{"id":6111,"date":"2018-12-07T16:42:43","date_gmt":"2018-12-08T00:42:43","guid":{"rendered":"https:\/\/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\/pt\/json-insights-analyze-usa-healthcare-data\/","title":{"rendered":"JSON para Insights: Analisando dados de sa\u00fade dos EUA."},"content":{"rendered":"

\"Nada \u00e9 certo, exceto a morte e os impostos.\"<\/p><\/blockquote>\n

Esse n\u00e3o \u00e9 um conjunto de dados criado com um canteiro de rosas<\/a> ou grama verde bem cuidada<\/a>. Um pouco mais s\u00e9rio. Vamos ver se podemos aprender algo rapidamente aqui.\u00a0<\/span>O conjunto de dados \u00e9 o seguinte.<\/span><\/p>\n

\"name\" : \"NCHS - Principais causas de morte: Estados Unidos\",<\/b> <\/b>
\n\"attribution\" : \"National Center for Health Statistics\" (Centro Nacional de Estat\u00edsticas de Sa\u00fade),<\/b><\/p>\n

O p\u00fablico d<\/span>ataset est\u00e1 dispon\u00edvel em\u00a0<\/span>https:\/\/data.cdc.gov\/api\/views\/bi63-dtpu\/rows.json?accessType=DOWNLOAD<\/span><\/a><\/p>\n

Etapa 1: <\/b>Fa\u00e7a o download do arquivo em um arquivo local (por exemplo, health.json).\u00a0Carregue esse arquivo em um dos n\u00f3s do cluster do Couchbase.<\/span><\/p>\n

Etapa 2<\/strong>Importar os dados para um compartimento chamado causa. Depois de criar o bucket, crie o \u00edndice prim\u00e1rio. Voc\u00ea precisar\u00e1 dele para fazer consultas.<\/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

Etapa 3<\/strong>. Inspecione a estrutura dos dados.<\/span><\/p>\n

Todos os dados s\u00e3o fornecidos em um \u00daNICO documento JSON. Por esse motivo, o INFER n\u00e3o ajuda. Voc\u00ea ter\u00e1 que inspecionar e entender a estrutura manualmente.\u00a0Esses dados s\u00e3o um conjunto de dados t\u00edpico do governo, com muitos dados em matrizes simples, com o significado de cada entidade fornecido nos metadados.<\/span><\/p>\n

Matriz simples:<\/span><\/p>\n

select data from cause ;<\/strong><\/pre>\n
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Ele simplesmente cont\u00e9m uma matriz de dados sem o esquema.  Para os conjuntos de dados p\u00fablicos, o esquema est\u00e1 no campo meta.<\/p>\n
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Vamos transformar a estrutura em pares simples de valores-chave JSON para que possamos lidar com esses bits de forma mais eficaz.  Voc\u00ea pode saber mais sobre como essa m\u00e1gica aconteceu neste artigo<\/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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Tarefa 1: Descubra a causa da maioria das mortes em um estado, por ano.<\/strong><\/h5>\n
A express\u00e3o de tabela comum (CTE) na cl\u00e1usula WITH (csdata) transforma os dados json complexos em JSON simples.   Voc\u00ea pode fazer isso dinamicamente ou fazer isso uma vez e INSERTAR de volta em um bucket, como discuti no artigo sobre Nomes de beb\u00eas de Nova York<\/a>. Neste artigo, eu uso 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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Nesse caso, todas as mortes na Calif\u00f3rnia est\u00e3o no topo, principalmente devido \u00e0 sua popula\u00e7\u00e3o.<\/p>\n
Tarefa 2. Descubra as principais causas de morte em cada estado no ano de 2016.<\/strong><\/p>\n
Consulta 2: <\/strong>Use o conjunto de resultados da consulta anterior e, em seguida, use a fun\u00e7\u00e3o de janela FIRST_VALUE() para determinar a causa principal.  O particionamento por estado (na cl\u00e1usula OVER BY) lhe dar\u00e1 as parti\u00e7\u00f5es por estado e ORDER BY dx.totdeaths dentro da cl\u00e1usula OVER BY lhe dar\u00e1 a causa principal em cada estado.<\/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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Tarefa 3.\u00a0\u00a0<\/strong>Descubra como o principal motivo mudou ao longo do ano, de 1999 a 2016, por estado.<\/p>\n
Consulta 3:\u00a0\u00a0<\/strong>Basta gerar o relat\u00f3rio para todos os anos (199-2016) e, em seguida, determinar o motivo principal e, finalmente, obter o motivo mais alto agrupando por estado, ano e obtendo MAX(topcount) para a causa do motivo principal.<\/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
Aqui est\u00e1 o resultado parcial.<\/p>\n
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A visualiza\u00e7\u00e3o disso nos d\u00e1 o seguinte histograma.<\/p>\n
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 <\/p>","protected":false},"excerpt":{"rendered":"
\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. 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