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IntegracionFinal.zpln
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IntegracionFinal.zpln
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{
"paragraphs": [
{
"title": "Carga de datos",
"text": "val datosDF = spark.read.format(\"csv\")\n .option(\"sep\", \"\\t\")\n .option(\"inferSchema\", \"true\")\n .option(\"header\", \"true\")\n .load(\"/datos/Datos_ENEMDU_PEA_v2.csv\")",
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"data": "\u001b[1m\u001b[34mdatosDF\u001b[0m: \u001b[1m\u001b[32morg.apache.spark.sql.DataFrame\u001b[0m = [id_persona: decimal(26,0), anio: int ... 15 more fields]\n"
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"dateFinished": "2020-08-01T23:14:26+0000",
"status": "FINISHED",
"focus": true,
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{
"title": "Carga de dataset con los nombres de la provincia en vez del num",
"text": "val data2 = datosDF.drop(\"id_persona\")\n\nval data3=data2.withColumn(\"provincia\", when($\"provincia\" === 1 , lit(\"Azuay\"))\n .otherwise(when($\"provincia\" === 2 , lit(\"Bovilar\"))\n .otherwise(when($\"provincia\" === 3, lit(\"Caniar\"))\n .otherwise(when($\"provincia\" === 4, lit(\"Carchi\"))\n .otherwise(when($\"provincia\" === 5, lit(\"Cotopaxi\"))\n .otherwise(when($\"provincia\" === 6, lit(\"Chimborazo\"))\n .otherwise(when($\"provincia\" === 7, lit(\"El Oro\"))\n .otherwise(when($\"provincia\" === 8, lit(\"Esmeraldas\"))\n .otherwise(when($\"provincia\" === 9, lit(\"Guayas\"))\n .otherwise(when($\"provincia\" === 10, lit(\"Imbabura\"))\n .otherwise(when($\"provincia\" === 11, lit(\"Loja\"))\n .otherwise(when($\"provincia\" === 12, lit(\"Los Rios\"))\n .otherwise(when($\"provincia\" === 13, lit(\"Manabi\"))\n .otherwise(when($\"provincia\" === 14, lit(\"Morona Santiago\"))\n .otherwise(when($\"provincia\" === 15, lit(\"Napo\"))\n .otherwise(when($\"provincia\" === 16, lit(\"Pastaza\"))\n .otherwise(when($\"provincia\" === 17, lit(\"Pichincha\"))\n .otherwise(when($\"provincia\" === 18, lit(\"Tungurahua\"))\n .otherwise(when($\"provincia\" === 19, lit(\"Zamora Chinchipe\"))\n .otherwise(when($\"provincia\" === 20, lit(\"Galapagos\"))\n .otherwise(when($\"provincia\" === 21, lit(\"Sucumbios\"))\n .otherwise(when($\"provincia\" === 22, lit(\"Orellana\"))\n .otherwise(when($\"provincia\" === 23, lit(\"Santo Domingo de los Tsachilas\"))\n .otherwise(when($\"provincia\" === 24, lit(\"Santa Elena\"))\n ) )) )))))))))))))))))))))",
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{
"title": "TODO Descartar Outliers",
"user": "anonymous",
"dateUpdated": "2020-08-01T23:14:27+0000",
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{
"text": "%md\n\n# KNN\nEn está tabla se puede observar la predicciòn de cual será el ingreso salarial segun la edad, sin embargo los datos de edad e ingreso no fueron filtrados; es decir, se pueden mejorar la clasificaciòn\nfiltrando las edades y sueldos con el análisis de los valores átipicos",
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"msg": [
{
"type": "HTML",
"data": "<div class=\"markdown-body\">\n<h1>KNN</h1>\n<p>En está tabla se puede observar la predicciòn de cual será el ingreso salarial segun la edad, sin embargo los datos de edad e ingreso no fueron filtrados; es decir, se pueden mejorar la clasificaciòn<br />\nfiltrando las edades y sueldos con el análisis de los valores átipicos</p>\n\n</div>"
}
]
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"jobName": "paragraph_1596315878686_-319868828",
"id": "paragraph_1596297384861_-1512466560",
"dateCreated": "2020-08-01T21:04:38+0000",
"dateStarted": "2020-08-01T23:14:27+0000",
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"status": "FINISHED",
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{
"title": "Variable Ingresos (A: KNN)",
"text": "import org.apache.spark._\nimport org.apache.spark.sql.SQLContext\nimport org.apache.spark.sql.types._\nimport org.apache.spark.sql.functions._\nimport org.apache.spark.sql._\nimport org.apache.spark.ml.feature.VectorAssembler\nimport org.apache.spark.ml.clustering.KMeans\n\nval header = datosDF.first()\nval dfh = datosDF.filter(_!=header)\ndfh.first()\n\nval nuevoDF= dfh.select($\"id_Persona\",$\"edad\",$\"ingreso_laboral\")\nval finalDF = nuevoDF.filter(\"ingreso_laboral is not null\" )\n\nval featureCols = Array(\"edad\",\"ingreso_laboral\")\nval assembler = new VectorAssembler().setInputCols(featureCols).setOutputCol(\"features\")\nval df2 = assembler.transform(finalDF)\n\nval Array(trainingData, testData) = df2.randomSplit(Array(0.3,0.6))\n\nval kmeans = new KMeans().setK(6).setFeaturesCol(\"features\").setPredictionCol(\"prediction\")\nval model = kmeans.fit(df2)\nval categories = model.transform(testData)\n\nz.show(categories.groupBy(\"prediction\").count())\n\nval grafico=categories.select($\"edad\",$\"ingreso_laboral\",$\"prediction\").groupBy(\"edad\",\"ingreso_laboral\",\"prediction\").agg(count(\"prediction\")).orderBy(\"edad\",\"ingreso_laboral\",\"prediction\")\nz.show(grafico)",
"user": "anonymous",
"dateUpdated": "2020-08-01T23:14:27+0000",
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"names": {
"prediction": "string",
"count": "string"
},
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},
"tableOptionSpecHash": "[{\"name\":\"useFilter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable filter for columns\"},{\"name\":\"showPagination\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable pagination for better navigation\"},{\"name\":\"showAggregationFooter\",\"valueType\":\"boolean\",\"defaultValue\":false,\"widget\":\"checkbox\",\"description\":\"Enable a footer for displaying aggregated values\"}]",
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"name": "edad",
"index": 0,
"aggr": "sum"
}
],
"groups": [],
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"name": "ingreso_laboral",
"index": 1,
"aggr": "sum"
}
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},
{
"type": "HTML",
"data": "<div class=\"result-alert alert-warning\" role=\"alert\"><button type=\"button\" class=\"close\" data-dismiss=\"alert\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button><strong>Output is truncated</strong> to 1000 rows. Learn more about <strong>zeppelin.spark.maxResult</strong></div>\n"
},
{
"type": "TEXT",
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"title": "Variable Ingresos (A): ¿En que rango de salarios se situan los encuestados?",
"text": "val data2 = datosDF.drop(\"id_persona\")\n\nval AIL = data2.withColumn(\"range\", when($\"ingreso_laboral\" > 0 and $\"ingreso_laboral\" <= 400, lit(\"1-400 Ingreso menor al salario básico\"))\n .otherwise(when($\"ingreso_laboral\" > 400 and $\"ingreso_laboral\" <= 1000, lit(\"Ingreso entre $400 y $1000\"))\n .otherwise(when($\"ingreso_laboral\" > 1000 and $\"ingreso_laboral\" <= 2000, lit(\"Ingreso mayor a $2000\"))\n ) ) ).groupBy(\"range\").count().orderBy(\"range\")\nval registros =data2.count.toDouble \nval tableAIL = AIL.select($\"range\",$\"count\"/registros*100).withColumnRenamed( \"((count / 622776.0) * 100)\",\"porcentaje\")\nz.show(tableAIL)\ntableAIL.createOrReplaceTempView(\"AIL\")",
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"title": "Variable Ingresos: ¿Cual es el ingreso laboral máximo y promedio de cada rama de actividad?",
"text": "z.show(datosDF.groupBy(\"rama_actividad\")\n .agg(\n max(\"ingreso_laboral\").as(\"SueldoMaximo\"),\n avg(\"ingreso_laboral\").as(\"SueldoPromedio\")\n ))\n \n // TODO Realizar un redondeo un un corte a los decimales\n ",
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"data": "rama_actividad\tSueldoMaximo\tSueldoPromedio\n11 - K. Actividades financieras y de seguros\t40000\t958.8430639887903\n07 - G. Comercio, reparación vehículos\t146030\t475.90089729398744\n08 - H. Transporte y almacenamiento\t17000\t505.5524149558663\n15 - O .Administración pública, defensa y seguridad social\t26771\t1066.8357761762086\n19 - S. Otras actividades de servicios\t9886\t299.0756843800322\nnull\t3640\t48.432308698495746\n01 - A. Agricultura, ganadería caza y silvicultura y pesca\t30015\t266.7872302821664\n21 - U Actividades de organizaciones extraterritoriales\t11850\t1709.1052631578948\n14 - N. Actividades y servicios administrativos y de apoyo\t17000\t482.4607137267178\n03 - C. Industrias manufactureras\t55000\t480.24952539614895\n04 - D. Suministros de electricidad, gas, aire acondicionado\t18000\t1051.2075471698113\n18 - R. Artes, entretenimiento y recreación\t10500\t531.7453793103448\n20 - T Actividades en hogares privados con servicio doméstico\t3455\t326.20922146636434\n22 - No especificado\t700\t407.1818181818182\n13 - M. Actividades profesionales, científicas y técnicas\t15000\t714.2027082478457\n16 - P. Enseñanza\t25194\t879.209165731388\n09 - I. Actividades de alojamiento y servicios de comida\t27000\t414.07745071005104\n10 - J. Información y comunicación\t120000\t680.4303797468355\n05 - E. Distribución de agua, alcantarillado\t14900\t704.983606557377\n06 - F. Construcción\t100000\t461.87737311686413\n17 - Q. Actividades, servicios sociales y de salud\t14848\t896.7538367926442\n12 - L. Actividades inmobiliarias\t82200\t764.2232841007819\n02 - B. Explotación de minas y canteras\t15000\t913.464972234088\n"
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{
"title": "Variable Ingresos: En base a un rango de edades ¿Quienes son los que reciben más ingresos en promedio?",
"text": "import org.apache.spark.sql.functions._\n\n\nz.show(datosDF.withColumn(\"edad\", when($\"edad\" >= 15 and $\"edad\" <= 25, lit(\"15-25\"))\n\t\t \t.when($\"edad\" > 25 and $\"edad\" <= 35, lit(\"26-35\"))\n\t\t \t.when($\"edad\" > 35 and $\"edad\" <= 45, lit(\"36-45\"))\n\t\t \t.when($\"edad\" > 45 and $\"edad\" <= 55, lit(\"46-55\"))\n\t\t \t.when($\"edad\" > 55 and $\"edad\" <= 65, lit(\"56-65\"))\n\t\t \t.when($\"edad\" > 65 and $\"edad\" <= 75, lit(\"66-75\"))\n\t\t \t.otherwise(\"otros\")\n ).groupBy(\"edad\").agg(avg($\"ingreso_laboral\")).orderBy(avg($\"ingreso_laboral\")) )\n ",
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{
"title": "Variable Ingresos (A.: Pivot): ¿ Cuál es el promedio del ingreso laboral de hombres y mujeres segun la sectorizacion?",
"text": "z.show( datosDF.groupBy(\"sectorizacion\").pivot(\"genero\").agg(avg(\"ingreso_laboral\")).orderBy($\"sectorizacion\").withColumn(\"2 - Mujer\", format_number($\"2 - Mujer\",2))\n.withColumn(\"1 - Hombre\",format_number($\"1 - Hombre\",2)) )",
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{
"title": "Variable Ingresos: ¿Cual es el porcentaje de personas que ganan menos del salario básico por provincia en el año 2015?",
"text": "val humanos = data3.select($\"provincia\",$\"anio\",$\"ingreso_laboral\").filter(pro(\"anio\") === 2015 && pro(\"ingreso_laboral\") < 400).groupBy(\"provincia\").count\n\nval result = humanos.select($\"provincia\",$\"count\"/62130.0*100).withColumnRenamed(\"((count / 62130.0) * 100)\",\"Porcentaje\").withColumn(\"Porcentaje\", format_number($\"Porcentaje\", 3))\nz.show( result )\nresult.select(sum(\"Porcentaje\")).show",
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"data": "org.apache.spark.sql.AnalysisException: Resolved attribute(s) anio#12616,ingreso_laboral#12626 missing from provincia#10060,anio#10011,ingreso_laboral#10021 in operator !Filter ((cast(anio#12616 as int) = 2015) && (cast(ingreso_laboral#12626 as int) < 400)). Attribute(s) with the same name appear in the operation: anio,ingreso_laboral. Please check if the right attribute(s) are used.;;\n!Filter ((cast(anio#12616 as int) = 2015) && (cast(ingreso_laboral#12626 as int) < 400))\n+- Project [provincia#10060, anio#10011, ingreso_laboral#10021]\n +- Project [anio#10011, mes#10012, CASE WHEN (cast(provincia#10013 as int) = 1) THEN Azuay ELSE CASE WHEN (cast(provincia#10013 as int) = 2) THEN Bovilar ELSE CASE WHEN (cast(provincia#10013 as int) = 3) THEN Caniar ELSE CASE WHEN (cast(provincia#10013 as int) = 4) THEN Carchi ELSE CASE WHEN (cast(provincia#10013 as int) = 5) THEN Cotopaxi ELSE CASE WHEN (cast(provincia#10013 as int) = 6) THEN Chimborazo ELSE CASE WHEN (cast(provincia#10013 as int) = 7) THEN El Oro ELSE CASE WHEN (cast(provincia#10013 as int) = 8) THEN Esmeraldas ELSE CASE WHEN (cast(provincia#10013 as int) = 9) THEN Guayas ELSE CASE WHEN (cast(provincia#10013 as int) = 10) THEN Imbabura ELSE CASE WHEN (cast(provincia#10013 as int) = 11) THEN Loja ELSE CASE WHEN (cast(provincia#10013 as int) = 12) THEN Los Rios ELSE CASE WHEN (cast(provincia#10013 as int) = 13) THEN Manabi ELSE CASE WHEN (cast(provincia#10013 as int) = 14) THEN Morona Santiago ELSE CASE WHEN (cast(provincia#10013 as int) = 15) THEN Napo ELSE CASE WHEN (cast(provincia#10013 as int) = 16) THEN Pastaza ELSE CASE WHEN (cast(provincia#10013 as int) = 17) THEN Pichincha ELSE CASE WHEN (cast(provincia#10013 as int) = 18) THEN Tungurahua ELSE CASE WHEN (cast(provincia#10013 as int) = 19) THEN Zamora Chinchipe ELSE CASE WHEN (cast(provincia#10013 as int) = 20) THEN Galapagos ELSE CASE WHEN (cast(provincia#10013 as int) = 21) THEN Sucumbios ELSE CASE WHEN (cast(provincia#10013 as int) = 22) THEN Orellana ELSE CASE WHEN (cast(provincia#10013 as int) = 23) THEN Santo Domingo de los Tsachilas ELSE CASE WHEN (cast(provincia#10013 as int) = 24) THEN Santa Elena END END END END END END END END END END END END END END END END END END END END END END END END AS provincia#10060, canton#10014, area#10015, genero#10016, edad#10017, estado_civil#10018, nivel_de_instruccion#10019, etnia#10020, ingreso_laboral#10021, condicion_actividad#10022, sectorizacion#10023, grupo_ocupacion#10024, rama_actividad#10025, factor_expansion#10026]\n +- Project [anio#10011, mes#10012, provincia#10013, canton#10014, area#10015, genero#10016, edad#10017, estado_civil#10018, nivel_de_instruccion#10019, etnia#10020, ingreso_laboral#10021, condicion_actividad#10022, sectorizacion#10023, grupo_ocupacion#10024, rama_actividad#10025, factor_expansion#10026]\n +- Relation[id_persona#10010,anio#10011,mes#10012,provincia#10013,canton#10014,area#10015,genero#10016,edad#10017,estado_civil#10018,nivel_de_instruccion#10019,etnia#10020,ingreso_laboral#10021,condicion_actividad#10022,sectorizacion#10023,grupo_ocupacion#10024,rama_actividad#10025,factor_expansion#10026] csv\n\n at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:43)\n at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:95)\n at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:369)\n at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:86)\n at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)\n at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:86)\n at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:95)\n at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:108)\n at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:105)\n at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)\n at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:105)\n at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)\n at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)\n at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)\n at org.apache.spark.sql.Dataset.<init>(Dataset.scala:176)\n at org.apache.spark.sql.Dataset.<init>(Dataset.scala:182)\n at org.apache.spark.sql.Dataset$.apply(Dataset.scala:64)\n at org.apache.spark.sql.Dataset.withTypedPlan(Dataset.scala:3417)\n at org.apache.spark.sql.Dataset.filter(Dataset.scala:1490)\n ... 123 elided\n"
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"title": "Variable Ingresos: TODO Algoritmo de tendencias y su gráfica (seria genial hacerlo por cada rama de actividad)",
"user": "anonymous",
"dateUpdated": "2020-08-01T21:04:38+0000",
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{
"title": "Variable Género (A: crosstab) : ¿Cual es la frecuencia de hombres y mujeres por provincia?",
"text": "\n\nval anioGeneroDF = data3.stat.crosstab(\"provincia\", \"genero\").orderBy(\"provincia_genero\")\nval r2= anioGeneroDF.select($\"provincia_genero\",anioGeneroDF(\"1 - Hombre\") + anioGeneroDF(\"2 - Mujer\") )\nz.show( anioGeneroDF.join(r2,Seq(\"provincia_genero\")).withColumnRenamed(\"(1 - Hombre + 2 - Mujer)\",\"TOTAL\") )",
"user": "anonymous",
"dateUpdated": "2020-08-01T23:02:08+0000",
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"2 - Mujer": "string",
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