted, imported and exported using the Zope
buttons beneath the list of reports. They can also be moved up and down in
the list using the up, down, top and bottom buttons. The order of reports
in portal_analytics controls the order that they will appear in portlets
throughout the site.The Report Rendering ProcessBy default, Analytics reports are rendered asynchronously using jQuery. This
improves site performance by allowing the body of page to render without
waiting for a response from Google Analytics. The basic flow of a request
that renders an Analytics report might go as follows:A user requests the content item front-page, which has a Google Analytics
portlet assigned in the right column.Plone renders front-page as usual. When it renders the Google Analytics
portlet, it looks up a loader component.Instead of requesting the actual results, the loader produces javascript
that will load the results after the page has finished loading. It also
produces javascript to load the visualization modules that the reports
will require.The javascript from the loader is included with front-page when it is
rendered. As soon as the page has finished loading, the javascript produced
by the loader is activated. It sends another request to the server that
includes the requested reports and any options for evaluating them.A browser view associated with the loader is called as part of the
asynchronous request. (The context for this browser view is still
front-page.) This browser view loads the specified reports.For each report in the request, the browser view looks up a renderer,
which is a multi-adapter on the current context (front-page), request
(the asynchronous request) and the report.The renderer renders the report or returns a cached result if one exists.
The browser view combines the results from all of the requested reports and
returns them.The results returned by the renderer are injected into the portlet. In the
process, jQuery evaluates any javascript in the results, including the
javascript that produces the visualization.Report PropertiesAnalytics reports are persistent Zope objects that store the arguments used to
query Google and the options needed to display the query result as a Google
Visualization. They store this information as properties on themselves. These
properties can be set using GenericSetup XML or through the web in the ZMI.It may be helpful to think of Analytics reports as having five logical
sections, each of which has its own properties:Report settingsQuery criteriaTable builderVisualization settingsReport bodyReport settingsThe report settings section consists of five properties that control the
display and behavior of the report. None of these properties accept
TAL or TALES.TitleThe title of the report in the management interface. This is the title that
that the user selects when assigning a portlet.DescriptionA brief description of the report. The description is mainly for developer
reference and never appears in the Plone user interface.I18n DomainThe domain for translating the report.CategoriesA list of categories to which the report belongs. Categories are used to
determine where the report can be displayed.PluginsPlugins are multi-adapters on the context, the request and the report
that extend the default functionality of the report. Two plugins ship
with collective.googleanalytics. See the sections onContextual Results
PluginandVariable Date Range Pluginfor more details.Note that some plugins add additional dimension, metric and visualization
choices, which are not available until the report is saved. As a result,
it is generally a good idea to save the report immediately after adding
or removing any plugin.Query criteriaThe query criteria section of the report is made up of all the properties
that begin with the word query. These properties determine the query that is
sent to Google to retrieve Analytics data. All of these properties accept
TALES expressions. They have access to the TALES variables defined in the
section onUsing TAL and TALES in Reportsas well as any TALES objects
provided by the selected plugins.Query MetricsA list of Google Analytics metrics to use in the query.Query DimensionsA list of Google Analytics dimensions to return in the query. This list can
include the special dimension variables date_range_dimension and
date_range_sort_dimension. For more information on using these dimension,
see the section on theVariable Date Range Plugin.Query FiltersA list of filters to use in the query. Filters are defined as strings or
TALES expressions that evaluate to strings in the format METRIC==VALUE,
where METRIC is the name of a Google Analytics metric or dimension,
VALUE is the desired value, and == is the appropriate logical operator.Query SortA list of metrics or dimensions on which to sort the query results. Sort
parameters are defined using strings or TALES expressions that evaluate to
strings containing the name of a Google Analytics dimension or metric. In
addition, the name of the dimension or metric can be preceded by a minus
sign (-) to change the sort order from ascending to descending.Query Start DateThe start date for query results. It must be a TALES expression that
evaluates to a Python datetime.date object. In reports that use theVariable Date Range Plugin, it is not necessary to specify the
start date or end date.Query End DateThe end date for the query results. See Query Start Date above for more
information.Query Maximum ResultsThe maximum number of results that the query can return. It must be a TALES
expression that evaluates to a positive integer.Table builderThe table builder section of the report includes three properties. Together
these properties are responsible for taking the query results returned by
Google and transforming them into a results table that can be used in as the
data source for a visualization or otherwise displayed in the report body.In order to perform this transformation, these three properties use TALES
expressions that return Python lists. The TALES expressions have access to
three special functions that allow them to extract data from the data feed
returned by Google:dimension(dimension, specified={}, aggregate=unique_list, default=[])Returns the value of the given dimension across the specified
dimensions and metrics using the specified aggregation method
(unique_list by default). If no values are found, the default value,
an empty list by default, is returned.For example, the following TALES expression would return a list of all
the browsers returned by the query:python:dimension('ga:browser')metric(metric, specified={}, aggregate=sum, default=0)Returns the value of the given metric across the specified
dimensions and metrics using the specified aggregation method (sum by
default). If no values are found, the default value, 0 by default, is
returned.To get the sum of the values of ‘ga:visits’ in records where
‘ga:browser’ equals ‘Mozilla,’ we could use this expression:python:metric('ga:visits', {'ga:browser': 'Mozilla'})In reports that use theVariable Date Range Plugin, the value
of the specified argument is often set to an element in the list
returned by the possible_dates method.possible_dates(dimensions=[], aggregate=unique_list)Returns a list of dictionaries containing all possible values for
the given date dimension in the current date range. If no dimensions
are specified, all of the date dimensions in the query are used.This method is commonly used in place of the dimension method in
reports that include date dimensions to ensure that the table contains
one row for each date unit in the date range.These three properties make up the table builder section of the report:Table Columns ExpressionThe titles for the table columns. It must be a TALES expression that evaluates
to a Python list of strings. If and where these titles appear depends on
the type of visualization. For the Table visualization, for example, they
appear as the table column headings.In most reports, the table columns expression is a static Python list:python:['Visits']It is, of course, possible to use TALES variables to populate the
columns list:python:[date_range_unit, 'Visits']In complex tables, the number of columns may be determined by the results
returned by the query. In this example, the first column is “Date” and the
names of the remaining columns are the names of the browsers returned
by the query:python:['Date'] + dimension('ga:browser')Table Row Repeat ExpressionThe expression that produces the set of row keys used generate the rows in
the results table. It is specified as a TALES expression that evaluates to
a Python iterable with one element for each row in the final table.When the report renderer is asked for the results table rows, it first
evaluates the row repeat expression. It then iterates over each element
in the resulting list and evaluates the table rows expression with
the current element assigned to the variable “row.”Typically the values of the row repeat expression are generated using the
dimension function or the possible_dates function:python:dimension('ga:pagePath')or:possible_datesSee the section onUsing TAL and TALES in Reportsfor more information
about the use of these functions.Table Rows ExpressionThe contents of each table row. It is must be a TALES expression that
evaluates to a Python list containing the value of the “cells” for that
table row. The table rows expression has access to two special TALES
varables:rowThe value of the row key for the row that is currently being evaluated.
These values come from the list produced by evaluating the table row
repeat expression.columnsThe list of table column headings produced by evaluating the table
columns expression.In tables with only one column, the value of the rows expression is
often the same as the value of the row key:python:[row]In two column tables, the value of one column is typically the row key,
and the other is a metric value looked up using the row key:python:[row, metric('ga:visits', {'ga:browser': row})]In complex, multi-column tables, it may be necessary to iterate over the
columns variable using a Python list comprehension:python:[row] + [metric('ga:visits', {'ga:browser': row, 'ga:operatingSystem': c}) for c in columns[1:]]Visualization settingsThe visualizaiton settings section of the report consists of the
visualization type and visualization options properties. These properties
are used to create javascript that uses the Google Visualizations API
to render the data table produced by the table builder section above.Visualization TypeThe type of Google Visualization to use to display the report results.
This property can be set to the name of any of the default visualizations
provided by Google.Visualization OptionsA list of options and values, in the format of TAL defines, that specify
the options for the visualization. The available options depend on the
type of visualization selected. It is important that the option expressions
evaluate to the data type that the visualization expects.For example, the height of a visualization that accepts an integer height
option could be set as follows:height python:300Report bodyThe report body consists of a single property that contains the TAL template
for the report. This block of TAL code is evaluated when the report is
rendered. TALES expressions within this code have access to the normal objects
described in the section onUsing TAL and TALES in Reports. They also
can access all of the public methods provided by the report renderer. In the
report body, these methods must be accessed using view/method_name or
python:view.method_name():profile_ids()Returns a list of Google Analytics profiles for which the report
is being evaluated.query_criteria()Returns the evaluated query criteria.data()Returns a list of dictionaries containing the values of the
dimensions and metrics for each entry in the data feed returned
by Google.columns()Returns the evaluated table column headings.rows()Returns the evaluated table rows.visualization()Returns the rendered visualization.dimension(dimension, specified={}, aggregate=unique_list, default=[])Returns the value of the given metric across the specified
dimensions and metrics using the specified aggregation method. See
the description in theTable buildersection above.metric(metric, specified={}, aggregate=sum, default=0)Returns the value of the given metric across the specified
dimensions and metrics using the specified aggregation method. See
the description in theTable buildersection above.possible_dates(dimensions=[], aggregate=unique_list)Returns a list of dictionaries containing all possible values for
the given date dimension in the current date range. See the description
in theTable buildersection above.Using TAL and TALES in ReportsMany of the properties of the Analytics report object accept TALES expressions
or TAL as their values. (For information about which properties accept TALES
and TAL, see the section onReport Propertiesabove.) All of the TAL code
and TALES expressions have access to a standard set of Python objects and
variables:contextThe object on which the current view is being called. In most cases, this
is the content object next to which the report will be displayed.requestThe current request object. Since Analytics reports are rendered
asynchronously, this request object is the asynchronous report request,
not the user’s original request. The URL of the original request can be
obtained by using request/request_url.dateAn alias for the datetime.date function.timedeltaAn alias for the datetime.timedelta function.unique_listA helper function that takes a Python list and returns a corresponding
list where all duplicated elements in the original list have been removed.
It differs from the Python set type in that it preserves the order of the
original list.Contextual Results PluginThis plugin provides tools to make reports page specific. It modifies the
default caching policy to cache report results on a per-page basis instead
of for the entire site. It also provides several helper TALES variables that
simplify the process of creating page-specific reports:page_urlThe relative URL of the current request. This is most commonly used in
the query filters property for creating page-specific reports.page_filterA Google Analytics filter expression that matches records where the
ga:pagePath record matches the current relative URL. It uses regular
expression matching to match both URLs with and without the trailing
slash.nextpage_filterA Google Analytics filter expression that matches records where the
ga:nextPagePath record matches the current relative URL. It uses regular
expression matching to match both URLs with and without the trailing
slash.previouspage_filterA Google Analytics filter expression that matches records where the
ga:previousPagePath record matches the current relative URL. It uses regular
expression matching to match both URLs with and without the trailing
slash.Variable Date Range PluginAnalytics reports can specify fixed start and end dates for their queries.
It is generally more useful, however, to allow the date range to be set when
the report is evaluated. The Variable Date Range Plugin provides this
functionality. In order to set the date range, it looks in the request for
one of these special keys:start_date and end_dateDates in the form of YYYYMMDD.date_rangeAn integer specifying the number of days prior to the current date to use
as the report start date. The end date is assumed to be the current date.
The date_range argument can also accept a string keyword that evaluates
to a particular date range depending on the current context. Current
keywords include:weekLast seven days.monthLast 30 days.quarterLast 90 days.yearLast 356 days.mtdMonth-to-date.ytdYear-to-date.publishedSince the item was published.Since Analytics reports are rendered asynchronously, these keys must be set
in the request sent by the asynchronous loader, not in the original request.Since dates for reports are dynamic, the plugin also provides two special
dimensions that are date sensitive. This allows the granularity of the report
results to be set based on the date range selected. (For example, if you specify
a date range of a year, you probably don’t want to segment your results by day.
Instead, viewing results by month would be a more appropriate choice.) The two
special dimensions are:date_range_dimensionThis is the dimension, selected based on the date range, that will be used
to segment the results.date_range_sort_dimensionThis is the date-related dimension that is used as a helper to ensure that
results segmented by date_range_dimension can be sorted chronologically.
For example, if date_range_dimension evaluates to ga:week,
date_rage_sort_dimension would evalute to ga:year. Using
date_range_sort_dimension (along with date_range_dimension) when sorting
prevents a situation in which week 52 of 2009 gets sorted before week 1
of 2010.Note that that these two dimensions must be selected from the list of query
dimensions to be included in the query. If they are not available in the list
of possible dimensions, be sure to save the report after selecting the Variable
Date Range Plugin from the list of plugins.The plugin also provides two helper variables that are useful in report
templates:date_range_unitA string containing the human-readable name of the dimension specified
by date_range_dimension (e.g. ‘Day’, ‘Month’, etc.)date_range_unit_pluralA convenience variable that contains date_range_unit with the letter
‘s’ appended.Creating a New ReportNow that you are familiar with the properties that make up an Analytics report,
it’s time to try creating a new report from scratch. In this example, we will
create a report that calculates and displays the site-wide bounce rate
over a period of time segmented by browser.This example presents a fairly complex report. For examples of simpler reports,
consult the default reports in portal_analytics. In many cases, you can
probably modify one of these reports to suit your needs by substituting
dimensions and metrics. If, however, you find that you need to create a more
complicated multi-dimensional report, read on:Navigate to the root of the site in the ZMI and click on the
portal_analytics tool.Click the Add Google Analytics Report button.We’ll give our new report the ID site-bounce-rate-browser-line,
following the naming convention of the default reports. This naming
convention is optional, but it helps to keep things organized. Then
click the add button.Click on the new report to edit it. Give it a title of Site Bounce Rate
By Browser: Line Chart and this description:This report displays the site-wide bounce rate segmented by the user’s
browser. It is useful for gauging how effective our site’s new multimedia
features are in each browser.Leave the i18n domain as collective.googleanalytics, the default value. If we
were going to translate this report, we might use the domain defined in our
site’s theme product.From the list of categories, select Site Wide.From the list of plugins, select Variable Date Range. After making your
selection, click the Save button to populate the list of dimensions with
the new options.Now the difficult part: determining the arguments for our query. If we
consult the common calculations page in the Google’s Dimensions and Metrics
Reference (see the section onWhere to Learn Morefor the link), we see
that bounce rate is calculated as follows:ga:bounces/ga:entrancesSo, set the query metrics to ga:bounces and ga:entrances.We also know that we want to segment our results by browser, so we’ll set
our query dimension to ga:browser. Be sure to also select
date_range_dimension and date_range_sort_dimension from the bottom
of the dimensions list.In the query filters enter:ga:entrances>10Strictly speaking, we wouldn’t need this filter. But for a site with a lot
of traffic, we probably don’t care about the results browsers for that
have fewer than 10 entrances in a given period of time. So, we use this
filter to eliminate them from the results.In the query sort box, enter the dimensions provided by the Variable Date
Range Plugin:date_range_dimension
date_range_sort_dimensionIn query maximum results, leave the default value, python:1000.Now that our query arguments are complete, we can work on our results
table. Let’s begin by drawing out what our table should look like:“Day”“Firefox”“Internet Explorer”“Safari”“Chrome”“5”60705463“6”64695968“7”63726568Etc.Note that the day column contains strings, not integers. This is necessary
so that the line chart visualization will treat these values as labels
instead of data.Great! Now we can write the expressions to generate the table. Enter this
expression in the table columns expression field:python:[date_range_unit] + dimension('ga:browser')This expression combines the value of the date_range_unit, which is
provided by the Variable Date Range Plugin, with all of the possible
values of the ga:browser dimension.For the table row repeat expression, enter:possible_datesThis expression will populate the row keys with dictionaries that contain
the values of date_range_dimension and date_range_sort_dimension. We use
possible_dates instead of dimension(date_range_dimension) because we want
one entry for every period of time in the current date range, even if there
aren’t any results for that particular period of time.16: In the table rows expression field, enter the following expression,removing the line breaks:python:[str(row[date_range_dimension])] +
[int(100*float(metric('ga:bounces', row))/(float(metric('ga:entrances', row)) + 0.0001))
    for c in columns[1:] if not row.update({'ga:browser': c})]Whoa! That looks complicated! If we break down the expression into its
parts, however, it’s easy to see what’s going on:[str(row[date_range_dimension])]This part of the expression creates a list with a single element: the value
of date_range_dimension as a string. Recall that, in this expression, row
is a dictionary that contains key-value pairs for date_range_expression
and date_range_sort_expression.Now let’s skip to the end of the expression:for c in columns[1:]This code serves as the repeat expression in a Python list comprehension
that generates the bounce rate for each browser for the specified date.
columns[1:] represents the list of browser names generated by
dimension(‘ga:browser’):if not row.update({'ga:browser': c})This tricky bit of code updates the row dictionary to include the value of
the current browser as it iterates over the list of browsers. That way we
can pass row to the metric() method to get value of the metric for the
date and browser we are currently evaluating. We use ‘if not’ because
the update method returns None, which evaluates to False.Finally, the rest of the expression is just the math used to calculate
the bounce rate:int(100*float(metric('ga:bounces', row))/(float(metric('ga:entrances', row)) + 0.0001))We have to convert the values we get back from metric() into floating point
numbers so that the division operates as we expect. We also add a tiny
number to the denominator to avoid getting a divide by zero error if the
value of ga:entrances is zero. Finally, we multiply the result by 100 to
get a percentage and round the result to the nearest integer.We’re almost done! From the visualization type drop down menu, choose
LineChart.In the visualization options box, enter these option definitions, one
per line:title string:Bounce Rate By Browser
height python:250
titleX python:date_range_unit
titleY string:Bounce Rate (%)
smoothLine python:TrueThese options are all aesthetic. Once you become familiar with Google
visualizations, you can adjust them to fit your personal preferences. For
a full list of the options available for each visualization, visit the
Google Visualization Gallery referenced in the section onWhere to Learn
More.In the report body field, enter this block of TAL code, which renders the
line chart visualization:<div tal:replace="structure view/visualization"></div>You’re done! Click the save button in the ZMI. Then test out your new
report on the site as described in the section aboutUsing Reports.Defining Reports in a Filesystem ProductAny product that imports a GenericSetup profile can define Analytics reports.
These reports should be defined in a file called analytics.xml in the
product’s GenericSetup profile directory. The easiest way to generate the XML
for a report is to create the report through the web and then export it.For example, after following the instructions above for creating a new report,
you could use the portal_setup tool in the ZMI to create a snapshot of the
site. Then you could navigate to the analytics.xml file in the snapshot and
copy and paste the appropriate XML into your product’s analytics.xml file.If you find that you need to write the GenericSetup XML for a report by hand,
consult the analytics.xml file in this product’s profiles/default directory
for guidance. Keep in mind that any XML or XML reserved characters must be
properly escaped.Where to Learn MoreCreating and managing Analytics reports requires knowledge of the Google
Analytics API, the Google Visualizations API, and Zope and Plone technologies
such as TAL and TALES.  These are resources that you may find helpful in
learning these technologies:Google Analytics APIGoogle Analytics Data Export API DocumentationThis is the best place to start for learning the ins and outs of Google
Analytics. Of particular interest are these pages:Data Feed ReferenceThis reference describes the arguments used to query Google.Data Feed Query ExplorerThis tool allows you to try out queries interactively, which can be
extremely helpful in the process of creating and debugging reports.Dimensions and Metrics ReferenceThis page describes each available dimension and metric. Also see the
subpages on valid combinations and common calculations.gdata API Referencegdata is the Python module that interacts with the Google API. This
documentation is most useful for developers who wish to contribute to or
extend collective.googleanalytics. The relevant documentation is divided into
two sections:gdata.analtyics.service ReferenceThis documentation describes the API for the analytics service objects
that gdata provides. collective.googleanalytics uses both the AccountsService and
the AnalyticsDataService.gdata.analytics ReferenceThis reference documents the response objects returned by a query to Google
Analytics.Google Visualizations APIGoogle Visualizations API DocumentationThe visualizations API documentation provides an overview of what Google
Visualizations are and how they work.Google Visualizations GalleryThis gallery provides examples of each type of visualization and documents
the options the options that each accepts.TAL and TALESUsing Zope Page TemplatesThis chapter from the Zope2 Book offers and introduction to TAL, TALES
and related technologies.Advanced Page TemplatesThis chapter from the Zope2 Book describes some of the more advanced
features of the TAL specification.Zope Page Template ReferenceThis appendix from the Zope2 Book provides a comprehensive overview of
TAL and TALES as they are used in Zope page templates.CreditsDevelopmentMatt YoderTom GrossCode ReviewDavid GlickUser DocumentationJon StahlOtherThanks to FamFamFam for the graph icons, which are part of theSilkset.Changelog1.6.1 (2017-08-22)Log virtual error page views based on HTTP response status
rather than Python exc_info (which is set even for handled
exceptions).
[davisagli]Use jQuery global instead of jq.
[davisagli]Plone 5.1 compatibility
[alecm]Port to plone.app.testing
[tomgross]Remove unused egg dependencies
[tomgross]implements -> implementer
[tomgross]1.6.0 (2017-02-21)Plone 5 compatibility
[enfold-josh, tomgross]1.5.0 (2016-07-27)Add virtual page views for search and error pages [tomgross]Use Python json instead of custom one [tomgross]Updated Spanish translations [macagua]Added more improvements about i18n [macagua]Move to OAuth2 credential authentication [frapell]1.4.4 (2014-10-15)Added more improvements about i18n [macagua]Added Spanish translations [macagua]Updated the core GA code with the latest snippet from Google [spanktar]Prepend “Copy of” if copy&paste a report. [frapell]Use SimpleTerm when creating the reports vocabulary. [frapell]Crop overlong profile names [tomgross]Pass viewlet view and manager [tomgross]1.4.3 (2013-05-28)french translations [kiorky]1.4.2 (2013-05-28)Fix release, add .mo [kiorky]1.4.1 (2013-05-28)Add dependency on unittest2 to support advance testing features under
Python 2.6.
[2013-04-06 - hvelarde]Add dependency on plone.app.testing and some tests for the control panel
configlet.
[2013-04-05 - hvelarde]Package distribution refactoring: package classifiers were updated;
documentation files were renamed as .rst; MANIFEST.in was fixed and some
missing testing dependencies were added.
[2013-04-05 - hvelarde]Add .docx, .pptx, and .xlsx to extensions tracked by the file
download plugin.
[2013-03-28 - davisagli]Improve i18n and include first version of the pt-br translation.
[2012-10-19 - tcurvelo]Improve handling of unicode values in vocabularies
[2012-10-23 - kiorky]Release & QA Stuff
[2013-05-28 - kiorky]1.4.1 - 2012-08-29Avoid calling Google to retrieve vocabularies if there’s no valid auth_token.
This should help avoid conflict errors on the analytics tool.
[2012-08-29 - davisagli]1.4 - 2012-08-27Update to continue working with Google’s new Management API and some bugs
in their backwards-compatibility for the Data Export API.
[2012-08-27 - davisagli]Avoid trying to look up the list of available web properties when simply
rendering the tracking code.
[2012-08-23 - davisagli]When a Forbidden exception is encountered and the stored access_token is
removed, also be sure to clear the reports_profile so that a new
authorization won’t encounter the same Forbidden exception immediately.
[2012-08-23 - davisagli]1.3 - 2012-04-09Add Plone 4.1 compatibility
[2012-03-28 - encolpe]Fixed leap day bug in date range calculation.
[2012-03-01 - yomatters]Updated file download tracking plugin to track at_download links.
[2011-07-26 - yomatters]1.2 - 2011-05-31Escaped CDATA tags to keep Chameleon happy.
[2011-05-27 - yomatters]Reject AuthSub tokens that generate a forbidden response.
[2011-04-27 - yomatters]Moved page view tracking after plugins so that custom variables get sent
along with the page view.
[2011-04-04 - yomatters]Added a plugin for tracking user type in a custom variable.
[2011-04-04 - yomatters]Update tracking code
[2011-04-15 - garbas]Added a plugin for tracking page load time.
[2011-05-05 - toutpt]1.1 - 2011-03-24Documented the domain registration process.
[2011-03-24 - yomatters]Made vocabularies safe for profiles containing non-ASCII characters.
[2010-12-23 - yomatters]1.0 - 2010-12-07Added icon expression for control panel in Plone 4.
[2010-12-07 - yomatters]Fixed permission bug so that the Google Analytics portlet can be made visible
to non-managers.
[2010-12-07 - yomatters]Allowed requesting Google API javascript over HTTPS if the current page is
being served securely.
[2010-11-04 - yomatters]Reduced logging level for query messages.
[2010-08-13 - yomatters]wrap inline javascript in CDATA, making it easier to use with Deliverance.
[2010-08-13 - garbas]1.0b3 - 2010-06-30Stored connections to Google Analytics on the ZODB connection instead
of the Analytics tool.
[2010-06-30 - yomatters]Resolved conflict between Analytics tools on multiple Plone sites on the
same Zope instance.
[2010-06-30 - yomatters]1.0b2 - 2010-06-18Changed to comma-separated strings in asynchronous loader for Plone 4
compatibility.
[2010-06-18 - yomatters]Fixed divide by zero error in Time on Site: Line Chart report.
[2010-06-18 - yomatters]Added basic timeout handling so that a request to Google can’t tie up a Zope
thread indefinitely.
[2010-06-15 - yomatters]Limited regular expressions generated by the contextual results plugin to
128 characters to conform to Google API restrictions.
[2010-06-07 - yomatters]Added upgrade step to 1.0b2.
[2010-06-07 - yomatters]Changed to AuthSub authentication.
[2010-06-04 - yomatters]Add tracking functionality, including external links, e-mail addresses,
comments and file downloads.
[2010-05-11 - yomatters]@@analytics-controlpanel failing to display under Plone 4 because of trying to
access self.context.request which for some reason is not availiable. Accesing
self.context.REQUEST works for for Plone 3.3.5 and Plone 4b2. Didn’t have time
to investigate this further, but I made it work.
[2010-05-07 garbas]1.0b1 - 2010-05-05Note: 1.0b1 changes the syntax for the table-building section of the report
(previously the report column labels and expressions) and the report body. The
upgrade step overwrites these properties for the default reports that ship with
the product. If you have customized these properties on the default reports, be
sure to rename the customized reports in portal_analytics before running the
upgrade step to avoid losing your changes.Changed loader to call @@analytics_async on the current context.
[2010-05-05 - yomatters]Added a link to Google Analytics in the control panel.
[2010-05-04 - yomatters]Standardized i18n domain to collective.googleanalytics.
[2010-04-30 - yomatters]Added possible_dates helper function to solve the problem where date-based
reports do not have results for every date in the range.
[2010-04-28 - yomatters]Make it Plone4 compatible.
[2010-04-26 - garbas]Changed table building interface so that rows are not directly tied to
results returned by Google. Added dimension and metric value-getter
functions.
[2010-04-22 - yomatters]Improved reporting when there is no data.
[2010-04-09 - yomatters]Refactored monolithic report into pluggable components.
[2010-04-09 - yomatters]Moved utility functions off of report class.
[2010-04-01 - yomatters]1.0a4 - 2010-03-24Only show the portlet on the context’s view template.
[2010-03-24 - yomatters]Add an upgrade step from 1.0a3 to 1.0a4.
[2010-03-24 - yomatters]Modify tests to match asynchronous javascript.
[2010-03-24 - yomatters]Use asynchronous view for the site-wide analytics viewlet.
[2010-03-24 - yomatters]Add a view for asynchronous results, and modify the portlet to use it.
[2010-03-19 - yomatters]1.0a3 - 2010-03-05Add upgrade step from 1.0a2 to 1.0a3.
[2010-03-05 - yomatters]Modify javascript registration and configuration javascript to be compatible
with Plone 3.1.
[2010-03-05 - yomatters]Add more default reports.
[2010-03-05 - yomatters]Load external javascript through a viewlet instead of through the
registry to add support for Plone < 3.3.
[2010-03-05 - yomatters]1.0a2 - 2010-02-10Add security assertions for report categories.
[2010-02-10 - yomatters]Update documentation to reflect date range changes.
[2010-02-10 - yomatters]Make date range independent of report and update tests and default reports
to reflect the new reality; add javascript to set the width of the
visualization based on the width of the container; add categories to reports
and adjust vocabularies to be category-specific; handle auth token expiration
error.
[2010-02-03 - yomatters]Make report IDs unique so that the same page can contain multiple copies
of the same report.
[2010-01-19 - yomatters]Fix a bug that caused an error if the query returned no results.
[2010-01-19 - yomatters]Fix bug in older Zopes that required the user to reenter the password every
time the configlet form was saved.
[2010-01-14 - yomatters]Add fallback for importing InitializeClass in Zopes prior to 2.12.
[2010-01-05 - yomatters]1.0a1 - 2009-12-23Initial release