diff --git a/analytics/hca-analytics/analytics_hca.py b/analytics/hca-analytics/analytics_hca.py index 2c857a741..ded660e11 100644 --- a/analytics/hca-analytics/analytics_hca.py +++ b/analytics/hca-analytics/analytics_hca.py @@ -8,6 +8,8 @@ import re from html import escape as escape_html +users_over_time_file_name = "users_over_time_history.json" + @cache def get_project_name(id): @@ -43,16 +45,29 @@ def adjust_table_index_key(val): def format_project_id_key(val): return ('' + escape_html(get_project_name(val)) + '', True) -def plot_users_over_time(**other_params): - return ac.show_plot_over_time( +def save_ga3_users_over_time_data(users_params, views_params, **other_params): + users_df = ac.get_data_df(["ga:30dayUsers"], ["ga:date"], df_processor=lambda df: df[::-1], **users_params, **other_params) + users_df.index = pd.to_datetime(users_df.index) + views_df = ac.get_data_df(["ga:pageviews"], ["ga:date"], df_processor=lambda df: df[::-1], **views_params, **other_params) + views_df.index = pd.to_datetime(views_df.index) + + df = ac.make_month_filter(["ga:30dayUsers"])(users_df.join(views_df)).rename(columns={"ga:30dayUsers": "Users", "ga:pageviews": "Total Pageviews"}) + df.to_json(users_over_time_file_name) + +def plot_users_over_time(load_json=True, use_api=True, **other_params): + old_data = pd.read_json(users_over_time_file_name) if load_json else None + df = ac.show_plot_over_time( "Monthly Activity Overview", - ["Users", "Total Unique Pageviews"], - ["ga:30dayUsers", "ga:uniquePageviews"], - df_filter=ac.make_month_filter(["ga:30dayUsers"]), - df_processor=lambda df: df[::-1], - change_dir=-1, + ["Users", "Total Pageviews"], + ["activeUsers", "screenPageViews"] if use_api else None, + dimensions="yearMonth", + sort_results=["yearMonth"], + df_processor=(lambda df: df.set_index(df.index + "01")[-2::-1]) if use_api else None, + pre_plot_df_processor=None if old_data is None else (lambda df: df.add(old_data, fill_value=0).astype("int")[::-1]) if use_api else (lambda df: old_data), + format_table=False, **other_params ) + return ac.format_change_over_time_table(df, change_dir=-1, **other_params) def plot_downloads(): diff --git a/analytics/hca-analytics/user-analytics.ipynb b/analytics/hca-analytics/user-analytics.ipynb index e4df65ae4..af0a14c09 100644 --- a/analytics/hca-analytics/user-analytics.ipynb +++ b/analytics/hca-analytics/user-analytics.ipynb @@ -11,13 +11,9 @@ ":class: analytics-logo\n", "```\n", "\n", - "# HCA DCP User Analytics - June vs May 2023\n", + "# HCA DCP User Analytics - July vs June 2023\n", "\n", - "This section displays metrics collected from Google Analytics HCA DCP Data Portal and Data Browser and compares June 2023 with the previous month (May 2023). The generation of this report is now coded in Jupiter notebooks and can be rerun easily and modified or consolidated as desired. \n", - "\n", - "### Geographic exclusions\n", - "\n", - "The data presented excludes access from Cambridge, UK and surrounds, Cambridge MA and surrounds, and San Francisco, CA and surrounds an attempt to exclude the HCA DCP development team member activity from the report.\n", + "This section displays metrics collected from Google Analytics HCA DCP Data Portal and Data Browser and compares July 2023 with the previous month (June 2023). The generation of this report is now coded in Jupiter notebooks and can be rerun easily and modified or consolidated as desired. \n", "\n", "\n", "### Key to tables\n", @@ -34,33 +30,6 @@ { "cell_type": "code", "execution_count": 19, - "id": "worthy-distinction", - "metadata": { - "scrolled": true, - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# !pip install google_auth_oauthlib \n", - "# !pip install install google-api-python-client\n", - "# !pip install pandas \n", - "# !pip install numpy\n", - "# !pip install matplotlib\n", - "# !pip install google-cloud-bigquery\n", - "# !pip install db-dtypes\n", - "\n", - "# DEV GUIDES\n", - "#https://developers.google.com/analytics/devguides/reporting\n", - " \n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, "id": "d76d1ebb", "metadata": { "tags": [ @@ -75,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "brave-victor", "metadata": { "scrolled": false, @@ -88,21 +57,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=713613812354-ccedl8colb27q3q6rvvvjqrpb5tcbuug.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8082%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fanalytics.readonly&state=9woWzElFvpWoPDJZ51SGvXwtKUsjer&access_type=offline\n" + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " (service, params)>,\n", - " {},\n", - " )" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -121,29 +78,49 @@ "import analytics.charts as ac\n", "import analytics_hca as hca\n", "\n", - "GA_PROPERTY = \"185740629\" # HCA Data Portal/Browser\n", + "GA_PROPERTY = \"361323030\" # data.humancellatlas.org - GA4\n", "# DCP_ANALYTICS_START = \"2019-03-01\"\n", "DCP_ANALYTICS_START = \"2021-01-01\"\n", "TODAY = 'today'\n", - "PERIOD = \"2023-06\"\n", - "PREV_PERIOD = \"2023-05\"\n", - "GEO_SEGMENT = \"gaid::q-RjXBSdQ-i18vXOOoXl5g\"\n", + "PERIOD = \"2023-07\"\n", + "PREV_PERIOD = \"2023-06\"\n", + "SECRET_NAME = 'ANALYTICS_REPORTING_CLIENT_SECRET_PATH'\n", "\n", + "GA3_END = \"2023-06-30\"\n", + "GA4_START = \"2023-07-01\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4ede9ed", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=713613812354-ccedl8colb27q3q6rvvvjqrpb5tcbuug.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8083%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fanalytics.readonly&state=wGAbiTRtNDrpv363FtBjrSPE5iL7V5&access_type=offline\n" + ] + } + ], + "source": [ "default_params = {\n", + " \"service_system\": ac.authenticate_ga4(SECRET_NAME),\n", " \"property\": GA_PROPERTY,\n", " \"index_key_formatter\": hca.adjust_table_index_key,\n", " \"period\": PERIOD,\n", - " \"prev_period\": PREV_PERIOD,\n", - " \"segment\": GEO_SEGMENT\n", - "}\n", - "\n", - "ac.authenticate_ga('ANALYTICS_REPORTING_CLIENT_SECRET_PATH')\n", - "\n" + " \"prev_period\": PREV_PERIOD\n", + "}" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "09374ca5", "metadata": { "tags": [ @@ -194,6 +171,10 @@ "\t\t\t}\n", "\t\t\t\n", "\t\t\t.anaColName:not(.anaIndex) {\n", + "\t\t\t\ttext-align: right;\n", + "\t\t\t}\n", + "\n", + "\t\t\t.anaColName.anaMultiCol {\n", "\t\t\t\ttext-align: center;\n", "\t\t\t}\n", "\t\t\t\n", @@ -239,6 +220,42 @@ "ac.init_tables()" ] }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d81273a4", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# ga3_service_system = ac.authenticate_ga(SECRET_NAME)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "290b5404", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# ga3_users_over_time_params = {\n", + "# \"start_date\": DCP_ANALYTICS_START,\n", + "# \"end_date\": GA3_END,\n", + "# \"service_system\": ga3_service_system,\n", + "# \"property\": \"185740629\",\n", + "# \"segment\": \"gaid::q-RjXBSdQ-i18vXOOoXl5g\"\n", + "# }\n", + "# \n", + "# hca.save_ga3_users_over_time_data({}, {}, **ga3_users_over_time_params)" + ] + }, { "cell_type": "markdown", "id": "ba6bbb95", @@ -257,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "id": "c767ea49", "metadata": { "scrolled": false, @@ -268,97 +285,17 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n" - ] } ], "source": [ - "users_over_time_table = hca.plot_users_over_time(start_date=DCP_ANALYTICS_START, end_date=TODAY, **default_params)" + "users_over_time_table = hca.plot_users_over_time(start_date=GA4_START, end_date=TODAY, **default_params)" ] }, { @@ -379,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "id": "eb64c71d", "metadata": { "scrolled": false, @@ -391,7 +328,7 @@ { "data": { "text/html": [ - "
Year
Month
Users
Total Unique Pageviews
2023
January
4475
(-4.58%)
21120
(+0.00%)
2022
December
4690
(-5.60%)
21119
(-10.65%)
November
4968
(+1.49%)
23636
(+0.83%)
October
4895
(+2.69%)
23442
(+1.14%)
September
4767
(+10.71%)
23177
(+2.43%)
August
4306
(+4.72%)
22627
(-5.70%)
July
4112
(-1.67%)
23995
(-1.70%)
June
4182
(-15.70%)
24409
(-9.11%)
May
4961
(+27.27%)
26857
(+14.76%)
April
3898
(+14.24%)
23403
(+7.76%)
March
3412
(+9.92%)
21717
(+20.34%)
February
3104
(+9.45%)
18046
(+0.12%)
January
2836
(+18.51%)
18024
(+21.77%)
2021
December
2393
(-18.52%)
14802
(-12.28%)
November
2937
(+13.66%)
16874
(+7.81%)
October
2584
(+7.44%)
15651
(+9.79%)
September
2405
(-3.61%)
14256
(-7.50%)
August
2495
(-15.51%)
15412
(-15.70%)
July
2953
(-15.99%)
18282
(-9.18%)
June
3515
(+6.64%)
20131
(+18.03%)
May
3296
(+6.22%)
17056
(+7.84%)
April
3103
(+1.27%)
15816
(-13.60%)
March
3064
(+16.59%)
18305
(+23.37%)
February
2628
(-4.33%)
14838
(-0.62%)
January
2747
(+98.63%)
14930
(+98.19%)
2020
December
1383
7533
November
0
0
October
0
0
September
0
0
August
0
0
July
0
0
June
0
0
May
0
0
April
0
0
March
0
0
February
0
0
January
0
0
" + "
Year
Month
Users
Total Pageviews
2023
July
7088
(-8.87%)
60969
(+38.11%)
June
7778
(+5.34%)
44146
(-9.50%)
May
7384
(+14.18%)
48780
(+23.50%)
April
6467
(-1.63%)
39497
(+1.89%)
March
6574
(+12.28%)
38763
(+25.85%)
February
5855
(+30.98%)
30800
(+19.40%)
January
4470
(-4.57%)
25796
(-1.06%)
2022
December
4684
(-5.66%)
26073
(-9.87%)
November
4965
(+1.51%)
28929
(-0.36%)
October
4891
(+2.62%)
29034
(+1.13%)
September
4766
(+10.73%)
28711
(+3.50%)
August
4304
(+4.67%)
27741
(-6.38%)
July
4112
(-1.67%)
29631
(-4.08%)
June
4182
(-15.69%)
30890
(-7.12%)
May
4960
(+27.21%)
33258
(+13.16%)
April
3899
(+14.27%)
29391
(+8.40%)
March
3412
(+9.92%)
27114
(+22.05%)
February
3104
(+9.45%)
22216
(-1.05%)
January
2836
(+18.46%)
22452
(+13.07%)
2021
December
2394
(-18.52%)
19856
(-18.65%)
November
2938
(+13.70%)
24409
(+6.30%)
October
2584
(+7.44%)
22963
(+12.43%)
September
2405
(-3.65%)
20424
(-5.88%)
August
2496
(-15.50%)
21700
(-15.11%)
July
2954
(+56.21%)
25562
(+53.22%)
June
1891
16683
May
0
0
April
0
0
March
0
0
February
0
0
January
0
0
" ], "text/plain": [ "" @@ -424,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "id": "92a2cb6c", "metadata": { "scrolled": true, @@ -436,7 +373,7 @@ { "data": { "text/html": [ - "
Total Users
4255
(+1.09%)
" + "
Total Users
7217
(+7.89%)
" ], "text/plain": [ "" @@ -447,12 +384,12 @@ } ], "source": [ - "ac.show_difference_table(\"Total Users\", None, \"ga:users\", None, **default_params)" + "ac.show_difference_table(\"Total Users\", None, \"totalUsers\", None, **default_params)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "id": "e3edce81", "metadata": { "scrolled": false, @@ -464,7 +401,7 @@ { "data": { "text/html": [ - "
Returning Users
1324
(+15.23%)
" + "
Returning Users
2415
(+16.89%)
" ], "text/plain": [ "" @@ -475,12 +412,12 @@ } ], "source": [ - "ac.show_difference_table(\"Returning Users\", None, \"ga:users\", None, filters=\"ga:userType==Returning Visitor\", **default_params)" + "ac.show_difference_table(\"Returning Users\", None, \"totalUsers\", None, dimension_filter=\"newVsReturning==returning\", **default_params)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "id": "c103a682", "metadata": { "tags": [ @@ -491,7 +428,7 @@ { "data": { "text/html": [ - "
Total Visits
7417
(+22.98%)
" + "
Total Visits
12774
(+14.69%)
" ], "text/plain": [ "" @@ -502,12 +439,12 @@ } ], "source": [ - "ac.show_difference_table(\"Total Visits\", None, \"ga:sessions\", None, **default_params)" + "ac.show_difference_table(\"Total Visits\", None, \"sessions\", None, **default_params)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "95ba261f", "metadata": { "scrolled": false, @@ -519,7 +456,7 @@ { "data": { "text/html": [ - "
Sessions Including Visits to Data Portal
4117
(+36.23%)
" + "
Sessions Including Visits to Data Portal
6569
(+7.41%)
" ], "text/plain": [ "" @@ -530,12 +467,12 @@ } ], "source": [ - "ac.show_difference_table(\"Sessions Including Visits to Data Portal\", None, \"ga:sessions\", None, filters=\"ga:pagePath!~/explore\", **default_params)" + "ac.show_difference_table(\"Sessions Including Visits to Data Portal\", None, \"sessions\", None, dimension_filter=\"pagePath!~/explore\", **default_params)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "id": "af69fbbe", "metadata": { "scrolled": false, @@ -547,7 +484,7 @@ { "data": { "text/html": [ - "
Sessions Including Visits to Data Browser
3300
(+9.67%)
" + "
Sessions Including Visits to Data Browser
9903
(+18.77%)
" ], "text/plain": [ "" @@ -558,7 +495,7 @@ } ], "source": [ - "ac.show_difference_table(\"Sessions Including Visits to Data Browser\", None, \"ga:sessions\", None, filters=\"ga:pagePath=~/explore\", **default_params)" + "ac.show_difference_table(\"Sessions Including Visits to Data Browser\", None, \"sessions\", None, dimension_filter=\"pagePath=~/explore\", **default_params)" ] }, { @@ -571,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "4f8f59ad", "metadata": { "tags": [ @@ -582,7 +519,7 @@ { "data": { "text/html": [ - "
Category
Users
Percentage of users
desktop
4265
(+22.42%)
84.52
(-4.88%)
mobile
764
(+77.67%)
15.14
(+38.06%)
tablet
17
(+142.86%)
0.34
(+88.71%)
" + "
Category
Users
Percentage of users
desktop
6536
(+5.10%)
89.50
(-3.44%)
mobile
735
(+56.05%)
10.06
(+43.38%)
tablet
32
(+60.00%)
0.44
(+47.01%)
" ], "text/plain": [ "" @@ -593,7 +530,7 @@ } ], "source": [ - "ac.show_difference_table([\"Users\", \"Percentage of users\"], \"Category\", \"ga:users\", \"ga:deviceCategory\", percentage_metrics={\"ga:users\"}, **default_params)" + "ac.show_difference_table([\"Users\", \"Percentage of users\"], \"Category\", \"totalUsers\", \"deviceCategory\", percentage_metrics={\"totalUsers\"}, **default_params)" ] }, { @@ -606,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "49c016fd", "metadata": { "tags": [ @@ -617,7 +554,7 @@ { "data": { "text/html": [ - "
Social Network
User Sessions
(not set)
7369
(+22.25%)
Facebook
47
(+2250.00%)
VKontakte
1
" + "
Social Network
User Sessions
LinkedIn
6
(+100.00%)
m.facebook.com
2
(+100.00%)
reddit.com
2
(+100.00%)
l.facebook.com
1
" ], "text/plain": [ "" @@ -628,7 +565,7 @@ } ], "source": [ - "ac.show_difference_table(\"User Sessions\", \"Social Network\", \"ga:sessions\", \"ga:socialNetwork\", **default_params)" + "ac.show_difference_table(\"User Sessions\", \"Social Network\", \"sessions\", \"sessionSource\", dimension_filter=\"sessionDefaultChannelGroup=@Social\", **default_params)" ] }, { @@ -649,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 34, "id": "744c6670", "metadata": { "scrolled": true, @@ -661,7 +598,7 @@ { "data": { "text/html": [ - "
Referrer
Users
(direct)
2117
(+23.58%)
google
1697
(+31.25%)
singlecell.broadinstitute.org
570
(+61.47%)
link.zhihu.com
220
(+22.22%)
cn.bing.com
75
(-1.32%)
ebi.ac.uk
66
(+26.92%)
bing
64
(+64.10%)
satijalab.org
46
(-30.30%)
jianshu.com
38
(+0.00%)
nature.com
38
(-15.56%)
baidu
34
(+142.86%)
github.com
23
(-8.00%)
m.facebook.com
23
ncbi.nlm.nih.gov
20
(+0.00%)
proteinatlas.org
20
(+42.86%)
covid19cellatlas.org
19
(+0.00%)
celltypist.org
18
en.wikipedia.org
18
(+0.00%)
lm.facebook.com
15
azimuth.hubmapconsortium.org
13
(+18.18%)
" + "
Referrer
Users
(direct)
3239
(+15.93%)
google
1828
(-3.64%)
singlecell.broadinstitute.org
1244
(+2.30%)
link.zhihu.com
315
(+7.88%)
cellxgene.cziscience.com
133
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(not set)
110
(+61.76%)
nature.com
106
(-4.50%)
bing
94
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cn.bing.com
93
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baidu
87
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satijalab.org
65
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ncbi.nlm.nih.gov
35
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github.com
30
(-9.09%)
ebi.ac.uk
30
(+7.14%)
jianshu.com
25
(+66.67%)
statics.teams.cdn.office.net
19
(+137.50%)
azimuth.hubmapconsortium.org
15
(+50.00%)
en.wikipedia.org
13
(-7.14%)
support.10xgenomics.com
12
(+0.00%)
covid19cellatlas.org
11
(-21.43%)
science.org
11
(-21.43%)
zhihu.com
10
(-9.09%)
academic.oup.com
9
(+80.00%)
heartcellatlas.org
7
(-36.36%)
mail.google.com
7
biorxiv.org
7
asap.epfl.ch
6
celltypist.org
6
(-14.29%)
tipz.io
6
bioconductor.org
5
" ], "text/plain": [ "" @@ -672,7 +609,7 @@ } ], "source": [ - "ac.show_difference_table(\"Users\", \"Referrer\", \"ga:users\",\"ga:source\", **default_params)" + "ac.show_difference_table(\"Users\", \"Referrer\", \"totalUsers\",\"sessionSource\", **default_params)" ] }, { @@ -693,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 35, "id": "cca3e2c5", "metadata": { "scrolled": true, @@ -705,7 +642,7 @@ { "data": { "text/html": [ - "
Country
Users
United States
1361
(+18.86%)
China
1061
(+14.83%)
Germany
329
(+117.88%)
United Kingdom
327
(+52.09%)
Japan
191
(+22.44%)
Hong Kong
149
(+12.88%)
Indonesia
147
South Korea
135
(+3.85%)
Spain
95
(+86.27%)
India
89
(+18.67%)
France
87
(+19.18%)
Canada
81
(-1.22%)
Australia
81
(+76.09%)
Switzerland
66
(+60.98%)
Nepal
65
(-54.86%)
Netherlands
64
(+20.75%)
Italy
63
(+23.53%)
Israel
60
(+66.67%)
Singapore
53
(+3.92%)
Sweden
52
(+13.04%)
" + "
Country
Users
United States
2090
(-2.88%)
China
1846
(+36.44%)
United Kingdom
436
(-5.83%)
Germany
361
(+1.12%)
Japan
346
(+21.40%)
South Korea
294
(+33.03%)
Canada
226
(+64.96%)
Hong Kong
150
(+8.70%)
France
144
(-11.66%)
India
126
(+3.28%)
Australia
116
(-10.08%)
Spain
112
(-11.11%)
Singapore
103
(+3.00%)
Sweden
97
(+22.78%)
Italy
92
(-20.69%)
Switzerland
84
(+5.00%)
Netherlands
70
(-5.41%)
Brazil
62
(+24.00%)
Taiwan
55
(-3.51%)
Israel
54
(-12.90%)
Austria
41
(+10.81%)
Belgium
34
(-45.16%)
Denmark
32
(-17.95%)
Poland
26
(+8.33%)
Portugal
20
Micronesia
20
Ireland
19
(-5.00%)
Vietnam
19
Türkiye
19
(-29.63%)
Czechia
15
" ], "text/plain": [ "" @@ -716,7 +653,7 @@ } ], "source": [ - "ac.show_difference_table(\"Users\", \"Country\", \"ga:users\",\"ga:country\", **default_params)" + "ac.show_difference_table(\"Users\", \"Country\", \"totalUsers\", \"country\", **default_params)" ] }, { @@ -737,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "id": "6729aa7f", "metadata": { "scrolled": true, @@ -749,7 +686,7 @@ { "data": { "text/html": [ - "" + "
Page
Entrances
3593
(+474.88%)
(not set)
1941
(+38.25%)
1010
(+30.15%)
325
(-12.87%)
232
(-3.33%)
226
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216
(-4.00%)
156
(+28.93%)
126
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103
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103
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74
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67
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64
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64
(-4.48%)
60
(-10.45%)
59
(+59.46%)
59
(-4.84%)
55
(+44.74%)
53
53
52
(+1.96%)
48
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47
46
(-6.12%)
45
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45
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42
41
39
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" ], "text/plain": [ "" @@ -760,22 +697,18 @@ } ], "source": [ - "ac.show_difference_table(\"Entrances\", \"Page\", \"ga:entrances\",\"ga:pagePath\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "62c6d14d", - "metadata": {}, - "source": [ - "
" + "ac.show_difference_table(\"Entrances\", \"Page\", \"sessions\", \"landingPage\", **default_params)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "4788a00e", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "## Downloads and exports\n", "### Download project metadata" @@ -783,359 +716,231 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 37, "id": "3f0a6b9f", "metadata": { "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n" - ] - }, - { - "data": { - "text/html": [ - "
Action
Users
Count
Download Project Full Metadata
28
32
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Full Manifest\", rows_type=\"fixed\", **{**default_params, \"index_key_formatter\": (lambda x: \"Download Project Full Metadata\")})" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Full Manifest\", rows_type=\"fixed\", **{**default_params, \"index_key_formatter\": (lambda x: \"Download Project Full Metadata\")})" ] }, { "attachments": {}, "cell_type": "markdown", "id": "7654fea0", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top metadata downloads" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "id": "7f57402e", "metadata": { "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n" - ] - }, - { - "data": { - "text/html": [ - "
Project
Downloads
6
6
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Downloads\", \"Project\", \"ga:hits\", \"ga:dimension15\", filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Full Manifest\", rows_limit=29, **{**default_params, \"index_key_formatter\": hca.format_project_id_key})" - ] - }, - { - "cell_type": "markdown", - "id": "f2864be9", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Downloads\", \"Project\", \"ga:hits\", \"ga:dimension15\", filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Full Manifest\", rows_limit=29, **{**default_params, \"index_key_formatter\": hca.format_project_id_key})" ] }, { "attachments": {}, "cell_type": "markdown", "id": "beb9dad7", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Download project manifest" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "id": "db613ae8", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Action
Users
Count
Download Project Manifest
335
(+7.03%)
602
(+6.74%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Manifest\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Manifest\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "40ea9b89", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Download project matrix" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 40, "id": "e18ca244", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Download Type
Users
Count
Project CGM Matrix
140
(-6.67%)
487
(-25.99%)
Project DCP Matrix
79
(-5.95%)
144
(-18.64%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Download Type\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension6\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Matrix\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Download Type\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension6\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Matrix\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "da28c4af", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Request curl command for selected data" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 41, "id": "cb9e47bf", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
curl Request From Search Results
Users
Count
Bulk Download
22
(+22.22%)
57
(+18.75%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"curl Request From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventAction==Request;ga:dimension1==Bulk Download\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"curl Request From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventAction==Request;ga:dimension1==Bulk Download\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "ae0dfd57", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Metadata request/download from selected data" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "id": "246c95a3", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Metadata Request/Download From Search Results
Users
Count
Cohort Manifest
11
(-21.43%)
16
(-48.39%)
Cohort Manifest Link
9
(-30.77%)
11
(-57.69%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Metadata Request/Download From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Manifest;ga:eventAction=~Request|Download;ga:dimension1=~Cohort Manifest|Cohort Manifest Link\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Metadata Request/Download From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Manifest;ga:eventAction=~Request|Download;ga:dimension1=~Cohort Manifest|Cohort Manifest Link\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "68028cea", - "metadata": {}, - "source": [ - "### Request export from selected data" - ] - }, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "### Request export from selected data" + ] + }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 43, "id": "18ac8d42", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Request Export From Search Results
Users
Count
Terra
20
(+17.65%)
24
(+4.35%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Request Export From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension3\", \"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Export;ga:eventAction==Request;ga:dimension1==Cohort Export;ga:dimension3==Terra\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Request Export From Search Results\", [\"ga:users\", \"ga:hits\"], [\"ga:dimension3\", \"ga:dimension1\", \"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Export;ga:eventAction==Request;ga:dimension1==Cohort Export;ga:dimension3==Terra\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "2ed08f5c", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top direct file download file types (from files tab)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 44, "id": "6ff15afb", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
File Type
Downloads
fastq.gz
25
(+733.33%)
bam
7
(+250.00%)
tsv.gz
5
loom
4
bai
2
(+100.00%)
csv.gz
1
h5ad.zip
1
tar.gz
1
zip.gz
1
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Downloads\", \"File Type\", \"ga:hits\", \"ga:dimension6\", filters=\"ga:eventCategory==File;ga:eventAction==Download\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "d658eb7d", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Downloads\", \"File Type\", \"ga:hits\", \"ga:dimension6\", filters=\"ga:eventCategory==File;ga:eventAction==Download\", **default_params)" ] }, { "cell_type": "markdown", "id": "3a9de92f", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Direct file downloads (from files tab)" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 45, "id": "51986a0d", "metadata": { "scrolled": false, @@ -1143,148 +948,47 @@ "remove-input" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Action
Users
Count
Download
8
(+33.33%)
20
(+81.82%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==File;ga:eventAction==Download\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==File;ga:eventAction==Download\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "5fd5afaa", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top \"export selected data\" queries" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 46, "id": "5fb87fc7", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Query
Selected for Export Count
download manifest\n", - "specimenOrgan: mouth\n", - "Project: Immune landscape of viral- and carcinogen-drived head and neck cancer\n", - "genusSpecies: Homo sapiens
8
export to terra, select species\n", - "Project: 1.3 Million Brain Cells from E18 Mice
7
export to terra\n", - "Project: 1.3 Million Brain Cells from E18 Mice
6
download manifest, select species\n", - "specimenOrgan: mouth\n", - "Project: Immune landscape of viral- and carcinogen-drived head and neck cancer\n", - "genusSpecies: Homo sapiens
5
export to terra, select species\n", - "genusSpecies: Homo sapiens\n", - "modelOrgan: brain, breast, eye, heart, liver, muscle, respiratory airway, skeletal muscle tissue, skin, skin of body, Unspecified\n", - "Project: A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra, A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors, A Single-Cell Atlas of the Human Healthy Airways., A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure, A cellular census of human lungs identifies novel cell states in health and in asthma, A multi-omics atlas of the human retina at single-cell resolution, A single-cell transcriptome atlas of the adult human retina, A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche, A survey of human brain transcriptome diversity at the single cell level, Cells of the adult human heart, Construction of a single-cell transcriptomic atlas of 58,243 liver cells from 4 donors and 4 recipient liver transplasnt patients to investigate early allograft dysfunction (EAD)., Differentiation of Human Intestinal Organoids with Endogenous Vascular Endothelial Cells, Developmental cell programs are co-opted in inflammatory skin disease., In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution, Massively parallel single-cell RNA-seq analysis of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors., Single Cell RNA-Sequencing of Human Limb Skeletal Muscle across Development and Myogenic Culture from Pluripotent Stem Cells, Single Cell RNAseq of primary pulmonary endothelial cells., Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney, Single-cell RNA-Seq reveals a developmental atlas of human prefrontal cortex, Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia., Single-cell transcriptional profiles in human and mouse skeletal muscle, Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs, Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle, Spatial and single-cell transcriptional landscape of human cerebellar development, The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors\n", - "fileFormat: .csv, .h5ad, .rds, .tsv, .tsv.gz, bam, cloupe, csv, csv.gz, fastq, fastq.gz, h5ad, h5ad.zip, jpg, loom, mtx.gz, RData.gz, rds.gz, tar, tar.gz, tif, tsv, tsv.gz, txt, txt.gz, xlsx, zip
5
get curl command\n", - "donorDisease: melanoma (disease)\n", - "Project: Melanoma infiltration of stromal and immune cells
4
export to terra\n", - "Project: 1.3 Million Brain Cells from E18 Mice\n", - "genusSpecies: Mus musculus\n", - "fileFormat: h5
4
get curl command\n", - "specimenOrgan: skin of body\n", - "Project: Developmental cell programs are co-opted in inflammatory skin disease.\n", - "genusSpecies: Homo sapiens
4
get curl command\n", - "Project: 1.3 Million Brain Cells from E18 Mice\n", - "genusSpecies: Mus musculus\n", - "fileFormat: fastq
4
get curl command, select species\n", - "donorDisease: melanoma (disease)\n", - "Project: Melanoma infiltration of stromal and immune cells
4
download manifest\n", - "donorDisease: melanoma (disease)\n", - "Project: Melanoma infiltration of stromal and immune cells\n", - "genusSpecies: Mus musculus\n", - "fileFormat: bai, bam, fastq.gz, loom
4
get curl command\n", - "organ: immune system\n", - "Project: Census of Immune Cells
3
(-40.00%)
download manifest\n", - "specimenOrgan: mouth\n", - "Project: Immune landscape of viral- and carcinogen-drived head and neck cancer\n", - "genusSpecies: Homo sapiens\n", - "fileFormat: tar
3
download manifest\n", - "genusSpecies: Mus musculus\n", - "organismAgeRange: {"ageMax": 4838400, "ageMin": 2419200, "ageUnit": "week"}\n", - "biologicalSex: male
3
export to terra\n", - "genusSpecies: Homo sapiens\n", - "modelOrgan: brain, breast, eye, heart, liver, muscle, respiratory airway, skeletal muscle tissue, skin, skin of body, Unspecified\n", - "Project: A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra, A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors, A Single-Cell Atlas of the Human Healthy Airways., A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure, A cellular census of human lungs identifies novel cell states in health and in asthma, A multi-omics atlas of the human retina at single-cell resolution, A single-cell transcriptome atlas of the adult human retina, A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche, A survey of human brain transcriptome diversity at the single cell level, Cells of the adult human heart, Construction of a single-cell transcriptomic atlas of 58,243 liver cells from 4 donors and 4 recipient liver transplasnt patients to investigate early allograft dysfunction (EAD)., Differentiation of Human Intestinal Organoids with Endogenous Vascular Endothelial Cells, Developmental cell programs are co-opted in inflammatory skin disease., In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution, Massively parallel single-cell RNA-seq analysis of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors., Single Cell RNA-Sequencing of Human Limb Skeletal Muscle across Development and Myogenic Culture from Pluripotent Stem Cells, Single Cell RNAseq of primary pulmonary endothelial cells., Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney, Single-cell RNA-Seq reveals a developmental atlas of human prefrontal cortex, Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia., Single-cell transcriptional profiles in human and mouse skeletal muscle, Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs, Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle, Spatial and single-cell transcriptional landscape of human cerebellar development, The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors\n", - "fileFormat: .csv, .h5ad, .rds, .tsv, .tsv.gz, bam, cloupe, csv, csv.gz, fastq, fastq.gz, h5ad, h5ad.zip, jpg, loom, mtx.gz, RData.gz, rds.gz, tar, tar.gz, tif, tsv, tsv.gz, txt, txt.gz, xlsx, zip
3
export to terra\n", - "specimenOrgan: brain\n", - "sampleEntityType: cell_lines\n", - "specimenDisease: normal\n", - "contentDescription: Gene expression matrix\n", - "genusSpecies: Homo sapiens\n", - "fileFormat: txt
3
get curl command, select species\n", - "genusSpecies: Homo sapiens, Mus musculus\n", - "modelOrgan: brain, breast, eye, heart, liver, muscle, respiratory airway, skeletal muscle tissue, skin, skin of body, Unspecified\n", - "Project: A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra, A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors, A Single-Cell Atlas of the Human Healthy Airways., A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure, A cellular census of human lungs identifies novel cell states in health and in asthma, A multi-omics atlas of the human retina at single-cell resolution, A single-cell transcriptome atlas of the adult human retina, A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche, A survey of human brain transcriptome diversity at the single cell level, Cells of the adult human heart, Construction of a single-cell transcriptomic atlas of 58,243 liver cells from 4 donors and 4 recipient liver transplasnt patients to investigate early allograft dysfunction (EAD)., Differentiation of Human Intestinal Organoids with Endogenous Vascular Endothelial Cells, Developmental cell programs are co-opted in inflammatory skin disease., In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution, Massively parallel single-cell RNA-seq analysis of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors., Single Cell RNA-Sequencing of Human Limb Skeletal Muscle across Development and Myogenic Culture from Pluripotent Stem Cells, Single Cell RNAseq of primary pulmonary endothelial cells., Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney, Single-cell RNA-Seq reveals a developmental atlas of human prefrontal cortex, Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia., Single-cell transcriptional profiles in human and mouse skeletal muscle, Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs, Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle, Spatial and single-cell transcriptional landscape of human cerebellar development, The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors\n", - "fileFormat: .csv, .h5ad, .rds, .tsv, .tsv.gz, bam, cloupe, csv, csv.gz, h5ad, h5ad.zip, jpg, loom, mtx.gz, RData.gz, rds.gz, tar, tar.gz, tif, tsv, tsv.gz, txt, txt.gz, xlsx, zip
3
3
download manifest\n", - "libraryConstructionApproach: Smart-seq2
3
3
get curl command\n", - "specimenOrgan: immune system\n", - "specimenOrganPart: lymph node\n", - "genusSpecies: Homo sapiens\n", - "fileFormat: tar
3
3
get curl command, select species\n", - "organ: immune system\n", - "Project: Census of Immune Cells
3
(-50.00%)
download manifest, select species\n", - "genusSpecies: Homo sapiens\n", - "modelOrgan: brain, breast, eye, heart, liver, muscle, respiratory airway, skeletal muscle tissue, skin, skin of body, Unspecified\n", - "Project: A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra, A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors, A Single-Cell Atlas of the Human Healthy Airways., A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure, A cellular census of human lungs identifies novel cell states in health and in asthma, A multi-omics atlas of the human retina at single-cell resolution, A single-cell transcriptome atlas of the adult human retina, A spatial multi-omics atlas of the human lung reveals a novel immune cell survival niche, A survey of human brain transcriptome diversity at the single cell level, Cells of the adult human heart, Construction of a single-cell transcriptomic atlas of 58,243 liver cells from 4 donors and 4 recipient liver transplasnt patients to investigate early allograft dysfunction (EAD)., Differentiation of Human Intestinal Organoids with Endogenous Vascular Endothelial Cells, Developmental cell programs are co-opted in inflammatory skin disease., In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution, Massively parallel single-cell RNA-seq analysis of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors., Single Cell RNA-Sequencing of Human Limb Skeletal Muscle across Development and Myogenic Culture from Pluripotent Stem Cells, Single Cell RNAseq of primary pulmonary endothelial cells., Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney, Single-cell RNA-Seq reveals a developmental atlas of human prefrontal cortex, Single-cell atlas of early human brain development highlights heterogeneity of human neuroepithelial cells and early radial glia., Single-cell transcriptional profiles in human and mouse skeletal muscle, Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs, Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle, Spatial and single-cell transcriptional landscape of human cerebellar development, The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors\n", - "fileFormat: .csv, .h5ad, .rds, .tsv, .tsv.gz, bam, cloupe, csv, csv.gz, fastq, fastq.gz, h5ad, h5ad.zip, jpg, loom, mtx.gz, RData.gz, rds.gz, tar, tar.gz, tif, tsv, tsv.gz, txt, txt.gz, xlsx, zip
3
get curl command\n", - "Project: 1.3 Million Brain Cells from E18 Mice\n", - "genusSpecies: Mus musculus\n", - "fileFormat: fastq, h5
3
get curl command\n", - "specimenOrgan: skeletal muscle organ\n", - "Project: Single-cell transcriptional profiles in human and mouse skeletal muscle, Single-nucleus cross-tissue molecular reference maps to decipher disease gene function.\n", - "genusSpecies: Homo sapiens\n", - "fileFormat: fastq.gz
3
download manifest, select species\n", - "genusSpecies: Mus musculus\n", - "organismAgeRange: {"ageMax": 4838400, "ageMin": 2419200, "ageUnit": "week"}\n", - "biologicalSex: male
3
download manifest, select species\n", - "libraryConstructionApproach: Smart-seq2
3
get curl command, select species\n", - "libraryConstructionApproach: 10X 5' v2 sequencing, 10x 5' v2, 10x 5' transcription profiling, 10x 5' v1\n", - "specimenOrgan: pancreas
3
get curl command, select species\n", - "Project: A human cell atlas of fetal gene expression., A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates\n", - "genusSpecies: Homo sapiens\n", - "fileFormat: fastq.gz
3
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table(\"Selected for Export Count\", \"Query\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~/explore/export/export-to-terra|/explore/export/get-curl-command|/explore/export/download-manifest\", split_vertical=[7, 11, 7], **default_params)" + "# ac.show_difference_table(\"Selected for Export Count\", \"Query\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~/explore/export/export-to-terra|/explore/export/get-curl-command|/explore/export/download-manifest\", split_vertical=[7, 11, 7], **default_params)" ] }, { "cell_type": "markdown", "id": "a017b0dd", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "## Entities\n", "### Entity tabs selections (project, samples, files)" @@ -1292,44 +996,28 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 47, "id": "615a50a1", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Entity Tab
Count
Projects
622
(+27.20%)
Samples
344
(+21.99%)
Files
198
(+31.13%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Count\", \"Entity Tab\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Entity;ga:eventAction==Select Tab\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "95ee5cfe", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Count\", \"Entity Tab\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Entity;ga:eventAction==Select Tab\", **default_params)" ] }, { "cell_type": "markdown", "id": "f60027fa", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "## Search facets\n", "### Top facets" @@ -1337,79 +1025,51 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 48, "id": "b7fde3a7", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Facet
Count
projectId
736
(+12.54%)
specimenOrgan
453
(-12.55%)
genusSpecies
333
(-7.76%)
specimenOrganPart
303
(+8.21%)
fileFormat
270
(-37.06%)
libraryConstructionApproach
254
(+22.71%)
specimenDisease
203
(+23.03%)
donorDisease
175
(+2.94%)
modelOrgan
162
(-13.37%)
selectedCellType
162
(-14.74%)
sampleEntityType
112
(-1.75%)
nucleicAcidSource
93
(+12.05%)
developmentStage
51
(-55.65%)
contentDescription
51
(+2.00%)
biologicalSex
45
(+73.08%)
workflow
44
(+25.71%)
fileSource
25
(+78.57%)
instrumentManufacturerModel
24
(+100.00%)
projectTitle
20
(-75.90%)
project
17
(+54.55%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Count\", \"Facet\", \"ga:hits\", \"ga:dimension9\", filters=\"ga:eventCategory==Search;ga:eventAction==Select\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "35c40808", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Count\", \"Facet\", \"ga:hits\", \"ga:dimension9\", filters=\"ga:eventCategory==Search;ga:eventAction==Select\", **default_params)" ] }, { "cell_type": "markdown", "id": "70ff5050", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top facet terms" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 49, "id": "a56a1474", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Facet
Term
Count
genusSpecies
Homo sapiens
303
(-6.77%)
specimenDisease
normal
83
(+5.06%)
nucleicAcidSource
single cell
71
(+9.23%)
sampleEntityType
specimens
69
(+1.47%)
donorDisease
normal
63
(-22.22%)
fileFormat
fastq.gz
50
(+28.21%)
projectId
1M Immune Cells
43
(+26.47%)
specimenOrgan
blood
42
(+20.00%)
projectId
1M Neurons
34
projectId
tabulaSapiens
33
(+26.92%)
modelOrgan
brain
30
(-6.25%)
genusSpecies
Mus musculus
29
(-12.12%)
specimenOrgan
brain
29
(-14.71%)
specimenOrgan
bone marrow
28
modelOrgan
lung
26
libraryConstructionApproach
10x 3' v2
25
fileFormat
bam
25
specimenOrganPart
bone marrow
25
(-16.67%)
specimenOrgan
kidney
25
modelOrgan
kidney
23
(-8.00%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table(\"Count\", [\"Facet\", \"Term\"], \"ga:hits\", [\"ga:dimension9\", \"ga:dimension10\"], filters=\"ga:eventCategory==Search;ga:eventAction==Select\", **default_params)" + "# ac.show_difference_table(\"Count\", [\"Facet\", \"Term\"], \"ga:hits\", [\"ga:dimension9\", \"ga:dimension10\"], filters=\"ga:eventCategory==Search;ga:eventAction==Select\", **default_params)" ] }, { "cell_type": "markdown", - "id": "0612d05b", + "id": "fab8c07e", "metadata": {}, "source": [ "
" @@ -1426,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 5, "id": "c38a26da", "metadata": { "scrolled": true, @@ -1438,7 +1098,7 @@ { "data": { "text/html": [ - "
Project
Count
524
(+7.60%)
297
(-11.34%)
255
(+26.24%)
154
(+17.56%)
142
(+32.71%)
138
117
106
(-23.19%)
102
100
(-4.76%)
96
(-11.11%)
96
(-25.00%)
92
(-29.23%)
84
(+10.53%)
83
(-14.43%)
83
82
(-2.38%)
82
(+2.50%)
81
80
(-36.00%)
" + "
Project
Count
738
(+17.70%)
256
(+64.10%)
253
(+54.27%)
240
(+21.83%)
238
(+36.00%)
225
(+92.31%)
206
(+18.39%)
198
(+75.22%)
191
(+151.32%)
173
(+58.72%)
161
(+62.63%)
159
(+0.00%)
135
(+56.98%)
134
(+54.02%)
132
131
(+65.82%)
130
127
126
(+23.53%)
124
123
118
116
113
103
" ], "text/plain": [ "" @@ -1449,131 +1109,99 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Project\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~^(\\\\/explore\\\\/projects\\\\/[0-9a-fA-F]{8}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{12})(\\\\/?\\\\?{0}|\\\\/?\\\\?{1}.*)$\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "57b26f26", - "metadata": {}, - "source": [ - "
" + "ac.show_difference_table(\"Count\", \"Project\", \"screenPageViews\", \"pagePathPlusQueryString\", dimension_filter=\"pagePathPlusQueryString=~^(\\\\/explore\\\\/projects\\\\/[0-9a-fA-F]{8}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{12})(\\\\/?\\\\?{0}|\\\\/?\\\\?{1}.*)$\", **default_params)" ] }, { "cell_type": "markdown", "id": "cd9060de", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Project detail supplementary links visits (from external resources)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 51, "id": "f714e61d", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Link
Visits
https://tabula-sapiens-portal.ds.czbiohub.org/home
9
(+28.57%)
https://descartes.brotmanbaty.org/bbi/human-gene-expression-during-development/
5
(+150.00%)
https://github.com/cssmillie/ulcerative_colitis
4
https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons
3
https://cellxgene.cziscience.com/collections/c9706a92-0e5f-46c1-96d8-20e42467f287
3
https://cellxgene.cziscience.com/collections/af893e86-8e9f-41f1-a474-ef05359b1fb7
3
https://cells-test.gi.ucsc.edu/?ds=early-brain
3
https://github.com/dpeerlab/Palantir/
3
http://www.jasonspencelab.com/protocols
3
https://singlecell.broadinstitute.org/single_cell/study/SCP259
3
http://retinalstemcellresearch.co.uk/CorneaCellAtlas/
3
https://developmentcellatlas.ncl.ac.uk/datasets/HCA_thymus/
2
(-50.00%)
ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_RAW.tar,ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_batch1.genes.tsv.gz,ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_batch1.total.tsne.df.tsv.gz,ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_batch2.genes.tsv.gz,ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_batch2.total.tsne.df.tsv.gz,ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/GSE96583_genes.txt.gz
2
https://www.ebi.ac.uk/gxa/sc/experiments/E-EHCA-2/Results
2
(+100.00%)
https://singlecell.broadinstitute.org/single_cell/study/SCP1479/single-nucleus-cross-tissue-molecular-reference-maps
2
(+0.00%)
https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-5061/Results
2
(+0.00%)
https://fbm.cellatlas.io/
2
https://insight.jci.org/articles/view/150861/sd/1
2
ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE117nnn/GSE117498/suppl/GSE117498_RAW.tar
2
https://github.com/agneantanaviciute/colonmesenchymescrnaseq
2
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table(\"Visits\", \"Link\", \"ga:hits\", [\"ga:dimension4\", \"ga:dimension6\", \"ga:eventLabel\"], filters=\"ga:eventCategory==Project;ga:eventAction==View External Resource\", **default_params)" + "# ac.show_difference_table(\"Visits\", \"Link\", \"ga:hits\", [\"ga:dimension4\", \"ga:dimension6\", \"ga:eventLabel\"], filters=\"ga:eventCategory==Project;ga:eventAction==View External Resource\", **default_params)" ] }, { "cell_type": "markdown", "id": "35122750", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Withdrawn/deprecated projects visits" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 52, "id": "51ac7d90", "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
View Withdrawn Project
2
(-50.00%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Count\", \"Project Type\", \"ga:hits\", \"ga:eventAction\", filters=\"ga:eventAction=~View Deprecated Project|View Withdrawn Project\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "b397bddb", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Count\", \"Project Type\", \"ga:hits\", \"ga:eventAction\", filters=\"ga:eventAction=~View Deprecated Project|View Withdrawn Project\", **default_params)" ] }, { "cell_type": "markdown", "id": "644f5c5b", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Analysis protocol portal links" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 53, "id": "6fb02aa1", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Analysis Protocol
Count
optimus_post_processing_v1.0.0
16
(-23.81%)
optimus_v4.2.3
4
(+33.33%)
optimus_v4.2.2
2
(-66.67%)
optimus_v1.3.2
1
(-75.00%)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table(\"Count\", \"Analysis Protocol\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Portal Link;ga:eventAction==Click\", **default_params)" + "# ac.show_difference_table(\"Count\", \"Analysis Protocol\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Portal Link;ga:eventAction==Click\", **default_params)" + ] + }, + { + "cell_type": "markdown", + "id": "0aea0a13", + "metadata": {}, + "source": [ + "
" ] }, { @@ -1587,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 54, "id": "5fc13339", "metadata": { "scrolled": true, @@ -1599,7 +1227,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1610,7 +1238,7 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Path\", \"ga:entrances\", \"ga:landingPagePath\", filters=\"ga:landingPagePath!~^\\/explore\", rows_limit=28, **default_params)" + "ac.show_difference_table(\"Count\", \"Path\", \"sessions\", \"landingPage\", dimension_filter=\"landingPage!~^\\/explore\", rows_limit=28, **default_params)" ] }, { @@ -1631,7 +1259,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 61, "id": "bc0752ae", "metadata": { "scrolled": true, @@ -1643,7 +1271,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1654,95 +1282,63 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Path\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath!~^\\/explore\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "659e9be5", - "metadata": {}, - "source": [ - "
" + "ac.show_difference_table(\"Count\", \"Path\", \"screenPageViews\", \"pagePath\", dimension_filter=\"pagePath!~^\\/explore\", **default_params)" ] }, { "cell_type": "markdown", "id": "88ccb4a9", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top searches" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 56, "id": "f8fc0670", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
Search Text
Count
H
3
P
2
SACS
2
pericyt
1
macrophages
1
me
1
me'l
1
me'la
1
me'le
1
me'le'no'm
1
melano
1
melanoma
1
melen
1
osteo
1
Brin
1
gill
1
pericyte
1
pericytes
1
sa
1
sa'ca
1
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ac.show_difference_table(\"Count\", \"Search Text\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Enter Text\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "ba21f903", - "metadata": {}, + "outputs": [], "source": [ - "
" + "# ac.show_difference_table(\"Count\", \"Search Text\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Enter Text\", **default_params)" ] }, { "cell_type": "markdown", "id": "03dd7c38", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top selected search results" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 57, "id": "353d5667", "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "ac.show_difference_table(\"Count\", \"Search Result\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Click\", **default_params)" + "# ac.show_difference_table(\"Count\", \"Search Result\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Click\", **default_params)" ] }, { diff --git a/analytics/hca-analytics/users_over_time_history.json b/analytics/hca-analytics/users_over_time_history.json new file mode 100644 index 000000000..8ae7916cb --- /dev/null +++ b/analytics/hca-analytics/users_over_time_history.json @@ -0,0 +1 @@ +{"Users":{"1685577600000":7778,"1682899200000":7384,"1680307200000":6467,"1677628800000":6574,"1675209600000":5855,"1672531200000":4470,"1669852800000":4684,"1667260800000":4965,"1664582400000":4891,"1661990400000":4766,"1659312000000":4304,"1656633600000":4112,"1654041600000":4182,"1651363200000":4960,"1648771200000":3899,"1646092800000":3412,"1643673600000":3104,"1640995200000":2836,"1638316800000":2394,"1635724800000":2938,"1633046400000":2584,"1630454400000":2405,"1627776000000":2496,"1625097600000":2954,"1622505600000":1891,"1619827200000":0,"1617235200000":0,"1614556800000":0,"1612137600000":0,"1609459200000":0},"Total Pageviews":{"1685577600000":44146,"1682899200000":48780,"1680307200000":39497,"1677628800000":38763,"1675209600000":30800,"1672531200000":25796,"1669852800000":26073,"1667260800000":28929,"1664582400000":29034,"1661990400000":28711,"1659312000000":27741,"1656633600000":29631,"1654041600000":30890,"1651363200000":33258,"1648771200000":29391,"1646092800000":27114,"1643673600000":22216,"1640995200000":22452,"1638316800000":19856,"1635724800000":24409,"1633046400000":22963,"1630454400000":20424,"1627776000000":21700,"1625097600000":25562,"1622505600000":16683,"1619827200000":0,"1617235200000":0,"1614556800000":0,"1612137600000":0,"1609459200000":0}} \ No newline at end of file diff --git a/analytics/lungmap-analytics/analytics.ipynb b/analytics/lungmap-analytics/analytics.ipynb index 40c5aed5c..033105798 100644 --- a/analytics/lungmap-analytics/analytics.ipynb +++ b/analytics/lungmap-analytics/analytics.ipynb @@ -11,9 +11,9 @@ ":class: analytics-logo\n", "```\n", "\n", - "# LungMAP User Analytics - June vs May 2023\n", + "# LungMAP User Analytics - July vs June 2023\n", "\n", - "This section displays metrics collected from Google Analytics for the LungMAP Data Browser at https://data-browser.lungmap.net and compares June 2023 with the previous month (May 2023). The generation of this report is now coded in Jupyter notebooks and can be rerun easily and modified or consolidated as desired.\n", + "This section displays metrics collected from Google Analytics for the LungMAP Data Browser at https://data-browser.lungmap.net and compares July 2023 with the previous month (June 2023). The generation of this report is now coded in Jupyter notebooks and can be rerun easily and modified or consolidated as desired.\n", "\n", "## Key to tables\n", "\n", @@ -72,28 +72,7 @@ "remove-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=713613812354-ccedl8colb27q3q6rvvvjqrpb5tcbuug.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8082%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fanalytics.readonly&state=j5RuDIaZjkKG0n6nS2QEgRO9nYVAZ2&access_type=offline\n" - ] - }, - { - "data": { - "text/plain": [ - "(,\n", - " (service, params)>,\n", - " {},\n", - " )" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from IPython.display import display\n", "\n", @@ -103,21 +82,43 @@ "import analytics.charts as ac\n", "import analytics_lungmap as lm\n", "\n", - "GA_PROPERTY = \"246040541\" # LungMAP Data Browser\n", + "GA_PROPERTY = \"362871218\" # data-browser.lungmap.net - GA4\n", "ANALYTICS_START = \"2021-05-01\"\n", "TODAY = 'today'\n", - "PERIOD = \"2023-06\"\n", - "PREV_PERIOD = \"2023-05\"\n", + "PERIOD = \"2023-07\"\n", + "PREV_PERIOD = \"2023-06\"\n", + "SECRET_NAME = 'ANALYTICS_REPORTING_CLIENT_SECRET_PATH'\n", "\n", + "GA3_END = \"2023-06-30\"\n", + "GA4_START = \"2023-07-01\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "09743293", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=713613812354-ccedl8colb27q3q6rvvvjqrpb5tcbuug.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8082%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fanalytics.readonly&state=jwvWUggxc64EU80ds7JhuawC8TzPVk&access_type=offline\n" + ] + } + ], + "source": [ "default_params = {\n", + " \"service_system\": ac.authenticate_ga4(SECRET_NAME),\n", " \"property\": GA_PROPERTY,\n", " \"index_key_formatter\": lm.adjust_table_index_key,\n", " \"period\": PERIOD,\n", " \"prev_period\": PREV_PERIOD\n", - "}\n", - "\n", - "ac.authenticate_ga('ANALYTICS_REPORTING_CLIENT_SECRET_PATH')\n", - "\n" + "}" ] }, { @@ -218,6 +219,49 @@ "ac.init_tables()" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4be30299", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=713613812354-ccedl8colb27q3q6rvvvjqrpb5tcbuug.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8083%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fanalytics.readonly&state=EE8pMxvNYtUpkRWcgeocgWDQkDPf15&access_type=offline\n" + ] + } + ], + "source": [ + "# ga3_service_system = ac.authenticate_ga(SECRET_NAME)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4d33899b", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# ga3_users_over_time_params = {\n", + "# \"start_date\": ANALYTICS_START,\n", + "# \"end_date\": GA3_END,\n", + "# \"service_system\": ga3_service_system,\n", + "# \"property\": \"246040541\",\n", + "# }\n", + "# \n", + "# lm.save_ga3_users_over_time_data({}, {}, **ga3_users_over_time_params)" + ] + }, { "cell_type": "markdown", "id": "837ebd50", @@ -229,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "4fb2a742", "metadata": { "scrolled": false, @@ -240,65 +284,17 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n", - "/Users/hunter/git-repos/data-browser/analytics/venv/src/analytics/analytics/hdgar-book/analytics_package/analytics/charts.py:189: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n", - " return make_index_code(index_runs, i, row_class, n) + \"\".join([make_item_code(item, row_class, i, c) for c, item in row.iteritems()])\n" - ] } ], "source": [ - "users_over_time_table = lm.plot_users_over_time(start_date=ANALYTICS_START, end_date=TODAY, **default_params)" + "users_over_time_table = lm.plot_users_over_time(start_date=GA4_START, end_date=TODAY, **default_params)" ] }, { @@ -319,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "f762cf5d", "metadata": { "tags": [ @@ -330,7 +326,7 @@ { "data": { "text/html": [ - "
Year
Month
Users
Total Pageviews
2023
January
100
(+38.89%)
540
(+21.62%)
2022
December
72
(-26.53%)
444
(-20.57%)
November
98
(+164.86%)
559
(+143.04%)
October
37
(+105.56%)
230
(+90.08%)
September
18
(+50.00%)
121
(+218.42%)
August
12
(+20.00%)
38
(+31.03%)
July
10
(-16.67%)
29
(-25.64%)
June
12
(+20.00%)
39
(+21.88%)
May
10
(+11.11%)
32
(-73.33%)
April
9
(+12.50%)
120
(+242.86%)
March
8
(-11.11%)
35
(+45.83%)
February
9
(-10.00%)
24
(-72.09%)
January
10
(-16.67%)
86
(+138.89%)
2021
December
12
(-14.29%)
36
(-18.18%)
November
14
(+133.33%)
44
(+69.23%)
October
6
(+500.00%)
26
(+36.84%)
September
1
(+0.00%)
19
(+533.33%)
August
1
(-66.67%)
3
(-90.62%)
July
3
32
June
0
0
May
0
0
" + "
Year
Month
Users
Total Pageviews
2023
July
143
(+14.40%)
657
(+33.27%)
June
125
(-32.80%)
493
(-55.78%)
May
186
(+44.19%)
1115
(+64.21%)
April
129
(-21.82%)
679
(-26.91%)
March
165
(+18.71%)
929
(+28.14%)
February
139
(+39.00%)
725
(+34.26%)
January
100
(+38.89%)
540
(+21.62%)
2022
December
72
(-26.53%)
444
(-20.57%)
November
98
(+164.86%)
559
(+143.04%)
October
37
(+105.56%)
230
(+90.08%)
September
18
(+50.00%)
121
(+218.42%)
August
12
(+20.00%)
38
(+31.03%)
July
10
(-16.67%)
29
(-25.64%)
June
12
(+20.00%)
39
(+21.88%)
May
10
(+11.11%)
32
(-73.33%)
April
9
(+12.50%)
120
(+242.86%)
March
8
(-11.11%)
35
(+45.83%)
February
9
(-10.00%)
24
(-72.09%)
January
10
(-16.67%)
86
(+138.89%)
2021
December
12
(-14.29%)
36
(-18.18%)
November
14
(+133.33%)
44
(+69.23%)
October
6
(+500.00%)
26
(+36.84%)
September
1
(+0.00%)
19
(+533.33%)
August
1
(-66.67%)
3
(-90.62%)
July
3
32
June
0
0
May
0
0
" ], "text/plain": [ "" @@ -385,7 +381,7 @@ } ], "source": [ - "ac.show_difference_table(\"Total Users\", None, \"ga:users\", None, **default_params)" + "ac.show_difference_table(\"Total Users\", None, \"totalUsers\", None, **default_params)" ] }, { @@ -412,7 +408,7 @@ } ], "source": [ - "ac.show_difference_table(\"Returning Users\", None, \"ga:users\", None, filters=\"ga:userType==Returning Visitor\", **default_params)" + "ac.show_difference_table(\"Returning Users\", None, \"totalUsers\", None, dimension_filter=\"newVsReturning==returning\", **default_params)" ] }, { @@ -439,7 +435,7 @@ } ], "source": [ - "ac.show_difference_table(\"Total Visits\", None, \"ga:sessions\", None, **default_params)" + "ac.show_difference_table(\"Total Visits\", None, \"sessions\", None, **default_params)" ] }, { @@ -474,7 +470,7 @@ } ], "source": [ - "ac.show_difference_table(\"Total\", None, \"ga:pageviews\", None, **default_params)" + "ac.show_difference_table(\"Total\", None, \"screenPageViews\", None, **default_params)" ] }, { @@ -510,7 +506,7 @@ } ], "source": [ - "ac.show_difference_table(\"Users\", \"Referrer\", \"ga:users\",\"ga:source\", **default_params)" + "ac.show_difference_table(\"Users\", \"Referrer\", \"totalUsers\",\"sessionSource\", **default_params)" ] }, { @@ -545,7 +541,7 @@ } ], "source": [ - "ac.show_difference_table(\"User Sessions\", \"Social Network\", \"ga:sessions\", \"ga:socialNetwork\", **default_params)" + "ac.show_difference_table(\"User Sessions\", \"Social Network\", \"sessions\", \"sessionSource\", dimension_filter=\"sessionDefaultChannelGroup=@Social\", **default_params)" ] }, { @@ -580,7 +576,7 @@ } ], "source": [ - "ac.show_difference_table([\"Users\", \"Percentage of users\"], \"Category\", \"ga:users\", \"ga:deviceCategory\", percentage_metrics={\"ga:users\"}, **default_params)" + "ac.show_difference_table([\"Users\", \"Percentage of users\"], \"Category\", \"totalUsers\", \"deviceCategory\", percentage_metrics={\"totalUsers\"}, **default_params)" ] }, { @@ -623,7 +619,7 @@ } ], "source": [ - "ac.show_difference_table(\"Page Views\", \"Page\", \"ga:pageviews\", \"ga:pagePath\", **default_params)" + "ac.show_difference_table(\"Page Views\", \"Page\", \"screenPageViews\", \"pagePath\", **default_params)" ] }, { @@ -666,7 +662,7 @@ } ], "source": [ - "ac.show_difference_table(\"Entrances\", \"Page\", \"ga:entrances\",\"ga:pagePath\", rows_limit=29, **default_params)" + "ac.show_difference_table(\"Entrances\", \"Page\", \"sessions\", \"landingPage\", **default_params)" ] }, { @@ -710,13 +706,17 @@ } ], "source": [ - "ac.show_difference_table(\"Users\", \"Country\", \"ga:users\",\"ga:country\", **default_params)" + "ac.show_difference_table(\"Users\", \"Country\", \"totalUsers\", \"country\", **default_params)" ] }, { "cell_type": "markdown", "id": "4788a00e", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "## Downloads and exports\n", "### Download project manifest" @@ -729,7 +729,8 @@ "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -747,13 +748,17 @@ } ], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Manifest\", rows_type=\"fixed\", **default_params)" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==Project;ga:eventAction==Download Project Manifest\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "3a9de92f", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Direct file downloads (from files tab)" ] @@ -765,7 +770,8 @@ "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -783,21 +789,17 @@ } ], "source": [ - "ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==File;ga:eventAction==Download\", rows_type=\"fixed\", **default_params)" - ] - }, - { - "cell_type": "markdown", - "id": "c323fa38", - "metadata": {}, - "source": [ - "
" + "# ac.show_difference_table([\"Users\", \"Count\"], \"Action\", [\"ga:users\", \"ga:hits\"], [\"ga:eventAction\", \"ga:eventCategory\"], filters=\"ga:eventCategory==File;ga:eventAction==Download\", rows_type=\"fixed\", **default_params)" ] }, { "cell_type": "markdown", "id": "5fd5afaa", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top \"export selected data\" queries" ] @@ -809,7 +811,8 @@ "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -906,13 +909,17 @@ } ], "source": [ - "ac.show_difference_table(\"Selected for Export Count\", \"Query\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~/explore/export/export-to-terra|/explore/export/get-curl-command|/explore/export/download-manifest\", split_vertical=[], **default_params)" + "# ac.show_difference_table(\"Selected for Export Count\", \"Query\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~/explore/export/export-to-terra|/explore/export/get-curl-command|/explore/export/download-manifest\", split_vertical=[], **default_params)" ] }, { "cell_type": "markdown", "id": "a017b0dd", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "## Entities\n", "### Entity tabs selections (project, samples, files)" @@ -925,7 +932,8 @@ "metadata": { "scrolled": false, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -943,7 +951,7 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Entity Tab\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Entity;ga:eventAction==Select Tab\", **default_params)" + "# ac.show_difference_table(\"Count\", \"Entity Tab\", \"ga:hits\", \"ga:eventLabel\", filters=\"ga:eventCategory==Entity;ga:eventAction==Select Tab\", **default_params)" ] }, { @@ -957,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "id": "c38a26da", "metadata": { "scrolled": false, @@ -969,7 +977,7 @@ { "data": { "text/html": [ - "
Project
Count
" + "" ], "text/plain": [ "" @@ -980,7 +988,7 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Project\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath=~^(\\\\/explore\\\\/projects\\\\/[0-9a-fA-F]{8}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{12})(\\\\/?\\\\?{0}|\\\\/?\\\\?{1}.*)$\", **default_params)" + "ac.show_difference_table(\"Count\", \"Project\", \"screenPageViews\", \"pagePathPlusQueryString\", dimension_filter=\"pagePathPlusQueryString=~^(\\\\/explore\\\\/projects\\\\/[0-9a-fA-F]{8}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{4}\\\\-[0-9a-fA-F]{12})(\\\\/?\\\\?{0}|\\\\/?\\\\?{1}.*)$\", **default_params)" ] }, { @@ -1032,7 +1040,7 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Path\", \"ga:pageviews\", \"ga:pagePath\", filters=\"ga:pagePath!~^\\/explore\", **default_params)" + "ac.show_difference_table(\"Count\", \"Path\", \"screenPageViews\", \"pagePath\", dimension_filter=\"pagePath!~^\\/explore\", **default_params)" ] }, { @@ -1045,7 +1053,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "9cdb1820", "metadata": { "scrolled": false, @@ -1057,7 +1065,7 @@ { "data": { "text/html": [ - "" + "
Path
Count
(not set)
25
(+25.00%)
14
(+366.67%)
1
1
1
" ], "text/plain": [ "" @@ -1068,13 +1076,17 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Path\", \"ga:entrances\", \"ga:landingPagePath\", filters=\"ga:landingPagePath!~^\\/explore\", **default_params)" + "ac.show_difference_table(\"Count\", \"Path\", \"sessions\", \"landingPage\", dimension_filter=\"landingPage!~^\\/explore\", **default_params)" ] }, { "cell_type": "markdown", "id": "88ccb4a9", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top searches" ] @@ -1086,7 +1098,8 @@ "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -1104,13 +1117,17 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Search Text\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Enter Text\", **default_params)" + "# ac.show_difference_table(\"Count\", \"Search Text\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Enter Text\", **default_params)" ] }, { "cell_type": "markdown", "id": "03dd7c38", - "metadata": {}, + "metadata": { + "tags": [ + "remove-cell" + ] + }, "source": [ "### Top selected search results" ] @@ -1122,7 +1139,8 @@ "metadata": { "scrolled": true, "tags": [ - "remove-input" + "remove-input", + "remove-cell" ] }, "outputs": [ @@ -1140,7 +1158,7 @@ } ], "source": [ - "ac.show_difference_table(\"Count\", \"Search Result\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Click\", **default_params)" + "# ac.show_difference_table(\"Count\", \"Search Result\", \"ga:hits\", [\"ga:eventLabel\", \"ga:eventAction\"], filters=\"ga:eventCategory==Search;ga:eventAction==Click\", **default_params)" ] }, { diff --git a/analytics/lungmap-analytics/analytics_lungmap.py b/analytics/lungmap-analytics/analytics_lungmap.py index bf945b2ad..863a66c5b 100644 --- a/analytics/lungmap-analytics/analytics_lungmap.py +++ b/analytics/lungmap-analytics/analytics_lungmap.py @@ -1,8 +1,11 @@ import analytics.charts as ac +import pandas as pd import json import re from html import escape as escape_html +users_over_time_file_name = "users_over_time_history.json" + def format_export_url_info(type, secondary_type, filter): result = escape_html(type.replace("-", " ")) if secondary_type: @@ -24,15 +27,27 @@ def adjust_table_index_key(val): return ('' + escape_html(val) + '', True) return val -def plot_users_over_time(**other_params): +def save_ga3_users_over_time_data(users_params, views_params, **other_params): + users_df = ac.get_data_df(["ga:30dayUsers"], ["ga:date"], df_processor=lambda df: df[::-1], **users_params, **other_params) + users_df.index = pd.to_datetime(users_df.index) + views_df = ac.get_data_df(["ga:pageviews"], ["ga:date"], df_processor=lambda df: df[::-1], **views_params, **other_params) + views_df.index = pd.to_datetime(views_df.index) + + df = ac.make_month_filter(["ga:30dayUsers"])(users_df.join(views_df)).rename(columns={"ga:30dayUsers": "Users", "ga:pageviews": "Total Pageviews"}) + df.to_json(users_over_time_file_name) + +def plot_users_over_time(load_json=True, use_api=True, **other_params): + old_data = pd.read_json(users_over_time_file_name) if load_json else None df = ac.show_plot_over_time( "Monthly Activity Overview", - ["Users Per Month", "Total Pageviews Per Month"], - ["ga:30dayUsers", "ga:pageviews"], - df_filter=ac.make_month_filter(["ga:30dayUsers"]), - df_processor=lambda df: df[::-1], + ["Users", "Total Pageviews"], + ["activeUsers", "screenPageViews"] if use_api else None, + dimensions="yearMonth", + sort_results=["yearMonth"], + df_processor=(lambda df: df.set_index(df.index + "01")[-2::-1]) if use_api else None, + pre_plot_df_processor=None if old_data is None else (lambda df: df.add(old_data, fill_value=0).astype("int")[::-1]) if use_api else (lambda df: old_data), format_table=False, **other_params - ).rename(columns={"Users Per Month": "Users", "Total Pageviews Per Month": "Total Pageviews"}) - return ac.format_change_over_time_table(df, change_dir=-1, **other_params) + ) + return ac.format_change_over_time_table(df, change_dir=-1, **other_params) diff --git a/analytics/lungmap-analytics/users_over_time_history.json b/analytics/lungmap-analytics/users_over_time_history.json new file mode 100644 index 000000000..1ef7de455 --- /dev/null +++ b/analytics/lungmap-analytics/users_over_time_history.json @@ -0,0 +1 @@ +{"Users":{"1685577600000":125,"1682899200000":186,"1680307200000":129,"1677628800000":165,"1675209600000":139,"1672531200000":100,"1669852800000":72,"1667260800000":98,"1664582400000":37,"1661990400000":18,"1659312000000":12,"1656633600000":10,"1654041600000":12,"1651363200000":10,"1648771200000":9,"1646092800000":8,"1643673600000":9,"1640995200000":10,"1638316800000":12,"1635724800000":14,"1633046400000":6,"1630454400000":1,"1627776000000":1,"1625097600000":3,"1622505600000":0,"1619827200000":0},"Total Pageviews":{"1685577600000":493,"1682899200000":1115,"1680307200000":679,"1677628800000":929,"1675209600000":725,"1672531200000":540,"1669852800000":444,"1667260800000":559,"1664582400000":230,"1661990400000":121,"1659312000000":38,"1656633600000":29,"1654041600000":39,"1651363200000":32,"1648771200000":120,"1646092800000":35,"1643673600000":24,"1640995200000":86,"1638316800000":36,"1635724800000":44,"1633046400000":26,"1630454400000":19,"1627776000000":3,"1625097600000":32,"1622505600000":0,"1619827200000":0}} \ No newline at end of file diff --git a/analytics/requirements.txt b/analytics/requirements.txt index 700c45b8b..7f3b333b8 100644 --- a/analytics/requirements.txt +++ b/analytics/requirements.txt @@ -1,5 +1,5 @@ alabaster==0.7.13 --e git+https://github.com/DataBiosphere/data-browser.git@5042e623bfe6ad8404be5f60e1ddb3c0e8e7d6b3#egg=analytics&subdirectory=analytics/hdgar-book/analytics_package +-e "git+https://github.com/DataBiosphere/data-browser.git@f674ffb9feae3fc9419e6c98d2ae34aacdc10704#egg=analytics&subdirectory=analytics/analytics_package" anyio==3.6.2 appdirs==1.4.4 appnope==0.1.3