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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b977bf15-ee73-4730-8651-f8a3caffb393",
+ "metadata": {},
+ "source": [
+ "# Analise estatistica com Teste CohenD\n",
+ "Teste de effect size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"dataframe\">\n",
+ "<caption>A data.frame: 6 × 39</caption>\n",
+ "<thead>\n",
+ "\t<tr><th></th><th scope=col>ANO_CENSO</th><th scope=col>NUM_SALAS</th><th scope=col>NUM_SALAS_UTILIZADAS</th><th scope=col>NUM_TV</th><th scope=col>NUM_VIDEOCASSETE</th><th scope=col>NUM_DVD</th><th scope=col>NUM_PARABOLICA</th><th scope=col>NUM_COPIADORA</th><th scope=col>NUM_RETROPROJETOR</th><th scope=col>NUM_IMPRESSORA</th><th scope=col>⋯</th><th scope=col>QTDE_PROF_COORDENADOR</th><th scope=col>QTDE_PROF_FONO</th><th scope=col>QTDE_PROF_NUTRICIONISTA</th><th scope=col>QTDE_PROF_PSICOLOGO</th><th scope=col>QTDE_PROF_ALIMENTACAO</th><th scope=col>QTDE_PROF_PEDAGOGIA</th><th scope=col>QTDE_PROF_SECRETARIO</th><th scope=col>QTDE_PROF_SEGURANCA</th><th scope=col>QTDE_PROF_MONITORES</th><th scope=col>QT_PROF_ADMIN</th></tr>\n",
+ "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col>⋯</th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n",
+ "</thead>\n",
+ "<tbody>\n",
+ "\t<tr><th scope=row>1</th><td>2013</td><td> 8</td><td> 8</td><td>3</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>4</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>2</th><td>2013</td><td> 5</td><td> 5</td><td>6</td><td>1</td><td>5</td><td>0</td><td>1</td><td>1</td><td>3</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>3</th><td>2013</td><td> 8</td><td> 8</td><td>3</td><td>1</td><td>2</td><td>0</td><td>1</td><td>2</td><td>5</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>4</th><td>2013</td><td>10</td><td>10</td><td>4</td><td>0</td><td>2</td><td>0</td><td>1</td><td>0</td><td>5</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>5</th><td>2013</td><td>12</td><td>12</td><td>6</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>7</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>6</th><td>2013</td><td> 4</td><td> 4</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "</tbody>\n",
+ "</table>\n"
+ ],
+ "text/latex": [
+ "A data.frame: 6 × 39\n",
+ "\\begin{tabular}{r|lllllllllllllllllllll}\n",
+ " & ANO\\_CENSO & NUM\\_SALAS & NUM\\_SALAS\\_UTILIZADAS & NUM\\_TV & NUM\\_VIDEOCASSETE & NUM\\_DVD & NUM\\_PARABOLICA & NUM\\_COPIADORA & NUM\\_RETROPROJETOR & NUM\\_IMPRESSORA & ⋯ & QTDE\\_PROF\\_COORDENADOR & QTDE\\_PROF\\_FONO & QTDE\\_PROF\\_NUTRICIONISTA & QTDE\\_PROF\\_PSICOLOGO & QTDE\\_PROF\\_ALIMENTACAO & QTDE\\_PROF\\_PEDAGOGIA & QTDE\\_PROF\\_SECRETARIO & QTDE\\_PROF\\_SEGURANCA & QTDE\\_PROF\\_MONITORES & QT\\_PROF\\_ADMIN\\\\\n",
+ " & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
+ "\\hline\n",
+ "\t1 & 2013 & 8 & 8 & 3 & 0 & 1 & 1 & 0 & 1 & 4 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t2 & 2013 & 5 & 5 & 6 & 1 & 5 & 0 & 1 & 1 & 3 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t3 & 2013 & 8 & 8 & 3 & 1 & 2 & 0 & 1 & 2 & 5 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t4 & 2013 & 10 & 10 & 4 & 0 & 2 & 0 & 1 & 0 & 5 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t5 & 2013 & 12 & 12 & 6 & 1 & 2 & 2 & 2 & 2 & 7 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t6 & 2013 & 4 & 4 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "A data.frame: 6 × 39\n",
+ "\n",
+ "| <!--/--> | ANO_CENSO <int> | NUM_SALAS <int> | NUM_SALAS_UTILIZADAS <int> | NUM_TV <int> | NUM_VIDEOCASSETE <int> | NUM_DVD <int> | NUM_PARABOLICA <int> | NUM_COPIADORA <int> | NUM_RETROPROJETOR <int> | NUM_IMPRESSORA <int> | ⋯ ⋯ | QTDE_PROF_COORDENADOR <int> | QTDE_PROF_FONO <int> | QTDE_PROF_NUTRICIONISTA <int> | QTDE_PROF_PSICOLOGO <int> | QTDE_PROF_ALIMENTACAO <int> | QTDE_PROF_PEDAGOGIA <int> | QTDE_PROF_SECRETARIO <int> | QTDE_PROF_SEGURANCA <int> | QTDE_PROF_MONITORES <int> | QT_PROF_ADMIN <int> |\n",
+ "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| 1 | 2013 | 8 | 8 | 3 | 0 | 1 | 1 | 0 | 1 | 4 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 2 | 2013 | 5 | 5 | 6 | 1 | 5 | 0 | 1 | 1 | 3 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 3 | 2013 | 8 | 8 | 3 | 1 | 2 | 0 | 1 | 2 | 5 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 4 | 2013 | 10 | 10 | 4 | 0 | 2 | 0 | 1 | 0 | 5 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 5 | 2013 | 12 | 12 | 6 | 1 | 2 | 2 | 2 | 2 | 7 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 6 | 2013 | 4 | 4 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "\n"
+ ],
+ "text/plain": [
+ " ANO_CENSO NUM_SALAS NUM_SALAS_UTILIZADAS NUM_TV NUM_VIDEOCASSETE NUM_DVD\n",
+ "1 2013 8 8 3 0 1 \n",
+ "2 2013 5 5 6 1 5 \n",
+ "3 2013 8 8 3 1 2 \n",
+ "4 2013 10 10 4 0 2 \n",
+ "5 2013 12 12 6 1 2 \n",
+ "6 2013 4 4 1 1 1 \n",
+ " NUM_PARABOLICA NUM_COPIADORA NUM_RETROPROJETOR NUM_IMPRESSORA ⋯\n",
+ "1 1 0 1 4 ⋯\n",
+ "2 0 1 1 3 ⋯\n",
+ "3 0 1 2 5 ⋯\n",
+ "4 0 1 0 5 ⋯\n",
+ "5 2 2 2 7 ⋯\n",
+ "6 0 1 1 1 ⋯\n",
+ " QTDE_PROF_COORDENADOR QTDE_PROF_FONO QTDE_PROF_NUTRICIONISTA\n",
+ "1 NA NA NA \n",
+ "2 NA NA NA \n",
+ "3 NA NA NA \n",
+ "4 NA NA NA \n",
+ "5 NA NA NA \n",
+ "6 NA NA NA \n",
+ " QTDE_PROF_PSICOLOGO QTDE_PROF_ALIMENTACAO QTDE_PROF_PEDAGOGIA\n",
+ "1 NA NA NA \n",
+ "2 NA NA NA \n",
+ "3 NA NA NA \n",
+ "4 NA NA NA \n",
+ "5 NA NA NA \n",
+ "6 NA NA NA \n",
+ " QTDE_PROF_SECRETARIO QTDE_PROF_SEGURANCA QTDE_PROF_MONITORES QT_PROF_ADMIN\n",
+ "1 NA NA NA NA \n",
+ "2 NA NA NA NA \n",
+ "3 NA NA NA NA \n",
+ "4 NA NA NA NA \n",
+ "5 NA NA NA NA \n",
+ "6 NA NA NA NA "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Carrega CSV\n",
+ "options(warn = -1)\n",
+ "library(effsize)\n",
+ "df = read.csv(\"../dados/escola_integers.csv\", sep=\"|\")\n",
+ "head(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c7eca10-6c0b-427d-be50-c4bf3d224f2d",
+ "metadata": {},
+ "source": [
+ "## Limpeza de Outliers\n",
+ "Para limpar dados que se diferenciam drasticamente do resto do conjunto de dados é utilizado a remoção dos outliers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Function to remove outliers using the IQR method\n",
+ "remove_outliers <- function(data) {\n",
+ " # Calculate the interquartile range (IQR)\n",
+ " Q1 <- quantile(data, 0.25)\n",
+ " Q3 <- quantile(data, 0.75)\n",
+ " IQR <- Q3 - Q1\n",
+ " \n",
+ " # Define the lower and upper bounds\n",
+ " lower_bound <- Q1 - 1.5 * IQR\n",
+ " upper_bound <- Q3 + 1.5 * IQR\n",
+ " \n",
+ " # Remove outliers\n",
+ " cleaned_data <- data[data >= lower_bound & data <= upper_bound]\n",
+ " \n",
+ " # Return the cleaned data\n",
+ " return(cleaned_data)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ca6c624-62c0-4fce-b3cd-c47d20c0b85e",
+ "metadata": {},
+ "source": [
+ "## Plot de Grafico\n",
+ "Funções para plot de gráficos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4c578156-afa1-4432-acb3-d6a143c0b349",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_ecdf <- function(x, x2){\n",
+ " plot(ecdf(x),\n",
+ " xlim = range(c(x, x2)), \n",
+ " col = \"blue\")\n",
+ " plot(ecdf(x2), \n",
+ " add = TRUE, \n",
+ " lty = \"dashed\",\n",
+ " col = \"red\")\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69c80406-3d20-408d-8fce-e4a7aea65dd4",
+ "metadata": {},
+ "source": [
+ "## Teste Cohen D\n",
+ "Utilizando testes de effect size é possivel determinar a magnitude da relação entre duas amostras\n",
+ "\n",
+ "Primeiro é selecionado duas amostras que foram agrupadas pelos anos do censo. \n",
+ "Em seguida é feito uma limpeza dos dados fazendo a retirada dos nulos, outliers e os ordenando. \n",
+ "Finalmente é executado o teste."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a9629868-ef70-4073-856b-fa3cee413898",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Cohen's d\n",
+ "\n",
+ "d estimate: -0.03455845 (negligible)\n",
+ "95 percent confidence interval:\n",
+ " lower upper \n",
+ "-0.04102476 -0.02809214 \n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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KMR7AAAqXB2llWr5M03xdU1+WCFCrJhg9SooV1bAFLFGjsAQOq8vGTGDJk0SQ4f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\ntwg78b/9iZGtHBuf69x56GvLtySn5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A\nKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACji/wED\nhCHmHljmmQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "Plot with title “ecdf(x)”"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 420,
+ "width": 420
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Dados agrupados por ano\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "# Amostras\n",
+ "X <- df[\"NUM_SALAS\"]\n",
+ "data1 <- X[ANO == 2013]\n",
+ "\n",
+ "Y <- df[\"NUM_SALAS\"]\n",
+ "data2 <- Y[ANO == 2014]\n",
+ "\n",
+ "# Remove zeros\n",
+ "data1 <- data1[data1 != 0]\n",
+ "data2 <- data2[data2 != 0]\n",
+ "\n",
+ "# Limpeza dos dados\n",
+ "data1 <- remove_outliers(na.omit(data1))\n",
+ "data2 <- remove_outliers(na.omit(data2))\n",
+ "\n",
+ "# Ajusta dados em histogramas\n",
+ "data1 <- hist(data1, breaks=100, PLOT=FALSE)\n",
+ "data2 <- hist(data2, breaks=100, PLOT=FALSE)\n",
+ "data1 <- data1$count\n",
+ "data2 <- data2$count\n",
+ "\n",
+ "# Executa teste Cohen D\n",
+ "print(cohen.d(data1, data2))\n",
+ "\n",
+ "# Plotagem dos dados\n",
+ "plot_ecdf(data1, data2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6c8c14d-5101-4201-b172-28e5b17016df",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos subsequentes\n",
+ "Execução do teste T entre uma coluna de um ano contra todas as colunas dos anos subsequentes para todos os anos do csv."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9aa86d14-0e60-4945-9d5f-d058b3c45654",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0));\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra de um ano\n",
+ " X <- df[col1]\n",
+ " data1 <- X[ANO == ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " data2 <- Y[ANO == ano+1]\n",
+ "\n",
+ " # Remove zeros\n",
+ " data1 <- data1[data1 != 0]\n",
+ " data2 <- data2[data2 != 0]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- remove_outliers(na.omit(data1))\n",
+ " data2 <- remove_outliers(na.omit(data2))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ " \n",
+ " # Ajusta dados em histogramas\n",
+ " data1 <- hist(data1, breaks=100, PLOT=FALSE)\n",
+ " data2 <- hist(data2, breaks=100, PLOT=FALSE)\n",
+ " data1 <- data1$count\n",
+ " data2 <- data2$count\n",
+ "\n",
+ " # Teste Cohen D\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_subsequente.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d52a6568-693a-4d59-b050-2f8d6899433e",
+ "metadata": {},
+ "source": [
+ "## Teste CohenD - Anos acumulados\n",
+ "O mesmo teste anterior, porém com o acumulo da amostra conforme o passar dos anos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bd76fa5b-a42c-434a-b3df-83f12f80a262",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Colunas do csv de saida\n",
+ "colunas = c(\"coluna1\", \"ano_coluna1\", \"coluna2\", \"ano_coluna2\", \"tamanho_amostra1\", \"estatistica_cohend\")\n",
+ "output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))\n",
+ "ano_start = min(df$ANO_CENSO)\n",
+ "\n",
+ "# Remove ANO_CENSO das iteracoes\n",
+ "atributos = names(df)\n",
+ "atributos = atributos[atributos != \"ANO_CENSO\"]\n",
+ " \n",
+ "# Separa os anos em amostras\n",
+ "ANO_COLUMN <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "for(ano in sort(unique(df$ANO_CENSO))){\n",
+ " for(col1 in atributos) {\n",
+ " for(col2 in atributos) {\n",
+ "\n",
+ " # Amostra acumulada dos anos\n",
+ " X <- df[col1]\n",
+ " data1 <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]\n",
+ "\n",
+ " # Amostra do ano seguinte\n",
+ " Y <- df[col2]\n",
+ " data2 <- Y[ANO_COLUMN == ano+1]\n",
+ "\n",
+ " # Remove zeros\n",
+ " data1 <- data1[data1 != 0]\n",
+ " data2 <- data2[data2 != 0]\n",
+ "\n",
+ " # Remove NaN, outliers e ordena\n",
+ " data1 <- remove_outliers(na.omit(data1))\n",
+ " data2 <- remove_outliers(na.omit(data2))\n",
+ "\n",
+ " # Pula casos em que não há dados nas amostras\n",
+ " if(length(data1) == 0 || length(data2) == 0){\n",
+ " next\n",
+ " }\n",
+ "\n",
+ " # Ajusta dados em histogramas\n",
+ " data1 <- hist(data1, breaks=100, PLOT=FALSE)\n",
+ " data2 <- hist(data2, breaks=100, PLOT=FALSE)\n",
+ " data1 <- data1$count\n",
+ " data2 <- data2$count\n",
+ "\n",
+ " # Teste Cohend\n",
+ " resultado = cohen.d(data1, data2)\n",
+ "\n",
+ " # Concatena resultados no dataframe\n",
+ " nova_linha = c(col1, ano, col2, ano+1, length(data1), resultado$estimate)\n",
+ " output_df = rbind(output_df, nova_linha)\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "output_csv = \"Result_COHEND/COHEND_acumulado.csv\"\n",
+ "colnames(output_df) <- colunas\n",
+ "write.csv(output_df, file = output_csv, row.names = FALSE)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "4.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Notebooks_R/TESTE_AD.ipynb b/Notebooks_R/TESTE_AD.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c8cc963bcde54e11ed52caac0a5cb6d382c34b70
--- /dev/null
+++ b/Notebooks_R/TESTE_AD.ipynb
@@ -0,0 +1,317 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b977bf15-ee73-4730-8651-f8a3caffb393",
+ "metadata": {},
+ "source": [
+ "# Analise estatistica com Teste AD"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ceab7323-0150-4a5c-8b6f-07464f948b9a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<table class=\"dataframe\">\n",
+ "<caption>A data.frame: 6 × 31</caption>\n",
+ "<thead>\n",
+ "\t<tr><th></th><th scope=col>ANO_CENSO</th><th scope=col>NUM_SALAS</th><th scope=col>NUM_SALAS_UTILIZADAS</th><th scope=col>NUM_TV</th><th scope=col>NUM_DVD</th><th scope=col>NUM_COPIADORA</th><th scope=col>NUM_IMPRESSORA</th><th scope=col>NUM_SOM</th><th scope=col>NUM_MULTIMIDIA</th><th scope=col>NUM_FOTO</th><th scope=col>⋯</th><th scope=col>QTDE_TABLET</th><th scope=col>QTDE_PROF_SERVICOS_GERAIS</th><th scope=col>QTDE_PROF_BIBLIOTECARIO</th><th scope=col>QTDE_PROF_SAUDE</th><th scope=col>QTDE_PROF_COORDENADOR</th><th scope=col>QTDE_PROF_ALIMENTACAO</th><th scope=col>QTDE_PROF_PEDAGOGIA</th><th scope=col>QTDE_PROF_SEGURANCA</th><th scope=col>QTDE_PROF_MONITORES</th><th scope=col>QT_PROF_ADMIN</th></tr>\n",
+ "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col>⋯</th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n",
+ "</thead>\n",
+ "<tbody>\n",
+ "\t<tr><th scope=row>1</th><td>2013</td><td> 8</td><td> 8</td><td>3</td><td>1</td><td>0</td><td>4</td><td>1</td><td>7</td><td>1</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>2</th><td>2013</td><td> 5</td><td> 5</td><td>6</td><td>5</td><td>1</td><td>3</td><td>7</td><td>1</td><td>2</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>3</th><td>2013</td><td> 8</td><td> 8</td><td>3</td><td>2</td><td>1</td><td>5</td><td>3</td><td>2</td><td>2</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>4</th><td>2013</td><td>10</td><td>10</td><td>4</td><td>2</td><td>1</td><td>5</td><td>2</td><td>2</td><td>3</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>5</th><td>2013</td><td>12</td><td>12</td><td>6</td><td>2</td><td>2</td><td>7</td><td>1</td><td>1</td><td>1</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "\t<tr><th scope=row>6</th><td>2013</td><td> 4</td><td> 4</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n",
+ "</tbody>\n",
+ "</table>\n"
+ ],
+ "text/latex": [
+ "A data.frame: 6 × 31\n",
+ "\\begin{tabular}{r|lllllllllllllllllllll}\n",
+ " & ANO\\_CENSO & NUM\\_SALAS & NUM\\_SALAS\\_UTILIZADAS & NUM\\_TV & NUM\\_DVD & NUM\\_COPIADORA & NUM\\_IMPRESSORA & NUM\\_SOM & NUM\\_MULTIMIDIA & NUM\\_FOTO & ⋯ & QTDE\\_TABLET & QTDE\\_PROF\\_SERVICOS\\_GERAIS & QTDE\\_PROF\\_BIBLIOTECARIO & QTDE\\_PROF\\_SAUDE & QTDE\\_PROF\\_COORDENADOR & QTDE\\_PROF\\_ALIMENTACAO & QTDE\\_PROF\\_PEDAGOGIA & QTDE\\_PROF\\_SEGURANCA & QTDE\\_PROF\\_MONITORES & QT\\_PROF\\_ADMIN\\\\\n",
+ " & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
+ "\\hline\n",
+ "\t1 & 2013 & 8 & 8 & 3 & 1 & 0 & 4 & 1 & 7 & 1 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t2 & 2013 & 5 & 5 & 6 & 5 & 1 & 3 & 7 & 1 & 2 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t3 & 2013 & 8 & 8 & 3 & 2 & 1 & 5 & 3 & 2 & 2 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t4 & 2013 & 10 & 10 & 4 & 2 & 1 & 5 & 2 & 2 & 3 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t5 & 2013 & 12 & 12 & 6 & 2 & 2 & 7 & 1 & 1 & 1 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\t6 & 2013 & 4 & 4 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & ⋯ & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n",
+ "\\end{tabular}\n"
+ ],
+ "text/markdown": [
+ "\n",
+ "A data.frame: 6 × 31\n",
+ "\n",
+ "| <!--/--> | ANO_CENSO <int> | NUM_SALAS <int> | NUM_SALAS_UTILIZADAS <int> | NUM_TV <int> | NUM_DVD <int> | NUM_COPIADORA <int> | NUM_IMPRESSORA <int> | NUM_SOM <int> | NUM_MULTIMIDIA <int> | NUM_FOTO <int> | ⋯ ⋯ | QTDE_TABLET <int> | QTDE_PROF_SERVICOS_GERAIS <int> | QTDE_PROF_BIBLIOTECARIO <int> | QTDE_PROF_SAUDE <int> | QTDE_PROF_COORDENADOR <int> | QTDE_PROF_ALIMENTACAO <int> | QTDE_PROF_PEDAGOGIA <int> | QTDE_PROF_SEGURANCA <int> | QTDE_PROF_MONITORES <int> | QT_PROF_ADMIN <int> |\n",
+ "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+ "| 1 | 2013 | 8 | 8 | 3 | 1 | 0 | 4 | 1 | 7 | 1 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 2 | 2013 | 5 | 5 | 6 | 5 | 1 | 3 | 7 | 1 | 2 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 3 | 2013 | 8 | 8 | 3 | 2 | 1 | 5 | 3 | 2 | 2 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 4 | 2013 | 10 | 10 | 4 | 2 | 1 | 5 | 2 | 2 | 3 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 5 | 2013 | 12 | 12 | 6 | 2 | 2 | 7 | 1 | 1 | 1 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "| 6 | 2013 | 4 | 4 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | ⋯ | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n",
+ "\n"
+ ],
+ "text/plain": [
+ " ANO_CENSO NUM_SALAS NUM_SALAS_UTILIZADAS NUM_TV NUM_DVD NUM_COPIADORA\n",
+ "1 2013 8 8 3 1 0 \n",
+ "2 2013 5 5 6 5 1 \n",
+ "3 2013 8 8 3 2 1 \n",
+ "4 2013 10 10 4 2 1 \n",
+ "5 2013 12 12 6 2 2 \n",
+ "6 2013 4 4 1 1 1 \n",
+ " NUM_IMPRESSORA NUM_SOM NUM_MULTIMIDIA NUM_FOTO ⋯ QTDE_TABLET\n",
+ "1 4 1 7 1 ⋯ NA \n",
+ "2 3 7 1 2 ⋯ NA \n",
+ "3 5 3 2 2 ⋯ NA \n",
+ "4 5 2 2 3 ⋯ NA \n",
+ "5 7 1 1 1 ⋯ NA \n",
+ "6 1 0 1 1 ⋯ NA \n",
+ " QTDE_PROF_SERVICOS_GERAIS QTDE_PROF_BIBLIOTECARIO QTDE_PROF_SAUDE\n",
+ "1 NA NA NA \n",
+ "2 NA NA NA \n",
+ "3 NA NA NA \n",
+ "4 NA NA NA \n",
+ "5 NA NA NA \n",
+ "6 NA NA NA \n",
+ " QTDE_PROF_COORDENADOR QTDE_PROF_ALIMENTACAO QTDE_PROF_PEDAGOGIA\n",
+ "1 NA NA NA \n",
+ "2 NA NA NA \n",
+ "3 NA NA NA \n",
+ "4 NA NA NA \n",
+ "5 NA NA NA \n",
+ "6 NA NA NA \n",
+ " QTDE_PROF_SEGURANCA QTDE_PROF_MONITORES QT_PROF_ADMIN\n",
+ "1 NA NA NA \n",
+ "2 NA NA NA \n",
+ "3 NA NA NA \n",
+ "4 NA NA NA \n",
+ "5 NA NA NA \n",
+ "6 NA NA NA "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Carrega CSV\n",
+ "options(warn = -1)\n",
+ "library(kSamples)\n",
+ "df = read.csv(\"../dados/escola_integers.csv\", sep=\"|\")\n",
+ "head(df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c7eca10-6c0b-427d-be50-c4bf3d224f2d",
+ "metadata": {},
+ "source": [
+ "## Limpeza de Outliers\n",
+ "Para limpar dados que se diferenciam drasticamente do resto do conjunto de dados é utilizado a remoção dos outliers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ee18ee6d-1dda-4c65-8347-f3b7e980e83c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Function to remove outliers using the IQR method\n",
+ "remove_outliers <- function(data) {\n",
+ " # Calculate the interquartile range (IQR)\n",
+ " Q1 <- quantile(data, 0.25)\n",
+ " Q3 <- quantile(data, 0.75)\n",
+ " IQR <- Q3 - Q1\n",
+ " \n",
+ " # Define the lower and upper bounds\n",
+ " lower_bound <- Q1 - 1.5 * IQR\n",
+ " upper_bound <- Q3 + 1.5 * IQR\n",
+ " \n",
+ " # Remove outliers\n",
+ " cleaned_data <- data[data >= lower_bound & data <= upper_bound]\n",
+ " \n",
+ " # Return the cleaned data\n",
+ " return(cleaned_data)\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ca6c624-62c0-4fce-b3cd-c47d20c0b85e",
+ "metadata": {},
+ "source": [
+ "## Plot de Grafico\n",
+ "Funções para plot de gráficos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4c578156-afa1-4432-acb3-d6a143c0b349",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_ecdf <- function(x, x2){\n",
+ " plot(ecdf(x),\n",
+ " xlim = range(c(x, x2)), \n",
+ " col = \"blue\")\n",
+ " plot(ecdf(x2), \n",
+ " add = TRUE, \n",
+ " lty = \"dashed\",\n",
+ " col = \"red\")\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69c80406-3d20-408d-8fce-e4a7aea65dd4",
+ "metadata": {},
+ "source": [
+ "## Teste Cohen D\n",
+ "Utilizando testes de effect size é possivel determinar a magnitude da relação entre duas amostras\n",
+ "\n",
+ "Primeiro é selecionado duas amostras que foram agrupadas pelos anos do censo. \n",
+ "Em seguida é feito uma limpeza dos dados fazendo a retirada dos nulos, outliers e os ordenando. \n",
+ "Finalmente é executado o teste."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a9629868-ef70-4073-856b-fa3cee413898",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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7f9qN/OBhivbuOfX940vlPbrw4Zcq3KocfkkfVSu1p6A4fzat/ZP9up0Pvu2OwZ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\n00+HUoW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qnV13//vbA/qe4FxEsOS9MG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\nTt3PW353AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "Plot with title “NUM_DVD 2014”"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 420,
+ "width": 420
+ }
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ " Anderson-Darling k-sample test.\n",
+ "\n",
+ "Number of samples: 2\n",
+ "Sample sizes: 10, 10\n",
+ "Number of ties: 6\n",
+ "\n",
+ "Mean of Anderson-Darling Criterion: 1\n",
+ "Standard deviation of Anderson-Darling Criterion: 0.69621\n",
+ "\n",
+ "T.AD = ( Anderson-Darling Criterion - mean)/sigma\n",
+ "\n",
+ "Null Hypothesis: All samples come from a common population.\n",
+ "\n",
+ " AD T.AD asympt. P-value\n",
+ "version 1: 4.8335 5.5063 0.0023038\n",
+ "version 2: 4.8900 5.5817 0.0021672\n",
+ "[1] 4.86175\n",
+ "[1] 0.0022355\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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nL0nvHsNjn85JrRbZt/stOQa1XuXccPqZ7Z2dJLeDnP9v38RzsT+tANaw8myEII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\nZdIkSRQ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l0AQUBkMUMFARHcF1BcUeox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\nAABJRU5ErkJggg==",
+ "text/plain": [
+ "Plot with title “NUM_DVD 2014”"
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 420,
+ "width": 420
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Dados agrupados por ano\n",
+ "ANO <- df[\"ANO_CENSO\"]\n",
+ "\n",
+ "col1 <- \"NUM_COMPUTADOR\"\n",
+ "col2 <- \"NUM_DVD\"\n",
+ "ano1 <- 2013\n",
+ "ano2 <- 2014\n",
+ "\n",
+ "# Amostras\n",
+ "X <- df[col1]\n",
+ "data1 <- X[ANO == ano1]\n",
+ "\n",
+ "Y <- df[col2]\n",
+ "data2 <- Y[ANO == ano2]\n",
+ "\n",
+ "# Remove zeros\n",
+ "data1 <- data1[data1 != 0]\n",
+ "data2 <- data2[data2 != 0]\n",
+ "\n",
+ "# Limpeza dos dados\n",
+ "data1 <- remove_outliers(na.omit(data1))\n",
+ "data2 <- remove_outliers(na.omit(data2))\n",
+ "\n",
+ "# histogramas\n",
+ "par(mfrow=c(1, 2)) # Ajusta plot\n",
+ "data1 <- hist(data1, breaks=10, PLOT=FALSE, main=paste(col1, as.character(ano1)), col=rgb(1,0,0,0.5))\n",
+ "data2 <- hist(data2, breaks=10, PLOT=FALSE, main=paste(col2, as.character(ano2)), col=rgb(0,0,1,0.5))\n",
+ "\n",
+ "# Normaliza\n",
+ "data1 <- data1$count / sum(data1$count)\n",
+ "data2 <- data2$count / sum(data2$count)\n",
+ "\n",
+ "plot(data1, main=paste(col1, as.character(ano1)), xlab=\"Value\", ylab=\"Normalized Frequency\", col=\"blue\")\n",
+ "plot(data2, main=paste(col2, as.character(ano2)), xlab=\"Value\", ylab=\"Normalized Frequency\", col=\"blue\")\n",
+ "\n",
+ "# Executa teste KS\n",
+ "resultado <- ad.test(list(data1, data2))\n",
+ "\n",
+ "print(resultado)\n",
+ "\n",
+ "print((resultado$ad[1, 1] + resultado$ad[2, 1])/2)\n",
+ "print((resultado$ad[1, 3] + resultado$ad[2, 3])/2)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "4.4.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Testes_hist/teste_ad.r b/Testes_hist/teste_ad.r
new file mode 100644
index 0000000000000000000000000000000000000000..e176a70fe2c1c53f84c53036975129dc355b5a21
--- /dev/null
+++ b/Testes_hist/teste_ad.r
@@ -0,0 +1,152 @@
+#!/usr/bin/env Rscript
+
+# Function to remove outliers using the IQR method
+remove_outliers <- function(data) {
+ # Calculate the interquartile range (IQR)
+ Q1 <- quantile(data, 0.25)
+ Q3 <- quantile(data, 0.75)
+ IQR <- Q3 - Q1
+
+ # Define the lower and upper bounds
+ lower_bound <- Q1 - 1.5 * IQR
+ upper_bound <- Q3 + 1.5 * IQR
+
+ # Remove outliers
+ cleaned_data <- data[data >= lower_bound & data <= upper_bound]
+
+ # Return the cleaned data
+ return(cleaned_data)
+}
+
+# MAIN ==========================================
+
+# Carrega CSV
+options(warn = -1)
+library(kSamples)
+df = read.csv("../dados/escola_integers.csv", sep="|")
+#head(df)
+
+# Colunas do csv de saida
+colunas = c("coluna1", "ano_coluna1", "coluna2", "ano_coluna2", "tamanho_amostra", "estatistica_ks", "p_valor")
+output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))
+
+# Remove ANO_CENSO das iteracoes
+atributos = names(df)
+atributos = atributos[atributos != "ANO_CENSO"]
+
+# Separa os anos em amostras
+ANO <- df["ANO_CENSO"]
+
+for(ano in sort(unique(df$ANO_CENSO))){
+ for(col1 in atributos) {
+ for(col2 in atributos) {
+
+ # Amostra de um ano
+ X <- df[col1]
+ data1 <- X[ANO == ano]
+
+ # Amostra do ano seguinte
+ Y <- df[col2]
+ data2 <- Y[ANO == ano+1]
+
+ # Remove zeros
+ data1 <- data1[data1 != 0]
+ data2 <- data2[data2 != 0]
+
+ # Remove NaN, outliers e ordena
+ data1 <- remove_outliers(na.omit(data1))
+ data2 <- remove_outliers(na.omit(data2))
+
+ # Pula casos em que não há dados nas amostras
+ if(length(data1) == 0 || length(data2) == 0){
+ next
+ }
+
+ # histogramas
+ par(mfrow=c(1, 2)) # Ajusta plot
+ data1 <- hist(data1, breaks=10, PLOT=FALSE, main=paste(col1, as.character(ano)), col=rgb(1,0,0,0.5))
+ data2 <- hist(data2, breaks=10, PLOT=FALSE, main=paste(col2, as.character(ano+1)), col=rgb(0,0,1,0.5))
+
+ data1 <- data1$count
+ data2 <- data2$count
+
+ # Teste AD
+ resultado = ad.test(list(data1, data2))
+
+ estatistica = (resultado$ad[1, 1] + resultado$ad[2, 1])/2
+ p_valor = (resultado$ad[1, 3] + resultado$ad[2, 3])/2
+
+ # Concatena resultados no dataframe
+ nova_linha = c(col1, ano, col2, ano+1, length(data1), estatistica, p_valor)
+ output_df = rbind(output_df, nova_linha)
+ }
+ }
+}
+output_csv = "../R_resultados/Histograma_norm/AD_subsequente.csv"
+colnames(output_df) <- colunas
+write.csv(output_df, file = output_csv, row.names = FALSE)
+
+# ACUMULADO =====================================
+
+# Colunas do csv de saida
+colunas = c("coluna1", "ano_coluna1", "coluna2", "ano_coluna2", "tamanho_amostra", "estatistica_ks", "p_valor")
+output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))
+ano_start = min(df$ANO_CENSO)
+
+# Remove ANO_CENSO das iteracoes
+atributos = names(df)
+atributos = atributos[atributos != "ANO_CENSO"]
+
+# Separa os anos em amostras
+ANO_COLUMN <- df["ANO_CENSO"]
+
+for(ano in sort(unique(df$ANO_CENSO))){
+ for(col1 in atributos) {
+ for(col2 in atributos) {
+
+ # Amostra acumulada dos anos
+ X <- df[col1]
+ data1 <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]
+
+ # Amostra do ano seguinte
+ Y <- df[col2]
+ data2 <- Y[ANO_COLUMN == ano+1]
+
+ # Remove zeros
+ data1 <- data1[data1 != 0]
+ data2 <- data2[data2 != 0]
+
+ # Remove NaN, outliers e ordena
+ data1 <- remove_outliers(na.omit(data1))
+ data2 <- remove_outliers(na.omit(data2))
+
+ # Pula casos em que não há dados nas amostras
+ if(length(data1) == 0 || length(data2) == 0){
+ next
+ }
+
+ # histogramas
+ par(mfrow=c(1, 2)) # Ajusta plot
+ data1 <- hist(data1, breaks=10, PLOT=FALSE, main=paste(col1, as.character(ano)), col=rgb(1,0,0,0.5))
+ data2 <- hist(data2, breaks=10, PLOT=FALSE, main=paste(col2, as.character(ano+1)), col=rgb(0,0,1,0.5))
+
+ data1 <- data1$count
+ data2 <- data2$count
+
+ # Teste AD
+ resultado = ad.test(list(data1, data2))
+
+ estatistica = (resultado$ad[1, 1] + resultado$ad[2, 1])/2
+ p_valor = (resultado$ad[1, 3] + resultado$ad[2, 3])/2
+
+ # Concatena resultados no dataframe
+ nova_linha = c(col1, ano, col2, ano+1, length(data1), estatistica, p_valor)
+ output_df = rbind(output_df, nova_linha)
+ }
+ }
+}
+output_csv = "../R_resultados/Histograma_norm/AD_acumulado.csv"
+colnames(output_df) <- colunas
+write.csv(output_df, file = output_csv, row.names = FALSE)
+
+
diff --git a/Testes_norm/teste_ad.r b/Testes_norm/teste_ad.r
new file mode 100644
index 0000000000000000000000000000000000000000..a20f582e308eb339cddee11c6e797fb899a61b56
--- /dev/null
+++ b/Testes_norm/teste_ad.r
@@ -0,0 +1,160 @@
+#!/usr/bin/env Rscript
+
+# Function to remove outliers using the IQR method
+remove_outliers <- function(data) {
+ # Calculate the interquartile range (IQR)
+ Q1 <- quantile(data, 0.25)
+ Q3 <- quantile(data, 0.75)
+ IQR <- Q3 - Q1
+
+ # Define the lower and upper bounds
+ lower_bound <- Q1 - 1.5 * IQR
+ upper_bound <- Q3 + 1.5 * IQR
+
+ # Remove outliers
+ cleaned_data <- data[data >= lower_bound & data <= upper_bound]
+
+ # Return the cleaned data
+ return(cleaned_data)
+}
+
+# MAIN ==========================================
+
+# Carrega CSV
+options(warn = -1)
+library(kSamples)
+df = read.csv("../dados/escola_integers.csv", sep="|")
+#head(df)
+
+# Colunas do csv de saida
+colunas = c("coluna1", "ano_coluna1", "coluna2", "ano_coluna2", "tamanho_amostra", "estatistica_ks", "p_valor")
+output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))
+
+# Remove ANO_CENSO das iteracoes
+atributos = names(df)
+atributos = atributos[atributos != "ANO_CENSO"]
+
+# Separa os anos em amostras
+ANO <- df["ANO_CENSO"]
+
+for(ano in sort(unique(df$ANO_CENSO))){
+ for(col1 in atributos) {
+ for(col2 in atributos) {
+
+ # Amostra de um ano
+ X <- df[col1]
+ data1 <- X[ANO == ano]
+
+ # Amostra do ano seguinte
+ Y <- df[col2]
+ data2 <- Y[ANO == ano+1]
+
+ # Remove zeros
+ data1 <- data1[data1 != 0]
+ data2 <- data2[data2 != 0]
+
+ # Remove NaN, outliers e ordena
+ data1 <- remove_outliers(na.omit(data1))
+ data2 <- remove_outliers(na.omit(data2))
+
+ # Pula casos em que não há dados nas amostras
+ if(length(data1) == 0 || length(data2) == 0){
+ next
+ }
+
+ # histogramas
+ par(mfrow=c(1, 2)) # Ajusta plot
+ data1 <- hist(data1, breaks=10, PLOT=FALSE, main=paste(col1, as.character(ano)), col=rgb(1,0,0,0.5))
+ data2 <- hist(data2, breaks=10, PLOT=FALSE, main=paste(col2, as.character(ano+1)), col=rgb(0,0,1,0.5))
+
+ # Normaliza
+ data1 <- data1$count / sum(data1$count)
+ data2 <- data2$count / sum(data2$count)
+
+ plot(data1, main=paste(col1, as.character(ano)), xlab="Value", ylab="Normalized Frequency", col="red")
+ plot(data2, main=paste(col2, as.character(ano+1)), xlab="Value", ylab="Normalized Frequency", col="blue")
+
+ # Teste AD
+ resultado = ad.test(list(data1, data2))
+
+ estatistica = (resultado$ad[1, 1] + resultado$ad[2, 1])/2
+ p_valor = (resultado$ad[1, 3] + resultado$ad[2, 3])/2
+
+ # Concatena resultados no dataframe
+ nova_linha = c(col1, ano, col2, ano+1, length(data1), estatistica, p_valor)
+ output_df = rbind(output_df, nova_linha)
+ }
+ }
+}
+output_csv = "../R_resultados/Histograma_norm/AD_subsequente.csv"
+colnames(output_df) <- colunas
+write.csv(output_df, file = output_csv, row.names = FALSE)
+
+# ACUMULADO =====================================
+
+# Colunas do csv de saida
+colunas = c("coluna1", "ano_coluna1", "coluna2", "ano_coluna2", "tamanho_amostra", "estatistica_ks", "p_valor")
+output_df = data.frame(matrix(ncol = length(colunas), nrow = 0))
+ano_start = min(df$ANO_CENSO)
+
+# Remove ANO_CENSO das iteracoes
+atributos = names(df)
+atributos = atributos[atributos != "ANO_CENSO"]
+
+# Separa os anos em amostras
+ANO_COLUMN <- df["ANO_CENSO"]
+
+for(ano in sort(unique(df$ANO_CENSO))){
+ for(col1 in atributos) {
+ for(col2 in atributos) {
+
+ # Amostra acumulada dos anos
+ X <- df[col1]
+ data1 <- X[ANO_COLUMN >= ano_start & ANO_COLUMN <= ano]
+
+ # Amostra do ano seguinte
+ Y <- df[col2]
+ data2 <- Y[ANO_COLUMN == ano+1]
+
+ # Remove zeros
+ data1 <- data1[data1 != 0]
+ data2 <- data2[data2 != 0]
+
+ # Remove NaN, outliers e ordena
+ data1 <- remove_outliers(na.omit(data1))
+ data2 <- remove_outliers(na.omit(data2))
+
+ # Pula casos em que não há dados nas amostras
+ if(length(data1) == 0 || length(data2) == 0){
+ next
+ }
+
+ # histogramas
+ par(mfrow=c(1, 2)) # Ajusta plot
+ data1 <- hist(data1, breaks=10, PLOT=FALSE, main=paste(col1, as.character(ano)), col=rgb(1,0,0,0.5))
+ data2 <- hist(data2, breaks=10, PLOT=FALSE, main=paste(col2, as.character(ano+1)), col=rgb(0,0,1,0.5))
+
+ # Normaliza
+ data1 <- data1$count / sum(data1$count)
+ data2 <- data2$count / sum(data2$count)
+
+ plot(data1, main=paste(col1, as.character(ano)), xlab="Value", ylab="Normalized Frequency", col="red")
+ plot(data2, main=paste(col2, as.character(ano+1)), xlab="Value", ylab="Normalized Frequency", col="blue")
+
+ # Teste AD
+ resultado = ad.test(list(data1, data2))
+
+ estatistica = (resultado$ad[1, 1] + resultado$ad[2, 1])/2
+ p_valor = (resultado$ad[1, 3] + resultado$ad[2, 3])/2
+
+ # Concatena resultados no dataframe
+ nova_linha = c(col1, ano, col2, ano+1, length(data1), estatistica, p_valor)
+ output_df = rbind(output_df, nova_linha)
+ }
+ }
+}
+output_csv = "../R_resultados/Histograma_norm/AD_acumulado.csv"
+colnames(output_df) <- colunas
+write.csv(output_df, file = output_csv, row.names = FALSE)
+
+
diff --git a/run.sh b/run.sh
index f232cf0179ff1badb4477865d3bb71f83446d754..c76388e2b8cd6e026cac9c5847d5fff014a85aeb 100644
--- a/run.sh
+++ b/run.sh
@@ -1,13 +1,21 @@
#!/bin/bash
-cd Testes_R
-source run.sh
-cd ..
+#cd Testes_R
+#source run.sh
+#cd ..
+#
+#cd Testes_hist
+#source run.sh
+#cd ..
+#
+#cd Testes_norm
+#source run.sh
+#cd ..
cd Testes_hist
-source run.sh
+Rscript ./teste_ad.r
cd ..
cd Testes_norm
-source run.sh
+Rscript ./teste_ad.r
cd ..