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feat (lab): food intake advanced analysis
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‎sql/food-intake/food-intake-analysis-advanced.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Análise com SQL Avançado\n",
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"## U.S. EPA Food Commodity Intake Database (FCID)\n",
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"### [https://fcid.foodrisk.org/](https://fcid.foodrisk.org/)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Ativando uma conexão de banco de dados em memória usando o SGBD H2:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%defaultDatasource jdbc:h2:mem:db"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Importando Tabelas do FCID"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"DROP TABLE IF EXISTS Crop_Group;\n",
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"DROP TABLE IF EXISTS FCID_Description;\n",
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"DROP TABLE IF EXISTS Recipes;\n",
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"DROP TABLE IF EXISTS Intake;\n",
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"\n",
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"CREATE TABLE Crop_Group (\n",
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" CGN VARCHAR(2),\n",
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" CGL VARCHAR(6),\n",
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" Crop_Group_Description VARCHAR(80),\n",
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" PRIMARY KEY (CGL)\n",
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") AS SELECT\n",
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" CGN, CGL, Crop_Group_Description\n",
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"FROM CSVREAD('../../data/food-intake/basics/FCID_Cropgroup_Description.csv');\n",
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"\n",
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"CREATE TABLE FCID_Description (\n",
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" CGN VARCHAR(2),\n",
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" CG_Subgroup VARCHAR(6),\n",
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" FCID_Code VARCHAR(10),\n",
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" FCID_Desc VARCHAR(55),\n",
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" PRIMARY KEY (FCID_Code),\n",
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") AS SELECT\n",
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" cgn, CG_Subgroup, FCID_Code, FCID_Desc\n",
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"FROM CSVREAD('../../data/food-intake/basics/FCID_Code_Description.csv');\n",
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"\n",
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"CREATE TABLE Recipes (\n",
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" Food_Code VARCHAR(8),\n",
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" Mod_Code VARCHAR(8),\n",
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" Ingredient_Num TINYINT,\n",
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" FCID_Code VARCHAR(10),\n",
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" Cooked_Status TINYINT,\n",
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" Food_Form TINYINT,\n",
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" Cooking_Method TINYINT,\n",
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" Commodity_Weight DECIMAL(5, 2),\n",
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" CSFII_9498_IND TINYINT,\n",
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" WWEIA_9904_IND TINYINT,\n",
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" WWEIA_0510_IND TINYINT,\n",
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" PRIMARY KEY(Food_Code, Mod_Code, Ingredient_Num),\n",
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" FOREIGN KEY(FCID_Code)\n",
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" REFERENCES FCID_Description(FCID_Code)\n",
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" ON DELETE NO ACTION\n",
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" ON UPDATE NO ACTION\n",
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") AS SELECT\n",
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" Food_Code, Mod_Code, Ingredient_Num, FCID_Code, Cooked_Status, Food_Form, Cooking_Method,\n",
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" Commodity_Weight, CSFII_9498_IND, WWEIA_9904_IND, WWEIA_0510_IND\n",
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"FROM CSVREAD('../../data/food-intake/recipes/Recipes_WWEIA_FCID_0510.csv');\n",
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"\n",
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"CREATE TABLE Intake (\n",
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" SeqN INTEGER NOT NULL,\n",
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" DayCode TINYINT NOT NULL,\n",
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" DraBF TINYINT,\n",
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" FCID_Code VARCHAR(10),\n",
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" Cooked_Status TINYINT,\n",
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" Food_Form TINYINT,\n",
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" Cooking_Method TINYINT,\n",
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" Intake DECIMAL(13,7),\n",
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" Intake_BW DECIMAL(13,10),\n",
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" PRIMARY KEY(SeqN, DayCode, FCID_Code, Cooked_Status, Food_Form, Cooking_Method),\n",
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" FOREIGN KEY(FCID_Code)\n",
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" REFERENCES FCID_Description(FCID_Code)\n",
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" ON DELETE NO ACTION\n",
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" ON UPDATE NO ACTION\n",
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") AS SELECT\n",
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" SEQN, DAYCODE, DRABF, FCID_Code, Cooked_Status, Food_Form, Cooking_Method, Intake,Intake_BW\n",
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"FROM CSVREAD('../../data/food-intake/consumption/Commodity_CSFFM_Intake_0510-cropped.csv');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualizando as Tabelas"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b89a3f80-02b3-4acb-bb7c-5d3d4f855e42",
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"version_major": 2,
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"version_minor": 0
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"SELECT * FROM Crop_Group LIMIT 10;"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"model_id": "9b197073-9158-4939-8e60-adfcfb546c1e",
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"SELECT * FROM FCID_Description LIMIT 10;"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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"data": {
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"model_id": "10c6feb9-2454-4656-bba0-ece47f008442",
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"SELECT * FROM Recipes LIMIT 10;"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"model_id": "7840e179-1311-409f-9ecf-6689a574ee1d",
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},
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"method": "display_data"
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"SELECT * FROM Intake LIMIT 10;"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Métricas\n",
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"\n",
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"Considere que a tabela Intake registra alimentos consumidos por 1.489 pessoas. Considere as seguintes métricas para um alimento:\n",
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"\n",
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"| Métrica | Descrição |\n",
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"| --- | --- |\n",
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"| Popularidade | número de pessoas (dentre as 1.489) que consumiram o alimento |\n",
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"| Intake_Sum | total consumido do alimento pelas 1.489 pessoas em gramas |\n",
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"| Intake_AVG | média de consumo do alimento em gramas |\n",
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"| Intake_AVG_BW | média de consumo do alimento x peso da pessoa |\n",
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"| Recipes | número de receitas (dentre as 7.154 receitas) que têm o produto como ingrediente |"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1) Construa uma View que apresente essas métricas por produto\n",
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"\n",
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"* Veja exemplo em: `/data/food-intake/computed/commodity-profile.csv`\n",
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"* Importante: esta tabela foi feita com um número maior de registros, portanto os valores não serão iguais aos seus"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2) Como você analisaria a correlação entre as métricas?\n",
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"\n",
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"* Por exemplo, produtos mais populares são mais consumidos (em número de pessoas ou em quantidade)?\n",
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"* Proponha uma ou mais queries para fazer esta análise"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3) Podemos criar grupos de consumidores conforme um perfil?\n",
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"* por exemplo, consumidores podem ser agrupados por alimentos que comem predominantemente?\n",
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"* como você associaria grupos a classes?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4) Que métricas podem ser analisadas para a comparação de perfis?\n",
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"* escreva uma query SQL que calcule pelo menos uma métrica comparativa"
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]
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},
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"language": "SQL",
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