{"id":6111,"date":"2018-12-07T16:42:43","date_gmt":"2018-12-08T00:42:43","guid":{"rendered":"http:\/\/www.couchbase.com\/blog\/?p=6111"},"modified":"2025-06-13T18:44:57","modified_gmt":"2025-06-14T01:44:57","slug":"json-insights-analyze-usa-healthcare-data","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/json-insights-analyze-usa-healthcare-data\/","title":{"rendered":"JSON to Insights: Analyzing US healthcare Data."},"content":{"rendered":"

\u201cNothing is certain except for death and taxes.\u201d<\/p><\/blockquote>\n

This isn\u2019t a dataset made with a bed of roses<\/a> or manicured green grass<\/a>. A bit more serious. Let\u2019s see if we can quickly learn anything here.\u00a0<\/span>The dataset is the following.<\/span><\/p>\n

“name” : “NCHS – Leading Causes of Death: United States”,<\/b> <\/b>
\n“attribution” : “National Center for Health Statistics”,<\/b><\/p>\n

The public d<\/span>ataset is available\u00a0at\u00a0<\/span>https:\/\/data.cdc.gov\/api\/views\/bi63-dtpu\/rows.json?accessType=DOWNLOAD<\/span><\/a><\/p>\n

Step 1: <\/b>Download the file into a local file (e.g. health.json).\u00a0Upload this file to one of the nodes in the Couchbase cluster.<\/span><\/p>\n

Step 2<\/strong>: import the data into a bucket called cause.\u00a0 After you create the bucket, create the primary index. You\u2019ll need this for querying.<\/span><\/p>\n

\/opt\/couchbase\/bin\/cbimport json -c couchbase:\/\/127.0.0.1 -u Administrator -p password -b cause -d file:\/\/health.json -g cause:0 -f sample<\/b><\/p>\n

>\u00a0CREATE PRIMARY INDEX ON cause;<\/b><\/strong><\/p>\n

Step 3<\/strong>. Inspect the structure of the data.<\/span><\/p>\n

All of the data is provided in a SINGLE JSON document.\u00a0 Because of this, INFER doesn’t help.\u00a0 You’ll have to inspect and understand the structure manually.\u00a0This data in typical government dataset with a lot of data in simple arrays with the meaning of each entity given at in the metadata.<\/span><\/p>\n

Simple array:<\/span><\/p>\n

select data from cause ;<\/strong><\/pre>\n
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This simply contains an array of data without the schema.\u00a0 For the public datasets, the schema is in the meta field.<\/p>\n
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Let\u2019s transform the structure into simple JSON key-value pairs so we can handle these bit more effectively.\u00a0 You can learn more about how this magic happened in this article<\/a>.<\/span><\/p>\n
WITH cs AS (\r\n  SELECT\r\n    meta.`view`.columns [*].fieldName f,\r\n    data\r\n  FROM\r\n    cause\r\n)\r\nSELECT\r\n  o\r\nFROM\r\n  cs UNNEST cs.data AS d1 \r\nLET o = OBJECT p :d1 [ARRAY_POSITION(cs.f, p)] FOR p IN cs.f END;<\/pre>\n
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Task1: Find out the cause for most deaths in a state, by year.<\/strong><\/h5>\n
The common table expression (CTE) in the WITH clause (csdata) transforms the complex json data into flat JSON.\u00a0 \u00a0You can do this dynamically or do this once and INSERT back into a bucket, as I’ve discussed in the article on New York baby names<\/a>. In this article, I use CTEs.<\/p>\n
WITH csdata as (\r\n  WITH cs AS (\r\n    SELECT\r\n      meta.`view`.columns [*].fieldName f,\r\n      data\r\n    FROM\r\n      cause\r\n  )\r\n  SELECT\r\n    o\r\n  FROM\r\n    cs UNNEST cs.data AS d1 LET o = OBJECT p :d1 [ARRAY_POSITION(cs.f, p)] FOR p IN cs.f END\r\n)\r\nSELECT\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name,\r\n  COUNT(c.o.cause_name),\r\n  SUM(TONUMBER(c.o.deaths)) totdeaths\r\nFROM\r\n  csdata as c\r\nWHERE\r\n  c.o.state <> \"United States\"\r\n  and c.o.cause_name <> \"All causes\"\r\nGROUP BY\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name\r\nORDER BY\r\n  totdeaths DESC,\r\n  c.o.state,\r\n  c.o.year<\/pre>\n
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In this case, all the deaths in California\u00a0come on top, mainly due to its population.<\/p>\n
Task 2. Find out leading causes of death in each state for the year 2016.<\/strong><\/p>\n
Query 2: <\/strong>Use the resultset from the previous query and then use the FIRST_VALUE() window function to determine the top cause.\u00a0 Partitioning by state\u00a0(in the OVER BY clause) will give you the partitions by state and ORDER BY dx.totdeaths\u00a0within the OVER BY clause will give you the top cause in every state.<\/p>\n
WITH csdata as (\r\n  WITH cs AS (\r\n    SELECT\r\n      meta.`view`.columns [*].fieldName f,\r\n      data\r\n    FROM\r\n      cause\r\n  )\r\n  SELECT\r\n    o\r\n  FROM\r\n    cs UNNEST cs.data AS d1 LET o = OBJECT p :d1 [ARRAY_POSITION(cs.f, p)] FOR p IN cs.f END\r\n),\r\nd2 as(\r\nSELECT\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name,\r\n  SUM(TONUMBER(c.o.deaths)) totdeaths\r\nFROM\r\n  csdata as c\r\nWHERE\r\n  c.o.state <> \"United States\"\r\n  and c.o.cause_name <> \"All causes\"\r\n  and c.o.year = \"2016\"\r\nGROUP BY\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name),\r\nd3 as (\r\nSELECT dx.state, dx.cause_name, dx.totdeaths,\r\n  FIRST_VALUE(dx.cause_name) OVER(PARTITION BY dx.state ORDER BY dx.totdeaths DESC) topreason,\r\n  FIRST_VALUE(dx.totdeaths) OVER(PARTITION BY dx.state ORDER BY dx.totdeaths DESC) topcount\r\nFROM d2 dx)\r\nSELECT d3\r\nFROM d3\r\nWHERE d3.topcount = d3.totdeaths\r\norder by d3.state<\/pre>\n
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Task 3.\u00a0\u00a0<\/strong>Find out how the top reason has changed by the year, from 1999 to 2016 by state.<\/p>\n
Query 3:\u00a0\u00a0<\/strong>Simply generate the report for all the years (199-2016) and then determine the top reason and finally get the highest reason by grouping by state, year and getting MAX(topcount) for the topreason cause.<\/p>\n
WITH csdata as (\r\n  WITH cs AS (\r\n    SELECT\r\n      meta.`view`.columns [*].fieldName f,\r\n      data\r\n    FROM\r\n      cause\r\n  )\r\n  SELECT\r\n    o\r\n  FROM\r\n    cs UNNEST cs.data AS d1 LET o = OBJECT p :d1 [ARRAY_POSITION(cs.f, p)] FOR p IN cs.f END\r\n),\r\nd2 as(\r\nSELECT\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name,\r\n  SUM(TONUMBER(c.o.deaths)) totdeaths\r\nFROM\r\n  csdata as c\r\nWHERE\r\n  c.o.state <> \"United States\" \r\n  and c.o.cause_name <> \"All causes\"\r\nGROUP BY\r\n  c.o.state,\r\n  c.o.year,\r\n  c.o.cause_name),\r\nd3 as (\r\nSELECT dx.state, dx.year,\r\n  FIRST_VALUE(dx.cause_name) OVER(PARTITION BY dx.state, dx.year ORDER BY dx.totdeaths DESC ) topreason,\r\n  FIRST_VALUE(dx.totdeaths) OVER(PARTITION BY dx.state, dx.year ORDER BY dx.totdeaths DESC) topcount\r\nFROM d2 dx)\r\nSELECT d3.state , d3.year , d3.topreason, max(d3.topcount) topcount\r\nFROM d3\r\nGROUP BY d3.state, d3.year, d3.topreason\r\norder by d3.state, d3.year<\/pre>\n
Here’s the partial result.<\/p>\n
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Visualizing this gives us the following histogram.<\/p>\n
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\u201cNothing is certain except for death and taxes.\u201d This isn\u2019t a dataset made with a bed of roses or manicured green grass. A bit more serious. Let\u2019s see if we can quickly learn anything here.\u00a0The dataset is the following. “name” […]<\/p>\n","protected":false},"author":55,"featured_media":7451,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,1815,1812],"tags":[2378,2318,2317,1261,1725],"ppma_author":[8929],"class_list":["post-6111","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-best-practices-and-tutorials","category-n1ql-query","tag-6-5","tag-healthcare","tag-insights","tag-json","tag-nosql-database"],"yoast_head":"\nJSON to Insights: Analyzing US healthcare Data. - The Couchbase Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/json-insights-analyze-usa-healthcare-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"JSON to Insights: Analyzing US healthcare Data.\" \/>\n<meta property=\"og:description\" content=\"\u201cNothing is certain except for death and taxes.\u201d This isn\u2019t a dataset made with a bed of roses or manicured green grass. 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