pax_global_header00006660000000000000000000000064130017346400014510gustar00rootroot0000000000000052 comment=a4b8021a74b8e8cbcd65010988e2707b75a69b59 examples-2.1/000077500000000000000000000000001300173464000131705ustar00rootroot00000000000000examples-2.1/.gitattributes000066400000000000000000000001701300173464000160610ustar00rootroot00000000000000# Force all checkouts to convert to LF line endings * text eol=lf # Treat these file types as binary *.pcap binary examples-2.1/.gitignore000066400000000000000000000000401300173464000151520ustar00rootroot00000000000000.DS_Store node_modules .vagrant examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/000077500000000000000000000000001300173464000241355ustar00rootroot00000000000000examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/000077500000000000000000000000001300173464000303555ustar00rootroot00000000000000examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/README.md000066400000000000000000000024261300173464000316400ustar00rootroot00000000000000## Ingest Data using Python scripts If you want to ingest data into Elasticsearch starting with the raw data files from [www.cdc.gov](http://www.cdc.gov/brfss/annual_data/annual_2013.html), follow the instructions below: ###### 1. Download files in this folder
###### 2. Download 2013 BRFSS data from cdc.gov website
- [2013 BRFSS ASCII.zip](http://www.cdc.gov/brfss/annual_data/2013/files/LLCP2013ASC.ZIP) Unzip and copy the files into the folder containing the files downloaded in step 1. ###### 3. Run Python script to process and index data
Run `process_brfss_data.py` (requires Python 3). When the script is done running, you will have a `brfss` index in your Elasticsearch instance ``` python3 process_brfss_data.py ``` NOTE: - It might take ~ 30-60 minutes for this step (depending on your machine) - We have also included a iPython Notebook version of the script `process_brfss_data.ipynb` in case you prefer running in a cell-by-cell mode. ##### 4. Check if data is available in Elasticsearch Check to see if all the data is available in Elasticsearch. If all goes well, you should get a `count` response of `491,773` when you run the following command. ```shell curl -XGET localhost:9200/brfss/_count -d '{ "query": { "match_all": {} } }' ``` examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/State.csv000066400000000000000000000035631300173464000321610ustar00rootroot00000000000000ID,State,Latitude,Longitude 1,Alabama,32.7396323,-86.8434593 2,Alaska,63.346191,-152.8370679 4,Arizona,34.2099643,-111.602401 5,Arkansas,34.8955256,-92.4446262 6,California,37.148573,-119.5406515 8,Colorado,38.9935752,-105.5077737 9,Connecticut,41.5797842,-72.7466666 10,Delaware,38.9935501,-75.4473739 11,District of Columbia,38.9041485,-77.0170942 12,Florida,28.4574302,-82.4091478 13,Georgia,32.629384,-83.4232125 15,Hawaii,19.809767,-155.5061027 16,Idaho,44.3020948,-114.5956254 17,Illinois,40.1028754,-89.1526108 18,Indiana,39.9030256,-86.2839503 19,Iowa,42.0700243,-93.4933473 20,Kansas,38.4985464,-98.3834298 21,Kentucky,37.5336807,-85.2929841 22,Louisiana,30.8577705,-91.803273 23,Maine,45.3906022,-68.6574869 24,Maryland,38.9466584,-76.6744939 25,Massachusetts,42.1565196,-71.4895915 26,Michigan,44.8410835,-85.6593197 27,Minnesota,46.3161343,-94.1994801 28,Mississippi,32.6864655,-89.6561493 29,Missouri,38.35075,-92.4567826 30,Montana,47.0511771,-109.6348174 31,Nebraska,41.5438105,-99.8123253 32,Nevada,39.3310928,-116.6151469 33,New Hampshire,43.6708595,-71.5811278 34,New Jersey,40.1072744,-74.6652012 35,New Mexico,34.4391265,-106.1261511 36,New York,42.9133974,-75.5962723 37,North Carolina,35.53971,-79.1308636 38,North Dakota,47.4569538,-100.4619304 39,Ohio,40.4149297,-82.7119975 40,Oklahoma,35.5894185,-97.4868683 41,Oregon,43.9715225,-120.6226269 42,Pennsylvania,40.9042486,-77.8280624 44,Rhode Island,41.5978358,-71.5252895 45,South Carolina,33.8741769,-80.8542699 46,South Dakota,44.4467957,-100.2381762 47,Tennessee,35.8585639,-86.3493573 48,Texas,31.4347032,-99.2818238 49,Utah,39.3349735,-111.6563633 50,Vermont,44.0605475,-72.673354 51,Virginia,37.5222512,-78.6681938 53,Washington,47.4162296,-120.5996231 54,West Virginia,38.6472854,-80.6183274 55,Wisconsin,44.628484,-89.7119299 56,Wyoming,42.9918024,-107.5419255 72,Puerto Rico,18.217648,-66.4107992 66,Guam,13.4383,144.7729285examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/activity.csv000066400000000000000000000040421300173464000327260ustar00rootroot00000000000000ID,Activity 1,"Active Gaming Devices (Wii Fit, Dance, Dance revolution)" 2,"Aerobics video or class" 3,"Backpacking" 4,"Badminton" 5,"Basketball " 6,"Bicycling machine exercise" 7,"Bicycling" 8,"Boating (Canoeing, rowing, kayaking, sailing for pleasure or camping)" 9,"Bowling " 10,"Boxing " 11,"Calisthenics" 12,"Canoeing/rowing in competition " 13,"Carpentry" 14,"Dancing-ballet, ballroom, Latin, hip hop, etc " 15,"Elliptical/EFX machine exercise " 16,"Fishing from river bank or boat " 17,"Frisbee " 18,"Gardening (spading, weeding, digging, filling)" 19,"Golf (with motorized cart) " 20,"Golf (without motorized cart) " 21,"Handball " 22,"Hiking cross-country " 23,"Hockey " 24,"Horseback riding " 25,"Hunting large game deer, elk " 26,"Hunting small game quail " 27,"Inline Skating " 28,"Jogging " 29,"Lacrosse " 30,"Mountain climbing" 31,"Mowing lawn " 32,"Paddleball " 33,"Painting/papering house " 34,"Pilates " 35,"Racquetball " 36,"Raking lawn " 37,"Running " 38,"Rock Climbing " 39,"Rope skipping " 40,"Rowing machine exercise " 41,"Rugby " 42,"Scuba diving" 43,"Skateboarding " 44,"Skating ice or roller " 45,"Sledding, tobogganing " 46,"Snorkeling " 47,"Snow blowing" 48,"Snow shoveling by hand " 49,"Snow skiing " 50,"Snowshoeing " 51,"Soccer " 52,"Softball/Baseball" 53,"Squash " 54,"Stair climbing/Stair master " 55,"Stream fishing in waders " 56,"Surfing " 57,"Swimming " 58,"Swimming in laps " 59,"Table tennis" 60,"Tai Chi " 61,"Tennis " 62,"Touch football " 63,"Volleyball " 64,"Walking " 66,"Waterskiing " 67,"Weight lifting " 68,"Wrestling" 69,"Yoga" 71,"Childcare" 72,"Farm/Ranch Work (caring for livestock, stacking hay, etc.) " 73,"Household Activities (vacuuming, dusting, home repair, etc.) " 74,"Karate/Martial Arts " 75,"Upper Body Cycle (wheelchair sports, ergometer, etc.) " 76,"Yard work (cutting/gathering wood, trimming hedges, etc.) " 77,"Dont know/Not Sure " 88,"No other activity" 98,"Other______ " 99,"Refused "examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/brfss_mapping.json000066400000000000000000000225521300173464000341100ustar00rootroot00000000000000{ "respondent": { "properties": { "Nutrition_Green_times_per_day": { "type": "double" }, "Number_of_Children": { "index": "not_analyzed", "type": "string" }, "Coordinates": { "type": "geo_point" }, "Nutrition_Fruit_times_per_day": { "type": "double" }, "College_Housing": { "index": "not_analyzed", "type": "string" }, "Height": { "type": "string" }, "Interview_Month": { "type": "long" }, "Difficulty_walking__climbing": { "index": "not_analyzed", "type": "string" }, "Activity_1_intensity": { "index": "not_analyzed", "type": "string" }, "BMI_Obesity": { "index": "not_analyzed", "type": "string" }, "Nutrition_Sugar_fruit_drinks": { "index": "not_analyzed", "type": "string" }, "Total_Vigorous_Activity_weekly_duration_minutes": { "type": "double" }, "Days_of_Poor_Health_past_30_days": { "type": "double" }, "Income": { "index": "not_analyzed", "type": "string" }, "Difficulty_decision_making": { "index": "not_analyzed", "type": "string" }, "Physical_activity_2": { "index": "not_analyzed", "type": "string" }, "Interview_Day": { "type": "long" }, "Difficulty_running_errands": { "index": "not_analyzed", "type": "string" }, "Activity_2_weekly_freq": { "type": "double" }, "Physical_activity_1": { "index": "not_analyzed", "type": "string" }, "Activity_2_MET": { "type": "double" }, "Physical_activity_2__frequency": { "index": "not_analyzed", "type": "string" }, "Activity_1_MET": { "type": "double" }, "Private_Residence": { "index": "not_analyzed", "type": "string" }, "Nutrition_Sugar_Drinks": { "index": "not_analyzed", "type": "string" }, "Activity_1_weekly_duration_minutes": { "type": "double" }, "File_Month": { "type": "long" }, "Interview_Year": { "type": "double" }, "Weight": { "type": "string" }, "Demo_edu_level": { "index": "not_analyzed", "type": "string" }, "Alcohol_Drinks_per_occasion": { "type": "long" }, "Limited_physical_activity": { "index": "not_analyzed", "type": "string" }, "Served_on_Active_Duty": { "index": "not_analyzed", "type": "string" }, "Interview_Date": { "format": "dateOptionalTime", "type": "date" }, "Nutrition_Beans_consumption": { "index": "not_analyzed", "type": "string" }, "General_Health": { "index": "not_analyzed", "type": "string" }, "Functional_capacity": { "type": "double" }, "Vigorous_Activity_2_weekly_duration_minutes": { "type": "double" }, "Nutrition_Fruit_juice_times_per_day": { "type": "double" }, "Alcohol_Binge_days_per_month": { "type": "double" }, "Longitude": { "type": "double" }, "Alcohol_Frequency": { "index": "not_analyzed", "type": "string" }, "Smoke_tried_to_quit": { "index": "not_analyzed", "type": "string" }, "Weight_kg": { "type": "double" }, "Had_Stroke": { "index": "not_analyzed", "type": "string" }, "Latitude": { "type": "double" }, "Muscle_strengthening_guideline": { "index": "not_analyzed", "type": "string" }, "State": { "index": "not_analyzed", "type": "string" }, "Max_O2_consumption": { "type": "double" }, "Total_Activity_weekly_duration_minutes": { "type": "double" }, "Activity_index": { "index": "not_analyzed", "type": "string" }, "Alcohol_Drinks_per_month": { "type": "double" }, "Physical_Health": { "type": "double" }, "Exercise": { "index": "not_analyzed", "type": "string" }, "Alcohol_Max_drinks": { "type": "double" }, "Alcohol_Binge_drinker": { "index": "not_analyzed", "type": "string" }, "Gender": { "index": "not_analyzed", "type": "string" }, "Last_Cholesterol_Check": { "index": "not_analyzed", "type": "string" }, "Is_Adult": { "index": "not_analyzed", "type": "string" }, "BMI": { "type": "double" }, "Mental_Health": { "type": "double" }, "Smoke_GT_100_cigarettes": { "index": "not_analyzed", "type": "string" }, "Nutrition_Orange_veg_times_per_day": { "type": "double" }, "Nutrition_Beans_times_per_day": { "type": "double" }, "Had_Diabetes": { "index": "not_analyzed", "type": "string" }, "Demo_income_group": { "index": "not_analyzed", "type": "string" }, "Physical_activity_1__duration": { "type": "double" }, "Strength_Activity_Weekly_frequency": { "type": "double" }, "Activity_yes__no": { "index": "not_analyzed", "type": "string" }, "Smoke_current_status": { "index": "not_analyzed", "type": "string" }, "Had_Heart_Attack": { "index": "not_analyzed", "type": "string" }, "Alcohol_any_drink_in_last_month": { "index": "not_analyzed", "type": "string" }, "SEQN": { "index": "not_analyzed", "type": "string" }, "BP_Meds": { "index": "not_analyzed", "type": "string" }, "Smoke_time_since_last_smoke": { "index": "not_analyzed", "type": "string" }, "Alcohol_Drinks_per_day": { "type": "long" }, "Nutrition_Fruit_juice_consumption": { "index": "not_analyzed", "type": "string" }, "Activity_2_intensity": { "index": "not_analyzed", "type": "string" }, "Educatio_Level": { "index": "not_analyzed", "type": "string" }, "BMI_Category": { "index": "not_analyzed", "type": "string" }, "Physical_activity_2__duration": { "type": "double" }, "Uses_special_equipment": { "index": "not_analyzed", "type": "string" }, "Activity_1_duration_minutes": { "type": "double" }, "Age_category": { "index": "not_analyzed", "type": "string" }, "Nutrition_Fruit_consumption": { "type": "double" }, "Nutrition_Orange_veg_consumption": { "index": "not_analyzed", "type": "string" }, "Activity_2_weekly_duration_minutes": { "type": "double" }, "Height_inches": { "type": "double" }, "Employment": { "index": "not_analyzed", "type": "string" }, "Nutrition_Vegetable_per_day": { "type": "double" }, "Nutrition_Vegetable_times_per_day": { "type": "double" }, "Aerobic_and_strengthening_guideline": { "index": "not_analyzed", "type": "string" }, "Had_Heart_Disease": { "index": "not_analyzed", "type": "string" }, "High_BP": { "index": "not_analyzed", "type": "string" }, "Difficulty_dressing__bathing": { "index": "not_analyzed", "type": "string" }, "Physical_activity_1__frequency": { "index": "not_analyzed", "type": "string" }, "Sleep_per_day_hours": { "type": "long" }, "Vigorous_Activity_1_weekly_duration_minutes": { "type": "double" }, "Nutrition_Greens_consumption": { "index": "not_analyzed", "type": "string" }, "High_Cholesterol": { "index": "not_analyzed", "type": "string" }, "Activity_2_duration_minutes": { "type": "double" }, "Activity_1_weekly_freq": { "type": "double" }, "Smoke_smoker_type": { "index": "not_analyzed", "type": "string" }, "Activity_category": { "index": "not_analyzed", "type": "string" }, "Marital": { "index": "not_analyzed", "type": "string" }, "Var": { "index": "not_analyzed", "type": "string" }, "Nutrition_Fruit_per_day": { "type": "double" }, "Height_m": { "type": "double" }, "Primary_Sampling_Unit": { "index": "not_analyzed", "type": "string" }, "Nutrition_Other_veg_consumption": { "index": "not_analyzed", "type": "string" }, "Cholesterol_Awareness": { "index": "not_analyzed", "type": "string" }, "Smoke_current_smoker": { "index": "not_analyzed", "type": "string" } } } } process_brfss_data.ipynb000066400000000000000000000454711300173464000352220ustar00rootroot00000000000000examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xport\n", "import pandas as pd\n", "import csv\n", "import numpy as np\n", "import re\n", "import elasticsearch\n", "import json\n", "import pprint as pprint\n", "\n", "es = elasticsearch.Elasticsearch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import data and read into a dataframe\n", "f = open('./LLCP2013.ASC', encoding='iso-8859-1')\n", "cdc = f.read().splitlines()\n", "t = pd.DataFrame({'Var': cdc})\n", "\n", "# Data references: \n", "# - Data: http://www.cdc.gov/brfss/annual_data/2013/files/LLCP2013ASC.ZIP\n", "# - Data Codebook: http://www.cdc.gov/brfss/annual_data/2013/pdf/codebook13_llcp.pdf\n", "# - Variable layout: http://www.cdc.gov/brfss/annual_data/2013/llcp_varlayout_13_onecolumn.html\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Each row in BRFSS data file correspondents to a respondent. The response to 321 questions is coded in\n", "# a single 2365 character long numeric string. The variable_list.csv file contains a maps the column number\n", "# to fields. For example, column 18-19 is a 2-digit code for the interview month\n", "var = pd.read_csv('./variable_list.csv')\n", "\n", "# We will only be looking at a subset of the columns in this analysis - these columns have been coded with a \n", "# Keep = Yes value in the variable list. \n", "varKeep = var[var['Keep'] == 'Yes']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Decode the numeric response into feature.\n", "for i, row in varKeep.iterrows():\n", " st = row['Starting Column'] - 1\n", " en = st + row['Field Length']\n", " #print(st, en)\n", " t[row['Variable Name']] = t['Var'].map(lambda x: x[st:en])\n", " #print(row['Variable Name'])\n", " \n", "# Create deep copy of variable\n", "t1 = t.copy(deep=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime\n", "def str_to_iso(text):\n", " if text != '':\n", " for fmt in (['%m%d%Y','%d%m%Y']):\n", " try:\n", " return datetime.isoformat(datetime.strptime(text, fmt))\n", " except ValueError:\n", " if text == '02292014':\n", " return datetime.isoformat(datetime.strptime('02282014', fmt))\n", " elif text == '09312014':\n", " return datetime.isoformat(datetime.strptime('09302014', fmt))\n", " print(text)\n", " pass \n", " raise ValueError('Changing date')\n", " else:\n", " \n", " return None\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# id to state map\n", "st = pd.read_csv('./State.csv')\n", "\n", "# Convert state value from string to int\n", "t1['_STATE'] = t1['_STATE'].map(lambda x: int(x))\n", "\n", "# Map numeric value of stateto state name\n", "st1 = st[['ID', 'State']].set_index('ID').to_dict('dict')['State']\n", "t1['_STATE'] = t1['_STATE'].replace(st1)\n", "\n", "# Grab avg coordinates for state\n", "lat = st.set_index('State')[['Latitude']].to_dict()['Latitude']\n", "lon = st.set_index('State')[['Longitude']].to_dict()['Longitude']\n", "t1['Latitude'] = t1['_STATE'].replace(lat)\n", "t1['Longitude'] = t1['_STATE'].replace(lon)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Convert interview date values into numeric\n", "t1['FMONTH'] = t1['FMONTH'].map(lambda x: int(x))\n", "t1['IMONTH'] = t1['IMONTH'].map(lambda x: int(x))\n", "t1['IDAY'] = t1['IDAY'].map(lambda x: int(x))\n", "t1['IDATE'] = t1['IDATE'].map(lambda x: str_to_iso(x))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Alcohol consumption\n", "\n", "t1['AVEDRNK2'] = t1['AVEDRNK2'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks per occasion\n", "t1['DRNK3GE5'] = t1['DRNK3GE5'].map(lambda x: int(x) if not str.isspace(x) else None) # binge days\n", "t1['MAXDRNKS'] = t1['MAXDRNKS'].map(lambda x: int(x) if not str.isspace(x) else None) # max drinks per occasion in last 30 days\n", "t1['_DRNKDY4'] = t1['_DRNKDY4'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks/day\n", "t1['_DRNKMO4'] = t1['_DRNKMO4'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks/month\n", "t1['DROCDY3_'] = t1['DROCDY3_'].map(lambda x: int(x) if not str.isspace(x) else None) # drink occasions in last 30 days\n", "\n", "choice = {'1':'No', '2':'Yes', '9': 'Missing'}\n", "t1['_RFBING5'] = t1['_RFBING5'].replace(choice) # binge drinker?\n", "\n", "choice = {'1':'Yes', '2':'No', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['DRNKANY5'] = t1['DRNKANY5'].replace(choice) # any drinks in last 30 days?\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Activity & exercise\n", "# Refer to the codebook ( http://www.cdc.gov/brfss/annual_data/2013/pdf/codebook13_llcp.pdf) for variable meaning\n", "\n", "t1['METVL11_'] = t1['METVL11_'].map(lambda x: int(x) if not str.isspace(x) else None)/10 \n", "t1['METVL21_'] = t1['METVL21_'].map(lambda x: int(x) if not str.isspace(x) else None)/10 \n", "t1['MAXVO2_'] = t1['MAXVO2_'].map(lambda x: int(x) if not str.isspace(x) else None) / 100 \n", "t1['FC60_'] = t1['FC60_'].map(lambda x: int(x) if not str.isspace(x) else None) / 100 \n", "t1['PADUR1_'] = t1['PADUR1_'].map(lambda x: int(x) if not str.isspace(x) else None) \n", "t1['PADUR2_'] = t1['PADUR2_'].map(lambda x: int(x) if not str.isspace(x) else None) \n", "t1['PAFREQ1_'] = t1['PAFREQ1_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000\n", "t1['PAFREQ2_'] = t1['PAFREQ2_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000\n", "t1['STRFREQ_'] = t1['STRFREQ_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000\n", "t1['PAMIN11_'] = t1['PAMIN11_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['PAMIN21_'] = t1['PAMIN21_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['PA1MIN_'] = t1['PA1MIN_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['PAVIG11_'] = t1['PAVIG11_'].map(lambda x: int(x) if not str.isspace(x) else None) \n", "t1['PAVIG21_'] = t1['PAVIG21_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['PA1VIGM_'] = t1['PA1VIGM_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['EXERHMM1'] = t1['EXERHMM1'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['EXERHMM2'] = t1['EXERHMM2'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "\n", "#t1['EXEROFT1'] = t1['EXEROFT1'].map(lambda x: exerFcn(x))\n", "#t1['EXEROFT2'] = t1['EXEROFT2'].map(lambda x: exerFcn(x))\n", "\n", "#t1['STRENGTH'] = t1['STRENGTH'].map(lambda x: exerFcn(x))\n", "\n", "choice = {'1':'Yes', '2':'No', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['EXERANY2'] = t1['EXERANY2'].replace(choice)\n", "\n", "choice={'1': 'Had exercise in last 30 days', \n", " '2': 'No exercise in last 30 days', \n", " '9': 'Don’t know/Not sure/Missing'}\n", "\n", "t1['_TOTINDA'] = t1['_TOTINDA'].replace(choice)\n", "\n", "\n", "choice = { '0' : 'Not Moderate / Vigorous or No Activity', \n", " '1' : 'Moderate',\n", " '2' : 'Vigorous'}\n", "\n", "t1['ACTIN11_'] = t1['ACTIN11_'].replace(choice)\n", "t1['ACTIN21_'] = t1['ACTIN21_'].replace(choice)\n", "\n", "\n", "choice = {'1' : 'Highly Active',\n", " '2' : 'Active', \n", " '3' : 'Insufficiently Active', \n", " '4' : 'Inactive', \n", " '9' : 'Don’t know' }\n", "\n", "t1['_PACAT1'] = t1['_PACAT1'].replace(choice)\n", "\n", "choice = {'1' : 'Met aerobic recommendations', \n", " '2' : 'Did not meet aerobic recommendations', \n", " '9' : 'Don’t know' }\n", "\n", "t1['_PAINDX1'] = t1['_PAINDX1'].replace(choice)\n", "\n", "choice = {'1' : 'Meet muscle strengthening recommendations',\n", " '2' : 'Did not meet muscle strengthening recommendations',\n", " '9' : 'Missing'}\n", "\n", "t1['_PASTRNG'] = t1['_PASTRNG'].replace(choice)\n", "\n", "choice = {'1' : 'Met both guidelines', \n", " '2' : 'Met aerobic guidelines only', \n", " '3' : 'Met strengthening guidelines only', \n", " '4' : 'Did not meet either guideline', \n", " '9' : 'Missing' }\n", "\n", "t1['_PAREC1'] = t1['_PAREC1'].replace(choice)\n", "\n", "\n", "choice = {'1' : 'Met both guidelines', \n", " '2' : 'Did not meet both guideline', \n", " '9' : 'Missing' }\n", "\n", "#t1['_PASTAE1'] = t1['_PASTAE1'].replace(choice)\n", "\n", "# Map activity code to activity names\n", "act = pd.read_csv('./activity.csv', encoding='iso-8859-1')\n", "act['Activity'] = act['Activity'].map(lambda x: re.sub(r'\\s*$','', x))\n", "\n", "t1['EXRACT11'] = t1['EXRACT11'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['EXRACT11'] = t1['EXRACT11'].replace(act.set_index('ID').to_dict()['Activity'])\n", "\n", "t1['EXRACT21'] = t1['EXRACT21'].map(lambda x: int(x) if not str.isspace(x) else '')\n", "t1['EXRACT21'] = t1['EXRACT21'].replace(act.set_index('ID').to_dict()['Activity'])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Height, Weight, Age\n", "\n", "# BMI\n", "t1['_BMI5'] = t1['_BMI5'].map(lambda x: int(x) if not str.isspace(x) else None)/100\n", "\n", "choice={'1': 'Underweight', \n", " '2': 'Normal weight', \n", " '3': 'Overweight',\n", " '4':'Obese'}\n", "\n", "t1['_BMI5CAT'] = t1['_BMI5CAT'].replace(choice)\n", "\n", "# Height & Weight\n", "t1['WTKG3'] = t1['WTKG3'].map(lambda x: int(x) if not str.isspace(x) else None)/100\n", "t1['HTM4'] = t1['HTM4'].map(lambda x: int(x) if not str.isspace(x) else None)/100\n", "t1['HTIN4'] = t1['HTIN4'].map(lambda x: int(x) if not str.isspace(x) else None)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Nutrition\n", "## NOTE: Values include two implied decimal places \n", "# Vegetable & Fruit intake per day\n", "t1['_FRUTSUM'] = t1['_FRUTSUM'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['_VEGESUM'] = t1['_VEGESUM'].map(lambda x: int(x) if not str.isspace(x) else None) \n", "\n", "# Food intake - times per day\n", "t1['FRUTDA1_'] = t1['FRUTDA1_'].map(lambda x: int(x) if not str.isspace(x) else None) \n", "t1['VEGEDA1_'] = t1['VEGEDA1_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['GRENDAY_'] = t1['GRENDAY_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['ORNGDAY_'] = t1['ORNGDAY_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['FTJUDA1_'] = t1['FTJUDA1_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['BEANDAY_'] = t1['BEANDAY_'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "\n", "# Salt intake and advice\n", "choice = {'1':'Yes', '2':'No', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['WTCHSALT'] = t1['WTCHSALT'].replace(choice)\n", "t1['DRADVISE'] = t1['DRADVISE'].replace(choice)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Demographics\n", "choice = {'1' : 'Did not graduate High School', \n", " '2' : 'Graduated High School',\n", " '3' : 'Attended College or Technical School', \n", " '4' : 'Graduated from College or Technical School', \n", " '9' : 'Don’t know/Not sure/Missing'}\n", "\n", "t1['_EDUCAG'] = t1['_EDUCAG'].replace(choice)\n", "\n", "choice = {'1' : 'Male', \n", " '2' : 'Female'}\n", "t1['SEX'] = t1['SEX'].replace(choice)\n", "\n", "choice = {'1' : '< $15000', \n", " '2' : '$15,000 - $25,000',\n", " '3' : '$25,000 - $35,000', \n", " '4' : '$35,000 - $50,000',\n", " '5' : '> $50,000', \n", " '9' : 'Don’t know/Not sure/Missing'}\n", "\n", "t1['_INCOMG'] = t1['_INCOMG'].replace(choice)\n", "\n", "choice = {'1':'Employed for wages', '2':'Self-employed', '3': 'Unemployed < 1 year', '4': 'Unemployed > 1 year', '5': 'Homemaker', '6' : 'Student', '7': 'Retired' , '8': 'Unable to work', '9': 'Refused'}\n", "t1['EMPLOY1'] = t1['EMPLOY1'].replace(choice)\n", "\n", "choice = {'1':'< Kindergarden', '2':'Elementary', '3': 'Some high-school', '4': 'High-school graduate', '5': 'College / tech school', '6' : 'College grade', '9': 'Refused'}\n", "t1['EDUCA'] = t1['EDUCA'].replace(choice)\n", "\n", "choice = {'1':'Married', '2':'Divored', '4': 'Separated', '3': 'Separated', '5': 'Never Married', '6':'Unmarried couple' , '9': 'Refused'}\n", "t1['MARITAL'] = t1['MARITAL'].replace(choice)\n", "\n", "choice = {'1':'Yes', '2':'No', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['VETERAN3'] = t1['VETERAN3'].replace(choice)\n", "\n", "# Age\n", "choice = {\n", "'01' : 'Age 18 to 24',\n", " '02' : 'Age 25 to 29', \n", " '03' : 'Age 30 to 34', \n", " '04' : 'Age 35 to 39', \n", " '05': 'Age 40 to 44', \n", " '06' : 'Age 45 to 49', \n", " '07': 'Age 50 to 54', \n", " '08': 'Age 55 to 59', \n", " '09': 'Age 60 to 64', \n", " '10': 'Age 65 to 69', \n", " '11': 'Age 70 to 74', \n", " '12': 'Age 75 to 79' , \n", " '13': 'Age 80 or older', \n", " '14': 'Don’t know/Refused/Missing'}\n", "\n", "\n", "t1['_AGEG5YR'] = t1['_AGEG5YR'].replace(choice)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# General health\n", "choice = {'5':'Poor', '3':'Good', '1':'Excellent', '2':'Very Good', '4':'Fair', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['GENHLTH'] = t1['GENHLTH'].replace(choice)\n", "\n", "choice = {'1':'Yes', '2':'No', '7':'Don\\'t know' , '9': 'Refused'}\n", "t1['QLACTLM2'] = t1['QLACTLM2'].replace(choice)\n", "t1['USEEQUIP'] = t1['USEEQUIP'].replace(choice)\n", "t1['DECIDE'] = t1['DECIDE'].replace(choice)\n", "t1['DIFFWALK'] = t1['DIFFWALK'].replace(choice)\n", "t1['DIFFDRES'] = t1['DIFFDRES'].replace(choice)\n", "t1['DIFFALON'] = t1['DIFFALON'].replace(choice)\n", "\n", "\n", "t1['MENTHLTH'] = t1['MENTHLTH'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['POORHLTH'] = t1['POORHLTH'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['SLEPTIM1'] = t1['SLEPTIM1'].map(lambda x: int(x) if not str.isspace(x) else None)\n", "t1['PHYSHLTH'] = t1['PHYSHLTH'].map(lambda x: int(x) if not str.isspace(x) else None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Map variable names to more descriptive names\n", "varDict = var[['Variable Name', 'DESC']].to_dict('split')\n", "varDict = dict(varDict['data'])\n", "t1.rename(columns=varDict, inplace=True)\n", "\n", "# Replace space / special characters with underscore\n", "t1.rename(columns=lambda x: re.sub(' ', '_', x), inplace=True)\n", "t1.rename(columns=lambda x: re.sub(r'\\(|\\-|\\/|\\|\\>|\\)|\\#', '', x), inplace=True)\n", "t1.rename(columns=lambda x: re.sub(r'\\>', 'GT', x), inplace=True)\n", "\n", "# Delete original row\n", "del(t1['Var'])\n", "\n", "t1.fillna('', inplace=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### Create and configure Elasticsearch index\n", "\n", "# Name of index and document type\n", "index_name = 'brfss';\n", "doc_name = 'respondent'\n", "\n", "# Delete donorschoose index if one does exist\n", "if es.indices.exists(index_name):\n", " es.indices.delete(index_name)\n", "\n", "# Create donorschoose index\n", "es.indices.create(index_name)\n", "\n", "# Add mapping\n", "with open('brfss_mapping.json') as json_mapping:\n", " d = json.load(json_mapping)\n", "\n", "es.indices.put_mapping(index=index_name, doc_type=doc_name, body=d)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "### Index Data into Elasticsearch\n", "\n", "for subj_id, subject in t1.iterrows():\n", " if subj_id % 1000 == 0:\n", " print(subj_id)\n", " thisResp = subject.to_dict()\n", " thisResp['Coordinates'] = [thisResp['Longitude'], thisResp['Latitude']]\n", " thisDoc = json.dumps(thisResp);\n", " #pprint.pprint(thisDoc)\n", "\n", " # write to elasticsearch\n", " es.index(index=index_name, doc_type=doc_name, id=subj_id, body=thisDoc)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 } process_brfss_data.py000066400000000000000000000321241300173464000345200ustar00rootroot00000000000000examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python# In[19]: import xport import pandas as pd import csv import numpy as np import re import elasticsearch import json import pprint as pprint es = elasticsearch.Elasticsearch() # Import data and read into a dataframe f = open('./LLCP2013.ASC', encoding='iso-8859-1') cdc = f.read().splitlines() t = pd.DataFrame({'Var': cdc}) # Data references: # - Data: http://www.cdc.gov/brfss/annual_data/2013/files/LLCP2013ASC.ZIP # - Data Codebook: http://www.cdc.gov/brfss/annual_data/2013/pdf/codebook13_llcp.pdf # - Variable layout: http://www.cdc.gov/brfss/annual_data/2013/llcp_varlayout_13_onecolumn.html # Each row in BRFSS data file correspondents to a respondent. The response to 321 questions is coded in # a single 2365 character long numeric string. The variable_list.csv file contains a maps the column number # to fields. For example, column 18-19 is a 2-digit code for the interview month var = pd.read_csv('./variable_list.csv') # We will only be looking at a subset of the columns in this analysis - these columns have been coded with a # Keep = Yes value in the variable list. varKeep = var[var['Keep'] == 'Yes'] # Decode the numeric response into feature. for i, row in varKeep.iterrows(): st = row['Starting Column'] - 1 en = st + row['Field Length'] #print(st, en) t[row['Variable Name']] = t['Var'].map(lambda x: x[st:en]) #print(row['Variable Name']) # Create deep copy of variable t1 = t.copy(deep=True) # Function to convert datetime from datetime import datetime def str_to_iso(text): if text != '': for fmt in (['%m%d%Y','%d%m%Y']): try: return datetime.isoformat(datetime.strptime(text, fmt)) except ValueError: if text == '02292014': return datetime.isoformat(datetime.strptime('02282014', fmt)) elif text == '09312014': return datetime.isoformat(datetime.strptime('09302014', fmt)) print(text) pass raise ValueError('Changing date') else: return None # id to state map st = pd.read_csv('./State.csv') # Convert state value from string to int t1['_STATE'] = t1['_STATE'].map(lambda x: int(x)) # Map numeric value of stateto state name st1 = st[['ID', 'State']].set_index('ID').to_dict('dict')['State'] t1['_STATE'] = t1['_STATE'].replace(st1) # Grab avg coordinates for state lat = st.set_index('State')[['Latitude']].to_dict()['Latitude'] lon = st.set_index('State')[['Longitude']].to_dict()['Longitude'] t1['Latitude'] = t1['_STATE'].replace(lat) t1['Longitude'] = t1['_STATE'].replace(lon) # Convert interview date values into numeric t1['FMONTH'] = t1['FMONTH'].map(lambda x: int(x)) t1['IMONTH'] = t1['IMONTH'].map(lambda x: int(x)) t1['IDAY'] = t1['IDAY'].map(lambda x: int(x)) t1['IDATE'] = t1['IDATE'].map(lambda x: str_to_iso(x)) # Alcohol consumption t1['AVEDRNK2'] = t1['AVEDRNK2'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks per occasion t1['DRNK3GE5'] = t1['DRNK3GE5'].map(lambda x: int(x) if not str.isspace(x) else None) # binge days t1['MAXDRNKS'] = t1['MAXDRNKS'].map(lambda x: int(x) if not str.isspace(x) else None) # max drinks per occasion in last 30 days t1['_DRNKDY4'] = t1['_DRNKDY4'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks/day t1['_DRNKMO4'] = t1['_DRNKMO4'].map(lambda x: int(x) if not str.isspace(x) else None) # drinks/month t1['DROCDY3_'] = t1['DROCDY3_'].map(lambda x: int(x) if not str.isspace(x) else None) # drink occasions in last 30 days choice = {'1':'No', '2':'Yes', '9': 'Missing'} t1['_RFBING5'] = t1['_RFBING5'].replace(choice) # binge drinker? choice = {'1':'Yes', '2':'No', '7':'Don\'t know' , '9': 'Refused'} t1['DRNKANY5'] = t1['DRNKANY5'].replace(choice) # any drinks in last 30 days? # Activity & exercise # Refer to the codebook ( http://www.cdc.gov/brfss/annual_data/2013/pdf/codebook13_llcp.pdf) for variable meaning t1['METVL11_'] = t1['METVL11_'].map(lambda x: int(x) if not str.isspace(x) else None)/10 t1['METVL21_'] = t1['METVL21_'].map(lambda x: int(x) if not str.isspace(x) else None)/10 t1['MAXVO2_'] = t1['MAXVO2_'].map(lambda x: int(x) if not str.isspace(x) else None) / 100 t1['FC60_'] = t1['FC60_'].map(lambda x: int(x) if not str.isspace(x) else None) / 100 t1['PADUR1_'] = t1['PADUR1_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PADUR2_'] = t1['PADUR2_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PAFREQ1_'] = t1['PAFREQ1_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000 t1['PAFREQ2_'] = t1['PAFREQ2_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000 t1['STRFREQ_'] = t1['STRFREQ_'].map(lambda x: int(x) if not str.isspace(x) else None) / 1000 t1['PAMIN11_'] = t1['PAMIN11_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PAMIN21_'] = t1['PAMIN21_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PA1MIN_'] = t1['PA1MIN_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PAVIG11_'] = t1['PAVIG11_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PAVIG21_'] = t1['PAVIG21_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PA1VIGM_'] = t1['PA1VIGM_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['EXERHMM1'] = t1['EXERHMM1'].map(lambda x: int(x) if not str.isspace(x) else None) t1['EXERHMM2'] = t1['EXERHMM2'].map(lambda x: int(x) if not str.isspace(x) else None) #t1['EXEROFT1'] = t1['EXEROFT1'].map(lambda x: exerFcn(x)) #t1['EXEROFT2'] = t1['EXEROFT2'].map(lambda x: exerFcn(x)) #t1['STRENGTH'] = t1['STRENGTH'].map(lambda x: exerFcn(x)) choice = {'1':'Yes', '2':'No', '7':'Don\'t know' , '9': 'Refused'} t1['EXERANY2'] = t1['EXERANY2'].replace(choice) choice={'1': 'Had exercise in last 30 days', '2': 'No exercise in last 30 days', '9': 'Don’t know/Not sure/Missing'} t1['_TOTINDA'] = t1['_TOTINDA'].replace(choice) choice = { '0' : 'Not Moderate / Vigorous or No Activity', '1' : 'Moderate', '2' : 'Vigorous'} t1['ACTIN11_'] = t1['ACTIN11_'].replace(choice) t1['ACTIN21_'] = t1['ACTIN21_'].replace(choice) choice = {'1' : 'Highly Active', '2' : 'Active', '3' : 'Insufficiently Active', '4' : 'Inactive', '9' : 'Don’t know' } t1['_PACAT1'] = t1['_PACAT1'].replace(choice) choice = {'1' : 'Met aerobic recommendations', '2' : 'Did not meet aerobic recommendations', '9' : 'Don’t know' } t1['_PAINDX1'] = t1['_PAINDX1'].replace(choice) choice = {'1' : 'Meet muscle strengthening recommendations', '2' : 'Did not meet muscle strengthening recommendations', '9' : 'Missing'} t1['_PASTRNG'] = t1['_PASTRNG'].replace(choice) choice = {'1' : 'Met both guidelines', '2' : 'Met aerobic guidelines only', '3' : 'Met strengthening guidelines only', '4' : 'Did not meet either guideline', '9' : 'Missing' } t1['_PAREC1'] = t1['_PAREC1'].replace(choice) choice = {'1' : 'Met both guidelines', '2' : 'Did not meet both guideline', '9' : 'Missing' } #t1['_PASTAE1'] = t1['_PASTAE1'].replace(choice) # Map activity code to activity names act = pd.read_csv('./activity.csv', encoding='iso-8859-1') act['Activity'] = act['Activity'].map(lambda x: re.sub(r'\s*$','', x)) t1['EXRACT11'] = t1['EXRACT11'].map(lambda x: int(x) if not str.isspace(x) else None) t1['EXRACT11'] = t1['EXRACT11'].replace(act.set_index('ID').to_dict()['Activity']) t1['EXRACT21'] = t1['EXRACT21'].map(lambda x: int(x) if not str.isspace(x) else '') t1['EXRACT21'] = t1['EXRACT21'].replace(act.set_index('ID').to_dict()['Activity']) # Height, Weight, Age, BMI t1['_BMI5'] = t1['_BMI5'].map(lambda x: int(x) if not str.isspace(x) else None)/100 choice={'1': 'Underweight', '2': 'Normal weight', '3': 'Overweight', '4':'Obese'} t1['_BMI5CAT'] = t1['_BMI5CAT'].replace(choice) # Height & Weight t1['WTKG3'] = t1['WTKG3'].map(lambda x: int(x) if not str.isspace(x) else None)/100 t1['HTM4'] = t1['HTM4'].map(lambda x: int(x) if not str.isspace(x) else None)/100 t1['HTIN4'] = t1['HTIN4'].map(lambda x: int(x) if not str.isspace(x) else None) # Nutrition ## NOTE: Values include two implied decimal places # Vegetable & Fruit intake per day t1['_FRUTSUM'] = t1['_FRUTSUM'].map(lambda x: int(x) if not str.isspace(x) else None) t1['_VEGESUM'] = t1['_VEGESUM'].map(lambda x: int(x) if not str.isspace(x) else None) # Food intake - times per day t1['FRUTDA1_'] = t1['FRUTDA1_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['VEGEDA1_'] = t1['VEGEDA1_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['GRENDAY_'] = t1['GRENDAY_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['ORNGDAY_'] = t1['ORNGDAY_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['FTJUDA1_'] = t1['FTJUDA1_'].map(lambda x: int(x) if not str.isspace(x) else None) t1['BEANDAY_'] = t1['BEANDAY_'].map(lambda x: int(x) if not str.isspace(x) else None) # Salt intake and advice choice = {'1':'Yes', '2':'No', '7':'Don\'t know' , '9': 'Refused'} t1['WTCHSALT'] = t1['WTCHSALT'].replace(choice) t1['DRADVISE'] = t1['DRADVISE'].replace(choice) # In[13]: # Demographics choice = {'1' : 'Did not graduate High School', '2' : 'Graduated High School', '3' : 'Attended College or Technical School', '4' : 'Graduated from College or Technical School', '9' : 'Don’t know/Not sure/Missing'} t1['_EDUCAG'] = t1['_EDUCAG'].replace(choice) choice = {'1' : 'Male', '2' : 'Female'} t1['SEX'] = t1['SEX'].replace(choice) choice = {'1' : '< $15000', '2' : '$15,000 - $25,000', '3' : '$25,000 - $35,000', '4' : '$35,000 - $50,000', '5' : '> $50,000', '9' : 'Don’t know/Not sure/Missing'} t1['_INCOMG'] = t1['_INCOMG'].replace(choice) choice = {'1':'Employed for wages', '2':'Self-employed', '3': 'Unemployed < 1 year', '4': 'Unemployed > 1 year', '5': 'Homemaker', '6' : 'Student', '7': 'Retired' , '8': 'Unable to work', '9': 'Refused'} t1['EMPLOY1'] = t1['EMPLOY1'].replace(choice) choice = {'1':'< Kindergarden', '2':'Elementary', '3': 'Some high-school', '4': 'High-school graduate', '5': 'College / tech school', '6' : 'College grade', '9': 'Refused'} t1['EDUCA'] = t1['EDUCA'].replace(choice) choice = {'1':'Married', '2':'Divored', '4': 'Separated', '3': 'Separated', '5': 'Never Married', '6':'Unmarried couple' , '9': 'Refused'} t1['MARITAL'] = t1['MARITAL'].replace(choice) choice = {'1':'Yes', '2':'No', '7':'Don\'t know' , '9': 'Refused'} t1['VETERAN3'] = t1['VETERAN3'].replace(choice) # Age choice = { '01' : 'Age 18 to 24', '02' : 'Age 25 to 29', '03' : 'Age 30 to 34', '04' : 'Age 35 to 39', '05': 'Age 40 to 44', '06' : 'Age 45 to 49', '07': 'Age 50 to 54', '08': 'Age 55 to 59', '09': 'Age 60 to 64', '10': 'Age 65 to 69', '11': 'Age 70 to 74', '12': 'Age 75 to 79' , '13': 'Age 80 or older', '14': 'Don’t know/Refused/Missing'} t1['_AGEG5YR'] = t1['_AGEG5YR'].replace(choice) # General health choice = {'5':'Poor', '3':'Good', '1':'Excellent', '2':'Very Good', '4':'Fair', '7':'Don\'t know' , '9': 'Refused'} t1['GENHLTH'] = t1['GENHLTH'].replace(choice) choice = {'1':'Yes', '2':'No', '7':'Don\'t know' , '9': 'Refused'} t1['QLACTLM2'] = t1['QLACTLM2'].replace(choice) t1['USEEQUIP'] = t1['USEEQUIP'].replace(choice) t1['DECIDE'] = t1['DECIDE'].replace(choice) t1['DIFFWALK'] = t1['DIFFWALK'].replace(choice) t1['DIFFDRES'] = t1['DIFFDRES'].replace(choice) t1['DIFFALON'] = t1['DIFFALON'].replace(choice) t1['MENTHLTH'] = t1['MENTHLTH'].map(lambda x: int(x) if not str.isspace(x) else None) t1['POORHLTH'] = t1['POORHLTH'].map(lambda x: int(x) if not str.isspace(x) else None) t1['SLEPTIM1'] = t1['SLEPTIM1'].map(lambda x: int(x) if not str.isspace(x) else None) t1['PHYSHLTH'] = t1['PHYSHLTH'].map(lambda x: int(x) if not str.isspace(x) else None) # Map variable names to more descriptive names varDict = var[['Variable Name', 'DESC']].to_dict('split') varDict = dict(varDict['data']) t1.rename(columns=varDict, inplace=True) # Replace space / special characters with underscore t1.rename(columns=lambda x: re.sub(' ', '_', x), inplace=True) t1.rename(columns=lambda x: re.sub(r'\(|\-|\/|\|\>|\)|\#', '', x), inplace=True) t1.rename(columns=lambda x: re.sub(r'\>', 'GT', x), inplace=True) # Delete original row del(t1['Var']) t1.fillna('', inplace=True) ### Create and configure Elasticsearch index # Name of index and document type index_name = 'brfss'; doc_name = 'respondent' # Delete donorschoose index if one does exist if es.indices.exists(index_name): es.indices.delete(index_name) # Create donorschoose index es.indices.create(index_name) # Add mapping with open('brfss_mapping.json') as json_mapping: d = json.load(json_mapping) es.indices.put_mapping(index=index_name, doc_type=doc_name, body=d) ### Index Data into Elasticsearch for subj_id, subject in t1.iterrows(): if subj_id % 1000 == 0: print(subj_id) thisResp = subject.to_dict() thisResp['Coordinates'] = [thisResp['Longitude'], thisResp['Latitude']] thisDoc = json.dumps(thisResp); #pprint.pprint(thisDoc) # write to elasticsearch es.index(index=index_name, doc_type=doc_name, id=subj_id, body=thisDoc) examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/Ingest Scripts - Python/variable_list.csv000066400000000000000000000236331300173464000337210ustar00rootroot00000000000000Question,Starting Column,Variable Name,Field Length,DESC,Keep 0,1,_STATE,2,State,Yes 1,16,FMONTH,2,File_Month,Yes 2,18,IDATE,8,Interview_Date,Yes 3,18,IMONTH,2,Interview_Month,Yes 4,20,IDAY,2,Interview_Day,Yes 5,22,IYEAR,4,Interview_Year,Yes 6,31,DISPCODE,4,,No 7,35,SEQNO,10,SEQN,Yes 8,35,_PSU,10,Primary_Sampling_Unit,Yes 9,62,CTELENUM,1,,No 10,63,PVTRESD1,1,Private_Residence,Yes 11,64,COLGHOUS,1,College_Housing,Yes 12,65,STATERES,1,,No 13,66,CELLFON3,1,,No 14,67,LADULT,1,Is_Adult,Yes 15,68,NUMADULT,2,,No 16,70,NUMMEN,2,,No 17,72,NUMWOMEN,2,,No 18,80,GENHLTH,1,General_Health,Yes 19,81,PHYSHLTH,2,Physical_Health,Yes 20,83,MENTHLTH,2,Mental_Health,Yes 21,85,POORHLTH,2,Days_of_Poor_Health_past_30_days,Yes 22,87,HLTHPLN1,1,,No 23,88,PERSDOC2,1,,No 24,89,MEDCOST,1,,No 25,90,CHECKUP1,1,,No 26,91,SLEPTIM1,2,Sleep_per_day_hours,Yes 27,93,BPHIGH4,1,High_BP,Yes 28,94,BPMEDS,1,BP_Meds,Yes 29,95,BLOODCHO,1,Cholesterol_Awareness,Yes 30,96,CHOLCHK,1,Last_Cholesterol_Check,Yes 31,97,TOLDHI2,1,High_Cholesterol,Yes 32,98,CVDINFR4,1,Had_Heart_Attack,Yes 33,99,CVDCRHD4,1,Had_Heart_Disease,Yes 34,100,CVDSTRK3,1,Had_Stroke,Yes 35,101,ASTHMA3,1,,No 36,102,ASTHNOW,1,,No 37,103,CHCSCNCR,1,,No 38,104,CHCOCNCR,1,,No 39,105,CHCCOPD1,1,,No 40,106,HAVARTH3,1,,No 41,107,ADDEPEV2,1,,No 42,108,CHCKIDNY,1,,No 43,109,DIABETE3,1,Had_Diabetes,Yes 44,146,VETERAN3,1,Served_on_Active_Duty,Yes 45,147,MARITAL,1,Marital,Yes 46,148,CHILDREN,2,Number_of_Children,Yes 47,150,EDUCA,1,Educatio_Level,Yes 48,151,EMPLOY1,1,Employment,Yes 49,152,INCOME2,2,Income,Yes 50,154,WEIGHT2,4,Weight,No 51,158,HEIGHT3,4,Height,No 52,170,NUMHHOL2,1,,No 53,171,NUMPHON2,1,,No 54,172,CPDEMO1,1,,No 55,173,CPDEMO4,3,,No 56,176,INTERNET,1,,No 57,177,RENTHOM1,1,,No 58,178,SEX,1,Gender,Yes 59,179,PREGNANT,1,,No 60,180,QLACTLM2,1,Limited_physical_activity,Yes 61,181,USEEQUIP,1,Uses_special_equipment,Yes 62,182,BLIND,1,,No 63,183,DECIDE,1,Difficulty_decision_making,Yes 64,184,DIFFWALK,1,Difficulty_walking__climbing,Yes 65,185,DIFFDRES,1,Difficulty_dressing__bathing,Yes 66,186,DIFFALON,1,Difficulty_running_errands,Yes 67,187,SMOKE100,1,Smoke_GT_100_cigarettes,Later 68,188,SMOKDAY2,1,Smoke_current_status,Later 69,189,STOPSMK2,1,Smoke_tried_to_quit,Later 70,190,LASTSMK2,2,Smoke_time_since_last_smoke,Later 71,192,USENOW3,1,Smoke_now,Later 72,193,ALCDAY5,3,Alcohol_Frequency,No 73,196,AVEDRNK2,2,Alcohol_Drinks_per_occasion,Yes 74,198,DRNK3GE5,2,Alcohol_Binge_days_per_month,Yes 75,200,MAXDRNKS,2,Alcohol_Max_drinks_in last_month,Yes 76,202,FRUITJU1,3,Nutrition_Fruit_juice_consumption,No 77,205,FRUIT1,3,Nutrition_Fruit_consumption,No 78,208,FVBEANS,3,Nutrition_Beans_consumption,No 79,211,FVGREEN,3,Nutrition_Greens_consumption,No 80,214,FVORANG,3,Nutrition_Orange_veg_consumption,No 81,217,VEGETAB1,3,Nutrition_Other_veg_consumption,No 82,220,EXERANY2,1,Exercise,Yes 83,221,EXRACT11,2,Physical_activity_1,Yes 84,223,EXEROFT1,3,Physical_activity_1__frequency,No 85,226,EXERHMM1,3,Physical_activity_1__duration,Yes 86,229,EXRACT21,2,Physical_activity_2,Yes 87,231,EXEROFT2,3,Physical_activity_2__frequency,No 88,234,EXERHMM2,3,Physical_activity_2__duration,Yes 89,237,STRENGTH,3,Strength_Activity_Weekly_frequency,No 90,240,LMTJOIN3,1,,No 91,241,ARTHDIS2,1,,No 92,242,ARTHSOCL,1,,No 93,243,JOINPAIN,2,,No 94,245,SEATBELT,1,,No 95,246,FLUSHOT6,1,,No 96,247,FLSHTMY2,6,,No 97,253,TETANUS,1,,No 98,254,PNEUVAC3,1,,No 99,255,HIVTST6,1,,No 100,256,HIVTSTD3,6,,No 101,262,WHRTST10,2,,No 102,270,PDIABTST,1,,Later 103,271,PREDIAB1,1,,Later 104,272,DIABAGE2,2,,Later 105,274,INSULIN,1,,Later 106,275,BLDSUGAR,3,,Later 107,278,FEETCHK2,3,,Later 108,281,DOCTDIAB,2,,Later 109,283,CHKHEMO3,2,,Later 110,285,FEETCHK,2,,Later 111,287,EYEEXAM,1,,Later 112,288,DIABEYE,1,,Later 113,289,DIABEDU,1,,Later 114,290,PAINACT2,2,,No 115,292,QLMENTL2,2,,No 116,294,QLSTRES2,2,,No 117,296,QLHLTH2,2,,No 118,298,MEDICARE,1,,No 119,299,HLTHCVRG,14,,No 120,313,DELAYMED,1,,No 121,314,DLYOTHER,25,,No 122,339,NOCOV121,1,,No 123,340,LSTCOVRG,1,,No 124,341,DRVISITS,2,,No 125,343,MEDSCOST,1,,No 126,344,CARERCVD,1,,No 127,345,MEDBILLS,1,,No 128,346,SSBSUGAR,3,Nutrition_Sugar_Drinks,No 129,349,SSBFRUT2,3,Nutrition_Sugar_fruit_drinks,No 130,352,WTCHSALT,1,Watching_salt_intake,Yes 131,353,LONGWTCH,3,,No 132,356,DRADVISE,1,Doctor_advised_salt_reduction,Yes 133,357,ASTHMAGE,2,,No 134,359,ASATTACK,1,,No 135,360,ASERVIST,2,,No 136,362,ASDRVIST,2,,No 137,364,ASRCHKUP,2,,No 138,366,ASACTLIM,3,,No 139,369,ASYMPTOM,1,,No 140,370,ASNOSLEP,1,,No 141,371,ASTHMED3,1,,No 142,372,ASINHALR,1,,No 143,373,HAREHAB1,1,,Later 144,374,STREHAB1,1,,Later 145,375,CVDASPRN,1,,Later 146,376,ASPUNSAF,1,,Later 147,377,RLIVPAIN,1,,Later 148,378,RDUCHART,1,,Later 149,379,RDUCSTRK,1,,Later 150,380,ARTTODAY,1,,No 151,381,ARTHWGT,1,,No 152,382,ARTHEXER,1,,No 153,383,ARTHEDU,1,,No 154,384,IMFVPLAC,2,,No 155,386,HPVADVC2,1,,No 156,387,HPVADSHT,2,,No 157,389,HADMAM,1,,No 158,390,HOWLONG,1,,No 159,391,PROFEXAM,1,,No 160,392,LENGEXAM,1,,No 161,393,HADPAP2,1,,No 162,394,LASTPAP2,1,,No 163,395,HADHYST2,1,,No 164,396,BLDSTOOL,1,,No 165,397,LSTBLDS3,1,,No 166,398,HADSIGM3,1,,No 167,399,HADSGCO1,1,,No 168,400,LASTSIG3,1,,No 169,401,PCPSAAD2,1,,Later 170,402,PCPSADI1,1,,Later 171,403,PCPSARE1,1,,Later 172,404,PSATEST1,1,,Later 173,405,PSATIME,1,,Later 174,406,PCPSARS1,1,,Later 175,407,PCPSADE1,1,,Later 176,408,PCDMDECN,4,,Later 177,412,RRCLASS2,1,,No 178,413,RRCOGNT2,1,,No 179,414,RRATWRK2,1,,No 180,415,RRHCARE3,1,,No 181,416,RRPHYSM2,1,,No 182,417,RREMTSM2,1,,No 183,418,MISNERVS,1,,Later 184,419,MISHOPLS,1,,Later 185,420,MISRSTLS,1,,Later 186,421,MISDEPRD,1,,Later 187,422,MISEFFRT,1,,Later 188,423,MISWTLES,1,,Later 189,424,MISNOWRK,2,,Later 190,426,MISTMNT,1,,Later 191,427,MISTRHLP,1,,Later 192,428,MISPHLPF,1,,Later 193,479,SCNTMONY,1,,No 194,480,SCNTMEAL,1,,No 195,481,SCNTPAID,1,,No 196,482,SCNTWRK1,2,,No 197,484,SCNTLPAD,1,,No 198,485,SCNTLWK1,2,,No 199,487,SCNTVOT1,1,,No 200,494,RCSGENDR,1,,No 201,529,RCSRLTN2,1,,No 202,530,CASTHDX2,1,,No 203,531,CASTHNO2,1,,No 204,532,EMTSUPRT,1,,No 205,533,LSATISFY,1,,No 206,580,CTELNUM1,1,,No 207,581,CELLFON2,1,,No 208,582,CADULT,1,,No 209,583,PVTRESD2,1,,No 210,584,CCLGHOUS,1,,No 211,585,CSTATE,1,,No 212,588,LANDLINE,1,,No 213,589,PCTCELL,3,,No 214,592,QSTVER,2,,No 215,594,QSTLANG,2,,No 216,1306,MSCODE,1,,No 217,1307,_STSTR,6,,No 218,1313,_STRWT,10,,No 219,1343,_RAWRAKE,10,,No 220,1353,_WT2RAKE,10,,No 221,1367,_IMPRACE,2,,No 222,1369,_IMPNPH,1,,No 223,1373,_IMPEDUC,1,,No 224,1374,_IMPMRTL,1,,No 225,1375,_IMPHOME,1,,No 226,1560,_CHISPNC,1,,No 227,1617,_CRACE1,2,,No 228,1619,_IMPCAGE,1,,No 229,1620,_IMPCRAC,1,,No 230,1621,_IMPCSEX,1,,No 231,1748,_CLLCPWT,10,,No 232,1884,_DUALUSE,1,,No 233,1885,_DUALCOR,10,,No 234,1895,_LLCPWT2,10,,No 235,1953,_LLCPWT,10,,No 236,2101,_RFHLTH,1,,No 237,2102,_HCVU651,1,,No 238,2103,_RFHYPE5,1,,Later 239,2104,_CHOLCHK,1,,Later 240,2105,_RFCHOL,1,,Later 241,2106,_LTASTH1,1,,No 242,2107,_CASTHM1,1,,No 243,2108,_ASTHMS1,1,,No 244,2109,_DRDXAR1,1,,No 245,2166,_PRACE1,2,,No 246,2168,_MRACE1,2,,No 247,2172,_HISPANC,1,,No 248,2173,_RACE,1,,No 249,2174,_RACEG21,1,,No 250,2175,_RACEGR3,1,,No 251,2176,_RACE_G1,1,,No 252,2177,_AGEG5YR,2,Age_category,Yes 253,2179,_AGE65YR,1,,No 254,2180,_AGE_G,1,,No 255,2181,HTIN4,3,Height_inches,Yes 256,2184,HTM4,3,Height_m,Yes 257,2187,WTKG3,5,Weight_kg,Yes 258,2192,_BMI5,4,BMI,Yes 259,2196,_BMI5CAT,1,BMI_Category,Yes 260,2197,_RFBMI5,1,BMI_Obesity,Yes 261,2198,_CHLDCNT,1,,No 262,2199,_EDUCAG,1,Demo_edu_level,Yes 263,2200,_INCOMG,1,Demo_income_group,Yes 264,2201,_SMOKER3,1,Smoke_smoker_type,Later 265,2202,_RFSMOK3,1,Smoke_current_smoker,Later 266,2203,DRNKANY5,1,Alcohol_any_drink_in_last_month,Yes 267,2204,DROCDY3_,3,Drink_occasions_per_day,Yes 268,2207,_RFBING5,1,Alcohol_Binge_drinker,Yes 269,2208,_DRNKDY4,4,Alcohol_Drinks_per_day,Yes 270,2212,_DRNKMO4,4,Alcohol_Drinks_per_month,Yes 271,2216,_RFDRHV4,1,,No 272,2217,_RFDRMN4,1,,No 273,2218,_RFDRWM4,1,,No 274,2219,FTJUDA1_,4,Nutrition_Fruit_juice_times_per_day,Yes 275,2223,FRUTDA1_,4,Nutrition_Fruit_times_per_day,Yes 276,2227,BEANDAY_,4,Nutrition_Beans_times_per_day,Yes 277,2231,GRENDAY_,4,Nutrition_Green_times_per_day,Yes 278,2235,ORNGDAY_,4,Nutrition_Orange_veg_times_per_day,Yes 279,2239,VEGEDA1_,4,Nutrition_Vegetable_times_per_day,Yes 280,2243,_MISFRTN,1,,No 281,2244,_MISVEGN,1,,No 282,2245,_FRTRESP,1,,No 283,2246,_VEGRESP,1,,No 284,2247,_FRUTSUM,6,Nutrition_Fruit_per_day,Yes 285,2253,_VEGESUM,6,Nutrition_Vegetable_per_day,Yes 286,2259,_FRTLT1,1,,No 287,2260,_VEGLT1,1,,No 288,2261,_FRT16,1,,No 289,2262,_VEG23,1,,No 290,2263,_FRUITEX,1,,No 291,2264,_VEGETEX,1,,No 292,2265,_FRTLT1,1,,No 293,2266,_VEGLT1,1,,No 294,2267,_TOTINDA,1,Activity_yes__no,Yes 295,2268,METVL11_,3,Activity_1_MET,Yes 296,2271,METVL21_,3,Activity_2_MET,Yes 297,2274,MAXVO2_,5,Max_O2_consumption,Yes 298,2279,FC60_,5,Functional_capacity,Yes 299,2284,ACTIN11_,1,Activity_1_intensity,Yes 300,2285,ACTIN21_,1,Activity_2_intensity,Yes 301,2286,PADUR1_,3,Activity_1_duration_minutes,Yes 302,2289,PADUR2_,3,Activity_2_duration_minutes,Yes 303,2292,PAFREQ1_,5,Activity_1_weekly_freq,Yes 304,2297,PAFREQ2_,5,Activity_2_weekly_freq,Yes 305,2302,_MINAC11,5,,No 306,2307,_MINAC21,5,,No 307,2312,STRFREQ_,5,Strength_Activity_Weekly_frequency,Yes 308,2317,PAMISS1_,1,,No 309,2318,PAMIN11_,5,Activity_1_weekly_duration_minutes,Yes 310,2323,PAMIN21_,5,Activity_2_weekly_duration_minutes,Yes 311,2328,PA1MIN_,5,Total_Activity_weekly_duration_minutes,Yes 312,2333,PAVIG11_,5,Vigorous_Activity_1_weekly_duration_minutes,Yes 313,2338,PAVIG21_,5,Vigorous_Activity_2_weekly_duration_minutes,Yes 314,2343,PA1VIGM_,5,Total_Vigorous_Activity_weekly_duration_minutes,Yes 315,2348,_PACAT1,1,Activity_category,Yes 316,2349,_PAINDX1,1,Activity_index,Yes 317,2350,_PA150R2,1,,No 318,2351,_PA300R2,1,,No 319,2352,_PA30021,1,,No 320,2353,_PASTRNG,1,Muscle_strengthening_guideline,Yes 321,2354,_PAREC1,1,Aerobic_and_strengthening_guideline,Yes 322,2355,_PASTAE1,1,,No 323,2356,_LMTACT1,1,,No 324,2357,_LMTWRK1,1,,No 325,2358,_LMTSCL1,1,,No 326,2359,_RFSEAT2,1,,No 327,2360,_RFSEAT3,1,,No 328,2361,_FLSHOT6,1,,No 329,2362,_PNEUMO2,1,,No 330,2363,_AIDTST3,1,,No 331,2364,_AGE80,2,,Noexamples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/README.md000066400000000000000000000105741300173464000254230ustar00rootroot00000000000000### Using Elasticsearch & Kibana to Analyze Health Behavior Survey data from CDC This example provides supplementary material to the Movember Data Dives - see [blog 1](https://www.elastic.co/blog/movember-data-dive-part-1) and [blog 2](https://www.elastic.co/blog/movember-data-dive-part-2) In this example, we will analyze the 2013 [Behavioral Risk Factor Surveillance System] (http://www.cdc.gov/brfss/annual_data/annual_2013.html) data using the Elastic stack. Every year, the Centers for Disease Control and Prevention (CDC) conducts approximately 500,000 telephone surveys to collect data on a variety of personal health-related topics, such as nutrition, drinking habits, physical activity and health history. We analyzed this data to explore exercise and nutrition patterns for male respondents as a part of our Movember Data Dive. For additional commentary on the analysis and unearthed insights, refer to the accompanying blogs [here](https://www.elastic.co/blog/movember-data-dive-part-1) (for analysis of physical activity and exercise patterns) and [here](https://www.elastic.co/blog/movember-data-dive-part-2) (for analysis of eating and drinking patterns). ##### Version Example has been tested in following versions: - Elasticsearch 1.7.0 - Kibana 4.1.0 ### Installation & Setup * Follow the [Installation & Setup Guide](https://github.com/elastic/examples/blob/master/Installation%20and%20Setup.md) to install and test the Elastic Stack (*you can skip this step if you already have a working installation of the Elastic Stack*) * Run Elasticsearch & Kibana ```shell /bin/elasticsearch /bin/kibana ``` * Check that Elasticsearch and Kibana are up and running. - Open `localhost:9200` in web browser -- should return status code 200 - Open `localhost:5601` in web browser -- should display Kibana UI. **Note:** By default, Elasticsearch runs on port 9200, and Kibana run on ports 5601. If you changed the default ports, change the above calls to use appropriate ports. ### Download & Ingest Data In this example, we ingest the data into Elasticsearch using the Elasticsearch Python client. Follow the instructions in the [ReadMe](https://github.com/elastic/examples/blob/master/ElasticStack_CDC_nutrition_exercise_patterns/Ingest%20Scripts%20-%20Python/README.md) in the [Ingest Scripts - Python](https://github.com/elastic/examples/tree/master/ElasticStack_CDC_nutrition_exercise_patterns/Ingest%20Scripts%20-%20Python) folder if you want to try this option. #### Check data availability Once the index is created, data will available in Elasticsearch. If all goes well, you should get a `count` response of `491773` when you run the following command. ```shell curl -XGET localhost:9200/brfss/_count -d '{ "query": { "match_all": {} } }' ``` #### Visualize Data in Kibana * Access Kibana by going to `http://localhost:5601` in a web browser * Connect Kibana to the `brfss` index in Elasticsearch * Click the **Settings** tab >> **Indices** tab >> **Create New**. Specify `brfss` as the index pattern name, select `Interview_Date` as the **Time-field name**, and click **Create** to define the index pattern. (Leave the **Use event times to create index names** box unchecked) * Load sample dashboard into Kibana * Click the **Settings** tab >> **Objects** tab >> **Import**, and select `brfss_kibana_dashboard.json` * Open dashboard(s) * Click on **Dashboard** tab and open either the `BRFSS: Nutrition` or `BRFSS: Fitness` dashboard. Voila! You should see one of the following dashboards. Happy Data Exploration! ![Kibana Dashboard Screenshot](https://cloud.githubusercontent.com/assets/5269751/11385430/a0e76804-92cd-11e5-99a7-0450c87ca375.png) ![Kibana Dashboard Screenshot](https://cloud.githubusercontent.com/assets/5269751/11385436/a542ed56-92cd-11e5-9f0a-8a243a55f054.png) ### We would love to hear from you! If you run into issues running this example or have suggestions to improve it, please use Github issues to let us know. Have an easy fix? Submit a pull request. We will try our best to respond in a timely manner! Have you created interesting examples using the Elastic Stack? Looking for a way to share your amazing work with the community? We would love to include your awesome work here. For more information on how to contribute, check out the **[Contribution](https://github.com/elastic/examples#contributing)** section! examples-2.1/ElasticStack_CDC_nutrition_exercise_patterns/brfss_kibana_dashboard.json000066400000000000000000001250251300173464000314700ustar00rootroot00000000000000[ { "_id": "BRFSS:-Fitness", "_type": "dashboard", "_source": { "title": "BRFSS: Fitness", "hits": 0, "description": "", "panelsJSON": 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