Projeto Mestrado - FATORES QUE CONTRIBUÍRAM PARA QUE AS IES PRIVADAS BRASILEIRAS SE DISTANCIASSEM DE SEU PONTO DE EQUILÍBRIO FINANCEIRO
Marcio Freire
15/03/2022
1 Introdução
FATORES QUE CONTRIBUÍRAM PARA QUE AS IES PRIVADAS BRASILEIRAS SE DISTANCIASSEM DE SEU PONTO DE EQUILÍBRIO FINANCEIRO
Este projeto R é disponibilizado para a comunidade como suporte a publicação da dissertação de mestrado.
O intuito da publicação é permitir ao pesquisador reproduzir, até para outras amostras, o resultado obtido.
Boa leitura.
2 Bibliotecas R Utilizadas
Carga de Bibliotecas utilizadas
library(readr)
library(dplyr)
library(tidyverse)
library(tibble)
library(stringr)
library(factoextra)
library (reshape2)
library(ggplot2)
library(hrbrthemes)
library(viridis)
library(pastecs)
library(rpart)
library(rpart.plot)
library(iml)
library(randomForest)
library(partykit)
library(patchwork)
library(reshape2)
library(ggplot2)
library(corrplot)
library(corrgram)
library(caret)
library(leaps)
library(h2o)
library(gtsummary)
library(knitr)
options(digits=12)3 Censo Superior 2017
Análise dos dados do Censo Superior 2017
3.1 Importação dos Microdados
Importação dos microdados do censo do ensino superior 2017. Download em: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados.
O arquivo contém as seguintes bases (CSV) que iremos importar (base fornecida antes antes de Set/2021):
- SUP_ALUNO_2017.CSV
- SUP_LOCAL_OFERTA_2017.CSV
- SUP_DOCENTE_2017.CSV
- SUP_IES_2017.CSV
- SUP_CURSO_2017.CSV
3.1.1 Instituições - IES - 2017
Importação e tratamento do arquivo relativo ao registro das IES em 2017
BaseIES = read_delim("./raw-data/SUP_IES_2017.CSV",
"|",
escape_double = FALSE,
col_types = cols(NU_ANO_CENSO= col_integer(),
CO_IES = col_integer(),
NO_IES = col_character(),
SG_IES = col_character(),
CO_MANTENEDORA = col_integer(),
NO_MANTENEDORA = col_character(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_REGIAO = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
QT_TEC_TOTAL = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_MASC = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_MASC = col_integer(),
QT_TEC_MEDIO_FEM = col_integer(),
QT_TEC_MEDIO_MASC = col_integer(),
QT_TEC_SUPERIOR_FEM = col_integer(),
QT_TEC_SUPERIOR_MASC = col_integer(),
QT_TEC_ESPECIALIZACAO_FEM = col_integer(),
QT_TEC_ESPECIALIZACAO_MASC = col_integer(),
QT_TEC_MESTRADO_FEM = col_integer(),
QT_TEC_MESTRADO_MASC = col_integer(),
QT_TEC_DOUTORADO_FEM = col_integer(),
QT_TEC_DOUTORADO_MASC = col_integer(),
IN_ACESSO_PORTAL_CAPES = col_integer(),
IN_ACESSO_OUTRAS_BASES = col_integer(),
IN_REPOSITORIO_INSTITUCIONAL = col_integer(),
IN_BUSCA_INTEGRADA = col_integer(),
IN_SERVICO_INTERNET = col_integer(),
IN_PARTICIPA_REDE_SOCIAL = col_integer(),
IN_CATALOGO_ONLINE = col_integer(),
QT_PERIODICO_ELETRONICO = col_integer(),
QT_LIVRO_ELETRONICO = col_integer(),
TP_REFERENTE = col_integer(),
VL_RECEITA_PROPRIA = col_double(),
VL_RECEITA_TRANSFERENCIA = col_double(),
VL_RECEITA_OUTRA = col_double(),
VL_DESPESA_PESSOAL_DOCENTE = col_double(),
VL_DESPESA_PESSOAL_TECNICO = col_double(),
VL_DESPESA_PESSOAL_ENCARGO = col_double(),
VL_DESPESA_CUSTEIO = col_double(),
VL_DESPESA_INVESTIMENTO = col_double(),
VL_DESPESA_PESQUISA = col_double(),
VL_DESPESA_OUTRA = col_double()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseIES = BaseIES %>% dplyr::select(everything()) %>%
mutate(NO_CATEGORIA_ADMINISTRATIVA = case_when(
TP_CATEGORIA_ADMINISTRATIVA == 1 ~ "PUBLICA FEDERAL",
TP_CATEGORIA_ADMINISTRATIVA == 2 ~ "PUBLICA ESTADUAL",
TP_CATEGORIA_ADMINISTRATIVA == 3 ~ "PUBLICA MUNICIPAL",
TP_CATEGORIA_ADMINISTRATIVA == 4 ~ "PRIVADA COM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 5 ~ "PRIVADA SEM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 6 ~ "PRIVADA CONFESSIONAL",
TP_CATEGORIA_ADMINISTRATIVA == 7 ~ "ESPECIAL"),
NO_ORGANIZACAO_ACADEMICA = case_when(
TP_ORGANIZACAO_ACADEMICA == 1 ~ "UNIVERSIDADE",
TP_ORGANIZACAO_ACADEMICA == 2 ~ "CENTRO UNIVERSITARIO",
TP_ORGANIZACAO_ACADEMICA == 3 ~ "FACULDADE",
TP_ORGANIZACAO_ACADEMICA == 4 ~ "INSTITUTO FEDERAL DE EDUCACAO - CIENCIA E TECNOLOGIA",
TP_ORGANIZACAO_ACADEMICA == 5 ~ "CENTRO FEDERAL DE EDUCACAO TECNOLOGICA"),
NO_REGIAO = case_when(
CO_REGIAO == 1 ~ "NORTE",
CO_REGIAO == 2 ~ "NORDESTE",
CO_REGIAO == 3 ~ "SUDESTE",
CO_REGIAO == 4 ~ "SUL",
CO_REGIAO == 5 ~ "CENTRO-OESTE"))
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseIES = BaseIES %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
saveRDS(BaseIES, "./data/ies_2017_data.rds")
rm(BaseIES)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2822542 150.8 4260781 227.6 4260781 227.6
## Vcells 4699639 35.9 10146329 77.5 7259225 55.43.1.2 Cursos das IES - 2017
Importação e tratamento do arquivo relativo ao registro dos cursos das IES em 2017
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseCurso = read_delim("./raw-data/SUP_CURSO_2017.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
CO_CURSO = col_integer(),
NO_CURSO = col_character(),
TP_SITUACAO = col_integer(),
CO_OCDE_AREA_GERAL = col_character(),
CO_OCDE_AREA_ESPECIFICA = col_character(),
CO_OCDE_AREA_DETALHADA = col_character(),
CO_OCDE = col_character(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
IN_GRATUITO = col_integer(),
TP_ATRIBUTO_INGRESSO = col_integer(),
NU_CARGA_HORARIA = col_integer(),
DT_INICIO_FUNCIONAMENTO = col_character(),
DT_AUTORIZACAO_CURSO = col_character(),
IN_AJUDA_DEFICIENTE = col_integer(),
IN_MATERIAL_DIGITAL = col_integer(),
IN_MATERIAL_AMPLIADO = col_integer(),
IN_MATERIAL_TATIL = col_integer(),
IN_MATERIAL_IMPRESSO = col_integer(),
IN_MATERIAL_AUDIO = col_integer(),
IN_MATERIAL_BRAILLE = col_integer(),
IN_MATERIAL_LIBRAS = col_integer(),
IN_DISCIPLINA_LIBRAS = col_integer(),
IN_TRADUTOR_LIBRAS = col_integer(),
IN_GUIA_INTERPRETE = col_integer(),
IN_RECURSOS_COMUNICACAO = col_integer(),
IN_RECURSOS_INFORMATICA = col_integer(),
IN_INTEGRAL = col_integer(),
IN_MATUTINO = col_integer(),
IN_VESPERTINO = col_integer(),
IN_NOTURNO = col_integer(),
NU_INTEGRALIZACAO_INTEGRAL = col_number(),
NU_INTEGRALIZACAO_MATUTINO = col_number(),
NU_INTEGRALIZACAO_VESPERTINO = col_number(),
NU_INTEGRALIZACAO_NOTURNO = col_number(),
NU_INTEGRALIZACAO_EAD = col_number(),
IN_OFERECE_DISC_SEMI_PRES = col_integer(),
NU_PERC_CARGA_SEMI_PRES = col_number(),
IN_POSSUI_LABORATORIO = col_integer(),
QT_INSC_VAGA_NOVA_INTEGRAL = col_integer(),
QT_INSC_VAGA_NOVA_MATUTINO = col_integer(),
QT_INSC_VAGA_NOVA_VESPERTINO = col_integer(),
QT_INSC_VAGA_NOVA_NOTURNO = col_integer(),
QT_INSC_VAGA_NOVA_EAD = col_integer(),
QT_INSC_VAGA_REMAN_INTEGRAL = col_integer(),
QT_INSC_VAGA_REMAN_MATUTINO = col_integer(),
QT_INSC_VAGA_REMAN_VESPERTINO = col_integer(),
QT_INSC_VAGA_REMAN_NOTURNO = col_integer(),
QT_INSC_VAGA_REMAN_EAD = col_integer(),
QT_INSC_PROG_ESP_INTEGRAL = col_integer(),
QT_INSC_PROG_ESP_MATUTINO = col_integer(),
QT_INSC_PROG_ESP_VESPERTINO = col_integer(),
QT_INSC_PROG_ESP_NOTURNO = col_integer(),
QT_INSC_PROG_ESP_EAD = col_integer(),
QT_INSC_PRINCIPAL_INTEGRAL = col_integer(),
QT_INSC_PRINCIPAL_MATUTINO = col_integer(),
QT_INSC_PRINCIPAL_VESPERTINO = col_integer(),
QT_INSC_PRINCIPAL_NOTURNO = col_integer(),
QT_INSC_PRINCIPAL_EAD = col_integer(),
QT_INSC_OUTRA_VAGA_INTEGRAL = col_integer(),
QT_INSC_OUTRA_VAGA_MATUTINO = col_integer(),
QT_INSC_OUTRA_VAGA_VESPERTINO = col_integer(),
QT_INSC_OUTRA_VAGA_NOTURNO = col_integer(),
QT_INSC_OUTRA_VAGA_EAD = col_integer(),
QT_INSC_ANUAL_INTEGRAL = col_integer(),
QT_INSC_ANUAL_MATUTINO = col_integer(),
QT_INSC_ANUAL_VESPERTINO = col_integer(),
QT_INSC_ANUAL_NOTURNO = col_integer(),
QT_INSC_ANUAL_EAD = col_integer(),
QT_VAGAS_NOVAS_INTEGRAL = col_integer(),
QT_VAGAS_NOVAS_MATUTINO = col_integer(),
QT_VAGAS_NOVAS_VESPERTINO = col_integer(),
QT_VAGAS_NOVAS_NOTURNO = col_integer(),
QT_VAGAS_NOVAS_EAD = col_integer(),
QT_VAGAS_REMAN_INTEGRAL = col_integer(),
QT_VAGAS_REMAN_MATUTINO = col_integer(),
QT_VAGAS_REMAN_VESPERTINO = col_integer(),
QT_VAGAS_REMAN_NOTURNO = col_integer(),
QT_VAGAS_REMAN_EAD = col_integer(),
QT_VAGAS_PROG_ESP_INTEGRAL = col_integer(),
QT_VAGAS_PROG_ESP_MATUTINO = col_integer(),
QT_VAGAS_PROG_ESP_VESPERTINO = col_integer(),
QT_VAGAS_PROG_ESP_NOTURNO = col_integer(),
QT_VAGAS_PROG_ESP_EAD = col_integer(),
QT_VAGAS_PRINCIPAL_INTEGRAL = col_integer(),
QT_VAGAS_PRINCIPAL_MATUTINO = col_integer(),
QT_VAGAS_PRINCIPAL_VESPERTINO = col_integer(),
QT_VAGAS_PRINCIPAL_NOTURNO = col_integer(),
QT_VAGAS_PRINCIPAL_EAD = col_integer(),
QT_VAGAS_OUTRAS_INTEGRAL = col_integer(),
QT_VAGAS_OUTRAS_MATUTINO = col_integer(),
QT_VAGAS_OUTRAS_VESPERTINO = col_integer(),
QT_VAGAS_OUTRAS_NOTURNO = col_integer(),
QT_VAGAS_OUTRAS_EAD = col_integer(),
QT_VAGAS_ANUAL_INTEGRAL = col_integer(),
QT_VAGAS_ANUAL_MATUTINO = col_integer(),
QT_VAGAS_ANUAL_VESPERTINO = col_integer(),
QT_VAGAS_ANUAL_NOTURNO = col_integer(),
QT_VAGAS_ANUAL_EAD = col_integer(),
QT_MATRICULA_TOTAL = col_integer(),
QT_CONCLUINTE_TOTAL = col_integer(),
QT_INGRESSO_TOTAL = col_integer(),
QT_INGRESSO_VAGA_NOVA = col_integer(),
QT_INGRESSO_PROCESSO_SELETIVO = col_integer(),
QT_VAGA_TOTAL = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseCurso = BaseCurso %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM ATIVIDADE",
TP_SITUACAO == 2 ~ "EXTINTO",
TP_SITUACAO == 3 ~ "EM EXTINCAO"),
NO_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA"),
NO_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
NO_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL"),
NO_IN_GRATUITO = case_when(
IN_GRATUITO == 0 ~ "NAO",
IN_GRATUITO == 1 ~ "SIM"),
NO_ATRIBUTO_INGRESSO = case_when(
TP_ATRIBUTO_INGRESSO == 0 ~ "NORMAL",
TP_ATRIBUTO_INGRESSO == 1 ~ "AREA BASICA DE INGRESSO",
TP_ATRIBUTO_INGRESSO == 2 ~ "BACHARELADO OU LICENCIATURA"))
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseCurso = BaseCurso %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseCurso, "./data/curso_2017_data.rds")
#liberando variável
rm(BaseMun)
rm(BaseCurso)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2823767 150.9 4260781 227.6 4260781 227.6
## Vcells 4706581 36.0 14140814 107.9 10159822 77.63.1.3 Docentes das IES - 2017
Importação e tratamento do arquivo relativo ao registro dos docentes das IES em 2017
#leitura base DOCENTE - 2017
BaseDocente = read_delim("./raw-data/SUP_DOCENTE_2017.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_DOCENTE_IES = col_integer(),
CO_DOCENTE = col_character(),
TP_SITUACAO = col_integer(),
TP_ESCOLARIDADE = col_integer(),
TP_REGIME_TRABALHO = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_COR_RACA = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
TP_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_ATUACAO_EAD = col_integer(),
IN_ATUACAO_EXTENSAO = col_integer(),
IN_ATUACAO_GESTAO = col_integer(),
IN_ATUACAO_GRAD_PRESENCIAL = col_integer(),
IN_ATUACAO_POS_EAD = col_integer(),
IN_ATUACAO_POS_PRESENCIAL = col_integer(),
IN_ATUACAO_SEQUENCIAL = col_integer(),
IN_ATUACAO_PESQUISA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
IN_SUBSTITUTO = col_integer(),
IN_EXERCICIO_DATA_REFERENCIA = col_integer(),
IN_VISITANTE = col_integer(),
TP_VISITANTE_IFES_VINCULO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
BaseDocente = BaseDocente %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM EXERCICIO",
TP_SITUACAO == 2 ~ "AFASTADO PARA QUALIFICACAO",
TP_SITUACAO == 3 ~ "AFASTADO PARA OUTROS ORGAOS",
TP_SITUACAO == 4 ~ "AFASTADO OUTROS MOTIVOS",
TP_SITUACAO == 5 ~ "AFASTADO SAUDE",
TP_SITUACAO == 6 ~ "FALECIDO",),
DS_ESCOLARIDADE = case_when(
TP_ESCOLARIDADE == 1 ~ "SEM GRADUACAO",
TP_ESCOLARIDADE == 2 ~ "GRADUACAO",
TP_ESCOLARIDADE == 3 ~ "ESPECIALIZACAO",
TP_ESCOLARIDADE == 4 ~ "MESTRADO",
TP_ESCOLARIDADE == 5 ~ "DOUTORADO"),
DS_REGIME_TRABALHO = case_when(
TP_REGIME_TRABALHO == 1 ~ "INTEGRAL EXCLUSIVA",
TP_REGIME_TRABALHO == 2 ~ "INTEGRAL NÃO EXCLUSIVA",
TP_REGIME_TRABALHO == 3 ~ "TEMPO PARCIAL",
TP_REGIME_TRABALHO == 4 ~ "HORISTA"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DT_NASC = lubridate::date(str_c(NU_ANO_NASCIMENTO,'-', NU_MES_NASCIMENTO, '-', NU_DIA_NASCIMENTO)))
#salvando no banco de dados
saveRDS(BaseDocente, "./data/docente_2017_data.rds")
#liberando variavel
rm(BaseDocente)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2825114 150.9 6196170 331.0 4260781 227.6
## Vcells 4904046 37.5 22557620 172.2 28197025 215.23.1.4 Locais de Oferta (pólos) das IES - 2017
Importação e tratamento do arquivo relativo ao registro dos pólos das IES em 2017
#leitura base DOCENTE - 2017
BaseLocalOferta = read_delim("./raw-data/SUP_LOCAL_OFERTA_2017.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
NO_LOCAL_OFERTA = col_character(),
CO_IES = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
IN_SEDE = col_integer(),
CO_CURSO_POLO = col_integer(),
CO_CURSO = col_integer(),
IN_LOCAL_OFERTA_NEAD = col_integer(),
IN_LOCAL_OFERTA_UAB = col_integer(),
IN_LOCAL_OFERTA_REITORIA = col_integer(),
IN_LOCAL_OFERTA_POLO = col_integer(),
IN_LOCAL_OFERTA_UNID_ACADEMICA = col_integer(),
QT_COMPUTADOR_DISCENTE = col_integer(),
IN_ACESSIBILIDADE = col_integer(),
IN_SINALIZACAO_TATIL = col_integer(),
IN_RAMPA_ACESSO_CORRIMAO = col_integer(),
IN_ENTRADA_SAIDA_DIMENSAO = col_integer(),
IN_AMBIENTE_DESOBSTRUIDO = col_integer(),
IN_SINALIZACAO_SONORA = col_integer(),
IN_SINALIZACAO_VISUAL = col_integer(),
IN_EQUIPAMENTO_ELETROMECANICO = col_integer(),
IN_BANHEIRO_ADAPTADO = col_integer(),
IN_ESPACO_ATENDIMENTO_ADAPTADO = col_integer(),
IN_MOBILIARIO_ADAPTADO = col_integer(),
IN_BEBEDOURO_LAVABO_ADAPTADO = col_integer(),
IN_RESTAURANTE_UNIVERSITARIO = col_integer(),
IN_QUADRA_POLIESPORTIVA = col_integer(),
IN_PISCINA = col_integer(),
IN_QUADRA_COBERTA_GINASIO = col_integer(),
IN_CINEMA = col_integer(),
IN_VESTIARIO = col_integer(),
IN_SERVICOS = col_integer(),
IN_ESTACIONAMENTO_DOCENTE = col_integer(),
IN_CANTINA_LANCHONETE = col_integer(),
IN_PISTA_ATLETISMO = col_integer(),
IN_CAMPO_PRATICA_ESPORTIVA = col_integer(),
IN_AUDITORIO_TEATRO = col_integer(),
IN_REDE_SEM_FIO_COMU_ACADEMICA = col_integer(),
IN_POSTO_ATENDIMENTO_1_SOCORRO = col_integer(),
IN_BICICLETARIO = col_integer(),
IN_ESTACIONAMENTO_ALUNO = col_integer(),
IN_MORADIA_ESTUDANTIL = col_integer(),
IN_EQUIPA_VIDEOCONFERENCIA = col_integer(),
IN_SALA_COORDENACAO_POLO = col_integer(),
IN_MICROCOMPUTADOR = col_integer(),
IN_POSSUI_INTERNET_BANDA_LARGA = col_integer(),
IN_SALA_ATENDIMENTO_TUTOR = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseLocalOferta = BaseLocalOferta %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseLocalOferta, "./data/localoferta_2017_data.rds")
#liberando vari?vel
rm(BaseLocalOferta)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2825086 150.9 6196170 331.0 4260781 227.6
## Vcells 4903395 37.5 23331958 178.1 28197025 215.23.1.5 Matrículas das IES - 2017
Importação e tratamento do arquivo relativo ao registro das matrículas das IES em 2017
#leitura base ALUNOS - 2017
BaseAluno = readr::read_delim("./raw-data/SUP_ALUNO_2017.CSV",
delim = "|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_CURSO = col_integer(),
CO_CURSO_POLO = col_integer(),
TP_TURNO = col_integer(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
CO_OCDE_AREA_GERAL = col_character(),
CO_OCDE_AREA_ESPECIFICA = col_character(),
CO_OCDE_AREA_DETALHADA = col_character(),
CO_OCDE = col_character(),
CO_ALUNO = col_character(),
CO_ALUNO_CURSO = col_integer(),
CO_ALUNO_CURSO_ORIGEM = col_integer(),
TP_COR_RACA = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
TP_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_SUPERDOTACAO = col_integer(),
IN_TGD_AUTISMO_INFANTIL = col_integer(),
IN_TGD_SINDROME_ASPERGER = col_integer(),
IN_TGD_SINDROME_RETT = col_integer(),
IN_TGD_TRANSTOR_DESINTEGRATIVO= col_integer(),
TP_SITUACAO = col_integer(),
QT_CARGA_HORARIA_TOTAL = col_integer(),
QT_CARGA_HORARIA_INTEG = col_integer(),
DT_INGRESSO_CURSO = col_character(),
IN_INGRESSO_VESTIBULAR = col_integer(),
IN_INGRESSO_ENEM = col_integer(),
IN_INGRESSO_AVALIACAO_SERIADA = col_integer(),
IN_INGRESSO_SELECAO_SIMPLIFICA= col_integer(),
IN_INGRESSO_OUTRO_TIPO_SELECAO= col_integer(),
IN_INGRESSO_VAGA_REMANESC = col_integer(),
IN_INGRESSO_VAGA_PROG_ESPECIAL= col_integer(),
IN_INGRESSO_TRANSF_EXOFFICIO = col_integer(),
IN_INGRESSO_DECISAO_JUDICIAL = col_integer(),
IN_INGRESSO_CONVENIO_PECG = col_integer(),
IN_INGRESSO_EGRESSO = col_integer(),
IN_INGRESSO_OUTRA_FORMA = col_integer(),
IN_RESERVA_VAGAS = col_integer(),
IN_RESERVA_ETNICO = col_integer(),
IN_RESERVA_DEFICIENCIA = col_integer(),
IN_RESERVA_ENSINO_PUBLICO = col_integer(),
IN_RESERVA_RENDA_FAMILIAR = col_integer(),
IN_RESERVA_OUTRA = col_integer(),
IN_FINANCIAMENTO_ESTUDANTIL = col_integer(),
IN_FIN_REEMB_FIES = col_integer(),
IN_FIN_REEMB_ESTADUAL = col_integer(),
IN_FIN_REEMB_MUNICIPAL = col_integer(),
IN_FIN_REEMB_PROG_IES = col_integer(),
IN_FIN_REEMB_ENT_EXTERNA = col_integer(),
IN_FIN_REEMB_OUTRA = col_integer(),
IN_FIN_NAOREEMB_PROUNI_INTEGR = col_integer(),
IN_FIN_NAOREEMB_PROUNI_PARCIAL= col_integer(),
IN_FIN_NAOREEMB_ESTADUAL = col_integer(),
IN_FIN_NAOREEMB_MUNICIPAL = col_integer(),
IN_FIN_NAOREEMB_PROG_IES = col_integer(),
IN_FIN_NAOREEMB_ENT_EXTERNA = col_integer(),
IN_FIN_NAOREEMB_OUTRA = col_integer(),
IN_APOIO_SOCIAL = col_integer(),
IN_APOIO_ALIMENTACAO = col_integer(),
IN_APOIO_BOLSA_PERMANENCIA = col_integer(),
IN_APOIO_BOLSA_TRABALHO = col_integer(),
IN_APOIO_MATERIAL_DIDATICO = col_integer(),
IN_APOIO_MORADIA = col_integer(),
IN_APOIO_TRANSPORTE = col_integer(),
IN_ATIVIDADE_EXTRACURRICULAR = col_integer(),
IN_COMPLEMENTAR_ESTAGIO = col_integer(),
IN_COMPLEMENTAR_EXTENSAO = col_integer(),
IN_COMPLEMENTAR_MONITORIA = col_integer(),
IN_COMPLEMENTAR_PESQUISA = col_integer(),
IN_BOLSA_ESTAGIO = col_integer(),
IN_BOLSA_EXTENSAO = col_integer(),
IN_BOLSA_MONITORIA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
TP_ESCOLA_CONCLUSAO_ENS_MEDIO = col_integer(),
IN_ALUNO_PARFOR = col_integer(),
TP_SEMESTRE_CONCLUSAO = col_integer(),
TP_SEMESTRE_REFERENCIA = col_integer(),
IN_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA_INTERN= col_integer(),
CO_IES_DESTINO = col_integer(),
CO_PAIS_DESTINO = col_integer(),
IN_MATRICULA = col_integer(),
IN_CONCLUINTE = col_integer(),
IN_INGRESSO_TOTAL = col_integer(),
IN_INGRESSO_VAGA_NOVA = col_integer(),
IN_INGRESSO_PROCESSO_SELETIVO = col_integer(),
NU_ANO_INGRESSO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
BaseAluno = BaseAluno %>% dplyr::select(everything()) %>%
mutate(DS_TURNO = case_when(
TP_TURNO == 1 ~ "MATUTINO",
TP_TURNO == 2 ~ "VESPERTINO",
TP_TURNO == 3 ~ "NOTURNO",
TP_TURNO == 4 ~ "INTEGRAL",
TRUE ~ "EAD"),
DS_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA",
TRUE ~ "NA"),
DS_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
DS_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL DE FORMACAO"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DS_SITUACAO = case_when(
TP_SITUACAO == 2 ~ "CURSANDO",
TP_SITUACAO == 3 ~ "MATRICULA TRANCADA",
TP_SITUACAO == 4 ~ "DESVINCULADO",
TP_SITUACAO == 5 ~ "TRANSFERIDO MESMA IES",
TP_SITUACAO == 6 ~ "FORMADO",
TP_SITUACAO == 7 ~ "FALECIDO"))
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseAluno = BaseAluno %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO_NASCIMENTO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseAluno, "./data/aluno_2017_data.rds")
rm(BaseAluno)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2825817 151.0 23017172 1229.3 13400303 715.7
## Vcells 8842955 67.5 1409938420 10757.0 1617645616 12341.73.2 Tratamento e Uniformização dos dados
A seção trata da uniformização, reconfiguração dos dados e montagem das bases de dados necessárias para a exploração e análise
3.2.1 Consolidação dos dados das IES - 2017
Agrupamentos e calculos para facilitar a consolidação nas mantenedoras.
#*carga dos dados 2017 importados por ies_import_data
ies_2017_full = readRDS("./data/ies_2017_data.rds")
#* SELECIONAR CAMPOS FINANCEIROS E AGRUPAMENTOS, LOCALIZAÇÃO, IDENTIFICAÇÃO DA IES.
ies_2017_ext = ies_2017_full %>% dplyr::select(c(1,2,3,5,6,11,13,37:54))
#criar indice de qualificação corpo técnico = IQCTA
ies_2017_IQCTA = ies_2017_full %>% dplyr::select(c(1,2,13:27))
ies_2017_IQCTA = ies_2017_IQCTA %>% mutate(
QT_TEC_D = QT_TEC_DOUTORADO_FEM+QT_TEC_DOUTORADO_MASC,
QT_TEC_M = QT_TEC_MESTRADO_FEM+QT_TEC_MESTRADO_MASC,
QT_TEC_E = QT_TEC_ESPECIALIZACAO_FEM+QT_TEC_ESPECIALIZACAO_MASC,
QT_TEC_G = QT_TEC_SUPERIOR_FEM+QT_TEC_SUPERIOR_MASC,
QT_TEC_MD= QT_TEC_MEDIO_FEM+QT_TEC_MEDIO_MASC,
QT_TEC_F = QT_TEC_FUNDAMENTAL_COMP_FEM+QT_TEC_FUNDAMENTAL_COMP_MASC)
ies_2017_IQCTA = ies_2017_IQCTA %>% dplyr::select(c(1,2, 18:23))
ies_2017_ext = ies_2017_ext %>% left_join(ies_2017_IQCTA)## Joining, by = c("NU_ANO_CENSO", "CO_IES")rm(ies_2017_full)
rm(ies_2017_IQCTA)
#* CRIAR COLUNAS COM TOTAL DE DOCENTES ATIVOS e IQCD
#*
docentes_2017_full = readRDS("./data/docente_2017_data.rds")
docentes_2017_consolidado_IES = docentes_2017_full %>%
dplyr::select(c(1,2,7,8)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QT_DOC = n(), #conta os registros
QT_DOC_D = sum(TP_ESCOLARIDADE == 5),
QT_DOC_M = sum(TP_ESCOLARIDADE == 4),
QT_DOC_E = sum(TP_ESCOLARIDADE == 3),
QT_DOC_G = sum(TP_ESCOLARIDADE == 2),
QT_DOC_SG = sum(TP_ESCOLARIDADE == 1))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2017_ext = ies_2017_ext %>% dplyr::select(everything()) %>%
left_join(docentes_2017_consolidado_IES, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
rm(docentes_2017_consolidado_IES)
rm(docentes_2017_full)
#* CRIAR COLUNAS COM DADOS DOS CURSOS
#*
cursos_2017_full = readRDS("./data/curso_2017_data.rds")
polos_2017_full = readRDS("./data/localoferta_2017_data.rds")
cursos_2017_consolidado_IES = cursos_2017_full %>%
dplyr::select(c(1,2,11,111,114)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES, TP_SITUACAO) %>%
dplyr::summarise(TVAGAS = sum (QT_VAGA_TOTAL), TINGRESSOS = sum(QT_INGRESSO_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_IES'. You can override
## using the `.groups` argument.cursos_2017_consolidado_IES = cursos_2017_consolidado_IES %>% dplyr::select(-c(3))
#POLOS
talunos_polos = cursos_2017_full %>% dplyr::select(1,2,5,9,109)
tpolos = polos_2017_full %>% dplyr::select(1,2,4,8,10)
polos_2017_consolidado_IES = talunos_polos %>% left_join(tpolos, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES", "CO_LOCAL_OFERTA" = "CO_LOCAL_OFERTA", "CO_CURSO" = "CO_CURSO"))
polos_2017_consolidado_IES[is.na(polos_2017_consolidado_IES)] = 0
polos_2017_consolidado_IES = polos_2017_consolidado_IES %>% group_by(NU_ANO_CENSO, CO_IES) %>% filter(IN_SEDE != 1) %>%
dplyr::summarise(TMATR_FSEDE = sum(QT_MATRICULA_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2017_ext = ies_2017_ext %>% dplyr::select(everything()) %>%
left_join(cursos_2017_consolidado_IES, c(TVAGAS, TINGRESSOS), by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES")) %>%
left_join(polos_2017_consolidado_IES, TMATR_FSEDE, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2017_ext[is.na(ies_2017_ext)] = 0
rm(cursos_2017_consolidado_IES)
rm(cursos_2017_full)
rm(polos_2017_full)
rm(talunos_polos)
rm(tpolos)
rm(polos_2017_consolidado_IES)
#* CRIAR COLUNAS COM TOTAL DE ALUNOS ATIVOS PRESENCIAL E EAD
#*
alunos_2017_full = readRDS("./data/aluno_2017_data.rds")
alunos_2017_consolidado_IES = alunos_2017_full %>%
dplyr::select(c(1,2,8,9,10,42,64,78)) %>% #pega ano censo, co_ies, situacao, modalidade e financiamento/bolsas
filter(TP_NIVEL_ACADEMICO == 1) %>%
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QTD_ALUNO_PRESENCIAL = sum(TP_MODALIDADE_ENSINO == 1 & TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_EAD = sum(TP_MODALIDADE_ENSINO == 2 &TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_ATIVO = QTD_ALUNO_PRESENCIAL+QTD_ALUNO_EAD,
QTD_ALUNO_EVADIDO = sum((TP_SITUACAO %in% c(3,4,7))),
QTD_ALUNO_FINANC = sum((IN_FINANCIAMENTO_ESTUDANTIL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNO_BOLSA = sum((IN_APOIO_SOCIAL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNOS = QTD_ALUNO_ATIVO + QTD_ALUNO_EVADIDO) #conta os registros## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2017_ext = ies_2017_ext %>% dplyr::select(everything()) %>%
left_join(alunos_2017_consolidado_IES, dplyr::select(QTD_ALUNO_PRESENCIAL,
QTD_ALUNO_EAD,
QTD_ALUNO_ATIVO,
QTD_ALUNO_EVADIDO,
QTD_ALUNO_FINANC,
QTD_ALUNO_BOLSA,
QTD_ALUNOS),
by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2017_ext[is.na(ies_2017_ext)] = 0
saveRDS(ies_2017_ext,"./data/ies_consolidado_2017_data.rds")
rm(alunos_2017_consolidado_IES)
rm(alunos_2017_full)Total de registros e dimensões:
dim(ies_2017_ext)## [1] 2448 47rm (ies_2017_ext)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2864910 153.1 18413738 983.4 17479135 933.5
## Vcells 8913059 68.1 1127950736 8605.6 1617645616 12341.73.2.2 Consolidação dos dados nas mantenedoras - 2017
Nesta etapa consolidaremos os dados das IES nas suas mantenedoras, agraupando os valores financeiros de acordo com as regras e criando as váriaveis de suporte
#*** TRATANDO, EXTRAINDO E CONVERTENDO 2017
#*carga dos dados tratados por ies_convert_data
ies_2017_ext = readRDS("./data/ies_consolidado_2017_data.rds")
# deixando somente as privadas
ies_2017_ext = ies_2017_ext %>% filter(NO_CATEGORIA_ADMINISTRATIVA %in% c("PRIVADA COM FINS LUCRATIVOS", "PRIVADA SEM FINS LUCRATIVOS"))
#primeiro selecionar TP_REFERENTE == 2 e somar todos os campos
tpr2_2017 = ies_2017_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 2) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#segundo passo, selecionar TP_REFERENTE == 1 e usar media para valores financeiros e soma para outros
tpr1_2017 = ies_2017_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 1) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = mean(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = mean (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = mean(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = mean(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = mean(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = mean(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = mean(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = mean(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = mean(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = mean(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#terceiro passo, juntar as 2 anteriores e somar novamente os campos de valores
mant_2017_agrupado = union_all(tpr1_2017, tpr2_2017)
mant_2017_agrupado = mant_2017_agrupado %>%
group_by(NU_ANO_CENSO,CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS)) ## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#validação com dados de matriculas divulgado em graduação privada (6.523.678)
total_2017 = sum(mant_2017_agrupado$QTD_ALUNO_ATIVO)Total de registros e dimensões. Também compara a quantidade de matriculas ativas em comparação com a sinopse estatística oficial 2017, para fins de validação:
Total de Matrículas ativas divulgada na sinopse = 6.241.307
total_2017## [1] 6241307dim(ies_2017_ext)## [1] 2152 47#salvando dataset
saveRDS(mant_2017_agrupado,"./data/mant_2017_agrupado_data.rds")
rm(mant_2017_agrupado)
rm(ies_2017_ext)
rm(tpr1_2017)
rm(tpr2_2017)
rm (total_2017)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 2867614 153.2 14730991 786.8 17479135 933.5
## Vcells 8920720 68.1 902360589 6884.5 1617645616 12341.73.3 Exploração e Análise de Dados
Esta seção se dedica a explorar as informações de acordo com cada visualização elaborada.
3.3.1 Análise de fatores que podem se correlacionar com o resultado do ponto de equilíbrio das mantenedoras
MONTAGEM E ANÁLISE
# MONTAGEM DO DATASET
mant_2017_agrupado = readRDS("./data/mant_2017_agrupado_data.rds")
ds_PEQ_2017 = mant_2017_agrupado %>% select_all() %>%
mutate(VDPEQ = ((VL_RECEITA_PROPRIA - #somente receitas com mensalidades
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA))/
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+
VL_DESPESA_PESSOAL_ENCARGO+VL_DESPESA_CUSTEIO+
VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)), #variavel distancia ponto de equilíbrio
VRAD = QTD_ALUNO_ATIVO/QT_DOC,
VRAF = QTD_ALUNO_ATIVO/QT_TEC_TOTAL, #alterado para alunos/funcionarios
VCFPA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO)/QTD_ALUNO_ATIVO,
VCTTA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)/QTD_ALUNO_ATIVO,
VTMA = (VL_RECEITA_PROPRIA/QTD_ALUNO_ATIVO),
VOCIO = TINGRESSOS/TVAGAS,
VEAD = QTD_ALUNO_EAD/QTD_ALUNO_ATIVO,
VPOL = TMATR_FSEDE/QTD_ALUNO_ATIVO,
IQCTA = ((5*QT_TEC_D+3*QT_TEC_M+2*QT_TEC_E+QT_TEC_G+0.75*QT_TEC_MD+0.5*QT_TEC_F)/
QT_TEC_TOTAL),
IQCDO = ((5*QT_DOC_D+3*QT_DOC_M+2*QT_DOC_E+1*QT_DOC_G)/QT_DOC),
VFINE = QTD_ALUNO_FINANC / QTD_ALUNO_ATIVO,
VBOLS = QTD_ALUNO_BOLSA / QTD_ALUNO_ATIVO,
VEVAD = QTD_ALUNO_EVADIDO / QTD_ALUNOS)
ds_PEQ_2017[is.na(ds_PEQ_2017)] = 0
ds_PEQ_2017 = ds_PEQ_2017 %>% dplyr::select(2,37:50)## Adding missing grouping variables: `NU_ANO_CENSO`ds_PEQ_2017 = (ds_PEQ_2017[,2:16])
rm(mant_2017_agrupado)ANÁLISE E REMOÇÃO DE OUTLIERS
Sumário antes da remoção:
tbl_summary(ds_PEQ_2017) #usando gtsummary| Characteristic | N = 1,4921 |
|---|---|
| CO_MANTENEDORA | 1,460 (748, 3,224) |
| VDPEQ | 0.07 (-0.11, 0.36) |
| VRAD | 14 (9, 22) |
| VRAF | 21 (12, 34) |
| VCFPA | 4,939 (2,963, 8,497) |
| VCTTA | 7,867 (4,690, 13,306) |
| VTMA | 8,279 (5,023, 13,440) |
| VOCIO | 0.30 (0.15, 0.48) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 0.00 (0.00, 0.30) |
| IQCTA | 1.10 (0.92, 1.36) |
| IQCDO | 3.05 (2.78, 3.34) |
| VFINE | 0.30 (0.04, 0.55) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.22 (0.16, 0.31) |
| 1 Median (IQR) | |
Boxplot antes do tratamento:
ds_boxplot_all = melt(ds_PEQ_2017, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))
ggplot(data = ds_boxplot_all, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
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Tratamento e remoção dos extremos, analisando VTMA e VCFPA 5% de cada lado = 95,9% / Perda total simultânea em VTMA e VCFPA = 13,3%
#Remoção de Registros inválidos - Outliers
q_vtma = quantile(ds_PEQ_2017$VTMA, probs = c(.05, .95), na.rm = F)
q_vcfpa = quantile(ds_PEQ_2017$VCFPA, probs = c(.05, .95), na.rm = F)
#dataset sem os outliers
ds_PEQ_2017_noOutliers = subset(ds_PEQ_2017, (ds_PEQ_2017$VTMA>=(q_vtma[1])&ds_PEQ_2017$VCFPA>=(q_vcfpa[1])) &
(ds_PEQ_2017$VTMA<=(q_vtma[2]) & ds_PEQ_2017$VCFPA<=(q_vcfpa[2])))
#dataset com os outliers excluidos
ds_PEQ_2017_Outliers = subset(ds_PEQ_2017, !(CO_MANTENEDORA %in% ds_PEQ_2017_noOutliers$CO_MANTENEDORA))Sumário após remoção:
tbl_summary(ds_PEQ_2017_noOutliers) #usando gtsummary| Characteristic | N = 1,2931 |
|---|---|
| CO_MANTENEDORA | 1,318 (693, 2,948) |
| VDPEQ | 0.07 (-0.09, 0.35) |
| VRAD | 15 (10, 22) |
| VRAF | 22 (13, 34) |
| VCFPA | 4,907 (3,173, 7,730) |
| VCTTA | 7,810 (5,096, 12,106) |
| VTMA | 8,511 (5,510, 12,671) |
| VOCIO | 0.30 (0.16, 0.47) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 0.00 (0.00, 0.29) |
| IQCTA | 1.08 (0.91, 1.34) |
| IQCDO | 3.05 (2.79, 3.33) |
| VFINE | 0.33 (0.07, 0.56) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.23 (0.16, 0.31) |
| 1 Median (IQR) | |
Boxplot após tratamento:
ds_boxplot_NoOutliers = melt(ds_PEQ_2017_noOutliers, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))
ggplot(data = ds_boxplot_NoOutliers, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Perda Total dos registros (em %)
perda = 1-(dim(ds_PEQ_2017_noOutliers)[1]/dim(ds_PEQ_2017)[1])
paste(round(100*perda, 2), "%", sep="")## [1] "13.34%"ds_RLM = ds_PEQ_2017_noOutliers
modelo_RLM = lm(VDPEQ ~ ., data = ds_RLM[,2:15])
summary(modelo_RLM)##
## Call:
## lm(formula = VDPEQ ~ ., data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.274452824 -0.209434751 -0.044441120 0.128052338 8.612141465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07131257e-01 1.20335202e-01 1.72129 0.085441 .
## VRAD 1.17830673e-03 1.37795495e-03 0.85511 0.392649
## VRAF 1.65196014e-03 6.74297114e-04 2.44990 0.014423 *
## VCFPA -1.02259074e-04 5.26072208e-06 -19.43822 < 2e-16 ***
## VCTTA -3.50844743e-06 1.18272694e-06 -2.96641 0.003069 **
## VTMA 6.81013184e-05 2.96696587e-06 22.95319 < 2e-16 ***
## VOCIO -3.45149729e-02 6.46646487e-02 -0.53375 0.593605
## VEAD -3.35047408e-01 1.59996532e-01 -2.09409 0.036448 *
## VPOL 1.06978223e-01 4.31284902e-02 2.48045 0.013249 *
## IQCTA 3.65590334e-02 3.81717677e-02 0.95775 0.338370
## IQCDO -4.52698037e-02 3.55321365e-02 -1.27405 0.202876
## VFINE -1.58466886e-02 4.95774357e-02 -0.31964 0.749297
## VBOLS -4.85969558e-02 9.10009388e-02 -0.53403 0.593416
## VEVAD -1.14043520e-01 1.14202812e-01 -0.99861 0.318175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.506172989 on 1279 degrees of freedom
## Multiple R-squared: 0.378821821, Adjusted R-squared: 0.372508048
## F-statistic: 59.9992731 on 13 and 1279 DF, p-value: < 2.220446e-16ANÁLISE DOS RESÍDUOS OUTLIERS
#falta fazer....ANÁLISE DE COMPONENTES PRINCIPAIS - PCA
# analise PCA - Componentes Principais
# Filtrando variáveis númericas
ds_pca = ds_PEQ_2017_noOutliers[,2:15]
# Calculando o desvio padrão das variáveis
sapply(ds_pca, sd)## VDPEQ VRAD VRAF VCFPA
## 6.38991212744e-01 1.41464290942e+01 2.47066933170e+01 4.21817258783e+03
## VCTTA VTMA VOCIO VEAD
## 1.43245484491e+04 6.67463700660e+03 2.28685733986e-01 1.04070146242e-01
## VPOL IQCTA IQCDO VFINE
## 3.41838369876e-01 3.78763960134e-01 4.32682383172e-01 3.00673589150e-01
## VBOLS VEVAD
## 1.57537284083e-01 1.25810533593e-01# PCA com a matriz de covariâncias
pca_cov = prcomp(ds_pca)
summary(pca_cov)## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 14812.34814 6382.17986 2720.04759 24.234593 11.6120398
## Proportion of Variance 0.82009 0.15225 0.02765 0.000000 0.0000000
## Cumulative Proportion 0.82009 0.97234 1.00000 1.000000 1.0000000
## PC6 PC7 PC8 PC9
## Standard deviation 0.507426728 0.398139178 0.372319965 0.343300115
## Proportion of Variance 0.000000000 0.000000000 0.000000000 0.000000000
## Cumulative Proportion 1.000000000 1.000000000 1.000000000 1.000000000
## PC10 PC11 PC12 PC13
## Standard deviation 0.282887131 0.22156398 0.155153754 0.122434782
## Proportion of Variance 0.000000000 0.00000000 0.000000000 0.000000000
## Cumulative Proportion 1.000000000 1.00000000 1.000000000 1.000000000
## PC14
## Standard deviation 0.0874722167
## Proportion of Variance 0.0000000000
## Cumulative Proportion 1.0000000000# PCA com a matriz de correlação (variáveis padronizadas)
pca_corr = prcomp(ds_pca, center = T, scale = T)
pca_corr$rotation## PC1 PC2 PC3 PC4
## VDPEQ -0.19096057135235 0.23143429541530 -0.3113773799918 0.4689284052442
## VRAD -0.35122638521922 0.44920144317683 0.1584885016034 -0.1478777393219
## VRAF -0.36692860049099 0.24290539871934 -0.0247011303075 -0.1678866238844
## VCFPA 0.54162982664114 0.15294002347061 0.0946707549859 0.0484791045727
## VCTTA 0.41512816673190 0.18088979359581 0.1247153773045 -0.1780801742175
## VTMA 0.40310541647096 0.38189574557699 -0.1360703128256 0.3027226334705
## VOCIO -0.02683887289188 0.29203609294397 -0.2612965111584 -0.2145193381683
## VEAD -0.16145457178312 0.28264867701169 0.5500505471274 -0.0313695721296
## VPOL -0.08822259303464 0.15439816317719 0.4171745573464 0.3968523210832
## IQCTA 0.09322190561304 -0.16617815569723 0.1867523658833 -0.3049966025308
## IQCDO 0.16479375392193 0.40415502923741 0.1126626274451 -0.1122437218200
## VFINE -0.01503819128838 0.30329115887489 -0.3444573169680 -0.1580550615838
## VBOLS 0.08167706706868 0.00480865366175 0.1792298764873 -0.3870626646125
## VEVAD 0.00578987712415 -0.10824880899520 0.3017849912968 0.3455919427860
## PC5 PC6 PC7 PC8
## VDPEQ -0.3793260359166 0.1999702561658 -0.2615330457111 0.249973965337179
## VRAD 0.1446728360062 -0.0140989236598 -0.0392705013981 0.012855672220989
## VRAF 0.2358437650210 0.1253997973275 -0.2277262850217 0.130428948553044
## VCFPA 0.0996446394547 -0.0626232056754 -0.0245746926984 -0.080263444711965
## VCTTA 0.0986846176196 0.1796650477478 -0.1202196927162 0.000510627141977
## VTMA -0.0931560636863 0.0575328568188 -0.1168072627869 0.037207694293308
## VOCIO -0.1709925979183 -0.4711352789845 0.2224593328463 0.420804912519396
## VEAD -0.0818505383470 -0.0795144131196 -0.0290066316165 -0.261220521279966
## VPOL -0.3707552361964 0.0136913174150 0.1233195365925 -0.162256622903412
## IQCTA -0.3640206087409 -0.2599085368778 -0.7615497707537 0.121278755683020
## IQCDO 0.1102880575559 -0.2146967275388 0.1480193129420 0.196275078175632
## VFINE 0.1745312698319 0.3148420497001 -0.2777225628540 -0.411973478708675
## VBOLS -0.3392029653739 0.6708356859783 0.2315333856243 0.370504160615250
## VEVAD 0.5363038205508 0.1153480453954 -0.2266183083605 0.529755673884956
## PC9 PC10 PC11 PC12
## VDPEQ -0.2324215522671 -0.0888527321311 -0.09402970874794 0.26456870241537
## VRAD -0.0549842680464 -0.1183810658042 -0.12444499045051 0.05802317657753
## VRAF 0.0913946254876 -0.3901262589168 0.50147257139335 -0.32274343930628
## VCFPA 0.0380079998321 -0.1632546458181 0.00214974768104 -0.38315650092984
## VCTTA 0.2451125228043 -0.3235427232492 0.13543837933917 0.70762584651104
## VTMA -0.0594065807423 -0.1654929223147 -0.10638032804213 -0.33920469428301
## VOCIO 0.5007007108713 0.0402562846338 -0.21841224334652 -0.00680202548186
## VEAD -0.1172345377201 -0.1160089096614 -0.51832734659075 -0.00648081612901
## VPOL 0.4531749592732 0.2398485673625 0.43352572697907 -0.01556436548499
## IQCTA -0.0182789115804 0.1408336358313 0.05569861040896 -0.06975459959129
## IQCDO -0.5351459956171 0.4639223864496 0.35183931282789 0.13376931290019
## VFINE 0.2673516347134 0.5341526401863 -0.12791727786003 -0.02580969807496
## VBOLS -0.0162220261680 0.1010089139807 -0.09090997957061 -0.18996874608928
## VEVAD 0.2031796930176 0.2529561793656 -0.19672258213963 -0.00505977699759
## PC13 PC14
## VDPEQ 0.1247969145956 0.34933165126485
## VRAD -0.7477923918233 0.09975548466479
## VRAF 0.3474529119362 -0.02080562339579
## VCFPA -0.0468785741405 0.68842503933152
## VCTTA 0.0540179457262 -0.06673770499026
## VTMA -0.1215082452641 -0.61841022569780
## VOCIO 0.1436954656152 0.03475630242904
## VEAD 0.4601353378505 -0.02776908263922
## VPOL -0.0809047458266 0.00652118220808
## IQCTA -0.1076701884773 -0.03655053593345
## IQCDO 0.1486606558098 -0.01131846026161
## VFINE 0.1050350639223 0.05348042459724
## VBOLS -0.0171348934030 0.01709851822268
## VEVAD 0.0262987986874 -0.01144511942342summary(pca_corr)$importance## PC1 PC2 PC3 PC4
## Standard deviation 1.59026054851 1.37465144695 1.19808771963 1.04857804514
## Proportion of Variance 0.18064000000 0.13498000000 0.10253000000 0.07854000000
## Cumulative Proportion 0.18064000000 0.31561000000 0.41814000000 0.49668000000
## PC5 PC6 PC7
## Standard deviation 1.03097651203 0.999492882898 0.954937780626
## Proportion of Variance 0.07592000000 0.071360000000 0.065140000000
## Cumulative Proportion 0.57260000000 0.643960000000 0.709100000000
## PC8 PC9 PC10
## Standard deviation 0.935103980542 0.882394385127 0.850572423587
## Proportion of Variance 0.062460000000 0.055620000000 0.051680000000
## Cumulative Proportion 0.771550000000 0.827170000000 0.878850000000
## PC11 PC12 PC13
## Standard deviation 0.81604532679 0.710608769127 0.592633788777
## Proportion of Variance 0.04757000000 0.036070000000 0.025090000000
## Cumulative Proportion 0.92641000000 0.962480000000 0.987570000000
## PC14
## Standard deviation 0.417190090407
## Proportion of Variance 0.012430000000
## Cumulative Proportion 1.000000000000# Gráfico das variáveis
fviz_pca_var(pca_corr,
col.var = "contrib", # Cor por contribuições para o PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE,
legend.title = "Contribuição"
)
Processo de análise de regressão múltipla
# Modelo de Regressão Linear Múltipla
ds_RLM = ds_PEQ_2017_noOutliers
summary(ds_RLM)## CO_MANTENEDORA VDPEQ VRAD
## Min. : 10.00000 Min. :-0.9725979870 Min. : 0.615384615
## 1st Qu.: 693.00000 1st Qu.:-0.0904209878 1st Qu.: 9.742857143
## Median : 1318.00000 Median : 0.0718982902 Median : 15.059259259
## Mean : 3713.58005 Mean : 0.1734566199 Mean : 18.137824452
## 3rd Qu.: 2948.00000 3rd Qu.: 0.3467122797 3rd Qu.: 22.285714286
## Max. :16843.00000 Max. :10.9004713483 Max. :136.800000000
## VRAF VCFPA VCTTA
## Min. : 0.5000000 Min. : 1156.88623 Min. : 1204.80056
## 1st Qu.: 13.2121212 1st Qu.: 3173.19233 1st Qu.: 5096.22143
## Median : 22.1500000 Median : 4907.13136 Median : 7810.45297
## Mean : 28.0096920 Mean : 6133.37167 Mean : 10067.99964
## 3rd Qu.: 34.2608696 3rd Qu.: 7730.47575 3rd Qu.: 12105.91235
## Max. :304.0000000 Max. :24385.21082 Max. :447303.58619
## VTMA VOCIO VEAD
## Min. : 1934.99213 Min. :0.000000000 Min. :0.0000000000
## 1st Qu.: 5509.88146 1st Qu.:0.159183673 1st Qu.:0.0000000000
## Median : 8510.74933 Median :0.296296296 Median :0.0000000000
## Mean :10138.47619 Mean :0.334409199 Mean :0.0214588745
## 3rd Qu.:12670.73274 3rd Qu.:0.468205128 3rd Qu.:0.0000000000
## Max. :42614.41378 Max. :1.475000000 Max. :1.0000000000
## VPOL IQCTA IQCDO
## Min. :0.000000000 Min. :0.458333333 Min. :1.33333333
## 1st Qu.:0.000000000 1st Qu.:0.911764706 1st Qu.:2.78947368
## Median :0.000000000 Median :1.080000000 Median :3.05128205
## Mean :0.200534324 Mean :1.179050020 Mean :3.07487118
## 3rd Qu.:0.288458583 3rd Qu.:1.339743590 3rd Qu.:3.33333333
## Max. :1.146341463 Max. :3.250000000 Max. :4.83333333
## VFINE VBOLS VEVAD
## Min. :0.0000000000 Min. :0.0000000000 Min. :0.000000000
## 1st Qu.:0.0691823899 1st Qu.:0.0000000000 1st Qu.:0.164273281
## Median :0.3312757202 Median :0.0000000000 Median :0.226765799
## Mean :0.3598668063 Mean :0.0383818069 Mean :0.248187807
## 3rd Qu.:0.5619174434 3rd Qu.:0.0000000000 3rd Qu.:0.312163290
## Max. :1.0000000000 Max. :1.0000000000 Max. :0.834813499# Correlograma
corrgram(ds_RLM[,c(2:15)],
#upper.panel=panel.cor,
upper.panel=panel.conf,
cex.labels=1.5, cex=1.2)## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
corrplot(cor(ds_RLM[,c(2:15)]), method="circle", type="lower", order="hclust", addCoef.col = "black")
# Modelo de regressão
modelo_RLM = lm(VDPEQ ~ ., data = ds_RLM[,2:15])
summary(modelo_RLM)##
## Call:
## lm(formula = VDPEQ ~ ., data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.274452824 -0.209434751 -0.044441120 0.128052338 8.612141465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07131257e-01 1.20335202e-01 1.72129 0.085441 .
## VRAD 1.17830673e-03 1.37795495e-03 0.85511 0.392649
## VRAF 1.65196014e-03 6.74297114e-04 2.44990 0.014423 *
## VCFPA -1.02259074e-04 5.26072208e-06 -19.43822 < 2e-16 ***
## VCTTA -3.50844743e-06 1.18272694e-06 -2.96641 0.003069 **
## VTMA 6.81013184e-05 2.96696587e-06 22.95319 < 2e-16 ***
## VOCIO -3.45149729e-02 6.46646487e-02 -0.53375 0.593605
## VEAD -3.35047408e-01 1.59996532e-01 -2.09409 0.036448 *
## VPOL 1.06978223e-01 4.31284902e-02 2.48045 0.013249 *
## IQCTA 3.65590334e-02 3.81717677e-02 0.95775 0.338370
## IQCDO -4.52698037e-02 3.55321365e-02 -1.27405 0.202876
## VFINE -1.58466886e-02 4.95774357e-02 -0.31964 0.749297
## VBOLS -4.85969558e-02 9.10009388e-02 -0.53403 0.593416
## VEVAD -1.14043520e-01 1.14202812e-01 -0.99861 0.318175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.506172989 on 1279 degrees of freedom
## Multiple R-squared: 0.378821821, Adjusted R-squared: 0.372508048
## F-statistic: 59.9992731 on 13 and 1279 DF, p-value: < 2.220446e-16tbl_regression(modelo_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.39 |
| VRAF | 0.00 | 0.00, 0.00 | 0.014 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | 0.003 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VOCIO | -0.03 | -0.16, 0.09 | 0.59 |
| VEAD | -0.34 | -0.65, -0.02 | 0.036 |
| VPOL | 0.11 | 0.02, 0.19 | 0.013 |
| IQCTA | 0.04 | -0.04, 0.11 | 0.34 |
| IQCDO | -0.05 | -0.11, 0.02 | 0.20 |
| VFINE | -0.02 | -0.11, 0.08 | 0.75 |
| VBOLS | -0.05 | -0.23, 0.13 | 0.59 |
| VEVAD | -0.11 | -0.34, 0.11 | 0.32 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.207131257318 | 0.120335201553 | 1.721285664088 | 0.085440820370 | -0.028944807614 | 0.443207322250 |
| VRAD | 0.001178306730 | 0.001377954946 | 0.855112667302 | 0.392648927674 | -0.001524993528 | 0.003881606988 |
| VRAF | 0.001651960142 | 0.000674297114 | 2.449899468436 | 0.014422899282 | 0.000329110242 | 0.002974810043 |
| VCFPA | -0.000102259074 | 0.000005260722 | -19.438220193596 | 0.000000000000 | -0.000112579667 | -0.000091938482 |
| VCTTA | -0.000003508447 | 0.000001182727 | -2.966405266934 | 0.003068974093 | -0.000005828745 | -0.000001188149 |
| VTMA | 0.000068101318 | 0.000002966966 | 22.953185623828 | 0.000000000000 | 0.000062280664 | 0.000073921973 |
| VOCIO | -0.034514972891 | 0.064664648749 | -0.533753349917 | 0.593604939857 | -0.161375406247 | 0.092345460465 |
| VEAD | -0.335047407898 | 0.159996531868 | -2.094091690521 | 0.036448246438 | -0.648931883586 | -0.021162932210 |
| VPOL | 0.106978223376 | 0.043128490232 | 2.480453704743 | 0.013249399305 | 0.022367867229 | 0.191588579523 |
| IQCTA | 0.036559033362 | 0.038171767716 | 0.957750598142 | 0.338369536438 | -0.038327122955 | 0.111445189679 |
| IQCDO | -0.045269803662 | 0.035532136535 | -1.274052395280 | 0.202876301583 | -0.114977477423 | 0.024437870099 |
| VFINE | -0.015846688570 | 0.049577435722 | -0.319635098892 | 0.749297165162 | -0.113108718165 | 0.081415341025 |
| VBOLS | -0.048596955789 | 0.091000938841 | -0.534026971671 | 0.593415675457 | -0.227124462865 | 0.129930551288 |
| VEVAD | -0.114043519883 | 0.114202811672 | -0.998605185054 | 0.318174873853 | -0.338088936650 | 0.110001896884 |
kable(broom::glance(modelo_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.378821821191 | 0.372508047677 | 0.506172988915 | 59.9992730727 | 0 | 13 | -947.275658194 | 1924.55131639 | 2002.02212207 | 327.69399013 | 1279 | 1293 |
plot(modelo_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo_RLM, order.by = ~., data = ds_RLM[,3:15], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo_RLM
## GQ = 1.026401727, df1 = 629, df2 = 629, p-value = 0.371967658
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo_RLM
## BP = 79.22568545, df = 13, p-value = 1.5425367e-11plot(modelo_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 -0.00700184241445 2.01399411085 0.86
## Alternative hypothesis: rho != 0car::vif(modelo_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 1.91614456669 1.39957582653 2.48315591462 1.44742291496 1.97762975051
## VOCIO VEAD VPOL IQCTA IQCDO
## 1.10274874807 1.39809939896 1.09606096205 1.05411118995 1.19191600715
## VFINE VBOLS VEVAD
## 1.12052942483 1.03638957416 1.04100348384plot(modelo_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo_RLM$residuals
## W = 0.5903292695, p-value < 2.220446e-16#modelo 2 - somente variáveis mais significativas - PCA
modelo2_RLM = lm(VDPEQ ~ VRAF+VCFPA+VCTTA+VTMA+VEAD+VPOL, data = ds_RLM[,2:15])
summary(modelo2_RLM)##
## Call:
## lm(formula = VDPEQ ~ VRAF + VCFPA + VCTTA + VTMA + VEAD + VPOL,
## data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.224839348 -0.214460839 -0.048034501 0.126582869 8.633460215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.58669505e-02 3.67821050e-02 2.60635 0.0092571 **
## VRAF 1.77092048e-03 6.07007001e-04 2.91746 0.0035902 **
## VCFPA -1.03354430e-04 5.00862459e-06 -20.63529 < 2.22e-16 ***
## VCTTA -3.63182454e-06 1.16041472e-06 -3.12976 0.0017888 **
## VTMA 6.74311812e-05 2.81053103e-06 23.99233 < 2.22e-16 ***
## VEAD -2.89836761e-01 1.41611625e-01 -2.04670 0.0408903 *
## VPOL 1.04885885e-01 4.29037169e-02 2.44468 0.0146315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.505641312 on 1286 degrees of freedom
## Multiple R-squared: 0.376733502, Adjusted R-squared: 0.373825571
## F-statistic: 129.553807 on 6 and 1286 DF, p-value: < 2.220446e-16anova(modelo_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 16.4166150 16.41661496 64.07457 2.6686e-15 ***
## VRAF 1 4.7686212 4.76862122 18.61208 1.7253e-05 ***
## VCFPA 1 27.3161095 27.31610950 106.61564 < 2.22e-16 ***
## VCTTA 1 0.9325749 0.93257493 3.63987 0.056635 .
## VTMA 1 147.2836529 147.28365293 574.85275 < 2.22e-16 ***
## VOCIO 1 0.0326195 0.03261950 0.12731 0.721291
## VEAD 1 0.5645652 0.56456522 2.20352 0.137943
## VPOL 1 1.5237631 1.52376311 5.94730 0.014875 *
## IQCTA 1 0.2521533 0.25215330 0.98416 0.321361
## IQCDO 1 0.4222797 0.42227972 1.64817 0.199441
## VFINE 1 0.0122349 0.01223493 0.04775 0.827055
## VBOLS 1 0.0615465 0.06154647 0.24022 0.624133
## VEVAD 1 0.2554969 0.25549686 0.99721 0.318175
## Residuals 1279 327.6939901 0.25621109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tbl_regression(modelo2_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAF | 0.00 | 0.00, 0.00 | 0.004 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | 0.002 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VEAD | -0.29 | -0.57, -0.01 | 0.041 |
| VPOL | 0.10 | 0.02, 0.19 | 0.015 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo2_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.095866950473 | 0.036782104977 | 2.60634758487 | 0.009257116908 | 0.023707435045 | 0.168026465902 |
| VRAF | 0.001770920481 | 0.000607007001 | 2.91746302300 | 0.003590226515 | 0.000580087844 | 0.002961753118 |
| VCFPA | -0.000103354430 | 0.000005008625 | -20.63529187382 | 0.000000000000 | -0.000113180402 | -0.000093528459 |
| VCTTA | -0.000003631825 | 0.000001160415 | -3.12976429083 | 0.001788827101 | -0.000005908338 | -0.000001355311 |
| VTMA | 0.000067431181 | 0.000002810531 | 23.99232762689 | 0.000000000000 | 0.000061917452 | 0.000072944910 |
| VEAD | -0.289836761359 | 0.141611624569 | -2.04670176083 | 0.040890307336 | -0.567651916697 | -0.012021606020 |
| VPOL | 0.104885884738 | 0.042903716919 | 2.44468060744 | 0.014631535341 | 0.020716927446 | 0.189054842029 |
kable(broom::glance(modelo2_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.376733502229 | 0.373825571446 | 0.505641312473 | 129.553806598 | 0 | 6 | -949.44546052 | 1914.89092104 | 1956.20868407 | 328.795654028 | 1286 | 1293 |
plot(modelo2_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo2_RLM, order.by = ~., data = ds_RLM[,3:12], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo2_RLM
## GQ = 1.099152259, df1 = 636, df2 = 636, p-value = 0.116747533
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo2_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo2_RLM
## BP = 72.20777676, df = 6, p-value = 1.44013996e-13plot(modelo2_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo2_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 -0.00621230018327 2.01242166748 0.848
## Alternative hypothesis: rho != 0car::vif(modelo2_RLM)## VRAF VCFPA VCTTA VTMA VEAD
## 1.13656443725 2.25560509203 1.39625824757 1.77831825373 1.09755818681
## VPOL
## 1.08694825159plot(modelo2_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo2_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo2_RLM$residuals
## W = 0.5890407642, p-value < 2.220446e-16faraway::vif(modelo2_RLM)## VRAF VCFPA VCTTA VTMA VEAD
## 1.13656443725 2.25560509203 1.39625824757 1.77831825373 1.09755818681
## VPOL
## 1.08694825159anova(modelo2_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAF 1 15.0090051 15.00900515 58.70388 3.5902e-14 ***
## VCFPA 1 30.9882315 30.98823151 121.20253 < 2.22e-16 ***
## VCTTA 1 0.8511937 0.85119372 3.32923 0.068291 .
## VTMA 1 149.8405091 149.84050913 586.06278 < 2.22e-16 ***
## VEAD 1 0.5236081 0.52360814 2.04796 0.152653
## VPOL 1 1.5280211 1.52802111 5.97646 0.014632 *
## Residuals 1286 328.7956540 0.25567314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#algoritmos de seleção de variáveis
ts2 = ds_PEQ_2017_noOutliers[,-c(1)]
modelo = lm(data = ts2, VDPEQ ~ .)
summary(modelo)##
## Call:
## lm(formula = VDPEQ ~ ., data = ts2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.274452824 -0.209434751 -0.044441120 0.128052338 8.612141465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.07131257e-01 1.20335202e-01 1.72129 0.085441 .
## VRAD 1.17830673e-03 1.37795495e-03 0.85511 0.392649
## VRAF 1.65196014e-03 6.74297114e-04 2.44990 0.014423 *
## VCFPA -1.02259074e-04 5.26072208e-06 -19.43822 < 2e-16 ***
## VCTTA -3.50844743e-06 1.18272694e-06 -2.96641 0.003069 **
## VTMA 6.81013184e-05 2.96696587e-06 22.95319 < 2e-16 ***
## VOCIO -3.45149729e-02 6.46646487e-02 -0.53375 0.593605
## VEAD -3.35047408e-01 1.59996532e-01 -2.09409 0.036448 *
## VPOL 1.06978223e-01 4.31284902e-02 2.48045 0.013249 *
## IQCTA 3.65590334e-02 3.81717677e-02 0.95775 0.338370
## IQCDO -4.52698037e-02 3.55321365e-02 -1.27405 0.202876
## VFINE -1.58466886e-02 4.95774357e-02 -0.31964 0.749297
## VBOLS -4.85969558e-02 9.10009388e-02 -0.53403 0.593416
## VEVAD -1.14043520e-01 1.14202812e-01 -0.99861 0.318175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.506172989 on 1279 degrees of freedom
## Multiple R-squared: 0.378821821, Adjusted R-squared: 0.372508048
## F-statistic: 59.9992731 on 13 and 1279 DF, p-value: < 2.220446e-16stats::step(modelo, direction = "both", trace = F)##
## Call:
## lm(formula = VDPEQ ~ VRAF + VCFPA + VCTTA + VTMA + VEAD + VPOL,
## data = ts2)
##
## Coefficients:
## (Intercept) VRAF VCFPA VCTTA
## 9.58669505e-02 1.77092048e-03 -1.03354430e-04 -3.63182454e-06
## VTMA VEAD VPOL
## 6.74311812e-05 -2.89836761e-01 1.04885885e-01#modelo 2 recomendado
par(mfrow = c(2,2))
plot(modelo2_RLM, which = c(1:4), pch = 20)
Processo de análise XAI - IA Explicada
# analise XAI
# Feature Importance
#carrega arquivo
ds_xai = ds_PEQ_2017_noOutliers[2:15]
head(ds_xai)## # A tibble: 6 × 14
## VDPEQ VRAD VRAF VCFPA VCTTA VTMA VOCIO VEAD VPOL IQCTA IQCDO
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.462 14.3 17.2 14398. 19271. 28174. 0.501 0 0.203 1.21 3.72
## 2 -0.245 20.4 21.7 14207. 19783. 14939. 0.404 0 0 1.12 3.82
## 3 0.00627 22.7 23.1 8821. 14748. 14840. 0.478 0.00874 0.375 1.03 3.59
## 4 0.422 24.3 22.5 9143. 14380. 20454. 0.363 0.130 0.195 1.17 4.06
## 5 -0.0626 13.7 14.9 13397. 29143. 27317. 0.0789 0.0224 0.0224 1.24 3.34
## 6 -0.134 17.4 18.3 13136. 20237. 17535. 0.630 0 0.216 0.924 3.78
## # … with 3 more variables: VFINE <dbl>, VBOLS <dbl>, VEVAD <dbl>
## # ℹ Use `colnames()` to see all variable names#criamos modelo randomForest
modelo_rf = randomForest(VDPEQ ~ ., data = ds_xai, ntree = 500, prox = T)
summary(modelo_rf)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 1293 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1293 -none- numeric
## importance 13 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 1671849 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1293 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms calltable(predict(modelo_rf), ds_xai$VDPEQ)##
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## [ reached getOption("max.print") -- omitted 1216 rows ]#previsor, modelo, dados, e previsor
previsor = Predictor$new(modelo, ds_xai[,-1] , y = ds_xai$VDPEQ )
#feature importance
importancia = FeatureImp$new(previsor, loss = "mae")
#dataframe
importancia$results## feature importance.05 importance importance.95 permutation.error
## 1 VTMA 2.104944276164 2.11323388023 2.15814562049 0.552599923851
## 2 VCFPA 2.036211079950 2.05946639278 2.10224002203 0.538539999037
## 3 VRAF 1.008693741679 1.01579248788 1.01652989621 0.265624575065
## 4 VEAD 1.011806444943 1.01516698900 1.01833166146 0.265461010287
## 5 VCTTA 1.010030494971 1.01451508669 1.01810610138 0.265290541143
## 6 VPOL 0.996171117467 1.00375222919 1.00929649806 0.262476108585
## 7 IQCTA 0.999777698728 1.00196050088 1.00355404465 0.262007580736
## 8 VOCIO 1.000375589584 1.00176123472 1.00250315329 0.261955473648
## 9 VEVAD 0.999537625540 1.00130003686 1.00256904597 0.261834872751
## 10 VBOLS 1.000574990938 1.00082174107 1.00124776925 0.261709800830
## 11 VRAD 0.998512274667 1.00028701471 1.00199479709 0.261569972604
## 12 VFINE 0.999754158403 1.00020129091 1.00037820614 0.261547556266
## 13 IQCDO 0.998376626748 1.00006517579 1.00059066555 0.261511962853plot(importancia)
#Feature Interaction
interacao = Interaction$new(previsor)
plot(interacao)
interacao$results## .feature .interaction
## 1: VRAD 1.22649136384e-16
## 2: VRAF 1.98887275099e-16
## 3: VCFPA 2.15605403855e-16
## 4: VCTTA 1.37643127083e-16
## 5: VTMA 2.21285583251e-16
## 6: VOCIO 1.71557679702e-16
## 7: VEAD 1.19577170444e-16
## 8: VPOL 1.65365340827e-16
## 9: IQCTA 9.87839113822e-17
## 10: IQCDO 6.96276558903e-17
## 11: VFINE 1.24665104396e-16
## 12: VBOLS 9.36666693109e-17
## 13: VEVAD 1.92904531121e-16#cria modelo substituto
arvore = TreeSurrogate$new(previsor, maxdepth = 3)
#plot a árvore
plot(arvore$tree)
4 Censo Superior 2018
Análise dos dados do Censo Superior 2018
4.1 Importação dos Microdados
Importação dos microdados do censo do ensino superior 2018. Download em: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados.
O arquivo contém as seguintes bases (CSV) que iremos importar (base fornecida antes antes de Set/2021):
- SUP_ALUNO_2018.CSV
- SUP_LOCAL_OFERTA_2018.CSV
- SUP_DOCENTE_2018.CSV
- SUP_IES_2018.CSV
- SUP_CURSO_2018.CSV
4.1.1 Instituições - IES - 2018
Importação e tratamento do arquivo relativo ao registro das IES em 2018
BaseIES = read_delim("./raw-data/SUP_IES_2018.CSV",
"|",
escape_double = FALSE,
col_types = cols(NU_ANO_CENSO= col_integer(),
CO_IES = col_integer(),
NO_IES = col_character(),
SG_IES = col_character(),
CO_MANTENEDORA = col_integer(),
NO_MANTENEDORA = col_character(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_REGIAO = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
QT_TEC_TOTAL = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_MASC = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_MASC = col_integer(),
QT_TEC_MEDIO_FEM = col_integer(),
QT_TEC_MEDIO_MASC = col_integer(),
QT_TEC_SUPERIOR_FEM = col_integer(),
QT_TEC_SUPERIOR_MASC = col_integer(),
QT_TEC_ESPECIALIZACAO_FEM = col_integer(),
QT_TEC_ESPECIALIZACAO_MASC = col_integer(),
QT_TEC_MESTRADO_FEM = col_integer(),
QT_TEC_MESTRADO_MASC = col_integer(),
QT_TEC_DOUTORADO_FEM = col_integer(),
QT_TEC_DOUTORADO_MASC = col_integer(),
IN_ACESSO_PORTAL_CAPES = col_integer(),
IN_ACESSO_OUTRAS_BASES = col_integer(),
IN_ASSINA_OUTRA_BASE = col_integer(),
IN_REPOSITORIO_INSTITUCIONAL = col_integer(),
IN_BUSCA_INTEGRADA = col_integer(),
IN_SERVICO_INTERNET = col_integer(),
IN_PARTICIPA_REDE_SOCIAL = col_integer(),
IN_CATALOGO_ONLINE = col_integer(),
QT_PERIODICO_ELETRONICO = col_integer(),
QT_LIVRO_ELETRONICO = col_integer(),
TP_REFERENTE = col_integer(),
VL_RECEITA_PROPRIA = col_double(),
VL_RECEITA_TRANSFERENCIA = col_double(),
VL_RECEITA_OUTRA = col_double(),
VL_DESPESA_PESSOAL_DOCENTE = col_double(),
VL_DESPESA_PESSOAL_TECNICO = col_double(),
VL_DESPESA_PESSOAL_ENCARGO = col_double(),
VL_DESPESA_CUSTEIO = col_double(),
VL_DESPESA_INVESTIMENTO = col_double(),
VL_DESPESA_PESQUISA = col_double(),
VL_DESPESA_OUTRA = col_double()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseIES = BaseIES %>% dplyr::select(everything()) %>%
mutate(NO_CATEGORIA_ADMINISTRATIVA = case_when(
TP_CATEGORIA_ADMINISTRATIVA == 1 ~ "PUBLICA FEDERAL",
TP_CATEGORIA_ADMINISTRATIVA == 2 ~ "PUBLICA ESTADUAL",
TP_CATEGORIA_ADMINISTRATIVA == 3 ~ "PUBLICA MUNICIPAL",
TP_CATEGORIA_ADMINISTRATIVA == 4 ~ "PRIVADA COM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 5 ~ "PRIVADA SEM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 6 ~ "PRIVADA CONFESSIONAL",
TP_CATEGORIA_ADMINISTRATIVA == 7 ~ "ESPECIAL"),
NO_ORGANIZACAO_ACADEMICA = case_when(
TP_ORGANIZACAO_ACADEMICA == 1 ~ "UNIVERSIDADE",
TP_ORGANIZACAO_ACADEMICA == 2 ~ "CENTRO UNIVERSITARIO",
TP_ORGANIZACAO_ACADEMICA == 3 ~ "FACULDADE",
TP_ORGANIZACAO_ACADEMICA == 4 ~ "INSTITUTO FEDERAL DE EDUCACAO - CIENCIA E TECNOLOGIA",
TP_ORGANIZACAO_ACADEMICA == 5 ~ "CENTRO FEDERAL DE EDUCACAO TECNOLOGICA"),
NO_REGIAO = case_when(
CO_REGIAO == 1 ~ "NORTE",
CO_REGIAO == 2 ~ "NORDESTE",
CO_REGIAO == 3 ~ "SUDESTE",
CO_REGIAO == 4 ~ "SUL",
CO_REGIAO == 5 ~ "CENTRO-OESTE"))
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseIES = BaseIES %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
saveRDS(BaseIES, "./data/ies_2018_data.rds")
rm(BaseIES)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3698948 197.6 11784793 629.4 17479135 933.5
## Vcells 17255439 131.7 577510778 4406.1 1617645616 12341.74.1.2 Cursos das IES - 2018
Importação e tratamento do arquivo relativo ao registro dos cursos das IES em 2018
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseCurso = read_delim("./raw-data/SUP_CURSO_2018.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
CO_CURSO = col_integer(),
NO_CURSO = col_character(),
TP_SITUACAO = col_integer(),
CO_CINE_ROTULO = col_character(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
IN_GRATUITO = col_integer(),
TP_ATRIBUTO_INGRESSO = col_integer(),
NU_CARGA_HORARIA = col_integer(),
DT_INICIO_FUNCIONAMENTO = col_character(),
DT_AUTORIZACAO_CURSO = col_character(),
IN_AJUDA_DEFICIENTE = col_integer(),
IN_MATERIAL_DIGITAL = col_integer(),
IN_MATERIAL_AMPLIADO = col_integer(),
IN_MATERIAL_TATIL = col_integer(),
IN_MATERIAL_IMPRESSO = col_integer(),
IN_MATERIAL_AUDIO = col_integer(),
IN_MATERIAL_BRAILLE = col_integer(),
IN_MATERIAL_LIBRAS = col_integer(),
IN_DISCIPLINA_LIBRAS = col_integer(),
IN_TRADUTOR_LIBRAS = col_integer(),
IN_GUIA_INTERPRETE = col_integer(),
IN_RECURSOS_COMUNICACAO = col_integer(),
IN_RECURSOS_INFORMATICA = col_integer(),
IN_INTEGRAL = col_integer(),
IN_MATUTINO = col_integer(),
IN_VESPERTINO = col_integer(),
IN_NOTURNO = col_integer(),
NU_INTEGRALIZACAO_INTEGRAL = col_number(),
NU_INTEGRALIZACAO_MATUTINO = col_number(),
NU_INTEGRALIZACAO_VESPERTINO = col_number(),
NU_INTEGRALIZACAO_NOTURNO = col_number(),
NU_INTEGRALIZACAO_EAD = col_number(),
IN_OFERECE_DISC_SEMI_PRES = col_integer(),
NU_PERC_CARGA_SEMI_PRES = col_number(),
IN_POSSUI_LABORATORIO = col_integer(),
QT_INSC_VAGA_NOVA_INTEGRAL = col_integer(),
QT_INSC_VAGA_NOVA_MATUTINO = col_integer(),
QT_INSC_VAGA_NOVA_VESPERTINO = col_integer(),
QT_INSC_VAGA_NOVA_NOTURNO = col_integer(),
QT_INSC_VAGA_NOVA_EAD = col_integer(),
QT_INSC_VAGA_REMAN_INTEGRAL = col_integer(),
QT_INSC_VAGA_REMAN_MATUTINO = col_integer(),
QT_INSC_VAGA_REMAN_VESPERTINO = col_integer(),
QT_INSC_VAGA_REMAN_NOTURNO = col_integer(),
QT_INSC_VAGA_REMAN_EAD = col_integer(),
QT_INSC_PROG_ESP_INTEGRAL = col_integer(),
QT_INSC_PROG_ESP_MATUTINO = col_integer(),
QT_INSC_PROG_ESP_VESPERTINO = col_integer(),
QT_INSC_PROG_ESP_NOTURNO = col_integer(),
QT_INSC_PROG_ESP_EAD = col_integer(),
QT_INSC_PRINCIPAL_INTEGRAL = col_integer(),
QT_INSC_PRINCIPAL_MATUTINO = col_integer(),
QT_INSC_PRINCIPAL_VESPERTINO = col_integer(),
QT_INSC_PRINCIPAL_NOTURNO = col_integer(),
QT_INSC_PRINCIPAL_EAD = col_integer(),
QT_INSC_OUTRA_VAGA_INTEGRAL = col_integer(),
QT_INSC_OUTRA_VAGA_MATUTINO = col_integer(),
QT_INSC_OUTRA_VAGA_VESPERTINO = col_integer(),
QT_INSC_OUTRA_VAGA_NOTURNO = col_integer(),
QT_INSC_OUTRA_VAGA_EAD = col_integer(),
QT_INSC_ANUAL_INTEGRAL = col_integer(),
QT_INSC_ANUAL_MATUTINO = col_integer(),
QT_INSC_ANUAL_VESPERTINO = col_integer(),
QT_INSC_ANUAL_NOTURNO = col_integer(),
QT_INSC_ANUAL_EAD = col_integer(),
QT_VAGAS_NOVAS_INTEGRAL = col_integer(),
QT_VAGAS_NOVAS_MATUTINO = col_integer(),
QT_VAGAS_NOVAS_VESPERTINO = col_integer(),
QT_VAGAS_NOVAS_NOTURNO = col_integer(),
QT_VAGAS_NOVAS_EAD = col_integer(),
QT_VAGAS_REMAN_INTEGRAL = col_integer(),
QT_VAGAS_REMAN_MATUTINO = col_integer(),
QT_VAGAS_REMAN_VESPERTINO = col_integer(),
QT_VAGAS_REMAN_NOTURNO = col_integer(),
QT_VAGAS_REMAN_EAD = col_integer(),
QT_VAGAS_PROG_ESP_INTEGRAL = col_integer(),
QT_VAGAS_PROG_ESP_MATUTINO = col_integer(),
QT_VAGAS_PROG_ESP_VESPERTINO = col_integer(),
QT_VAGAS_PROG_ESP_NOTURNO = col_integer(),
QT_VAGAS_PROG_ESP_EAD = col_integer(),
QT_VAGAS_PRINCIPAL_INTEGRAL = col_integer(),
QT_VAGAS_PRINCIPAL_MATUTINO = col_integer(),
QT_VAGAS_PRINCIPAL_VESPERTINO = col_integer(),
QT_VAGAS_PRINCIPAL_NOTURNO = col_integer(),
QT_VAGAS_PRINCIPAL_EAD = col_integer(),
QT_VAGAS_OUTRAS_INTEGRAL = col_integer(),
QT_VAGAS_OUTRAS_MATUTINO = col_integer(),
QT_VAGAS_OUTRAS_VESPERTINO = col_integer(),
QT_VAGAS_OUTRAS_NOTURNO = col_integer(),
QT_VAGAS_OUTRAS_EAD = col_integer(),
QT_VAGAS_ANUAL_INTEGRAL = col_integer(),
QT_VAGAS_ANUAL_MATUTINO = col_integer(),
QT_VAGAS_ANUAL_VESPERTINO = col_integer(),
QT_VAGAS_ANUAL_NOTURNO = col_integer(),
QT_VAGAS_ANUAL_EAD = col_integer(),
QT_MATRICULA_TOTAL = col_integer(),
QT_CONCLUINTE_TOTAL = col_integer(),
QT_INGRESSO_TOTAL = col_integer(),
QT_INGRESSO_VAGA_NOVA = col_integer(),
QT_INGRESSO_PROCESSO_SELETIVO = col_integer(),
QT_VAGA_TOTAL = col_integer(),
QT_INSCRITO_TOTAL = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseCurso = BaseCurso %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM ATIVIDADE",
TP_SITUACAO == 2 ~ "EXTINTO",
TP_SITUACAO == 3 ~ "EM EXTINCAO"),
NO_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA"),
NO_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
NO_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL"),
NO_IN_GRATUITO = case_when(
IN_GRATUITO == 0 ~ "NAO",
IN_GRATUITO == 1 ~ "SIM"),
NO_ATRIBUTO_INGRESSO = case_when(
TP_ATRIBUTO_INGRESSO == 0 ~ "NORMAL",
TP_ATRIBUTO_INGRESSO == 1 ~ "AREA BASICA DE INGRESSO",
TP_ATRIBUTO_INGRESSO == 2 ~ "BACHARELADO OU LICENCIATURA")
)
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseCurso = BaseCurso %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseCurso, "./data/curso_2018_data.rds")
#liberando variável
rm(BaseMun)
rm(BaseCurso)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3694019 197.3 11784793 629.4 17479135 933.5
## Vcells 15137520 115.5 462008623 3524.9 1617645616 12341.74.1.3 Docentes das IES - 2018
Importação e tratamento do arquivo relativo ao registro dos docentes das IES em 2018
#leitura base DOCENTE - 2018
BaseDocente = read_delim("./raw-data/SUP_DOCENTE_2018.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_DOCENTE_IES = col_integer(),
ID_DOCENTE = col_character(),
TP_SITUACAO = col_integer(),
TP_ESCOLARIDADE = col_integer(),
TP_REGIME_TRABALHO = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_COR_RACA = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
IN_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_ATUACAO_EAD = col_integer(),
IN_ATUACAO_EXTENSAO = col_integer(),
IN_ATUACAO_GESTAO = col_integer(),
IN_ATUACAO_GRAD_PRESENCIAL = col_integer(),
IN_ATUACAO_POS_EAD = col_integer(),
IN_ATUACAO_POS_PRESENCIAL = col_integer(),
IN_ATUACAO_SEQUENCIAL = col_integer(),
IN_ATUACAO_PESQUISA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
IN_SUBSTITUTO = col_integer(),
IN_EXERCICIO_DATA_REFERENCIA = col_integer(),
IN_VISITANTE = col_integer(),
TP_VISITANTE_IFES_VINCULO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
BaseDocente = BaseDocente %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM EXERCICIO",
TP_SITUACAO == 2 ~ "AFASTADO PARA QUALIFICACAO",
TP_SITUACAO == 3 ~ "AFASTADO PARA OUTROS ORGAOS",
TP_SITUACAO == 4 ~ "AFASTADO OUTROS MOTIVOS",
TP_SITUACAO == 5 ~ "AFASTADO SAUDE",
TP_SITUACAO == 6 ~ "FALECIDO",),
DS_ESCOLARIDADE = case_when(
TP_ESCOLARIDADE == 1 ~ "SEM GRADUACAO",
TP_ESCOLARIDADE == 2 ~ "GRADUACAO",
TP_ESCOLARIDADE == 3 ~ "ESPECIALIZACAO",
TP_ESCOLARIDADE == 4 ~ "MESTRADO",
TP_ESCOLARIDADE == 5 ~ "DOUTORADO"),
DS_REGIME_TRABALHO = case_when(
TP_REGIME_TRABALHO == 1 ~ "INTEGRAL EXCLUSIVA",
TP_REGIME_TRABALHO == 2 ~ "INTEGRAL NÃO EXCLUSIVA",
TP_REGIME_TRABALHO == 3 ~ "TEMPO PARCIAL",
TP_REGIME_TRABALHO == 4 ~ "HORISTA"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DT_NASC = lubridate::date(str_c(NU_ANO_NASCIMENTO,'-', NU_MES_NASCIMENTO, '-', NU_DIA_NASCIMENTO))
)
#salvando no banco de dados
saveRDS(BaseDocente, "./data/docente_2018_data.rds")
#liberando variavel
rm(BaseDocente)4.1.4 Locais de Oferta (pólos) das IES - 2018
Importação e tratamento do arquivo relativo ao registro dos pólos das IES em 2017
#leitura base LOCAIS OFERTA - 2018
BaseLocalOferta = read_delim("./raw-data/SUP_LOCAL_OFERTA_2018.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
NO_LOCAL_OFERTA = col_character(),
CO_IES = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
IN_SEDE = col_integer(),
CO_CURSO_POLO = col_integer(),
CO_CURSO = col_integer(),
IN_LOCAL_OFERTA_NEAD = col_integer(),
IN_LOCAL_OFERTA_UAB = col_integer(),
IN_LOCAL_OFERTA_REITORIA = col_integer(),
IN_LOCAL_OFERTA_POLO = col_integer(),
IN_LOCAL_OFERTA_UNID_ACADEMICA = col_integer(),
QT_COMPUTADOR_DISCENTE = col_integer(),
IN_ACESSIBILIDADE = col_integer(),
IN_SINALIZACAO_TATIL = col_integer(),
IN_RAMPA_ACESSO_CORRIMAO = col_integer(),
IN_ENTRADA_SAIDA_DIMENSAO = col_integer(),
IN_AMBIENTE_DESOBSTRUIDO = col_integer(),
IN_SINALIZACAO_SONORA = col_integer(),
IN_SINALIZACAO_VISUAL = col_integer(),
IN_EQUIPAMENTO_ELETROMECANICO = col_integer(),
IN_BANHEIRO_ADAPTADO = col_integer(),
IN_ESPACO_ATENDIMENTO_ADAPTADO = col_integer(),
IN_MOBILIARIO_ADAPTADO = col_integer(),
IN_BEBEDOURO_LAVABO_ADAPTADO = col_integer(),
IN_RESTAURANTE_UNIVERSITARIO = col_integer(),
IN_QUADRA_POLIESPORTIVA = col_integer(),
IN_PISCINA = col_integer(),
IN_QUADRA_COBERTA_GINASIO = col_integer(),
IN_CINEMA = col_integer(),
IN_VESTIARIO = col_integer(),
IN_SERVICOS = col_integer(),
IN_ESTACIONAMENTO_DOCENTE = col_integer(),
IN_CANTINA_LANCHONETE = col_integer(),
IN_PISTA_ATLETISMO = col_integer(),
IN_CAMPO_PRATICA_ESPORTIVA = col_integer(),
IN_AUDITORIO_TEATRO = col_integer(),
IN_REDE_SEM_FIO_COMU_ACADEMICA = col_integer(),
IN_POSTO_ATENDIMENTO_1_SOCORRO = col_integer(),
IN_BICICLETARIO = col_integer(),
IN_ESTACIONAMENTO_ALUNO = col_integer(),
IN_MORADIA_ESTUDANTIL = col_integer(),
IN_EQUIPA_VIDEOCONFERENCIA = col_integer(),
IN_SALA_COORDENACAO_POLO = col_integer(),
IN_MICROCOMPUTADOR = col_integer(),
IN_POSSUI_INTERNET_BANDA_LARGA = col_integer(),
IN_SALA_ATENDIMENTO_TUTOR = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseLocalOferta = BaseLocalOferta %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseLocalOferta, "./data/localoferta_2018_data.rds")
#liberando vari?vel
rm(BaseLocalOferta)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3693700 197.3 11784793 629.4 17479135 933.5
## Vcells 15134295 115.5 369606899 2819.9 1617645616 12341.74.1.5 Matrículas das IES - 2018
Importação e tratamento do arquivo relativo ao registro das matrículas das IES em 2018
#leitura base MATRICULAS - 2018
BaseAluno = readr::read_delim("./raw-data/SUP_ALUNO_2018.CSV",
delim = "|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_CURSO = col_integer(),
CO_CURSO_POLO = col_integer(),
TP_TURNO = col_integer(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
CO_CINE_ROTULO = col_character(),
ID_ALUNO = col_character(),
CO_ALUNO_CURSO = col_integer(),
CO_ALUNO_CURSO_ORIGEM = col_integer(),
TP_COR_RACA = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
IN_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_SUPERDOTACAO = col_integer(),
IN_TGD_AUTISMO = col_integer(),
IN_TGD_SINDROME_ASPERGER = col_integer(),
IN_TGD_SINDROME_RETT = col_integer(),
IN_TGD_TRANSTOR_DESINTEGRATIVO= col_integer(),
TP_SITUACAO = col_integer(),
QT_CARGA_HORARIA_TOTAL = col_integer(),
QT_CARGA_HORARIA_INTEG = col_integer(),
DT_INGRESSO_CURSO = col_character(),
IN_INGRESSO_VESTIBULAR = col_integer(),
IN_INGRESSO_ENEM = col_integer(),
IN_INGRESSO_AVALIACAO_SERIADA = col_integer(),
IN_INGRESSO_SELECAO_SIMPLIFICA= col_integer(),
IN_INGRESSO_OUTRO_TIPO_SELECAO= col_integer(),
IN_INGRESSO_VAGA_REMANESC = col_integer(),
IN_INGRESSO_VAGA_PROG_ESPECIAL= col_integer(),
IN_INGRESSO_TRANSF_EXOFFICIO = col_integer(),
IN_INGRESSO_DECISAO_JUDICIAL = col_integer(),
IN_INGRESSO_CONVENIO_PECG = col_integer(),
IN_INGRESSO_EGRESSO = col_integer(),
IN_INGRESSO_OUTRA_FORMA = col_integer(),
IN_RESERVA_VAGAS = col_integer(),
IN_RESERVA_ETNICO = col_integer(),
IN_RESERVA_DEFICIENCIA = col_integer(),
IN_RESERVA_ENSINO_PUBLICO = col_integer(),
IN_RESERVA_RENDA_FAMILIAR = col_integer(),
IN_RESERVA_OUTRA = col_integer(),
IN_FINANCIAMENTO_ESTUDANTIL = col_integer(),
IN_FIN_REEMB_FIES = col_integer(),
IN_FIN_REEMB_ESTADUAL = col_integer(),
IN_FIN_REEMB_MUNICIPAL = col_integer(),
IN_FIN_REEMB_PROG_IES = col_integer(),
IN_FIN_REEMB_ENT_EXTERNA = col_integer(),
IN_FIN_REEMB_OUTRA = col_integer(),
IN_FIN_NAOREEMB_PROUNI_INTEGR = col_integer(),
IN_FIN_NAOREEMB_PROUNI_PARCIAL= col_integer(),
IN_FIN_NAOREEMB_ESTADUAL = col_integer(),
IN_FIN_NAOREEMB_MUNICIPAL = col_integer(),
IN_FIN_NAOREEMB_PROG_IES = col_integer(),
IN_FIN_NAOREEMB_ENT_EXTERNA = col_integer(),
IN_FIN_NAOREEMB_OUTRA = col_integer(),
IN_APOIO_SOCIAL = col_integer(),
IN_APOIO_ALIMENTACAO = col_integer(),
IN_APOIO_BOLSA_PERMANENCIA = col_integer(),
IN_APOIO_BOLSA_TRABALHO = col_integer(),
IN_APOIO_MATERIAL_DIDATICO = col_integer(),
IN_APOIO_MORADIA = col_integer(),
IN_APOIO_TRANSPORTE = col_integer(),
IN_ATIVIDADE_EXTRACURRICULAR = col_integer(),
IN_COMPLEMENTAR_ESTAGIO = col_integer(),
IN_COMPLEMENTAR_EXTENSAO = col_integer(),
IN_COMPLEMENTAR_MONITORIA = col_integer(),
IN_COMPLEMENTAR_PESQUISA = col_integer(),
IN_BOLSA_ESTAGIO = col_integer(),
IN_BOLSA_EXTENSAO = col_integer(),
IN_BOLSA_MONITORIA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
TP_ESCOLA_CONCLUSAO_ENS_MEDIO = col_integer(),
IN_ALUNO_PARFOR = col_integer(),
TP_SEMESTRE_CONCLUSAO = col_integer(),
TP_SEMESTRE_REFERENCIA = col_integer(),
IN_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA_INTERN= col_integer(),
CO_IES_DESTINO = col_integer(),
CO_PAIS_DESTINO = col_integer(),
IN_MATRICULA = col_integer(),
IN_CONCLUINTE = col_integer(),
IN_INGRESSO_TOTAL = col_integer(),
IN_INGRESSO_VAGA_NOVA = col_integer(),
IN_INGRESSO_PROCESSO_SELETIVO = col_integer(),
NU_ANO_INGRESSO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
BaseAluno = BaseAluno %>% dplyr::select(everything()) %>%
mutate(DS_TURNO = case_when(
TP_TURNO == 1 ~ "MATUTINO",
TP_TURNO == 2 ~ "VESPERTINO",
TP_TURNO == 3 ~ "NOTURNO",
TP_TURNO == 4 ~ "INTEGRAL",
TRUE ~ "EAD"),
DS_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA",
TRUE ~ "NA"),
DS_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
DS_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL DE FORMACAO"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DS_SITUACAO = case_when(
TP_SITUACAO == 2 ~ "CURSANDO",
TP_SITUACAO == 3 ~ "MATRICULA TRANCADA",
TP_SITUACAO == 4 ~ "DESVINCULADO",
TP_SITUACAO == 5 ~ "TRANSFERIDO MESMA IES",
TP_SITUACAO == 6 ~ "FORMADO",
TP_SITUACAO == 7 ~ "FALECIDO"))
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseAluno = BaseAluno %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO_NASCIMENTO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseAluno, "./data/aluno_2018_data.rds")
rm(BaseAluno)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3694163 197.3 19690538 1051.6 17479135 933.5
## Vcells 19335482 147.6 1618022230 12344.6 1683739966 12846.04.2 Tratamento e Uniformização dos dados
A seção trata da uniformização, reconfiguração dos dados e montagem das bases de dados necessárias para a exploração e análise
4.2.1 Consolidação dos dados das IES - 2018
Agrupamentos e calculos para facilitar a consolidação nas mantenedoras.
#*carga dos dados 2017 importados por ies_import_data
ies_2018_full = readRDS("./data/ies_2018_data.rds")
#* SELECIONAR CAMPOS FINANCEIROS E AGRUPAMENTOS, LOCALIZAÇÃO, IDENTIFICAÇÃO DA IES.
ies_2018_ext = ies_2018_full %>% dplyr::select(c(1,2,3,5,6,11,13,38:55))
#criar indice de qualificação corpo técnico = IQCTA
ies_2018_IQCTA = ies_2018_full %>% dplyr::select(c(1,2,13:27))
ies_2018_IQCTA = ies_2018_IQCTA %>% mutate(
QT_TEC_D = QT_TEC_DOUTORADO_FEM+QT_TEC_DOUTORADO_MASC,
QT_TEC_M = QT_TEC_MESTRADO_FEM+QT_TEC_MESTRADO_MASC,
QT_TEC_E = QT_TEC_ESPECIALIZACAO_FEM+QT_TEC_ESPECIALIZACAO_MASC,
QT_TEC_G = QT_TEC_SUPERIOR_FEM+QT_TEC_SUPERIOR_MASC,
QT_TEC_MD= QT_TEC_MEDIO_FEM+QT_TEC_MEDIO_MASC,
QT_TEC_F = QT_TEC_FUNDAMENTAL_COMP_FEM+QT_TEC_FUNDAMENTAL_COMP_MASC)
ies_2018_IQCTA = ies_2018_IQCTA %>% dplyr::select(c(1,2, 18:23))
ies_2018_ext = ies_2018_ext %>% left_join(ies_2018_IQCTA)## Joining, by = c("NU_ANO_CENSO", "CO_IES")rm(ies_2018_full)
rm(ies_2018_IQCTA)
#* CRIAR COLUNAS COM TOTAL DE DOCENTES ATIVOS e IQCD
#*
docentes_2018_full = readRDS("./data/docente_2018_data.rds")
docentes_2018_consolidado_IES = docentes_2018_full %>%
dplyr::select(c(1,2,7,8)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QT_DOC = n(), #conta os registros
QT_DOC_D = sum(TP_ESCOLARIDADE == 5),
QT_DOC_M = sum(TP_ESCOLARIDADE == 4),
QT_DOC_E = sum(TP_ESCOLARIDADE == 3),
QT_DOC_G = sum(TP_ESCOLARIDADE == 2),
QT_DOC_SG = sum(TP_ESCOLARIDADE == 1))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2018_ext = ies_2018_ext %>% dplyr::select(everything()) %>%
left_join(docentes_2018_consolidado_IES, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
rm(docentes_2018_consolidado_IES)
rm(docentes_2018_full)
#* CRIAR COLUNAS COM DADOS DOS CURSOS
#*
cursos_2018_full = readRDS("./data/curso_2018_data.rds")
polos_2018_full = readRDS("./data/localoferta_2018_data.rds")
cursos_2018_consolidado_IES = cursos_2018_full %>%
dplyr::select(c(1,2,11,108,111)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES, TP_SITUACAO) %>%
dplyr::summarise(TVAGAS = sum (QT_VAGA_TOTAL), TINGRESSOS = sum(QT_INGRESSO_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_IES'. You can override
## using the `.groups` argument.cursos_2018_consolidado_IES = cursos_2018_consolidado_IES %>% dplyr::select(-c(3))
#POLOS
talunos_polos = cursos_2018_full %>% dplyr::select(1,2,5,9,106)
tpolos = polos_2018_full %>% dplyr::select(1,2,4,8,10)
polos_2018_consolidado_IES = talunos_polos %>% left_join(tpolos, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES", "CO_LOCAL_OFERTA" = "CO_LOCAL_OFERTA", "CO_CURSO" = "CO_CURSO"))
polos_2018_consolidado_IES[is.na(polos_2018_consolidado_IES)] = 0
polos_2018_consolidado_IES = polos_2018_consolidado_IES %>% group_by(NU_ANO_CENSO, CO_IES) %>% filter(IN_SEDE != 1) %>%
dplyr::summarise(TMATR_FSEDE = sum(QT_MATRICULA_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2018_ext = ies_2018_ext %>% dplyr::select(everything()) %>%
left_join(cursos_2018_consolidado_IES, c(TVAGAS, TINGRESSOS), by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES")) %>%
left_join(polos_2018_consolidado_IES, TMATR_FSEDE, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2018_ext[is.na(ies_2018_ext)] = 0
rm(cursos_2018_consolidado_IES)
rm(cursos_2018_full)
rm(polos_2018_full)
rm(talunos_polos)
rm(tpolos)
rm(polos_2018_consolidado_IES)
#* CRIAR COLUNAS COM TOTAL DE ALUNOS ATIVOS PRESENCIAL E EAD
#*
alunos_2018_full = readRDS("./data/aluno_2018_data.rds")
alunos_2018_consolidado_IES = alunos_2018_full %>%
dplyr::select(c(1,2,8,9,10,39,61,75)) %>% #pega ano censo, co_ies, situacao, modalidade e financiamento/bolsas
filter(TP_NIVEL_ACADEMICO == 1) %>%
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QTD_ALUNO_PRESENCIAL = sum(TP_MODALIDADE_ENSINO == 1 & TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_EAD = sum(TP_MODALIDADE_ENSINO == 2 &TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_ATIVO = QTD_ALUNO_PRESENCIAL+QTD_ALUNO_EAD,
QTD_ALUNO_EVADIDO = sum((TP_SITUACAO %in% c(3,4,7))),
QTD_ALUNO_FINANC = sum((IN_FINANCIAMENTO_ESTUDANTIL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNO_BOLSA = sum((IN_APOIO_SOCIAL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNOS = QTD_ALUNO_ATIVO + QTD_ALUNO_EVADIDO) #conta os registros## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2018_ext = ies_2018_ext %>% dplyr::select(everything()) %>%
left_join(alunos_2018_consolidado_IES, dplyr::select(QTD_ALUNO_PRESENCIAL,
QTD_ALUNO_EAD,
QTD_ALUNO_ATIVO,
QTD_ALUNO_EVADIDO,
QTD_ALUNO_FINANC,
QTD_ALUNO_BOLSA,
QTD_ALUNOS),
by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2018_ext[is.na(ies_2018_ext)] = 0
saveRDS(ies_2018_ext,"./data/ies_consolidado_2018_data.rds")
rm(alunos_2018_consolidado_IES)
rm(alunos_2018_full)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3700301 197.7 15752431 841.3 18747399 1001.3
## Vcells 19453261 148.5 1294417784 9875.7 1683739966 12846.0Total de registros e dimensões:
dim(ies_2018_ext)## [1] 2537 47rm (ies_2018_ext)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3693511 197.3 12601945 673.1 18747399 1001.3
## Vcells 19327592 147.5 1035534228 7900.5 1683739966 12846.04.2.2 Consolidação dos dados nas mantenedoras - 2018
Nesta etapa consolidaremos os dados das IES nas suas mantenedoras, agraupando os valores financeiros de acordo com as regras e criando as váriaveis de suporte
#*** TRATANDO, EXTRAINDO E CONVERTENDO 2018
#*carga dos dados tratados por ies_convert_data
ies_2018_ext = readRDS("./data/ies_consolidado_2018_data.rds")
# deixando somente as privadas
ies_2018_ext = ies_2018_ext %>% filter(NO_CATEGORIA_ADMINISTRATIVA %in% c("PRIVADA COM FINS LUCRATIVOS", "PRIVADA SEM FINS LUCRATIVOS"))
#primeiro selecionar TP_REFERENTE == 2 e somar todos os campos
tpr2_2018 = ies_2018_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 2) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#segundo passo, selecionar TP_REFERENTE == 1 e usar media para valores financeiros e soma para outros
tpr1_2018 = ies_2018_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 1) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = mean(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = mean (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = mean(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = mean(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = mean(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = mean(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = mean(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = mean(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = mean(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = mean(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#terceiro passo, juntar as 2 anteriores e somar novamente os campos de valores
mant_2018_agrupado = union_all(tpr1_2018, tpr2_2018)
mant_2018_agrupado = mant_2018_agrupado %>%
group_by(NU_ANO_CENSO,CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS)) ## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#validação com dados de matriculas divulgado em graduação privada (6.373.274)
total_2018 = sum(mant_2018_agrupado$QTD_ALUNO_ATIVO)Total de registros e dimensões. Também compara a quantidade de matriculas ativas em comparação com a sinopse estatística oficial 2018, para fins de validação:
Total de Matrículas ativas divulgada na sinopse = 6.241.307
total_2018## [1] 6373274dim(ies_2018_ext)## [1] 2238 47#salvando dataset
saveRDS(mant_2018_agrupado,"./data/mant_2018_agrupado_data.rds")
rm(mant_2018_agrupado)
rm(ies_2018_ext)
rm(tpr1_2018)
rm(tpr2_2018)
rm (total_2018)4.3 Exploração e Análise de Dados
Esta seção se dedica a explorar as informações de acordo com cada visualização elaborada.
4.3.1 Análise de fatores que podem se correlacionar com o resultado do ponto de equilíbrio das mantenedoras
MONTAGEM E ANÁLISE
# MONTAGEM DO DATASET
mant_2018_agrupado = readRDS("./data/mant_2018_agrupado_data.rds")
ds_PEQ_2018 = mant_2018_agrupado %>% select_all() %>%
mutate(VDPEQ = ((VL_RECEITA_PROPRIA - #somente receitas com mensalidades
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA))/
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+
VL_DESPESA_PESSOAL_ENCARGO+VL_DESPESA_CUSTEIO+
VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)), #variavel distancia ponto de equilíbrio
VRAD = QTD_ALUNO_ATIVO/QT_DOC,
VRAF = QTD_ALUNO_ATIVO/QT_TEC_TOTAL, #alterado para alunos/funcionarios
VCFPA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO)/QTD_ALUNO_ATIVO,
VCTTA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)/QTD_ALUNO_ATIVO,
VTMA = (VL_RECEITA_PROPRIA/QTD_ALUNO_ATIVO),
VOCIO = TINGRESSOS/TVAGAS,
VEAD = QTD_ALUNO_EAD/QTD_ALUNO_ATIVO,
VPOL = TMATR_FSEDE/QTD_ALUNO_ATIVO,
IQCTA = ((5*QT_TEC_D+3*QT_TEC_M+2*QT_TEC_E+QT_TEC_G+0.75*QT_TEC_MD+0.5*QT_TEC_F)/
QT_TEC_TOTAL),
IQCDO = ((5*QT_DOC_D+3*QT_DOC_M+2*QT_DOC_E+1*QT_DOC_G)/QT_DOC),
VFINE = QTD_ALUNO_FINANC / QTD_ALUNO_ATIVO,
VBOLS = QTD_ALUNO_BOLSA / QTD_ALUNO_ATIVO,
VEVAD = QTD_ALUNO_EVADIDO / QTD_ALUNOS)
ds_PEQ_2018[is.na(ds_PEQ_2018)] = 0
ds_PEQ_2018 = ds_PEQ_2018 %>% dplyr::select(2,37:50)## Adding missing grouping variables: `NU_ANO_CENSO`ds_PEQ_2018 = (ds_PEQ_2018[,2:16])
rm(mant_2018_agrupado)ANÁLISE E REMOÇÃO DE OUTLIERS
Sumário antes da remoção:
tbl_summary(ds_PEQ_2018) #usando gtsummary| Characteristic | N = 1,5581 |
|---|---|
| CO_MANTENEDORA | 1,654 (805, 12,550) |
| VDPEQ | 0.06 (-0.11, 0.34) |
| VRAD | 14 (8, 21) |
| VRAF | 19 (10, 31) |
| VCFPA | 5,044 (2,934, 8,974) |
| VCTTA | 7,978 (4,768, 14,631) |
| VTMA | 8,293 (4,936, 14,401) |
| VOCIO | 0.27 (0.13, 0.44) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 1.00 (1.00, 1.00) |
| IQCTA | 1.10 (0.93, 1.41) |
| IQCDO | 3.09 (2.80, 3.41) |
| VFINE | 0.25 (0.01, 0.55) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.23 (0.16, 0.32) |
| 1 Median (IQR) | |
Boxplot antes do tratamento:
ds_boxplot_all = melt(ds_PEQ_2018, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2018, id.vars = "CO_MANTENEDORA", measure.vars =
## c("VCFPA", : The melt generic in data.table has been passed a grouped_df and
## will attempt to redirect to the relevant reshape2 method; please note that
## reshape2 is deprecated, and this redirection is now deprecated as well. To
## continue using melt methods from reshape2 while both libraries are attached,
## e.g. melt.list, you can prepend the namespace like reshape2::melt(ds_PEQ_2018).
## In the next version, this warning will become an error.ggplot(data = ds_boxplot_all, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Tratamento e remoção dos extremos, analisando VTMA e VCFPA 5% de cada lado = 95,9% / Perda total simultânea em VTMA e VCFPA = 13,3%
#Remoção de Registros inválidos - Outliers
q_vtma = quantile(ds_PEQ_2018$VTMA, probs = c(.05, .95), na.rm = F)
q_vcfpa = quantile(ds_PEQ_2018$VCFPA, probs = c(.05, .95), na.rm = F)
#dataset sem os outliers
ds_PEQ_2018_noOutliers = subset(ds_PEQ_2018, (ds_PEQ_2018$VTMA>=(q_vtma[1])&ds_PEQ_2018$VCFPA>=(q_vcfpa[1])) &
(ds_PEQ_2018$VTMA<=(q_vtma[2]) & ds_PEQ_2018$VCFPA<=(q_vcfpa[2])))
#dataset com os outliers excluidos
ds_PEQ_2018_Outliers = subset(ds_PEQ_2018, !(CO_MANTENEDORA %in% ds_PEQ_2018_noOutliers$CO_MANTENEDORA))Sumário após remoção:
tbl_summary(ds_PEQ_2018_noOutliers) #usando gtsummary| Characteristic | N = 1,3401 |
|---|---|
| CO_MANTENEDORA | 1,444 (743, 3,420) |
| VDPEQ | 0.06 (-0.10, 0.33) |
| VRAD | 15 (9, 22) |
| VRAF | 20 (12, 32) |
| VCFPA | 5,041 (3,149, 8,208) |
| VCTTA | 7,936 (5,109, 13,070) |
| VTMA | 8,400 (5,411, 13,521) |
| VOCIO | 0.26 (0.13, 0.42) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 1.00 (1.00, 1.00) |
| IQCTA | 1.09 (0.92, 1.39) |
| IQCDO | 3.09 (2.80, 3.41) |
| VFINE | 0.29 (0.04, 0.57) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.23 (0.17, 0.32) |
| 1 Median (IQR) | |
Boxplot após tratamento:
ds_boxplot_NoOutliers = melt(ds_PEQ_2018_noOutliers, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2018_noOutliers, id.vars = "CO_MANTENEDORA",
## measure.vars = c("VCFPA", : The melt generic in data.table has been passed
## a grouped_df and will attempt to redirect to the relevant reshape2 method;
## please note that reshape2 is deprecated, and this redirection is now
## deprecated as well. To continue using melt methods from reshape2 while both
## libraries are attached, e.g. melt.list, you can prepend the namespace like
## reshape2::melt(ds_PEQ_2018_noOutliers). In the next version, this warning will
## become an error.ggplot(data = ds_boxplot_NoOutliers, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Perda Total dos registros (em %)
perda = 1-(dim(ds_PEQ_2018_noOutliers)[1]/dim(ds_PEQ_2018)[1])
paste(round(100*perda, 2), "%", sep="")## [1] "13.99%"ANÁLISE DOS RESÍDUOS OUTLIERS
#falta fazer....ANÁLISE DE COMPONENTES PRINCIPAIS - PCA
# analise PCA - Componentes Principais
# Filtrando variáveis númericas
ds_pca = ds_PEQ_2018_noOutliers[,2:15]
# Calculando o desvio padrão das variáveis
sapply(ds_pca, sd)## VDPEQ VRAD VRAF VCFPA
## 4.82903366614e-01 1.45319682024e+01 2.35749783187e+01 4.47210122765e+03
## VCTTA VTMA VOCIO VEAD
## 1.33011512320e+04 7.20501057597e+03 2.33379007514e-01 1.22203372707e-01
## VPOL IQCTA IQCDO VFINE
## 3.14560571117e-01 3.85788103039e-01 4.47127251490e-01 3.07922261149e-01
## VBOLS VEVAD
## 1.53521892142e-01 1.36233185177e-01# PCA com a matriz de covariâncias
pca_cov = prcomp(ds_pca)
summary(pca_cov)## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 14089.82275 6514.07837 2806.46675 23.3842359 11.549932
## Proportion of Variance 0.79782 0.17053 0.03165 0.0000000 0.000000
## Cumulative Proportion 0.79782 0.96834 1.00000 1.0000000 1.000000
## PC6 PC7 PC8 PC9
## Standard deviation 0.417099685 0.37919746 0.356755706 0.31026179
## Proportion of Variance 0.000000000 0.00000000 0.000000000 0.00000000
## Cumulative Proportion 1.000000000 1.00000000 1.000000000 1.00000000
## PC10 PC11 PC12 PC13
## Standard deviation 0.285312206 0.226206897 0.152582058 0.13060877
## Proportion of Variance 0.000000000 0.000000000 0.000000000 0.00000000
## Cumulative Proportion 1.000000000 1.000000000 1.000000000 1.00000000
## PC14
## Standard deviation 0.104335816
## Proportion of Variance 0.000000000
## Cumulative Proportion 1.000000000# PCA com a matriz de correlação (variáveis padronizadas)
pca_corr = prcomp(ds_pca, center = T, scale = T)
summary(pca_corr)$importance## PC1 PC2 PC3 PC4
## Standard deviation 1.6203879448 1.41382720087 1.16745082297 1.06849911461
## Proportion of Variance 0.1875500000 0.14278000000 0.09735000000 0.08155000000
## Cumulative Proportion 0.1875500000 0.33033000000 0.42768000000 0.50923000000
## PC5 PC6 PC7
## Standard deviation 1.01874215467 1.00366559857 0.958685475305
## Proportion of Variance 0.07413000000 0.07195000000 0.065650000000
## Cumulative Proportion 0.58336000000 0.65531000000 0.720960000000
## PC8 PC9 PC10
## Standard deviation 0.901029401339 0.884700565339 0.832552833834
## Proportion of Variance 0.057990000000 0.055910000000 0.049510000000
## Cumulative Proportion 0.778950000000 0.834860000000 0.884370000000
## PC11 PC12 PC13
## Standard deviation 0.816495869286 0.679036772293 0.587579837037
## Proportion of Variance 0.047620000000 0.032940000000 0.024660000000
## Cumulative Proportion 0.931990000000 0.964920000000 0.989580000000
## PC14
## Standard deviation 0.381897786477
## Proportion of Variance 0.010420000000
## Cumulative Proportion 1.000000000000# Gráfico das variáveis
fviz_pca_var(pca_corr,
col.var = "contrib", # Cor por contribuições para o PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE,
legend.title = "Contribuição"
)
Processo de análise de regressão múltipla
# Modelo de Regressão Linear Múltipla
ds_RLM = ds_PEQ_2018_noOutliers
summary(ds_RLM)## CO_MANTENEDORA VDPEQ VRAD
## Min. : 10.00000 Min. :-0.9600663398 Min. : 0.30000000
## 1st Qu.: 743.25000 1st Qu.:-0.0965505685 1st Qu.: 8.59880952
## Median : 1443.50000 Median : 0.0615518076 Median : 14.77534905
## Mean : 4509.44851 Mean : 0.1465815626 Mean : 17.61634202
## 3rd Qu.: 3420.50000 3rd Qu.: 0.3274134066 3rd Qu.: 21.62589031
## Max. :17201.00000 Max. : 3.2395967225 Max. :143.07925408
## VRAF VCFPA VCTTA
## Min. : 0.2500000 Min. : 1055.90551 Min. : 1062.22222
## 1st Qu.: 11.5084489 1st Qu.: 3148.76416 1st Qu.: 5108.80910
## Median : 19.8290625 Median : 5041.22142 Median : 7935.83090
## Mean : 25.7449521 Mean : 6333.46085 Mean : 10492.65320
## 3rd Qu.: 32.2777778 3rd Qu.: 8208.10457 3rd Qu.: 13069.54055
## Max. :228.5454545 Max. :27069.60010 Max. :401851.74063
## VTMA VOCIO VEAD
## Min. : 1666.66667 Min. :0.000000000 Min. :0.0000000000
## 1st Qu.: 5411.04627 1st Qu.:0.133745207 1st Qu.:0.0000000000
## Median : 8400.48212 Median :0.264518546 Median :0.0000000000
## Mean :10419.11405 Mean :0.310584125 Mean :0.0326465854
## 3rd Qu.:13521.32855 3rd Qu.:0.420055947 3rd Qu.:0.0000000000
## Max. :44793.25727 Max. :1.146788991 Max. :1.0000000000
## VPOL IQCTA IQCDO
## Min. :0.000000000 Min. :0.500000000 Min. :1.80000000
## 1st Qu.:1.000000000 1st Qu.:0.924515483 1st Qu.:2.80298957
## Median :1.000000000 Median :1.090061047 Median :3.09143156
## Mean :0.852433087 Mean :1.199872333 Mean :3.10979880
## 3rd Qu.:1.000000000 3rd Qu.:1.386994949 3rd Qu.:3.40712396
## Max. :1.675213675 Max. :3.250000000 Max. :4.86666667
## VFINE VBOLS VEVAD
## Min. :0.0000000000 Min. :0.0000000000 Min. :0.000000000
## 1st Qu.:0.0361206028 1st Qu.:0.0000000000 1st Qu.:0.166666667
## Median :0.2899816443 Median :0.0000000000 Median :0.234699044
## Mean :0.3455605815 Mean :0.0360210809 Mean :0.256184570
## 3rd Qu.:0.5728645786 3rd Qu.:0.0000000000 3rd Qu.:0.323552308
## Max. :1.0000000000 Max. :1.0000000000 Max. :0.956521739# Correlograma
corrgram(ds_RLM[,c(2:15)],
#upper.panel=panel.cor,
upper.panel=panel.conf,
cex.labels=1.5, cex=1.2)## Warning in par(usr): argument 1 does not name a graphical parameter
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## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
corrplot(cor(ds_RLM[,c(2:15)]), method="circle", type="lower", order="hclust", addCoef.col = "black")
# Modelo de regressão
modelo_RLM = lm(VDPEQ ~ ., data = ds_RLM[,2:15])
summary(modelo_RLM)##
## Call:
## lm(formula = VDPEQ ~ ., data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6159451745 -0.1909281084 -0.0492455069 0.1186798303 2.1608384694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.33122512e-02 9.01796254e-02 1.03474 0.30098013
## VRAD 1.27188086e-03 9.38008513e-04 1.35594 0.17534994
## VRAF 1.89446202e-03 4.97703610e-04 3.80641 0.00014743 ***
## VCFPA -7.78000371e-05 3.51849467e-06 -22.11174 < 2.22e-16 ***
## VCTTA -7.40648994e-06 8.90579046e-07 -8.31649 2.2218e-16 ***
## VTMA 5.38330622e-05 1.99195833e-06 27.02519 < 2.22e-16 ***
## VOCIO -1.22566922e-01 4.35228775e-02 -2.81615 0.00493230 **
## VEAD -2.29783814e-01 9.24448329e-02 -2.48563 0.01305462 *
## VPOL 2.68291890e-02 3.20414128e-02 0.83733 0.40255882
## IQCTA 2.45246338e-02 2.58886401e-02 0.94731 0.34365214
## IQCDO -1.96261754e-03 2.41520076e-02 -0.08126 0.93524661
## VFINE 2.88389805e-02 3.35604440e-02 0.85931 0.39032227
## VBOLS 2.67335240e-02 6.41880290e-02 0.41649 0.67712058
## VEVAD -7.76696492e-02 7.34336786e-02 -1.05768 0.29039204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.355422261 on 1326 degrees of freedom
## Multiple R-squared: 0.463546936, Adjusted R-squared: 0.458287592
## F-statistic: 88.1377899 on 13 and 1326 DF, p-value: < 2.220446e-16tbl_regression(modelo_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.18 |
| VRAF | 0.00 | 0.00, 0.00 | <0.001 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VOCIO | -0.12 | -0.21, -0.04 | 0.005 |
| VEAD | -0.23 | -0.41, -0.05 | 0.013 |
| VPOL | 0.03 | -0.04, 0.09 | 0.40 |
| IQCTA | 0.02 | -0.03, 0.08 | 0.34 |
| IQCDO | 0.00 | -0.05, 0.05 | 0.94 |
| VFINE | 0.03 | -0.04, 0.09 | 0.39 |
| VBOLS | 0.03 | -0.10, 0.15 | 0.68 |
| VEVAD | -0.08 | -0.22, 0.07 | 0.29 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.093312251165 | 0.090179625377 | 1.034737622550 | 0.300980129958 | -0.083598046817 | 0.270222549147 |
| VRAD | 0.001271880858 | 0.000938008513 | 1.355937436070 | 0.175349937285 | -0.000568261689 | 0.003112023404 |
| VRAF | 0.001894462022 | 0.000497703610 | 3.806406032444 | 0.000147432740 | 0.000918089659 | 0.002870834384 |
| VCFPA | -0.000077800037 | 0.000003518495 | -22.111739379642 | 0.000000000000 | -0.000084702460 | -0.000070897614 |
| VCTTA | -0.000007406490 | 0.000000890579 | -8.316487992467 | 0.000000000000 | -0.000009153588 | -0.000005659392 |
| VTMA | 0.000053833062 | 0.000001991958 | 27.025194936489 | 0.000000000000 | 0.000049925329 | 0.000057740796 |
| VOCIO | -0.122566921833 | 0.043522877522 | -2.816149317597 | 0.004932300643 | -0.207948128498 | -0.037185715167 |
| VEAD | -0.229783814440 | 0.092444832895 | -2.485631778904 | 0.013054618574 | -0.411137893767 | -0.048429735113 |
| VPOL | 0.026829188957 | 0.032041412826 | 0.837328525520 | 0.402558815508 | -0.036028201136 | 0.089686579050 |
| IQCTA | 0.024524633844 | 0.025888640144 | 0.947312555138 | 0.343652135505 | -0.026262525929 | 0.075311793617 |
| IQCDO | -0.001962617544 | 0.024152007589 | -0.081261051978 | 0.935246610692 | -0.049342930354 | 0.045417695266 |
| VFINE | 0.028838980548 | 0.033560444003 | 0.859314630820 | 0.390322273350 | -0.036998375995 | 0.094676337091 |
| VBOLS | 0.026733523974 | 0.064188029045 | 0.416487690495 | 0.677120578221 | -0.099187639456 | 0.152654687405 |
| VEVAD | -0.077669649241 | 0.073433678644 | -1.057684303376 | 0.290392037423 | -0.221728508616 | 0.066389210135 |
kable(broom::glance(modelo_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.463546935888 | 0.458287592122 | 0.355422260545 | 88.1377899087 | 0 | 13 | -508.179512371 | 1046.35902474 | 1124.36539814 | 167.506927844 | 1326 | 1340 |
plot(modelo_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo_RLM, order.by = ~., data = ds_RLM[,3:15], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo_RLM
## GQ = 0.9279689319, df1 = 653, df2 = 652, p-value = 0.830047185
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo_RLM
## BP = 173.6552202, df = 13, p-value < 2.220446e-16plot(modelo_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 0.0111783706003 1.97757371347 0.702
## Alternative hypothesis: rho != 0car::vif(modelo_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 1.96948993121 1.45926952353 2.62438999406 1.48735533433 2.18333849015
## VOCIO VEAD VPOL IQCTA IQCDO
## 1.09357886157 1.35276698488 1.07677065639 1.05732248149 1.23611842705
## VFINE VBOLS VEVAD
## 1.13195431091 1.02929541274 1.06083470267plot(modelo_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo_RLM$residuals
## W = 0.855122908, p-value < 2.220446e-16#modelo 2 - somente variáveis mais significativas - PCA
modelo2_RLM = lm(VDPEQ ~ VRAF+VCFPA+VCTTA+VTMA+VEAD+VOCIO, data = ds_RLM[,2:15])
summary(modelo2_RLM)##
## Call:
## lm(formula = VDPEQ ~ VRAF + VCFPA + VCTTA + VTMA + VEAD + VOCIO,
## data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6360831035 -0.1912127069 -0.0474161643 0.1215856252 2.1308588040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.43036106e-01 2.66045823e-02 5.37637 8.9641e-08 ***
## VRAF 2.14444780e-03 4.41059602e-04 4.86204 1.2998e-06 ***
## VCFPA -7.95795397e-05 3.35041956e-06 -23.75211 < 2.22e-16 ***
## VCTTA -7.29985741e-06 8.77429326e-07 -8.31960 < 2.22e-16 ***
## VTMA 5.45275257e-05 1.90115456e-06 28.68127 < 2.22e-16 ***
## VEAD -1.82013629e-01 8.03766719e-02 -2.26451 0.023703 *
## VOCIO -1.07026926e-01 4.20118902e-02 -2.54754 0.010960 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.355196029 on 1333 degrees of freedom
## Multiple R-squared: 0.46140129, Adjusted R-squared: 0.458976989
## F-statistic: 190.323491 on 6 and 1333 DF, p-value: < 2.220446e-16anova(modelo_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 13.97211431 13.97211431 110.60452 < 2.22e-16 ***
## VRAF 1 6.24367236 6.24367236 49.42548 3.2888e-12 ***
## VCFPA 1 17.71579918 17.71579918 140.23987 < 2.22e-16 ***
## VCTTA 1 2.89151154 2.89151154 22.88947 1.9079e-06 ***
## VTMA 1 101.83396161 101.83396161 806.12686 < 2.22e-16 ***
## VOCIO 1 0.76887583 0.76887583 6.08649 0.0137475 *
## VEAD 1 0.84731548 0.84731548 6.70743 0.0097064 **
## VPOL 1 0.08257768 0.08257768 0.65369 0.4189407
## IQCTA 1 0.10443496 0.10443496 0.82672 0.3633889
## IQCDO 1 0.00003837 0.00003837 0.00030 0.9860974
## VFINE 1 0.11096504 0.11096504 0.87841 0.3488084
## VBOLS 1 0.02947728 0.02947728 0.23334 0.6291345
## VEVAD 1 0.14131926 0.14131926 1.11870 0.2903920
## Residuals 1326 167.50692784 0.12632498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tbl_regression(modelo2_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAF | 0.00 | 0.00, 0.00 | <0.001 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VEAD | -0.18 | -0.34, -0.02 | 0.024 |
| VOCIO | -0.11 | -0.19, -0.02 | 0.011 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo2_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.143036106261 | 0.026604582297 | 5.37637105770 | 0.000000089641 | 0.090844694047 | 0.195227518476 |
| VRAF | 0.002144447799 | 0.000441059602 | 4.86203630462 | 0.000001299838 | 0.001279201231 | 0.003009694367 |
| VCFPA | -0.000079579540 | 0.000003350420 | -23.75211170668 | 0.000000000000 | -0.000086152209 | -0.000073006870 |
| VCTTA | -0.000007299857 | 0.000000877429 | -8.31959588958 | 0.000000000000 | -0.000009021150 | -0.000005578565 |
| VTMA | 0.000054527526 | 0.000001901155 | 28.68126919681 | 0.000000000000 | 0.000050797945 | 0.000058257107 |
| VEAD | -0.182013628777 | 0.080376671935 | -2.26450815137 | 0.023702921662 | -0.339692180937 | -0.024335076616 |
| VOCIO | -0.107026925516 | 0.042011890176 | -2.54753892450 | 0.010960071058 | -0.189443550353 | -0.024610300678 |
kable(broom::glance(modelo2_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.461401289762 | 0.458976989491 | 0.355196028716 | 190.323490557 | 0 | 6 | -510.853959961 | 1037.70791992 | 1079.31131906 | 168.176903681 | 1333 | 1340 |
plot(modelo2_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo2_RLM, order.by = ~., data = ds_RLM[,3:12], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo2_RLM
## GQ = 1.170377782, df1 = 660, df2 = 659, p-value = 0.0217818066
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo2_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo2_RLM
## BP = 165.9760169, df = 6, p-value < 2.220446e-16plot(modelo2_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo2_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 0.010279813678 1.97939838418 0.678
## Alternative hypothesis: rho != 0car::vif(modelo2_RLM)## VRAF VCFPA VCTTA VTMA VEAD
## 1.14747053182 2.38268162827 1.44559663121 1.99135399964 1.02393125133
## VOCIO
## 1.02026360396plot(modelo2_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo2_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo2_RLM$residuals
## W = 0.8580888634, p-value < 2.220446e-16faraway::vif(modelo2_RLM)## VRAF VCFPA VCTTA VTMA VEAD
## 1.14747053182 2.38268162827 1.44559663121 1.99135399964 1.02393125133
## VOCIO
## 1.02026360396anova(modelo2_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAF 1 16.56596772 16.56596772 131.30480 < 2.22e-16 ***
## VCFPA 1 19.95198472 19.95198472 158.14297 < 2.22e-16 ***
## VCTTA 1 2.78956050 2.78956050 22.11055 2.8411e-06 ***
## VTMA 1 103.34176100 103.34176100 819.10515 < 2.22e-16 ***
## VEAD 1 0.60401307 0.60401307 4.78751 0.02884 *
## VOCIO 1 0.81880005 0.81880005 6.48995 0.01096 *
## Residuals 1333 168.17690368 0.12616422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#algoritmos de seleção de variáveis
ts2 = ds_PEQ_2018_noOutliers[,-c(1)]
modelo = lm(data = ts2, VDPEQ ~ .)
summary(modelo)##
## Call:
## lm(formula = VDPEQ ~ ., data = ts2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6159451745 -0.1909281084 -0.0492455069 0.1186798303 2.1608384694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.33122512e-02 9.01796254e-02 1.03474 0.30098013
## VRAD 1.27188086e-03 9.38008513e-04 1.35594 0.17534994
## VRAF 1.89446202e-03 4.97703610e-04 3.80641 0.00014743 ***
## VCFPA -7.78000371e-05 3.51849467e-06 -22.11174 < 2.22e-16 ***
## VCTTA -7.40648994e-06 8.90579046e-07 -8.31649 2.2218e-16 ***
## VTMA 5.38330622e-05 1.99195833e-06 27.02519 < 2.22e-16 ***
## VOCIO -1.22566922e-01 4.35228775e-02 -2.81615 0.00493230 **
## VEAD -2.29783814e-01 9.24448329e-02 -2.48563 0.01305462 *
## VPOL 2.68291890e-02 3.20414128e-02 0.83733 0.40255882
## IQCTA 2.45246338e-02 2.58886401e-02 0.94731 0.34365214
## IQCDO -1.96261754e-03 2.41520076e-02 -0.08126 0.93524661
## VFINE 2.88389805e-02 3.35604440e-02 0.85931 0.39032227
## VBOLS 2.67335240e-02 6.41880290e-02 0.41649 0.67712058
## VEVAD -7.76696492e-02 7.34336786e-02 -1.05768 0.29039204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.355422261 on 1326 degrees of freedom
## Multiple R-squared: 0.463546936, Adjusted R-squared: 0.458287592
## F-statistic: 88.1377899 on 13 and 1326 DF, p-value: < 2.220446e-16stats::step(modelo, direction = "both", trace = F)##
## Call:
## lm(formula = VDPEQ ~ VRAF + VCFPA + VCTTA + VTMA + VOCIO + VEAD,
## data = ts2)
##
## Coefficients:
## (Intercept) VRAF VCFPA VCTTA
## 1.43036106e-01 2.14444780e-03 -7.95795397e-05 -7.29985741e-06
## VTMA VOCIO VEAD
## 5.45275257e-05 -1.07026926e-01 -1.82013629e-01#modelo 2 recomendado
par(mfrow = c(2,2))
plot(modelo2_RLM, which = c(1:4), pch = 20)
Processo de análise XAI - IA Explicada
# analise XAI
# Feature Importance
#carrega arquivo
ds_xai = ds_PEQ_2018_noOutliers[2:15]
head(ds_xai)## # A tibble: 6 × 14
## VDPEQ VRAD VRAF VCFPA VCTTA VTMA VOCIO VEAD VPOL IQCTA IQCDO
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.438 14.4 22.9 15126. 20498. 29480. 0.428 0.0143 0.201 1.12 3.83
## 2 -0.196 19.2 21.0 15353. 20183. 16237. 0.381 0 0 1.12 3.86
## 3 -0.0164 20.4 19.4 10278. 17107. 16826. 0.424 0.0198 0.378 1.14 3.64
## 4 0.216 23.0 20.9 10327. 16705. 20317. 0.277 0.132 0.225 1.13 4.13
## 5 0.190 11.8 14.9 13006. 26083. 31045. 0.0806 0.0322 0.0322 1.22 3.31
## 6 -0.0136 17.8 20.1 13892. 19408. 19145. 0.647 0 0.226 0.948 3.88
## # … with 3 more variables: VFINE <dbl>, VBOLS <dbl>, VEVAD <dbl>
## # ℹ Use `colnames()` to see all variable names#criamos modelo randomForest
modelo_rf = randomForest(VDPEQ ~ ., data = ds_xai, ntree = 500, prox = T)
summary(modelo_rf)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 1340 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1340 -none- numeric
## importance 13 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 1795600 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1340 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call#previsor, modelo, dados, e previsor
previsor = Predictor$new(modelo, ds_xai[,-1] , y = ds_xai$VDPEQ )
#feature importance
importancia = FeatureImp$new(previsor, loss = "mae")
#dataframe
importancia$results## feature importance.05 importance importance.95 permutation.error
## 1 VTMA 2.036360658433 2.097405731505 2.11246479540 0.493079157474
## 2 VCFPA 1.901602387750 1.937098129302 1.94284049877 0.455392439905
## 3 VCTTA 1.068433884751 1.075339513897 1.08065435132 0.252801588909
## 4 VRAF 1.016011989865 1.017239765387 1.02169582443 0.239142917811
## 5 VEAD 1.008261850792 1.011848119227 1.01649273553 0.237875395602
## 6 VOCIO 1.005243278290 1.009774441021 1.01219868112 0.237387894549
## 7 VRAD 0.998970464898 1.003519267793 1.00434657272 0.235917365743
## 8 VPOL 1.001908328061 1.002473148989 1.00319760789 0.235671433652
## 9 VEVAD 1.000859154772 1.001782018614 1.00325114497 0.235508955798
## 10 IQCTA 0.999934526258 1.001471670224 1.00241186505 0.235435995988
## 11 VBOLS 0.999935679269 1.000212662130 1.00077234192 0.235140015749
## 12 IQCDO 0.999844081006 0.999991017717 1.00001899038 0.235087909359
## 13 VFINE 0.997833332207 0.999096083242 1.00158795891 0.234877519195plot(importancia)
#Feature Interaction
interacao = Interaction$new(previsor)
plot(interacao)
interacao$results## .feature .interaction
## 1: VRAD 1.78785934567e-16
## 2: VRAF 1.27622136173e-16
## 3: VCFPA 3.56850484971e-16
## 4: VCTTA 2.30039823629e-16
## 5: VTMA 2.52710851483e-16
## 6: VOCIO 1.44942549161e-16
## 7: VEAD 7.87082243800e-17
## 8: VPOL 8.48277240385e-17
## 9: IQCTA 1.58015265486e-16
## 10: IQCDO 1.25482984698e-16
## 11: VFINE 1.09487032863e-16
## 12: VBOLS 1.27259223993e-16
## 13: VEVAD 1.38576797918e-16#cria modelo substituto
arvore = TreeSurrogate$new(previsor, maxdepth = 3)
#plot a árvore
plot(arvore$tree)
5 Censo Superior 2019
Análise dos dados do Censo Superior 2019
5.1 Importação dos Microdados
Importação dos microdados do censo do ensino superior 2019. Download em: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados.
O arquivo contém as seguintes bases (CSV) que iremos importar (base fornecida antes antes de Set/2021):
- SUP_ALUNO_2019.CSV
- SUP_LOCAL_OFERTA_2019.CSV
- SUP_DOCENTE_2019.CSV
- SUP_IES_2019.CSV
- SUP_CURSO_2019.CSV
5.1.1 Instituições - IES - 2019
Importação e tratamento do arquivo relativo ao registro das IES em 2019
BaseIES = read_delim("./raw-data/SUP_IES_2019.CSV",
"|",
escape_double = FALSE,
col_types = cols(NU_ANO_CENSO= col_integer(),
CO_IES = col_integer(),
NO_IES = col_character(),
SG_IES = col_character(),
CO_MANTENEDORA = col_integer(),
NO_MANTENEDORA = col_character(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_REGIAO = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
QT_TEC_TOTAL = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_MASC = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_MASC = col_integer(),
QT_TEC_MEDIO_FEM = col_integer(),
QT_TEC_MEDIO_MASC = col_integer(),
QT_TEC_SUPERIOR_FEM = col_integer(),
QT_TEC_SUPERIOR_MASC = col_integer(),
QT_TEC_ESPECIALIZACAO_FEM = col_integer(),
QT_TEC_ESPECIALIZACAO_MASC = col_integer(),
QT_TEC_MESTRADO_FEM = col_integer(),
QT_TEC_MESTRADO_MASC = col_integer(),
QT_TEC_DOUTORADO_FEM = col_integer(),
QT_TEC_DOUTORADO_MASC = col_integer(),
IN_ACESSO_PORTAL_CAPES = col_integer(),
IN_ACESSO_OUTRAS_BASES = col_integer(),
IN_ASSINA_OUTRA_BASE = col_integer(),
IN_REPOSITORIO_INSTITUCIONAL = col_integer(),
IN_BUSCA_INTEGRADA = col_integer(),
IN_SERVICO_INTERNET = col_integer(),
IN_PARTICIPA_REDE_SOCIAL = col_integer(),
IN_CATALOGO_ONLINE = col_integer(),
QT_PERIODICO_ELETRONICO = col_integer(),
QT_LIVRO_ELETRONICO = col_integer(),
TP_REFERENTE = col_integer(),
VL_RECEITA_PROPRIA = col_double(),
VL_RECEITA_TRANSFERENCIA = col_double(),
VL_RECEITA_OUTRA = col_double(),
VL_DESPESA_PESSOAL_DOCENTE = col_double(),
VL_DESPESA_PESSOAL_TECNICO = col_double(),
VL_DESPESA_PESSOAL_ENCARGO = col_double(),
VL_DESPESA_CUSTEIO = col_double(),
VL_DESPESA_INVESTIMENTO = col_double(),
VL_DESPESA_PESQUISA = col_double(),
VL_DESPESA_OUTRA = col_double()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseIES = BaseIES %>% dplyr::select(everything()) %>%
mutate(NO_CATEGORIA_ADMINISTRATIVA = case_when(
TP_CATEGORIA_ADMINISTRATIVA == 1 ~ "PUBLICA FEDERAL",
TP_CATEGORIA_ADMINISTRATIVA == 2 ~ "PUBLICA ESTADUAL",
TP_CATEGORIA_ADMINISTRATIVA == 3 ~ "PUBLICA MUNICIPAL",
TP_CATEGORIA_ADMINISTRATIVA == 4 ~ "PRIVADA COM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 5 ~ "PRIVADA SEM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 6 ~ "PRIVADA CONFESSIONAL",
TP_CATEGORIA_ADMINISTRATIVA == 7 ~ "ESPECIAL"),
NO_ORGANIZACAO_ACADEMICA = case_when(
TP_ORGANIZACAO_ACADEMICA == 1 ~ "UNIVERSIDADE",
TP_ORGANIZACAO_ACADEMICA == 2 ~ "CENTRO UNIVERSITARIO",
TP_ORGANIZACAO_ACADEMICA == 3 ~ "FACULDADE",
TP_ORGANIZACAO_ACADEMICA == 4 ~ "INSTITUTO FEDERAL DE EDUCACAO - CIENCIA E TECNOLOGIA",
TP_ORGANIZACAO_ACADEMICA == 5 ~ "CENTRO FEDERAL DE EDUCACAO TECNOLOGICA"),
NO_REGIAO = case_when(
CO_REGIAO == 1 ~ "NORTE",
CO_REGIAO == 2 ~ "NORDESTE",
CO_REGIAO == 3 ~ "SUDESTE",
CO_REGIAO == 4 ~ "SUL",
CO_REGIAO == 5 ~ "CENTRO-OESTE"),
)
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseIES = BaseIES %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
saveRDS(BaseIES, "./data/ies_2019_data.rds")
rm(BaseIES)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3698272 197.6 10081556 538.5 18747399 1001.3
## Vcells 19692468 150.3 828427383 6320.4 1683739966 12846.05.1.2 Cursos das IES - 2019
Importação e tratamento do arquivo relativo ao registro dos cursos das IES em 2019
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseCurso = read_delim("./raw-data/SUP_CURSO_2019.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
CO_CURSO = col_integer(),
NO_CURSO = col_character(),
TP_SITUACAO = col_integer(),
CO_CINE_ROTULO = col_character(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
IN_GRATUITO = col_integer(),
TP_ATRIBUTO_INGRESSO = col_integer(),
NU_CARGA_HORARIA = col_integer(),
DT_INICIO_FUNCIONAMENTO = col_character(),
DT_AUTORIZACAO_CURSO = col_character(),
IN_AJUDA_DEFICIENTE = col_integer(),
IN_MATERIAL_DIGITAL = col_integer(),
IN_MATERIAL_AMPLIADO = col_integer(),
IN_MATERIAL_TATIL = col_integer(),
IN_MATERIAL_IMPRESSO = col_integer(),
IN_MATERIAL_AUDIO = col_integer(),
IN_MATERIAL_BRAILLE = col_integer(),
IN_MATERIAL_LIBRAS = col_integer(),
IN_DISCIPLINA_LIBRAS = col_integer(),
IN_TRADUTOR_LIBRAS = col_integer(),
IN_GUIA_INTERPRETE = col_integer(),
IN_RECURSOS_COMUNICACAO = col_integer(),
IN_RECURSOS_INFORMATICA = col_integer(),
IN_INTEGRAL = col_integer(),
IN_MATUTINO = col_integer(),
IN_VESPERTINO = col_integer(),
IN_NOTURNO = col_integer(),
NU_INTEGRALIZACAO_INTEGRAL = col_number(),
NU_INTEGRALIZACAO_MATUTINO = col_number(),
NU_INTEGRALIZACAO_VESPERTINO = col_number(),
NU_INTEGRALIZACAO_NOTURNO = col_number(),
NU_INTEGRALIZACAO_EAD = col_number(),
IN_OFERECE_DISC_SEMI_PRES = col_integer(),
NU_PERC_CARGA_SEMI_PRES = col_number(),
IN_POSSUI_LABORATORIO = col_integer(),
QT_INSC_VAGA_NOVA_INTEGRAL = col_integer(),
QT_INSC_VAGA_NOVA_MATUTINO = col_integer(),
QT_INSC_VAGA_NOVA_VESPERTINO = col_integer(),
QT_INSC_VAGA_NOVA_NOTURNO = col_integer(),
QT_INSC_VAGA_NOVA_EAD = col_integer(),
QT_INSC_VAGA_REMAN_INTEGRAL = col_integer(),
QT_INSC_VAGA_REMAN_MATUTINO = col_integer(),
QT_INSC_VAGA_REMAN_VESPERTINO = col_integer(),
QT_INSC_VAGA_REMAN_NOTURNO = col_integer(),
QT_INSC_VAGA_REMAN_EAD = col_integer(),
QT_INSC_PROG_ESP_INTEGRAL = col_integer(),
QT_INSC_PROG_ESP_MATUTINO = col_integer(),
QT_INSC_PROG_ESP_VESPERTINO = col_integer(),
QT_INSC_PROG_ESP_NOTURNO = col_integer(),
QT_INSC_PROG_ESP_EAD = col_integer(),
QT_INSC_PRINCIPAL_INTEGRAL = col_integer(),
QT_INSC_PRINCIPAL_MATUTINO = col_integer(),
QT_INSC_PRINCIPAL_VESPERTINO = col_integer(),
QT_INSC_PRINCIPAL_NOTURNO = col_integer(),
QT_INSC_PRINCIPAL_EAD = col_integer(),
QT_INSC_OUTRA_VAGA_INTEGRAL = col_integer(),
QT_INSC_OUTRA_VAGA_MATUTINO = col_integer(),
QT_INSC_OUTRA_VAGA_VESPERTINO = col_integer(),
QT_INSC_OUTRA_VAGA_NOTURNO = col_integer(),
QT_INSC_OUTRA_VAGA_EAD = col_integer(),
QT_INSC_ANUAL_INTEGRAL = col_integer(),
QT_INSC_ANUAL_MATUTINO = col_integer(),
QT_INSC_ANUAL_VESPERTINO = col_integer(),
QT_INSC_ANUAL_NOTURNO = col_integer(),
QT_INSC_ANUAL_EAD = col_integer(),
QT_VAGAS_NOVAS_INTEGRAL = col_integer(),
QT_VAGAS_NOVAS_MATUTINO = col_integer(),
QT_VAGAS_NOVAS_VESPERTINO = col_integer(),
QT_VAGAS_NOVAS_NOTURNO = col_integer(),
QT_VAGAS_NOVAS_EAD = col_integer(),
QT_VAGAS_REMAN_INTEGRAL = col_integer(),
QT_VAGAS_REMAN_MATUTINO = col_integer(),
QT_VAGAS_REMAN_VESPERTINO = col_integer(),
QT_VAGAS_REMAN_NOTURNO = col_integer(),
QT_VAGAS_REMAN_EAD = col_integer(),
QT_VAGAS_PROG_ESP_INTEGRAL = col_integer(),
QT_VAGAS_PROG_ESP_MATUTINO = col_integer(),
QT_VAGAS_PROG_ESP_VESPERTINO = col_integer(),
QT_VAGAS_PROG_ESP_NOTURNO = col_integer(),
QT_VAGAS_PROG_ESP_EAD = col_integer(),
QT_VAGAS_PRINCIPAL_INTEGRAL = col_integer(),
QT_VAGAS_PRINCIPAL_MATUTINO = col_integer(),
QT_VAGAS_PRINCIPAL_VESPERTINO = col_integer(),
QT_VAGAS_PRINCIPAL_NOTURNO = col_integer(),
QT_VAGAS_PRINCIPAL_EAD = col_integer(),
QT_VAGAS_OUTRAS_INTEGRAL = col_integer(),
QT_VAGAS_OUTRAS_MATUTINO = col_integer(),
QT_VAGAS_OUTRAS_VESPERTINO = col_integer(),
QT_VAGAS_OUTRAS_NOTURNO = col_integer(),
QT_VAGAS_OUTRAS_EAD = col_integer(),
QT_VAGAS_ANUAL_INTEGRAL = col_integer(),
QT_VAGAS_ANUAL_MATUTINO = col_integer(),
QT_VAGAS_ANUAL_VESPERTINO = col_integer(),
QT_VAGAS_ANUAL_NOTURNO = col_integer(),
QT_VAGAS_ANUAL_EAD = col_integer(),
QT_MATRICULA_TOTAL = col_integer(),
QT_CONCLUINTE_TOTAL = col_integer(),
QT_INGRESSO_TOTAL = col_integer(),
QT_INGRESSO_VAGA_NOVA = col_integer(),
QT_INGRESSO_PROCESSO_SELETIVO = col_integer(),
QT_VAGA_TOTAL = col_integer(),
QT_INSCRITO_TOTAL = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseCurso = BaseCurso %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM ATIVIDADE",
TP_SITUACAO == 2 ~ "EXTINTO",
TP_SITUACAO == 3 ~ "EM EXTINCAO"),
NO_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA"),
NO_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
NO_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL"),
NO_IN_GRATUITO = case_when(
IN_GRATUITO == 0 ~ "NAO",
IN_GRATUITO == 1 ~ "SIM"),
NO_ATRIBUTO_INGRESSO = case_when(
TP_ATRIBUTO_INGRESSO == 0 ~ "NORMAL",
TP_ATRIBUTO_INGRESSO == 1 ~ "AREA BASICA DE INGRESSO",
TP_ATRIBUTO_INGRESSO == 2 ~ "BACHARELADO OU LICENCIATURA")
)
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseCurso = BaseCurso %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseCurso, "./data/curso_2019_data.rds")
#liberando variável
rm(BaseMun)
rm(BaseCurso)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3698236 197.6 10081556 538.5 18747399 1001.3
## Vcells 19630160 149.8 662741907 5056.4 1683739966 12846.05.1.3 Docentes das IES - 2019
Importação e tratamento do arquivo relativo ao registro dos docentes das IES em 2019
#leitura base DOCENTE - 2019
BaseDocente = read_delim("./raw-data/SUP_DOCENTE_2019.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_DOCENTE_IES = col_integer(),
ID_DOCENTE = col_character(),
TP_SITUACAO = col_integer(),
TP_ESCOLARIDADE = col_integer(),
TP_REGIME_TRABALHO = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_COR_RACA = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
IN_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_ATUACAO_EAD = col_integer(),
IN_ATUACAO_EXTENSAO = col_integer(),
IN_ATUACAO_GESTAO = col_integer(),
IN_ATUACAO_GRAD_PRESENCIAL = col_integer(),
IN_ATUACAO_POS_EAD = col_integer(),
IN_ATUACAO_POS_PRESENCIAL = col_integer(),
IN_ATUACAO_SEQUENCIAL = col_integer(),
IN_ATUACAO_PESQUISA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
IN_SUBSTITUTO = col_integer(),
IN_EXERCICIO_DATA_REFERENCIA = col_integer(),
IN_VISITANTE = col_integer(),
TP_VISITANTE_IFES_VINCULO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
BaseDocente = BaseDocente %>% dplyr::select(everything()) %>%
mutate(DS_SITUACAO = case_when(
TP_SITUACAO == 1 ~ "EM EXERCICIO",
TP_SITUACAO == 2 ~ "AFASTADO PARA QUALIFICACAO",
TP_SITUACAO == 3 ~ "AFASTADO PARA OUTROS ORGAOS",
TP_SITUACAO == 4 ~ "AFASTADO OUTROS MOTIVOS",
TP_SITUACAO == 5 ~ "AFASTADO SAUDE",
TP_SITUACAO == 6 ~ "FALECIDO",),
DS_ESCOLARIDADE = case_when(
TP_ESCOLARIDADE == 1 ~ "SEM GRADUACAO",
TP_ESCOLARIDADE == 2 ~ "GRADUACAO",
TP_ESCOLARIDADE == 3 ~ "ESPECIALIZACAO",
TP_ESCOLARIDADE == 4 ~ "MESTRADO",
TP_ESCOLARIDADE == 5 ~ "DOUTORADO"),
DS_REGIME_TRABALHO = case_when(
TP_REGIME_TRABALHO == 1 ~ "INTEGRAL EXCLUSIVA",
TP_REGIME_TRABALHO == 2 ~ "INTEGRAL NÃO EXCLUSIVA",
TP_REGIME_TRABALHO == 3 ~ "TEMPO PARCIAL",
TP_REGIME_TRABALHO == 4 ~ "HORISTA"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DT_NASC = lubridate::date(str_c(NU_ANO_NASCIMENTO,'-', NU_MES_NASCIMENTO, '-', NU_DIA_NASCIMENTO))
)
#salvando no banco de dados
saveRDS(BaseDocente, "./data/docente_2019_data.rds")
#liberando variavel
rm(BaseDocente)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3698128 197.6 10081556 538.5 18747399 1001.3
## Vcells 19627882 149.8 530193526 4045.1 1683739966 12846.05.1.4 Locais de Oferta (pólos) das IES - 2019
Importação e tratamento do arquivo relativo ao registro dos pólos das IES em 2017
#leitura base LOCAIS OFERTA - 2019
BaseLocalOferta = read_delim("./raw-data/SUP_LOCAL_OFERTA_2019.CSV",
"|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_LOCAL_OFERTA = col_integer(),
NO_LOCAL_OFERTA = col_character(),
CO_IES = col_integer(),
CO_UF = col_integer(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
IN_SEDE = col_integer(),
CO_CURSO_POLO = col_integer(),
CO_CURSO = col_integer(),
IN_LOCAL_OFERTA_NEAD = col_integer(),
IN_LOCAL_OFERTA_UAB = col_integer(),
IN_LOCAL_OFERTA_REITORIA = col_integer(),
IN_LOCAL_OFERTA_POLO = col_integer(),
IN_LOCAL_OFERTA_UNID_ACADEMICA = col_integer(),
QT_COMPUTADOR_DISCENTE = col_integer(),
IN_ACESSIBILIDADE = col_integer(),
IN_SINALIZACAO_TATIL = col_integer(),
IN_RAMPA_ACESSO_CORRIMAO = col_integer(),
IN_ENTRADA_SAIDA_DIMENSAO = col_integer(),
IN_AMBIENTE_DESOBSTRUIDO = col_integer(),
IN_SINALIZACAO_SONORA = col_integer(),
IN_SINALIZACAO_VISUAL = col_integer(),
IN_EQUIPAMENTO_ELETROMECANICO = col_integer(),
IN_BANHEIRO_ADAPTADO = col_integer(),
IN_ESPACO_ATENDIMENTO_ADAPTADO = col_integer(),
IN_MOBILIARIO_ADAPTADO = col_integer(),
IN_BEBEDOURO_LAVABO_ADAPTADO = col_integer(),
IN_RESTAURANTE_UNIVERSITARIO = col_integer(),
IN_QUADRA_POLIESPORTIVA = col_integer(),
IN_PISCINA = col_integer(),
IN_QUADRA_COBERTA_GINASIO = col_integer(),
IN_CINEMA = col_integer(),
IN_VESTIARIO = col_integer(),
IN_SERVICOS = col_integer(),
IN_ESTACIONAMENTO_DOCENTE = col_integer(),
IN_CANTINA_LANCHONETE = col_integer(),
IN_PISTA_ATLETISMO = col_integer(),
IN_CAMPO_PRATICA_ESPORTIVA = col_integer(),
IN_AUDITORIO_TEATRO = col_integer(),
IN_REDE_SEM_FIO_COMU_ACADEMICA = col_integer(),
IN_POSTO_ATENDIMENTO_1_SOCORRO = col_integer(),
IN_BICICLETARIO = col_integer(),
IN_ESTACIONAMENTO_ALUNO = col_integer(),
IN_MORADIA_ESTUDANTIL = col_integer(),
IN_EQUIPA_VIDEOCONFERENCIA = col_integer(),
IN_SALA_COORDENACAO_POLO = col_integer(),
IN_MICROCOMPUTADOR = col_integer(),
IN_POSSUI_INTERNET_BANDA_LARGA = col_integer(),
IN_SALA_ATENDIMENTO_TUTOR = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
#
#FAZ JOIN COM CADASTRO DE MUNICIPIOS(SUBIR DATATABLE BaseMun)
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseLocalOferta = BaseLocalOferta %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseLocalOferta, "./data/localoferta_2019_data.rds")
#liberando vari?vel
rm(BaseLocalOferta)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3697890 197.5 10081556 538.5 18747399 1001.3
## Vcells 19626921 149.8 424154821 3236.1 1683739966 12846.05.1.5 Matrículas das IES - 2019
Importação e tratamento do arquivo relativo ao registro das matrículas das IES em 2019
#leitura base MATRICULAS - 2019
BaseAluno = readr::read_delim("./raw-data/SUP_ALUNO_2019.CSV",
delim = "|",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
CO_IES = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
CO_CURSO = col_integer(),
CO_CURSO_POLO = col_integer(),
TP_TURNO = col_integer(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
CO_CINE_ROTULO = col_character(),
ID_ALUNO = col_character(),
CO_ALUNO_CURSO = col_integer(),
CO_ALUNO_CURSO_ORIGEM = col_integer(),
TP_COR_RACA = col_integer(),
TP_SEXO = col_integer(),
NU_ANO_NASCIMENTO = col_integer(),
NU_MES_NASCIMENTO = col_integer(),
NU_DIA_NASCIMENTO = col_integer(),
NU_IDADE = col_integer(),
TP_NACIONALIDADE = col_integer(),
CO_PAIS_ORIGEM = col_integer(),
CO_UF_NASCIMENTO = col_integer(),
CO_MUNICIPIO_NASCIMENTO = col_integer(),
IN_DEFICIENCIA = col_integer(),
IN_DEFICIENCIA_AUDITIVA = col_integer(),
IN_DEFICIENCIA_FISICA = col_integer(),
IN_DEFICIENCIA_INTELECTUAL = col_integer(),
IN_DEFICIENCIA_MULTIPLA = col_integer(),
IN_DEFICIENCIA_SURDEZ = col_integer(),
IN_DEFICIENCIA_SURDOCEGUEIRA = col_integer(),
IN_DEFICIENCIA_BAIXA_VISAO = col_integer(),
IN_DEFICIENCIA_CEGUEIRA = col_integer(),
IN_DEFICIENCIA_SUPERDOTACAO = col_integer(),
IN_TGD_AUTISMO = col_integer(),
IN_TGD_SINDROME_ASPERGER = col_integer(),
IN_TGD_SINDROME_RETT = col_integer(),
IN_TGD_TRANSTOR_DESINTEGRATIVO= col_integer(),
TP_SITUACAO = col_integer(),
QT_CARGA_HORARIA_TOTAL = col_integer(),
QT_CARGA_HORARIA_INTEG = col_integer(),
DT_INGRESSO_CURSO = col_character(),
IN_INGRESSO_VESTIBULAR = col_integer(),
IN_INGRESSO_ENEM = col_integer(),
IN_INGRESSO_AVALIACAO_SERIADA = col_integer(),
IN_INGRESSO_SELECAO_SIMPLIFICA= col_integer(),
IN_INGRESSO_OUTRO_TIPO_SELECAO= col_integer(),
IN_INGRESSO_VAGA_REMANESC = col_integer(),
IN_INGRESSO_VAGA_PROG_ESPECIAL= col_integer(),
IN_INGRESSO_TRANSF_EXOFFICIO = col_integer(),
IN_INGRESSO_DECISAO_JUDICIAL = col_integer(),
IN_INGRESSO_CONVENIO_PECG = col_integer(),
IN_INGRESSO_EGRESSO = col_integer(),
IN_INGRESSO_OUTRA_FORMA = col_integer(),
IN_RESERVA_VAGAS = col_integer(),
IN_RESERVA_ETNICO = col_integer(),
IN_RESERVA_DEFICIENCIA = col_integer(),
IN_RESERVA_ENSINO_PUBLICO = col_integer(),
IN_RESERVA_RENDA_FAMILIAR = col_integer(),
IN_RESERVA_OUTRA = col_integer(),
IN_FINANCIAMENTO_ESTUDANTIL = col_integer(),
IN_FIN_REEMB_FIES = col_integer(),
IN_FIN_REEMB_ESTADUAL = col_integer(),
IN_FIN_REEMB_MUNICIPAL = col_integer(),
IN_FIN_REEMB_PROG_IES = col_integer(),
IN_FIN_REEMB_ENT_EXTERNA = col_integer(),
IN_FIN_REEMB_OUTRA = col_integer(),
IN_FIN_NAOREEMB_PROUNI_INTEGR = col_integer(),
IN_FIN_NAOREEMB_PROUNI_PARCIAL= col_integer(),
IN_FIN_NAOREEMB_ESTADUAL = col_integer(),
IN_FIN_NAOREEMB_MUNICIPAL = col_integer(),
IN_FIN_NAOREEMB_PROG_IES = col_integer(),
IN_FIN_NAOREEMB_ENT_EXTERNA = col_integer(),
IN_FIN_NAOREEMB_OUTRA = col_integer(),
IN_APOIO_SOCIAL = col_integer(),
IN_APOIO_ALIMENTACAO = col_integer(),
IN_APOIO_BOLSA_PERMANENCIA = col_integer(),
IN_APOIO_BOLSA_TRABALHO = col_integer(),
IN_APOIO_MATERIAL_DIDATICO = col_integer(),
IN_APOIO_MORADIA = col_integer(),
IN_APOIO_TRANSPORTE = col_integer(),
IN_ATIVIDADE_EXTRACURRICULAR = col_integer(),
IN_COMPLEMENTAR_ESTAGIO = col_integer(),
IN_COMPLEMENTAR_EXTENSAO = col_integer(),
IN_COMPLEMENTAR_MONITORIA = col_integer(),
IN_COMPLEMENTAR_PESQUISA = col_integer(),
IN_BOLSA_ESTAGIO = col_integer(),
IN_BOLSA_EXTENSAO = col_integer(),
IN_BOLSA_MONITORIA = col_integer(),
IN_BOLSA_PESQUISA = col_integer(),
TP_ESCOLA_CONCLUSAO_ENS_MEDIO = col_integer(),
IN_ALUNO_PARFOR = col_integer(),
TP_SEMESTRE_CONCLUSAO = col_integer(),
TP_SEMESTRE_REFERENCIA = col_integer(),
IN_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA = col_integer(),
TP_MOBILIDADE_ACADEMICA_INTERN= col_integer(),
CO_IES_DESTINO = col_integer(),
CO_PAIS_DESTINO = col_integer(),
IN_MATRICULA = col_integer(),
IN_CONCLUINTE = col_integer(),
IN_INGRESSO_TOTAL = col_integer(),
IN_INGRESSO_VAGA_NOVA = col_integer(),
IN_INGRESSO_PROCESSO_SELETIVO = col_integer(),
NU_ANO_INGRESSO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas nao existentes
BaseAluno = BaseAluno %>% dplyr::select(everything()) %>%
mutate(DS_TURNO = case_when(
TP_TURNO == 1 ~ "MATUTINO",
TP_TURNO == 2 ~ "VESPERTINO",
TP_TURNO == 3 ~ "NOTURNO",
TP_TURNO == 4 ~ "INTEGRAL",
TRUE ~ "EAD"),
DS_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA",
TRUE ~ "NA"),
DS_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
DS_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL DE FORMACAO"),
DS_SEXO = case_when(
TP_SEXO == 1 ~ "FEMININO",
TP_SEXO == 2 ~ "MASCULINO"),
DS_COR_RACA = case_when(
TP_COR_RACA == 0 ~ "NAO DECLARADO",
TP_COR_RACA == 1 ~ "BRANCA",
TP_COR_RACA == 2 ~ "PRETA",
TP_COR_RACA == 3 ~ "PARDA",
TP_COR_RACA == 4 ~ "AMARELA",
TP_COR_RACA == 5 ~ "INDIGENA",
TP_COR_RACA == 9 ~ "SEM INFO",),
DS_NACIONALIDADE = case_when(
TP_NACIONALIDADE == 1 ~ "BRASILEIRA",
TP_NACIONALIDADE == 2 ~ "BRASILEIRO NATURALIZADO",
TP_NACIONALIDADE == 3 ~ "ESTRANGEIRO"),
DS_SITUACAO = case_when(
TP_SITUACAO == 2 ~ "CURSANDO",
TP_SITUACAO == 3 ~ "MATRICULA TRANCADA",
TP_SITUACAO == 4 ~ "DESVINCULADO",
TP_SITUACAO == 5 ~ "TRANSFERIDO MESMA IES",
TP_SITUACAO == 6 ~ "FORMADO",
TP_SITUACAO == 7 ~ "FALECIDO"))
BaseMun = readRDS("./data/cad_municipios_data.rds")
BaseAluno = BaseAluno %>%
left_join(dplyr::select(BaseMun, SGL_UF, DS_MESORREGIAO, DS_MICRORREGIAO, NOME_MUNICIPIO, LATITUDE, LONGITUDE, CO_MUNICIPIO), by = c("CO_MUNICIPIO_NASCIMENTO" = "CO_MUNICIPIO"))
#salvando no banco de dados
saveRDS(BaseAluno, "./data/aluno_2019_data.rds")
rm(BaseAluno)
rm(BaseMun)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3698368 197.6 19813626 1058.2 18747399 1001.3
## Vcells 19633835 149.8 1540067937 11749.8 1726045989 13168.75.2 Tratamento e Uniformização dos dados
A seção trata da uniformização, reconfiguração dos dados e montagem das bases de dados necessárias para a exploração e análise
5.2.1 Consolidação dos dados das IES - 2019
Agrupamentos e calculos para facilitar a consolidação nas mantenedoras.
#*carga dos dados 2017 importados por ies_import_data
ies_2019_full = readRDS("./data/ies_2019_data.rds")
#* SELECIONAR CAMPOS FINANCEIROS E AGRUPAMENTOS, LOCALIZAÇÃO, IDENTIFICAÇÃO DA IES.
ies_2019_ext = ies_2019_full %>% dplyr::select(c(1,2,3,5,6,11,13,38:55))
#criar indice de qualificação corpo técnico = IQCTA
ies_2019_IQCTA = ies_2019_full %>% dplyr::select(c(1,2,13:27))
ies_2019_IQCTA = ies_2019_IQCTA %>% mutate(
QT_TEC_D = QT_TEC_DOUTORADO_FEM+QT_TEC_DOUTORADO_MASC,
QT_TEC_M = QT_TEC_MESTRADO_FEM+QT_TEC_MESTRADO_MASC,
QT_TEC_E = QT_TEC_ESPECIALIZACAO_FEM+QT_TEC_ESPECIALIZACAO_MASC,
QT_TEC_G = QT_TEC_SUPERIOR_FEM+QT_TEC_SUPERIOR_MASC,
QT_TEC_MD= QT_TEC_MEDIO_FEM+QT_TEC_MEDIO_MASC,
QT_TEC_F = QT_TEC_FUNDAMENTAL_COMP_FEM+QT_TEC_FUNDAMENTAL_COMP_MASC)
ies_2019_IQCTA = ies_2019_IQCTA %>% dplyr::select(c(1,2, 18:23))
ies_2019_ext = ies_2019_ext %>% left_join(ies_2019_IQCTA)## Joining, by = c("NU_ANO_CENSO", "CO_IES")rm(ies_2019_full)
rm(ies_2019_IQCTA)
#* CRIAR COLUNAS COM TOTAL DE DOCENTES ATIVOS e IQCD
#*
docentes_2019_full = readRDS("./data/docente_2019_data.rds")
docentes_2019_consolidado_IES = docentes_2019_full %>%
dplyr::select(c(1,2,7,8)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QT_DOC = n(), #conta os registros
QT_DOC_D = sum(TP_ESCOLARIDADE == 5),
QT_DOC_M = sum(TP_ESCOLARIDADE == 4),
QT_DOC_E = sum(TP_ESCOLARIDADE == 3),
QT_DOC_G = sum(TP_ESCOLARIDADE == 2),
QT_DOC_SG = sum(TP_ESCOLARIDADE == 1))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2019_ext = ies_2019_ext %>% dplyr::select(everything()) %>%
left_join(docentes_2019_consolidado_IES, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
rm(docentes_2019_consolidado_IES)
rm(docentes_2019_full)
#* CRIAR COLUNAS COM DADOS DOS CURSOS
#*
cursos_2019_full = readRDS("./data/curso_2019_data.rds")
polos_2019_full = readRDS("./data/localoferta_2019_data.rds")
cursos_2019_consolidado_IES = cursos_2019_full %>%
dplyr::select(c(1,2,11,108,111)) %>% #pega somente ano censo, co_ies e situacao
filter(TP_SITUACAO == 1) %>% #filtra somente os ativos
group_by(NU_ANO_CENSO, CO_IES, TP_SITUACAO) %>%
dplyr::summarise(TVAGAS = sum (QT_VAGA_TOTAL), TINGRESSOS = sum(QT_INGRESSO_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_IES'. You can override
## using the `.groups` argument.cursos_2019_consolidado_IES = cursos_2019_consolidado_IES %>% dplyr::select(-c(3))
#POLOS
talunos_polos = cursos_2019_full %>% dplyr::select(1,2,5,9,106)
tpolos = polos_2019_full %>% dplyr::select(1,2,4,8,10)
polos_2019_consolidado_IES = talunos_polos %>% left_join(tpolos, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES", "CO_LOCAL_OFERTA" = "CO_LOCAL_OFERTA", "CO_CURSO" = "CO_CURSO"))
polos_2019_consolidado_IES[is.na(polos_2019_consolidado_IES)] = 0
polos_2019_consolidado_IES = polos_2019_consolidado_IES %>% group_by(NU_ANO_CENSO, CO_IES) %>% filter(IN_SEDE != 1) %>%
dplyr::summarise(TMATR_FSEDE = sum(QT_MATRICULA_TOTAL))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2019_ext = ies_2019_ext %>% dplyr::select(everything()) %>%
left_join(cursos_2019_consolidado_IES, c(TVAGAS, TINGRESSOS), by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES")) %>%
left_join(polos_2019_consolidado_IES, TMATR_FSEDE, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2019_ext[is.na(ies_2019_ext)] = 0
rm(cursos_2019_consolidado_IES)
rm(cursos_2019_full)
rm(polos_2019_full)
rm(talunos_polos)
rm(tpolos)
rm(polos_2019_consolidado_IES)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3704429 197.9 15850901 846.6 18747399 1001.3
## Vcells 19745082 150.7 1232054350 9399.9 1726045989 13168.7#* CRIAR COLUNAS COM TOTAL DE ALUNOS ATIVOS PRESENCIAL E EAD
#*
alunos_2019_full = readRDS("./data/aluno_2019_data.rds")
alunos_2019_consolidado_IES = alunos_2019_full %>%
dplyr::select(c(1,2,8,9,10,39,61,75)) %>% #pega ano censo, co_ies, situacao, modalidade e financiamento/bolsas
filter(TP_NIVEL_ACADEMICO == 1) %>%
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QTD_ALUNO_PRESENCIAL = sum(TP_MODALIDADE_ENSINO == 1 & TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_EAD = sum(TP_MODALIDADE_ENSINO == 2 &TP_SITUACAO %in% c(2,6)),
QTD_ALUNO_ATIVO = QTD_ALUNO_PRESENCIAL+QTD_ALUNO_EAD,
QTD_ALUNO_EVADIDO = sum((TP_SITUACAO %in% c(3,4,7))),
QTD_ALUNO_FINANC = sum((IN_FINANCIAMENTO_ESTUDANTIL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNO_BOLSA = sum((IN_APOIO_SOCIAL == 1 & TP_SITUACAO %in% c(2,6))),
QTD_ALUNOS = QTD_ALUNO_ATIVO + QTD_ALUNO_EVADIDO) #conta os registros## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2019_ext = ies_2019_ext %>% dplyr::select(everything()) %>%
left_join(alunos_2019_consolidado_IES, dplyr::select(QTD_ALUNO_PRESENCIAL,
QTD_ALUNO_EAD,
QTD_ALUNO_ATIVO,
QTD_ALUNO_EVADIDO,
QTD_ALUNO_FINANC,
QTD_ALUNO_BOLSA,
QTD_ALUNOS),
by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2019_ext[is.na(ies_2019_ext)] = 0
saveRDS(ies_2019_ext,"./data/ies_consolidado_2019_data.rds")
rm(alunos_2019_consolidado_IES)
rm(alunos_2019_full)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3704424 197.9 12680721 677.3 18747399 1001.3
## Vcells 19754143 150.8 985643480 7519.9 1726045989 13168.7Total de registros e dimensões:
dim(ies_2019_ext)## [1] 2608 47rm (ies_2019_ext)5.2.2 Consolidação dos dados nas mantenedoras - 2019
Nesta etapa consolidaremos os dados das IES nas suas mantenedoras, agraupando os valores financeiros de acordo com as regras e criando as váriaveis de suporte
#*** TRATANDO, EXTRAINDO E CONVERTENDO 2019
#*carga dos dados tratados por ies_convert_data
ies_2019_ext = readRDS("./data/ies_consolidado_2019_data.rds")
# deixando somente as privadas
ies_2019_ext = ies_2019_ext %>% filter(NO_CATEGORIA_ADMINISTRATIVA %in% c("PRIVADA COM FINS LUCRATIVOS", "PRIVADA SEM FINS LUCRATIVOS"))
#primeiro selecionar TP_REFERENTE == 2 e somar todos os campos
tpr2_2019 = ies_2019_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 2) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#segundo passo, selecionar TP_REFERENTE == 1 e usar media para valores financeiros e soma para outros
tpr1_2019 = ies_2019_ext %>% dplyr::select(c(1,4,5,7:18,26:47)) %>% filter(TP_REFERENTE == 1) %>% dplyr::select(-c(5))%>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = mean(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = mean (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = mean(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = mean(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = mean(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = mean(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = mean(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = mean(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = mean(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = mean(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#terceiro passo, juntar as 2 anteriores e somar novamente os campos de valores
mant_2019_agrupado = union_all(tpr1_2019, tpr2_2019)
mant_2019_agrupado = mant_2019_agrupado %>%
group_by(NU_ANO_CENSO,CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(VL_RECEITA_PROPRIA = sum(VL_RECEITA_PROPRIA),
VL_RECEITA_TRANSFERENCIA = sum (VL_RECEITA_TRANSFERENCIA),
VL_RECEITA_OUTRA = sum(VL_RECEITA_OUTRA),
VL_DESPESA_PESSOAL_DOCENTE = sum(VL_DESPESA_PESSOAL_DOCENTE),
VL_DESPESA_PESSOAL_TECNICO = sum(VL_DESPESA_PESSOAL_TECNICO),
VL_DESPESA_PESSOAL_ENCARGO = sum(VL_DESPESA_PESSOAL_ENCARGO),
VL_DESPESA_CUSTEIO = sum(VL_DESPESA_CUSTEIO),
VL_DESPESA_INVESTIMENTO = sum(VL_DESPESA_INVESTIMENTO),
VL_DESPESA_PESQUISA = sum(VL_DESPESA_PESQUISA),
VL_DESPESA_OUTRA = sum(VL_DESPESA_OUTRA),
QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
TMATR_FSEDE = sum(TMATR_FSEDE),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS)) ## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#validação com dados de matriculas divulgado em graduação privada (6.523.678)
total_2019 = sum(mant_2019_agrupado$QTD_ALUNO_ATIVO)Total de registros e dimensões. Também compara a quantidade de matriculas ativas em comparação com a sinopse estatística oficial 2019, para fins de validação:
Total de Matrículas ativas divulgada na sinopse = 6.523.678
total_2019## [1] 6523678dim(mant_2019_agrupado)## [1] 1562 36#salvando dataset
saveRDS(mant_2019_agrupado,"./data/mant_2019_agrupado_data.rds")
rm(mant_2019_agrupado)
rm(ies_2019_ext)
rm(tpr1_2019)
rm(tpr2_2019)
rm (total_2019)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3697962 197.5 10144577 541.8 18747399 1001.3
## Vcells 19627936 149.8 788514784 6015.9 1726045989 13168.75.3 Exploração e Análise de Dados
Esta seção se dedica a explorar as informações de acordo com cada visualização elaborada.
5.3.1 Análise de fatores que podem se correlacionar com o resultado do ponto de equilíbrio das mantenedoras
MONTAGEM E ANÁLISE
# MONTAGEM DO DATASET
mant_2019_agrupado = readRDS("./data/mant_2019_agrupado_data.rds")
ds_PEQ_2019 = mant_2019_agrupado %>% select_all() %>%
mutate(VDPEQ = ((VL_RECEITA_PROPRIA - #somente receitas com mensalidades
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA))/
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+
VL_DESPESA_PESSOAL_ENCARGO+VL_DESPESA_CUSTEIO+
VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)), #variavel distancia ponto de equilíbrio
VRAD = QTD_ALUNO_ATIVO/QT_DOC,
VRAF = QTD_ALUNO_ATIVO/QT_TEC_TOTAL, #alterado para alunos/funcionarios
VCFPA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO)/QTD_ALUNO_ATIVO,
VCTTA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)/QTD_ALUNO_ATIVO,
VTMA = (VL_RECEITA_PROPRIA/QTD_ALUNO_ATIVO),
VOCIO = TINGRESSOS/TVAGAS,
VEAD = QTD_ALUNO_EAD/QTD_ALUNO_ATIVO,
VPOL = TMATR_FSEDE/QTD_ALUNO_ATIVO,
IQCTA = ((5*QT_TEC_D+3*QT_TEC_M+2*QT_TEC_E+QT_TEC_G+0.75*QT_TEC_MD+0.5*QT_TEC_F)/
QT_TEC_TOTAL),
IQCDO = ((5*QT_DOC_D+3*QT_DOC_M+2*QT_DOC_E+1*QT_DOC_G)/QT_DOC),
VFINE = QTD_ALUNO_FINANC / QTD_ALUNO_ATIVO,
VBOLS = QTD_ALUNO_BOLSA / QTD_ALUNO_ATIVO,
VEVAD = QTD_ALUNO_EVADIDO / QTD_ALUNOS)
ds_PEQ_2019[is.na(ds_PEQ_2019)] = 0
ds_PEQ_2019 = ds_PEQ_2019 %>% dplyr::select(2,37:50)## Adding missing grouping variables: `NU_ANO_CENSO`ds_PEQ_2019 = (ds_PEQ_2019[,2:16])
rm(mant_2019_agrupado)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3699693 197.6 10144577 541.8 18747399 1001.3
## Vcells 19656772 150.0 630811828 4812.8 1726045989 13168.7ANÁLISE E REMOÇÃO DE OUTLIERS
Sumário antes da remoção:
tbl_summary(ds_PEQ_2019) #usando gtsummary| Characteristic | N = 1,5621 |
|---|---|
| CO_MANTENEDORA | 1,880 (848, 14,969) |
| VDPEQ | 0.06 (-0.14, 0.34) |
| VRAD | 13 (7, 21) |
| VRAF | 18 (10, 32) |
| VCFPA | 5,160 (2,868, 9,127) |
| VCTTA | 8,232 (4,825, 15,131) |
| VTMA | 8,408 (4,780, 14,816) |
| VOCIO | 0.26 (0.13, 0.43) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 1.00 (1.00, 1.00) |
| IQCTA | 1.14 (0.95, 1.44) |
| IQCDO | 3.12 (2.84, 3.44) |
| VFINE | 0.22 (0.00, 0.52) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.24 (0.16, 0.33) |
| 1 Median (IQR) | |
Boxplot antes do tratamento:
ds_boxplot_all = melt(ds_PEQ_2019, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2019, id.vars = "CO_MANTENEDORA", measure.vars =
## c("VCFPA", : The melt generic in data.table has been passed a grouped_df and
## will attempt to redirect to the relevant reshape2 method; please note that
## reshape2 is deprecated, and this redirection is now deprecated as well. To
## continue using melt methods from reshape2 while both libraries are attached,
## e.g. melt.list, you can prepend the namespace like reshape2::melt(ds_PEQ_2019).
## In the next version, this warning will become an error.ggplot(data = ds_boxplot_all, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Tratamento e remoção dos extremos, analisando VTMA e VCFPA 5% de cada lado = 95,9% / Perda total simultânea em VTMA e VCFPA = 13,3%
#Remoção de Registros inválidos - Outliers
q_vtma = quantile(ds_PEQ_2019$VTMA, probs = c(.05, .95), na.rm = F)
q_vcfpa = quantile(ds_PEQ_2019$VCFPA, probs = c(.05, .95), na.rm = F)
#dataset sem os outliers
ds_PEQ_2019_noOutliers = subset(ds_PEQ_2019, (ds_PEQ_2019$VTMA>=(q_vtma[1])&ds_PEQ_2019$VCFPA>=(q_vcfpa[1])) &
(ds_PEQ_2019$VTMA<=(q_vtma[2]) & ds_PEQ_2019$VCFPA<=(q_vcfpa[2])))
#dataset com os outliers excluidos
ds_PEQ_2019_Outliers = subset(ds_PEQ_2019, !(CO_MANTENEDORA %in% ds_PEQ_2019_noOutliers$CO_MANTENEDORA))Sumário após remoção:
tbl_summary(ds_PEQ_2019_noOutliers) #usando gtsummary| Characteristic | N = 1,3401 |
|---|---|
| CO_MANTENEDORA | 1,602 (788, 12,658) |
| VDPEQ | 0.06 (-0.11, 0.32) |
| VRAD | 14 (8, 21) |
| VRAF | 20 (11, 33) |
| VCFPA | 5,126 (3,029, 8,401) |
| VCTTA | 8,094 (5,115, 13,417) |
| VTMA | 8,564 (5,350, 13,569) |
| VOCIO | 0.26 (0.13, 0.42) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 1.00 (1.00, 1.00) |
| IQCTA | 1.13 (0.94, 1.40) |
| IQCDO | 3.12 (2.84, 3.43) |
| VFINE | 0.25 (0.02, 0.55) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.24 (0.17, 0.33) |
| 1 Median (IQR) | |
Boxplot após tratamento:
ds_boxplot_NoOutliers = melt(ds_PEQ_2019_noOutliers, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2019_noOutliers, id.vars = "CO_MANTENEDORA",
## measure.vars = c("VCFPA", : The melt generic in data.table has been passed
## a grouped_df and will attempt to redirect to the relevant reshape2 method;
## please note that reshape2 is deprecated, and this redirection is now
## deprecated as well. To continue using melt methods from reshape2 while both
## libraries are attached, e.g. melt.list, you can prepend the namespace like
## reshape2::melt(ds_PEQ_2019_noOutliers). In the next version, this warning will
## become an error.ggplot(data = ds_boxplot_NoOutliers, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Perda Total dos registros (em %)
perda = 1-(dim(ds_PEQ_2019_noOutliers)[1]/dim(ds_PEQ_2019)[1])
paste(round(100*perda, 2), "%", sep="")## [1] "14.21%"ANÁLISE DOS RESÍDUOS OUTLIERS
#falta fazer....ANÁLISE DE COMPONENTES PRINCIPAIS - PCA
# analise PCA - Componentes Principais
# Filtrando variáveis númericas
ds_pca = ds_PEQ_2019_noOutliers[,3:15]
# Calculando o desvio padrão das variáveis
sapply(ds_pca, sd)## VRAD VRAF VCFPA VCTTA
## 1.60960549347e+01 2.85387560950e+01 4.68116710084e+03 1.49260240051e+04
## VTMA VOCIO VEAD VPOL
## 7.90757849866e+03 2.33325842063e-01 1.47393098646e-01 3.17541815305e-01
## IQCTA IQCDO VFINE VBOLS
## 3.97642101374e-01 4.53997609127e-01 3.13439769014e-01 1.57258239730e-01
## VEVAD
## 1.33792867305e-01# PCA com a matriz de covariâncias
pca_cov = prcomp(ds_pca)
summary(pca_cov)## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 15758.99378 7083.21880 2951.53024 28.3543532 13.4530497
## Proportion of Variance 0.80834 0.16330 0.02836 0.0000000 0.0000000
## Cumulative Proportion 0.80834 0.97164 1.00000 1.0000000 1.0000000
## PC6 PC7 PC8 PC9
## Standard deviation 0.422286692 0.390021627 0.308689961 0.298506967
## Proportion of Variance 0.000000000 0.000000000 0.000000000 0.000000000
## Cumulative Proportion 1.000000000 1.000000000 1.000000000 1.000000000
## PC10 PC11 PC12 PC13
## Standard deviation 0.225967963 0.156140458 0.12874038 0.119615489
## Proportion of Variance 0.000000000 0.000000000 0.00000000 0.000000000
## Cumulative Proportion 1.000000000 1.000000000 1.00000000 1.000000000# PCA com a matriz de correlação (variáveis padronizadas)
pca_corr = prcomp(ds_pca, center = T, scale = T)
summary(pca_corr)$importance## PC1 PC2 PC3 PC4
## Standard deviation 1.5825853593 1.41353734074 1.11852832507 1.05823558633
## Proportion of Variance 0.1926600000 0.15370000000 0.09624000000 0.08614000000
## Cumulative Proportion 0.1926600000 0.34636000000 0.44260000000 0.52874000000
## PC5 PC6 PC7
## Standard deviation 0.998496413031 0.966183347283 0.941711175227
## Proportion of Variance 0.076690000000 0.071810000000 0.068220000000
## Cumulative Proportion 0.605430000000 0.677240000000 0.745460000000
## PC8 PC9 PC10
## Standard deviation 0.900028550273 0.853042589479 0.817925985137
## Proportion of Variance 0.062310000000 0.055980000000 0.051460000000
## Cumulative Proportion 0.807770000000 0.863750000000 0.915210000000
## PC11 PC12 PC13
## Standard deviation 0.71218665032 0.596015046721 0.489757677832
## Proportion of Variance 0.03902000000 0.027330000000 0.018450000000
## Cumulative Proportion 0.95422000000 0.981550000000 1.000000000000# Gráfico das variáveis
fviz_pca_var(pca_corr,
col.var = "contrib", # Cor por contribuições para o PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE,
legend.title = "Contribuição"
)
Processo de análise de regressão múltipla
# Modelo de Regressão Linear Múltipla
ds_RLM = ds_PEQ_2019_noOutliers
summary(ds_RLM)## CO_MANTENEDORA VDPEQ VRAD
## Min. : 10.00000 Min. :-0.9369590738 Min. : 0.444444444
## 1st Qu.: 788.50000 1st Qu.:-0.1145428348 1st Qu.: 8.262500000
## Median : 1601.50000 Median : 0.0596653923 Median : 14.375000000
## Mean : 5198.20299 Mean : 0.1438124028 Mean : 17.501248171
## 3rd Qu.:12658.25000 3rd Qu.: 0.3195233566 3rd Qu.: 21.111002541
## Max. :17505.00000 Max. : 4.1810171095 Max. :184.868243243
## VRAF VCFPA VCTTA
## Min. : 0.386363636 Min. : 1110.01866 Min. : 1247.93878
## 1st Qu.: 11.340818078 1st Qu.: 3029.42673 1st Qu.: 5114.70471
## Median : 20.000000000 Median : 5126.29940 Median : 8094.32071
## Mean : 26.767234401 Mean : 6440.76495 Mean : 10775.60110
## 3rd Qu.: 33.060246679 3rd Qu.: 8401.45746 3rd Qu.: 13416.62671
## Max. :504.000000000 Max. :28227.18087 Max. :469731.66721
## VTMA VOCIO VEAD
## Min. : 1587.03833 Min. :0.000000000 Min. :0.0000000000
## 1st Qu.: 5349.93044 1st Qu.:0.132489754 1st Qu.:0.0000000000
## Median : 8563.82902 Median :0.262068903 Median :0.0000000000
## Mean :10763.78093 Mean :0.309303456 Mean :0.0462910071
## 3rd Qu.:13568.75201 3rd Qu.:0.422761525 3rd Qu.:0.0000000000
## Max. :49053.96947 Max. :1.247422680 Max. :1.0000000000
## VPOL IQCTA IQCDO
## Min. :0.000000000 Min. :0.312500000 Min. :1.33333333
## 1st Qu.:0.998658245 1st Qu.:0.943384641 1st Qu.:2.83600440
## Median :1.000000000 Median :1.126893939 Median :3.11764706
## Mean :0.841811184 Mean :1.226582940 Mean :3.13912398
## 3rd Qu.:1.000000000 3rd Qu.:1.400000000 3rd Qu.:3.42857143
## Max. :2.038461538 Max. :3.250000000 Max. :5.00000000
## VFINE VBOLS VEVAD
## Min. :0.0000000000 Min. :0.0000000000 Min. :0.000000000
## 1st Qu.:0.0178388797 1st Qu.:0.0000000000 1st Qu.:0.171351977
## Median :0.2544295630 Median :0.0000000000 Median :0.243370752
## Mean :0.3296330945 Mean :0.0379487164 Mean :0.260379331
## 3rd Qu.:0.5520438528 3rd Qu.:0.0000000000 3rd Qu.:0.329304161
## Max. :1.0000000000 Max. :1.0000000000 Max. :0.864182692# Correlograma
corrgram(ds_RLM[,c(2:15)],
#upper.panel=panel.cor,
upper.panel=panel.conf,
cex.labels=1.5, cex=1.2)## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
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## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
corrplot(cor(ds_RLM[,c(2:15)]), method="circle", type="lower", order="hclust", addCoef.col = "black")
# Modelo de regressão
modelo_RLM = lm(VDPEQ ~ ., data = ds_RLM[,2:15])
summary(modelo_RLM)##
## Call:
## lm(formula = VDPEQ ~ ., data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.476366445 -0.195398846 -0.051722808 0.129614333 3.579104739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.55227302e-01 9.25316740e-02 1.67756 0.0936690 .
## VRAD 2.11964298e-03 9.00085519e-04 2.35494 0.0186710 *
## VRAF 1.15195851e-03 4.17915840e-04 2.75644 0.0059236 **
## VCFPA -7.37888214e-05 3.50671065e-06 -21.04218 < 2.22e-16 ***
## VCTTA -6.38525411e-06 8.36271430e-07 -7.63538 4.2868e-14 ***
## VTMA 4.93142153e-05 1.96781363e-06 25.06041 < 2.22e-16 ***
## VOCIO -1.81436515e-01 4.58487099e-02 -3.95729 7.9822e-05 ***
## VEAD -2.45902688e-01 8.48143327e-02 -2.89931 0.0038015 **
## VPOL 8.17357684e-02 3.35123958e-02 2.43897 0.0148596 *
## IQCTA 1.96289616e-02 2.67154994e-02 0.73474 0.4626274
## IQCDO -1.90449334e-02 2.51499814e-02 -0.75725 0.4490320
## VFINE 3.47420388e-02 3.44187363e-02 1.00939 0.3129702
## VBOLS -3.62505594e-02 6.63843078e-02 -0.54607 0.5851088
## VEVAD -1.60399485e-01 7.93961670e-02 -2.02024 0.0435590 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.37546905 on 1326 degrees of freedom
## Multiple R-squared: 0.420870045, Adjusted R-squared: 0.415192301
## F-statistic: 74.1262722 on 13 and 1326 DF, p-value: < 2.220446e-16tbl_regression(modelo_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.019 |
| VRAF | 0.00 | 0.00, 0.00 | 0.006 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VOCIO | -0.18 | -0.27, -0.09 | <0.001 |
| VEAD | -0.25 | -0.41, -0.08 | 0.004 |
| VPOL | 0.08 | 0.02, 0.15 | 0.015 |
| IQCTA | 0.02 | -0.03, 0.07 | 0.46 |
| IQCDO | -0.02 | -0.07, 0.03 | 0.45 |
| VFINE | 0.03 | -0.03, 0.10 | 0.31 |
| VBOLS | -0.04 | -0.17, 0.09 | 0.59 |
| VEVAD | -0.16 | -0.32, 0.00 | 0.044 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.155227301741 | 0.092531674046 | 1.677558558633 | 0.093668977060 | -0.026297138617 | 0.336751742098 |
| VRAD | 0.002119642984 | 0.000900085519 | 2.354935103421 | 0.018671014191 | 0.000353896047 | 0.003885389921 |
| VRAF | 0.001151958512 | 0.000417915840 | 2.756436588612 | 0.005923585467 | 0.000332110177 | 0.001971806848 |
| VCFPA | -0.000073788821 | 0.000003506711 | -21.042175648557 | 0.000000000000 | -0.000080668127 | -0.000066909516 |
| VCTTA | -0.000006385254 | 0.000000836271 | -7.635384735286 | 0.000000000000 | -0.000008025813 | -0.000004744695 |
| VTMA | 0.000049314215 | 0.000001967814 | 25.060409435196 | 0.000000000000 | 0.000045453848 | 0.000053174583 |
| VOCIO | -0.181436515263 | 0.045848709850 | -3.957287257486 | 0.000079821839 | -0.271380434277 | -0.091492596248 |
| VEAD | -0.245902688171 | 0.084814332663 | -2.899305818365 | 0.003801487427 | -0.412287598308 | -0.079517778034 |
| VPOL | 0.081735768440 | 0.033512395791 | 2.438971207790 | 0.014859581265 | 0.015992670697 | 0.147478866182 |
| IQCTA | 0.019628961555 | 0.026715499355 | 0.734740582417 | 0.462627414106 | -0.032780293105 | 0.072038216216 |
| IQCDO | -0.019044933450 | 0.025149981448 | -0.757254373672 | 0.449032017065 | -0.068383026103 | 0.030293159203 |
| VFINE | 0.034742038845 | 0.034418736275 | 1.009393214435 | 0.312970244108 | -0.032779076540 | 0.102263154230 |
| VBOLS | -0.036250559421 | 0.066384307823 | -0.546071211853 | 0.585108846375 | -0.166480282922 | 0.093979164080 |
| VEVAD | -0.160399484503 | 0.079396166966 | -2.020242168251 | 0.043559013696 | -0.316155282975 | -0.004643686030 |
kable(broom::glance(modelo_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.42087004544 | 0.415192300787 | 0.375469050354 | 74.1262721724 | 0 | 13 | -581.704637505 | 1193.40927501 | 1271.4156484 | 186.935512308 | 1326 | 1340 |
plot(modelo_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo_RLM, order.by = ~., data = ds_RLM[,3:15], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo_RLM
## GQ = 1.062070251, df1 = 653, df2 = 652, p-value = 0.221019588
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo_RLM
## BP = 129.5701698, df = 13, p-value < 2.220446e-16plot(modelo_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 0.0208174600066 1.95799001267 0.458
## Alternative hypothesis: rho != 0car::vif(modelo_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 1.99360322974 1.35107678770 2.55941340973 1.47984108453 2.29978502542
## VOCIO VEAD VPOL IQCTA IQCDO
## 1.08695661807 1.48431161342 1.07558586943 1.07187366651 1.23826830891
## VFINE VBOLS VEVAD
## 1.10542765933 1.03511826359 1.07175935651plot(modelo_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo_RLM$residuals
## W = 0.8616876764, p-value < 2.220446e-16#modelo 2 - somente variáveis mais significativas - PCA
modelo2_RLM = lm(VDPEQ ~ VRAD+VRAF+VCFPA+VCTTA+VTMA+VEAD+VOCIO+VPOL+VEVAD, data = ds_RLM[,2:15])
summary(modelo2_RLM)##
## Call:
## lm(formula = VDPEQ ~ VRAD + VRAF + VCFPA + VCTTA + VTMA + VEAD +
## VOCIO + VPOL + VEVAD, data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.480540086 -0.192350807 -0.048426710 0.128267181 3.593474403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.33568982e-01 4.61130516e-02 2.89655 0.0038346 **
## VRAD 2.05931688e-03 8.74060532e-04 2.35603 0.0186156 *
## VRAF 1.16175103e-03 4.15475691e-04 2.79619 0.0052452 **
## VCFPA -7.40445089e-05 3.45555321e-06 -21.42769 < 2.22e-16 ***
## VCTTA -6.40064484e-06 8.21829633e-07 -7.78829 1.3588e-14 ***
## VTMA 4.93191371e-05 1.92418460e-06 25.63119 < 2.22e-16 ***
## VEAD -2.51539567e-01 8.32991309e-02 -3.01971 0.0025784 **
## VOCIO -1.85610362e-01 4.53587278e-02 -4.09205 4.5328e-05 ***
## VPOL 8.29082677e-02 3.31498134e-02 2.50102 0.0125032 *
## VEVAD -1.63682473e-01 7.88754201e-02 -2.07520 0.0381596 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.375229218 on 1330 degrees of freedom
## Multiple R-squared: 0.419864885, Adjusted R-squared: 0.415939158
## F-statistic: 106.952153 on 9 and 1330 DF, p-value: < 2.220446e-16anova(modelo_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 10.21652403 10.21652403 72.46943 < 2.22e-16 ***
## VRAF 1 2.94782156 2.94782156 20.90995 5.2634e-06 ***
## VCFPA 1 17.43386837 17.43386837 123.66462 < 2.22e-16 ***
## VCTTA 1 2.00903784 2.00903784 14.25082 0.00016706 ***
## VTMA 1 98.05883622 98.05883622 695.56616 < 2.22e-16 ***
## VOCIO 1 1.77213764 1.77213764 12.57040 0.00040554 ***
## VEAD 1 1.60307026 1.60307026 11.37115 0.00076743 ***
## VPOL 1 0.87921482 0.87921482 6.23658 0.01263424 *
## IQCTA 1 0.09622264 0.09622264 0.68254 0.40886160
## IQCDO 1 0.07333675 0.07333675 0.52020 0.47088200
## VFINE 1 0.16503171 0.16503171 1.17063 0.27946884
## VBOLS 1 0.02081826 0.02081826 0.14767 0.70083233
## VEVAD 1 0.57538052 0.57538052 4.08138 0.04355901 *
## Residuals 1326 186.93551231 0.14097701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tbl_regression(modelo2_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.019 |
| VRAF | 0.00 | 0.00, 0.00 | 0.005 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VEAD | -0.25 | -0.41, -0.09 | 0.003 |
| VOCIO | -0.19 | -0.27, -0.10 | <0.001 |
| VPOL | 0.08 | 0.02, 0.15 | 0.013 |
| VEVAD | -0.16 | -0.32, -0.01 | 0.038 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo2_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.133568982472 | 0.046113051626 | 2.89655483125 | 0.003834575962 | 0.043106738314 | 0.224031226631 |
| VRAD | 0.002059316876 | 0.000874060532 | 2.35603462427 | 0.018615640437 | 0.000344629289 | 0.003774004463 |
| VRAF | 0.001161751029 | 0.000415475691 | 2.79619495077 | 0.005245237134 | 0.000346691907 | 0.001976810152 |
| VCFPA | -0.000074044509 | 0.000003455553 | -21.42768590969 | 0.000000000000 | -0.000080823438 | -0.000067265580 |
| VCTTA | -0.000006400645 | 0.000000821830 | -7.78828674308 | 0.000000000000 | -0.000008012869 | -0.000004788421 |
| VTMA | 0.000049319137 | 0.000001924185 | 25.63118799506 | 0.000000000000 | 0.000045544369 | 0.000053093905 |
| VEAD | -0.251539567041 | 0.083299130925 | -3.01971418245 | 0.002578353305 | -0.414951574145 | -0.088127559937 |
| VOCIO | -0.185610361536 | 0.045358727782 | -4.09205395766 | 0.000045328128 | -0.274592811464 | -0.096627911608 |
| VPOL | 0.082908267665 | 0.033149813405 | 2.50101762721 | 0.012503185395 | 0.017876646284 | 0.147939889045 |
| VEVAD | -0.163682473058 | 0.078875420075 | -2.07520255235 | 0.038159622077 | -0.318416268747 | -0.008948677369 |
kable(broom::glance(modelo2_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.419864884841 | 0.415939158498 | 0.375229218059 | 106.952153089 | 0 | 9 | -582.866507706 | 1187.73301541 | 1244.93768923 | 187.259964894 | 1330 | 1340 |
plot(modelo2_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo2_RLM, order.by = ~., data = ds_RLM[,3:12], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo2_RLM
## GQ = 1.032943036, df1 = 657, df2 = 656, p-value = 0.339054106
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo2_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo2_RLM
## BP = 116.0070518, df = 9, p-value < 2.220446e-16plot(modelo2_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo2_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 0.0215278753112 1.95658400904 0.432
## Alternative hypothesis: rho != 0car::vif(modelo2_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 1.88238819939 1.33705292194 2.48846043167 1.43099839196 2.20174881546
## VEAD VOCIO VPOL VEVAD
## 1.43358193382 1.06520867355 1.05378327905 1.05909903698plot(modelo2_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo2_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo2_RLM$residuals
## W = 0.8617858322, p-value < 2.220446e-16faraway::vif(modelo2_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 1.88238819939 1.33705292194 2.48846043167 1.43099839196 2.20174881546
## VEAD VOCIO VPOL VEVAD
## 1.43358193382 1.06520867355 1.05378327905 1.05909903698anova(modelo2_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 10.21652403 10.21652403 72.56210 < 2.22e-16 ***
## VRAF 1 2.94782156 2.94782156 20.93668 5.1901e-06 ***
## VCFPA 1 17.43386837 17.43386837 123.82276 < 2.22e-16 ***
## VCTTA 1 2.00903784 2.00903784 14.26904 0.00016545 ***
## VTMA 1 98.05883622 98.05883622 696.45561 < 2.22e-16 ***
## VEAD 1 1.23486788 1.23486788 8.77056 0.00311564 **
## VOCIO 1 2.14034003 2.14034003 15.20161 0.00010144 ***
## VPOL 1 0.87921482 0.87921482 6.24456 0.01257738 *
## VEVAD 1 0.60633730 0.60633730 4.30647 0.03815962 *
## Residuals 1330 187.25996489 0.14079697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#algoritmos de seleção de variáveis
ts2 = ds_PEQ_2019_noOutliers[,-c(1)]
modelo = lm(data = ts2, VDPEQ ~ .)
summary(modelo)##
## Call:
## lm(formula = VDPEQ ~ ., data = ts2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.476366445 -0.195398846 -0.051722808 0.129614333 3.579104739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.55227302e-01 9.25316740e-02 1.67756 0.0936690 .
## VRAD 2.11964298e-03 9.00085519e-04 2.35494 0.0186710 *
## VRAF 1.15195851e-03 4.17915840e-04 2.75644 0.0059236 **
## VCFPA -7.37888214e-05 3.50671065e-06 -21.04218 < 2.22e-16 ***
## VCTTA -6.38525411e-06 8.36271430e-07 -7.63538 4.2868e-14 ***
## VTMA 4.93142153e-05 1.96781363e-06 25.06041 < 2.22e-16 ***
## VOCIO -1.81436515e-01 4.58487099e-02 -3.95729 7.9822e-05 ***
## VEAD -2.45902688e-01 8.48143327e-02 -2.89931 0.0038015 **
## VPOL 8.17357684e-02 3.35123958e-02 2.43897 0.0148596 *
## IQCTA 1.96289616e-02 2.67154994e-02 0.73474 0.4626274
## IQCDO -1.90449334e-02 2.51499814e-02 -0.75725 0.4490320
## VFINE 3.47420388e-02 3.44187363e-02 1.00939 0.3129702
## VBOLS -3.62505594e-02 6.63843078e-02 -0.54607 0.5851088
## VEVAD -1.60399485e-01 7.93961670e-02 -2.02024 0.0435590 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.37546905 on 1326 degrees of freedom
## Multiple R-squared: 0.420870045, Adjusted R-squared: 0.415192301
## F-statistic: 74.1262722 on 13 and 1326 DF, p-value: < 2.220446e-16stats::step(modelo, direction = "both", trace = F)##
## Call:
## lm(formula = VDPEQ ~ VRAD + VRAF + VCFPA + VCTTA + VTMA + VOCIO +
## VEAD + VPOL + VEVAD, data = ts2)
##
## Coefficients:
## (Intercept) VRAD VRAF VCFPA
## 1.33568982e-01 2.05931688e-03 1.16175103e-03 -7.40445089e-05
## VCTTA VTMA VOCIO VEAD
## -6.40064484e-06 4.93191371e-05 -1.85610362e-01 -2.51539567e-01
## VPOL VEVAD
## 8.29082677e-02 -1.63682473e-01#modelo 2 recomendado
par(mfrow = c(2,2))
plot(modelo2_RLM, which = c(1:4), pch = 20)
Processo de análise XAI - IA Explicada
# analise XAI
# Feature Importance
#carrega arquivo
ds_xai = ds_PEQ_2019_noOutliers[2:15]
head(ds_xai)## # A tibble: 6 × 14
## VDPEQ VRAD VRAF VCFPA VCTTA VTMA VOCIO VEAD VPOL IQCTA IQCDO VFINE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.426 15.1 16.8 16218. 21434. 30565. 0.430 0.0422 0.227 1.14 3.94 0.329
## 2 -0.210 20.2 21.1 15126. 20124. 15903. 0.456 0 0 1.14 3.91 0.470
## 3 -0.0268 20.9 18.7 10617. 17671. 17197. 0.338 0.0595 0.399 1.16 3.67 0.277
## 4 0.137 21.6 20.8 10599. 18009. 20471. 0.232 0.178 0.283 1.11 4.18 0.534
## 5 0.0184 11.6 17.1 13348. 31937. 32524. 0.0910 0.156 0.156 1.21 3.31 0.376
## 6 0.0633 18.3 18.1 14829. 20060. 21330. 0.519 0 0.227 0.993 3.95 0.373
## # … with 2 more variables: VBOLS <dbl>, VEVAD <dbl>
## # ℹ Use `colnames()` to see all variable names#criamos modelo randomForest
modelo_rf = randomForest(VDPEQ ~ ., data = ds_xai, ntree = 500, prox = T)
summary(modelo_rf)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 1340 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1340 -none- numeric
## importance 13 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 1795600 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1340 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call#previsor, modelo, dados, e previsor
previsor = Predictor$new(modelo, ds_xai[,-1] , y = ds_xai$VDPEQ )
#feature importance
importancia = FeatureImp$new(previsor, loss = "mae")
#dataframe
importancia$results## feature importance.05 importance importance.95 permutation.error
## 1 VTMA 1.986141095670 2.001967163650 2.03307185409 0.497246300318
## 2 VCFPA 1.826195515844 1.846765438757 1.87883429251 0.458697474489
## 3 VCTTA 1.048349574225 1.053605939835 1.05502838322 0.261693430886
## 4 VOCIO 1.012940214691 1.019767606013 1.02166972231 0.253288704471
## 5 VEAD 1.013301894214 1.017426886701 1.02176635107 0.252707319302
## 6 VPOL 1.005021303059 1.008887685216 1.01021522088 0.250586362263
## 7 VRAD 1.003658658117 1.007631590471 1.01379122729 0.250274374896
## 8 VRAF 1.002142503807 1.003840014333 1.00718887912 0.249332627578
## 9 VEVAD 1.000799677904 1.001457211968 1.00353193801 0.248740789869
## 10 VFINE 1.000443980611 1.001411041192 1.00243476780 0.248729322025
## 11 VBOLS 0.999793525902 1.000518445170 1.00136625337 0.248507620052
## 12 IQCTA 0.998869419874 0.999389670584 1.00019655057 0.248227256319
## 13 IQCDO 0.997722778512 0.999183767427 1.00041863076 0.248176114330plot(importancia)
#Feature Interaction
interacao = Interaction$new(previsor)
plot(interacao)
interacao$results## .feature .interaction
## 1: VRAD 2.45633577674e-16
## 2: VRAF 1.85053215343e-16
## 3: VCFPA 2.08714129919e-16
## 4: VCTTA 2.12902539152e-16
## 5: VTMA 2.00072537361e-16
## 6: VOCIO 7.95712413577e-17
## 7: VEAD 1.04345557092e-16
## 8: VPOL 2.30693776049e-16
## 9: IQCTA 1.27164789325e-16
## 10: IQCDO 2.41854323050e-16
## 11: VFINE 1.03358851163e-16
## 12: VBOLS 1.42154313877e-16
## 13: VEVAD 1.15530064578e-16#cria modelo substituto
arvore = TreeSurrogate$new(previsor, maxdepth = 3)
#plot a árvore
plot(arvore$tree)
6 Censo Superior 2020
6.1 Importação dos Microdados
Importação dos microdados do censo do ensino superior 2020. Com a alteração na publicação detalhada, os dados foram obtidos já consolidados e descaracterizados em visita a sala segura do INEP, em 16/09/2022. Faça o download em: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados.
Microdados INEP - Públicos
Importação e tratamento do arquivo relativo ao registro das IES em 2020
O arquivo contém as seguintes bases (CSV) que iremos importar (base fornecida antes antes de Set/2021):
- MICRODADOS_CADASTRO_IES_2020.CSV
- MICRODADOS_CADASTRO_CURSOS_2020.CSV
6.1.1 Instituições - IES - 2020
Importação e tratamento do arquivo relativo ao registro das IES em 2020
BaseIES = read_delim("./raw-data/MICRODADOS_CADASTRO_IES_2020.CSV",
";",
escape_double = FALSE,
col_types = cols(NO_REGIAO_IES = col_character(),
CO_REGIAO_IES = col_integer(),
NO_UF_IES = col_character(),
SG_UF_IES = col_character(),
CO_UF_IES = col_integer(),
NO_MUNICIPIO_IES = col_character(),
CO_MUNICIPIO_IES = col_integer(),
IN_CAPITAL_IES = col_integer(),
NO_MESORREGIAO_IES = col_character(),
CO_MESORREGIAO_IES = col_integer(),
NO_MICRORREGIAO_IES = col_character(),
CO_MICRORREGIAO_IES = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
NO_MANTENEDORA = col_character(),
CO_MANTENEDORA = col_integer(),
CO_IES = col_integer(),
NO_IES = col_character(),
SG_IES = col_character(),
DS_ENDERECO_IES = col_character(),
DS_NUMERO_ENDERECO_IES = col_character(),
DS_COMPLEMENTO_ENDERECO_IES = col_character(),
NO_BAIRRO_IES = col_character(),
NU_CEP_IES = col_integer(),
QT_TEC_TOTAL = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_INCOMP_MASC = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_FEM = col_integer(),
QT_TEC_FUNDAMENTAL_COMP_MASC = col_integer(),
QT_TEC_MEDIO_FEM = col_integer(),
QT_TEC_MEDIO_MASC = col_integer(),
QT_TEC_SUPERIOR_FEM = col_integer(),
QT_TEC_SUPERIOR_MASC = col_integer(),
QT_TEC_ESPECIALIZACAO_FEM = col_integer(),
QT_TEC_ESPECIALIZACAO_MASC = col_integer(),
QT_TEC_MESTRADO_FEM = col_integer(),
QT_TEC_MESTRADO_MASC = col_integer(),
QT_TEC_DOUTORADO_FEM = col_integer(),
QT_TEC_DOUTORADO_MASC = col_integer(),
IN_ACESSO_PORTAL_CAPES = col_integer(),
IN_ACESSO_OUTRAS_BASES = col_integer(),
IN_ASSINA_OUTRA_BASE = col_integer(),
IN_REPOSITORIO_INSTITUCIONAL = col_integer(),
IN_BUSCA_INTEGRADA = col_integer(),
IN_SERVICO_INTERNET = col_integer(),
IN_PARTICIPA_REDE_SOCIAL = col_integer(),
IN_CATALOGO_ONLINE = col_integer(),
QT_PERIODICO_ELETRONICO = col_integer(),
QT_LIVRO_ELETRONICO = col_integer(),
QT_DOC_TOTAL = col_integer(),
QT_DOC_EXE = col_integer(),
QT_DOC_EX_FEMI = col_integer(),
QT_DOC_EX_MASC = col_integer(),
QT_DOC_EX_SEM_GRAD = col_integer(),
QT_DOC_EX_GRAD = col_integer(),
QT_DOC_EX_ESP = col_integer(),
QT_DOC_EX_MEST = col_integer(),
QT_DOC_EX_DOUT = col_integer(),
QT_DOC_EX_INT = col_integer(),
QT_DOC_EX_INT_DE = col_integer(),
QT_DOC_EX_INT_SEM_DE = col_integer(),
QT_DOC_EX_PARC = col_integer(),
QT_DOC_EX_HOR = col_integer(),
QT_DOC_EX_0_29 = col_integer(),
QT_DOC_EX_30_34 = col_integer(),
QT_DOC_EX_35_39 = col_integer(),
QT_DOC_EX_40_44 = col_integer(),
QT_DOC_EX_45_49 = col_integer(),
QT_DOC_EX_50_54 = col_integer(),
QT_DOC_EX_55_59 = col_integer(),
QT_DOC_EX_60_MAIS = col_integer(),
QT_DOC_EX_BRANCA = col_integer(),
QT_DOC_EX_PRETA = col_integer(),
QT_DOC_EX_PARDA = col_integer(),
QT_DOC_EX_AMARELA = col_integer(),
QT_DOC_EX_INDIGENA = col_integer(),
QT_DOC_EX_COR_ND = col_integer(),
QT_DOC_EX_BRA = col_integer(),
QT_DOC_EX_EST = col_integer(),
QT_DOC_EX_COM_DEFICIENCIA = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseIES = BaseIES %>% dplyr::select(everything()) %>%
mutate(NO_CATEGORIA_ADMINISTRATIVA = case_when(
TP_CATEGORIA_ADMINISTRATIVA == 1 ~ "PUBLICA FEDERAL",
TP_CATEGORIA_ADMINISTRATIVA == 2 ~ "PUBLICA ESTADUAL",
TP_CATEGORIA_ADMINISTRATIVA == 3 ~ "PUBLICA MUNICIPAL",
TP_CATEGORIA_ADMINISTRATIVA == 4 ~ "PRIVADA COM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 5 ~ "PRIVADA SEM FINS LUCRATIVOS",
TP_CATEGORIA_ADMINISTRATIVA == 6 ~ "PRIVADA CONFESSIONAL",
TP_CATEGORIA_ADMINISTRATIVA == 7 ~ "ESPECIAL"),
NO_ORGANIZACAO_ACADEMICA = case_when(
TP_ORGANIZACAO_ACADEMICA == 1 ~ "UNIVERSIDADE",
TP_ORGANIZACAO_ACADEMICA == 2 ~ "CENTRO UNIVERSITARIO",
TP_ORGANIZACAO_ACADEMICA == 3 ~ "FACULDADE",
TP_ORGANIZACAO_ACADEMICA == 4 ~ "INSTITUTO FEDERAL DE EDUCACAO - CIENCIA E TECNOLOGIA",
TP_ORGANIZACAO_ACADEMICA == 5 ~ "CENTRO FEDERAL DE EDUCACAO TECNOLOGICA")
)
saveRDS(BaseIES, "./data/ies_2020_data.rds")
rm(BaseIES)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3703439 197.8 10144577 541.8 18747399 1001.3
## Vcells 20364587 155.4 504649463 3850.2 1726045989 13168.76.1.2 Cursos das IES - 2020
Importação e tratamento do arquivo relativo ao registro dos cursos das IES em 2020
BaseCurso = read_delim("./raw-data/MICRODADOS_CADASTRO_CURSOS_2020.CSV",
";",
escape_double = FALSE,
col_types =
cols(NU_ANO_CENSO = col_integer(),
NO_REGIAO = col_character(),
CO_REGIAO = col_integer(),
NO_UF = col_character(),
SG_UF = col_character(),
CO_UF = col_integer(),
NO_MUNICIPIO = col_character(),
CO_MUNICIPIO = col_integer(),
IN_CAPITAL = col_integer(),
TP_ORGANIZACAO_ACADEMICA = col_integer(),
TP_CATEGORIA_ADMINISTRATIVA = col_integer(),
TP_REDE = col_integer(),
CO_IES = col_integer(),
NO_CINE_ROTULO = col_character(),
CO_CINE_ROTULO = col_character(),
CO_CINE_AREA_GERAL = col_character(),
NO_CINE_AREA_GERAL = col_character(),
CO_CINE_AREA_ESPECIFICA = col_character(),
NO_CINE_AREA_ESPECIFICA = col_character(),
CO_CINE_AREA_DETALHADA = col_character(),
NO_CINE_AREA_DETALHADA = col_character(),
TP_GRAU_ACADEMICO = col_integer(),
TP_MODALIDADE_ENSINO = col_integer(),
TP_NIVEL_ACADEMICO = col_integer(),
QT_CURSO = col_integer(),
QT_VG_TOTAL = col_integer(),
QT_VG_TOTAL_DIURNO = col_integer(),
QT_VG_TOTAL_NOTURNO = col_integer(),
QT_VG_TOTAL_EAD = col_integer(),
QT_VG_NOVA = col_integer(),
QT_VG_PROC_SELETIVO = col_integer(),
QT_VG_REMANESC = col_integer(),
QT_VG_PROG_ESPECIAL = col_integer(),
QT_INSCRITO_TOTAL = col_integer(),
QT_INSCRITO_TOTAL_DIURNO = col_integer(),
QT_INSCRITO_TOTAL_NOTURNO = col_integer(),
QT_INSCRITO_TOTAL_EAD = col_integer(),
QT_INSC_VG_NOVA = col_integer(),
QT_INSC_PROC_SELETIVO = col_integer(),
QT_INSC_VG_REMANESC = col_integer(),
QT_INSC_VG_PROG_ESPECIAL = col_integer(),
QT_ING = col_integer(),
QT_ING_FEM = col_integer(),
QT_ING_MASC = col_integer(),
QT_ING_DIURNO = col_integer(),
QT_ING_NOTURNO = col_integer(),
QT_ING_VG_NOVA = col_integer(),
QT_ING_VESTIBULAR = col_integer(),
QT_ING_ENEM = col_integer(),
QT_ING_AVALIACAO_SERIADA = col_integer(),
QT_ING_SELECAO_SIMPLIFICA = col_integer(),
QT_ING_EGR = col_integer(),
QT_ING_OUTRO_TIPO_SELECAO = col_integer(),
QT_ING_PROC_SELETIVO = col_integer(),
QT_ING_VG_REMANESC = col_integer(),
QT_ING_VG_PROG_ESPECIAL = col_integer(),
QT_ING_OUTRA_FORMA = col_integer(),
QT_ING_0_17 = col_integer(),
QT_ING_18_24 = col_integer(),
QT_ING_25_29 = col_integer(),
QT_ING_30_34 = col_integer(),
QT_ING_35_39 = col_integer(),
QT_ING_40_49 = col_integer(),
QT_ING_50_59 = col_integer(),
QT_ING_60_MAIS = col_integer(),
QT_ING_BRANCA = col_integer(),
QT_ING_PRETA = col_integer(),
QT_ING_PARDA = col_integer(),
QT_ING_AMARELA = col_integer(),
QT_ING_INDIGENA = col_integer(),
QT_ING_CORND = col_integer(),
QT_MAT = col_integer(),
QT_MAT_FEM = col_integer(),
QT_MAT_MASC = col_integer(),
QT_MAT_DIURNO = col_integer(),
QT_MAT_NOTURNO = col_integer(),
QT_MAT_0_17 = col_integer(),
QT_MAT_18_24 = col_integer(),
QT_MAT_25_29 = col_integer(),
QT_MAT_30_34 = col_integer(),
QT_MAT_35_39 = col_integer(),
QT_MAT_40_49 = col_integer(),
QT_MAT_50_59 = col_integer(),
QT_MAT_60_MAIS = col_integer(),
QT_MAT_BRANCA = col_integer(),
QT_MAT_PRETA = col_integer(),
QT_MAT_PARDA = col_integer(),
QT_MAT_AMARELA = col_integer(),
QT_MAT_INDIGENA = col_integer(),
QT_MAT_CORND = col_integer(),
QT_CONC = col_integer(),
QT_CONC_FEM = col_integer(),
QT_CONC_MASC = col_integer(),
QT_CONC_DIURNO = col_integer(),
QT_CONC_NOTURNO = col_integer(),
QT_CONC_0_17 = col_integer(),
QT_CONC_18_24 = col_integer(),
QT_CONC_25_29 = col_integer(),
QT_CONC_30_34 = col_integer(),
QT_CONC_35_39 = col_integer(),
QT_CONC_40_49 = col_integer(),
QT_CONC_50_59 = col_integer(),
QT_CONC_60_MAIS = col_integer(),
QT_CONC_BRANCA = col_integer(),
QT_CONC_PRETA = col_integer(),
QT_CONC_PARDA = col_integer(),
QT_CONC_AMARELA = col_integer(),
QT_CONC_INDIGENA = col_integer(),
QT_CONC_CORND = col_integer(),
QT_ING_NACBRAS = col_integer(),
QT_ING_NACESTRANG = col_integer(),
QT_MAT_NACBRAS = col_integer(),
QT_MAT_NACESTRANG = col_integer(),
QT_CONC_NACBRAS = col_integer(),
QT_CONC_NACESTRANG = col_integer(),
QT_ALUNO_DEFICIENTE = col_integer(),
QT_ING_DEFICIENTE = col_integer(),
QT_MAT_DEFICIENTE = col_integer(),
QT_CONC_DEFICIENTE = col_integer(),
QT_ING_FINANC = col_integer(),
QT_ING_FINANC_REEMB = col_integer(),
QT_ING_FIES = col_integer(),
QT_ING_RPFIES = col_integer(),
QT_ING_FINANC_REEMB_OUTROS = col_integer(),
QT_ING_FINANC_NREEMB = col_integer(),
QT_ING_PROUNII = col_integer(),
QT_ING_PROUNIP = col_integer(),
QT_ING_NRPFIES = col_integer(),
QT_ING_FINANC_NREEMB_OUTROS = col_integer(),
QT_MAT_FINANC = col_integer(),
QT_MAT_FINANC_REEMB = col_integer(),
QT_MAT_FIES = col_integer(),
QT_MAT_RPFIES = col_integer(),
QT_MAT_FINANC_REEMB_OUTROS = col_integer(),
QT_MAT_FINANC_NREEMB = col_integer(),
QT_MAT_PROUNII = col_integer(),
QT_MAT_PROUNIP = col_integer(),
QT_MAT_NRPFIES = col_integer(),
QT_MAT_FINANC_NREEMB_OUTROS = col_integer(),
QT_CONC_FINANC = col_integer(),
QT_CONC_FINANC_REEMB = col_integer(),
QT_CONC_FIES = col_integer(),
QT_CONC_RPFIES = col_integer(),
QT_CONC_FINANC_REEMB_OUTROS = col_integer(),
QT_CONC_FINANC_NREEMB = col_integer(),
QT_CONC_PROUNII = col_integer(),
QT_CONC_PROUNIP = col_integer(),
QT_CONC_NRPFIES = col_integer(),
QT_CONC_FINANC_NREEMB_OUTROS = col_integer(),
QT_ING_RESERVA_VAGA = col_integer(),
QT_ING_RVREDEPUBLICA = col_integer(),
QT_ING_RVETNICO = col_integer(),
QT_ING_RVPDEF = col_integer(),
QT_ING_RVSOCIAL_RF = col_integer(),
QT_ING_RVOUTROS = col_integer(),
QT_MAT_RESERVA_VAGA = col_integer(),
QT_MAT_RVREDEPUBLICA = col_integer(),
QT_MAT_RVETNICO = col_integer(),
QT_MAT_RVPDEF = col_integer(),
QT_MAT_RVSOCIAL_RF = col_integer(),
QT_MAT_RVOUTROS = col_integer(),
QT_CONC_RESERVA_VAGA = col_integer(),
QT_CONC_RVREDEPUBLICA = col_integer(),
QT_CONC_RVETNICO = col_integer(),
QT_CONC_RVPDEF = col_integer(),
QT_CONC_RVSOCIAL_RF = col_integer(),
QT_CONC_RVOUTROS = col_integer(),
QT_SIT_TRANCADA = col_integer(),
QT_SIT_DESVINCULADO = col_integer(),
QT_SIT_TRANSFERIDO = col_integer(),
QT_SIT_FALECIDO = col_integer(),
QT_ING_PROCESCPUBLICA = col_integer(),
QT_ING_PROCESCPRIVADA = col_integer(),
QT_ING_PROCNAOINFORMADA = col_integer(),
QT_MAT_PROCESCPUBLICA = col_integer(),
QT_MAT_PROCESCPRIVADA = col_integer(),
QT_MAT_PROCNAOINFORMADA = col_integer(),
QT_CONC_PROCESCPUBLICA = col_integer(),
QT_CONC_PROCESCPRIVADA = col_integer(),
QT_CONC_PROCNAOINFORMADA = col_integer(),
QT_PARFOR = col_integer(),
QT_ING_PARFOR = col_integer(),
QT_MAT_PARFOR = col_integer(),
QT_CONC_PARFOR = col_integer(),
QT_APOIO_SOCIAL = col_integer(),
QT_ING_APOIO_SOCIAL = col_integer(),
QT_MAT_APOIO_SOCIAL = col_integer(),
QT_CONC_APOIO_SOCIAL = col_integer(),
QT_ATIV_EXTRACURRICULAR = col_integer(),
QT_ING_ATIV_EXTRACURRICULAR = col_integer(),
QT_MAT_ATIV_EXTRACURRICULAR = col_integer(),
QT_CONC_ATIV_EXTRACURRICULAR = col_integer(),
QT_MOB_ACADEMICA = col_integer(),
QT_ING_MOB_ACADEMICA = col_integer(),
QT_MAT_MOB_ACADEMICA = col_integer(),
QT_CONC_MOB_ACADEMICA = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseCurso = BaseCurso %>% dplyr::select(everything()) %>%
mutate(NO_GRAU_ACADEMICO = case_when(
TP_GRAU_ACADEMICO == 1 ~ "BACHARELADO",
TP_GRAU_ACADEMICO == 2 ~ "LICENCIATURA",
TP_GRAU_ACADEMICO == 3 ~ "TECNOLOGICO",
TP_GRAU_ACADEMICO == 4 ~ "BACHARELADO E LICENCIATURA"),
NO_MODALIDADE_ENSINO = case_when(
TP_MODALIDADE_ENSINO == 1 ~ "PRESENCIAL",
TP_MODALIDADE_ENSINO == 2 ~ "CURSO A DISTANCIA"),
NO_NIVEL_ACADEMICO = case_when(
TP_NIVEL_ACADEMICO == 1 ~ "GRADUACAO",
TP_NIVEL_ACADEMICO == 2 ~ "SEQUENCIAL"))
#salvando no banco de dados
saveRDS(BaseCurso, "./data/curso_2020_data.rds")
#liberando variável
rm(BaseCurso)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3702555 197.8 10144577 541.8 18747399 1001.3
## Vcells 19704112 150.4 403719571 3080.2 1726045989 13168.76.1.3 Dados INEP - Sala Segura
Importação e tratamento do arquivo relativo ao registro das Mantenedoras em 2020
BaseSEDAP2020 = read_delim("./raw-data/SEDAP_2020_1.csv",
";",
escape_double = FALSE,
col_types =
cols(CO_MANTENEDORA = col_integer(),
VL_RECEITA_PROPRIA = col_double(),
VL_RECEITA_TRANSFERENCIA = col_double(),
VL_RECEITA_OUTRA = col_double(),
VL_DESPESA_PESSOAL_DOCENTE = col_double(),
VL_DESPESA_PESSOAL_TECNICO = col_double(),
VL_DESPESA_PESSOAL_ENCARGO = col_double(),
VL_DESPESA_CUSTEIO = col_double(),
VL_DESPESA_INVESTIMENTO = col_double(),
VL_DESPESA_PESQUISA = col_double(),
VL_DESPESA_OUTRA = col_double(),
QT_TEC_TOTAL = col_integer(),
IQCTA = col_double(),
QT_VAGA_TOTAL = col_integer(),
QT_ING = col_integer(),
QT_MAT = col_integer(),
QT_CONC = col_integer(),
QT_INSC = col_integer(),
QT_CURSO = col_integer(),
TMATR_FSEDE = col_integer(),
TALUNOS = col_integer(),
TALUNOS_PRESENCIAL = col_integer(),
TALUNOS_EAD = col_integer(),
TALUNOS_EV = col_integer(),
TALUNOS_PRESENCIAL_EV = col_integer(),
TALUNOS_EAD_EV = col_integer(),
TALUNOS_FO = col_integer(),
TALUNOS_PRESENCIAL_FO = col_integer(),
TALUNOS_EAD_FO = col_integer()),
locale = locale(encoding = "ISO-8859-1"))
#tratando os dados e colunas não existentes
#
BaseSEDAP2020[is.na(BaseSEDAP2020)] = 0
#salvando no banco de dados
saveRDS(BaseSEDAP2020, "./data/sedap_mant_2020_data.rds")
#liberando variável
rm(BaseSEDAP2020)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3702157 197.8 10144577 541.8 18747399 1001.3
## Vcells 19695511 150.3 322975657 2464.2 1726045989 13168.76.2 Tratamento e Uniformização dos dados
A seção trata da uniformização, reconfiguração dos dados e montagem das bases de dados necessárias para a exploração e análise
6.2.1 Consolidação dos dados das IES - 2020
Agrupamentos e calculos para facilitar a consolidação nas mantenedoras.
#*carga dos dados 2017 importados por ies_import_data
ies_2020_full = readRDS("./data/ies_2020_data.rds")
#* SELECIONAR SOMENTE aCAMPOS NECESSÁRIOS.
ies_2020_ext = ies_2020_full %>% dplyr::select(c(1,18,19,17,16,8,26,82,83))
#criar indice de qualificação corpo técnico = IQCTA
ies_2020_IQCTA = ies_2020_full %>% dplyr::select(c(1,18,27:40))
ies_2020_IQCTA = ies_2020_IQCTA %>% mutate(
QT_TEC_D = QT_TEC_DOUTORADO_FEM+QT_TEC_DOUTORADO_MASC,
QT_TEC_M = QT_TEC_MESTRADO_FEM+QT_TEC_MESTRADO_MASC,
QT_TEC_E = QT_TEC_ESPECIALIZACAO_FEM+QT_TEC_ESPECIALIZACAO_MASC,
QT_TEC_G = QT_TEC_SUPERIOR_FEM+QT_TEC_SUPERIOR_MASC,
QT_TEC_MD= QT_TEC_MEDIO_FEM+QT_TEC_MEDIO_MASC,
QT_TEC_F = QT_TEC_FUNDAMENTAL_COMP_FEM+QT_TEC_FUNDAMENTAL_COMP_MASC+
QT_TEC_FUNDAMENTAL_INCOMP_FEM+QT_TEC_FUNDAMENTAL_INCOMP_MASC)
ies_2020_IQCTA = ies_2020_IQCTA %>% dplyr::select(c(1,2, 17:22))
ies_2020_ext = ies_2020_ext %>% left_join(ies_2020_IQCTA)## Joining, by = c("NU_ANO_CENSO", "CO_IES")rm(ies_2020_IQCTA)
#* CRIAR COLUNAS COM TOTAL DE DOCENTES ATIVOS e IQCD
#*
docentes_2020_consolidado_IES = ies_2020_full %>%
dplyr::select(c(1,18,52,55:59)) %>% #pega somente ano censo, co_ies e em exercicio
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QT_DOC = sum(QT_DOC_EXE),
QT_DOC_D = sum(QT_DOC_EX_DOUT),
QT_DOC_M = sum(QT_DOC_EX_MEST),
QT_DOC_E = sum(QT_DOC_EX_ESP),
QT_DOC_G = sum(QT_DOC_EX_GRAD),
QT_DOC_SG = sum(QT_DOC_EX_SEM_GRAD))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2020_ext = ies_2020_ext %>% dplyr::select(everything()) %>%
left_join(docentes_2020_consolidado_IES, by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
rm(docentes_2020_consolidado_IES)
rm(docentes_2020_full)## Warning in rm(docentes_2020_full): objeto 'docentes_2020_full' não encontrado#* CRIAR COLUNAS COM DADOS DOS CURSOS
#*
cursos_2020_full = readRDS("./data/curso_2020_data.rds")
cursos_2020_consolidado_IES = cursos_2020_full %>%
dplyr::select(c(1,13,42,26)) %>% #pega somente ano censo, co_ies e situacao
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(TVAGAS = sum (QT_VG_TOTAL), TINGRESSOS = sum(QT_ING))## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2020_ext = ies_2020_ext %>% dplyr::select(everything()) %>%
left_join(cursos_2020_consolidado_IES, c(TVAGAS, TINGRESSOS), by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2020_ext[is.na(ies_2020_ext)] = 0
rm(cursos_2020_consolidado_IES)
rm(cursos_2020_full)
#* CRIAR COLUNAS COM TOTAL DE ALUNOS ATIVOS PRESENCIAL E EAD
#*
cursos_2020_full = readRDS("./data/curso_2020_data.rds")
alunos_2020_consolidado_IES = cursos_2020_full %>%
dplyr::select(c(1,13,24,23,72,168,169,171,130,187)) %>% #pega ano censo, co_ies, situacao, modalidade e financiamento/bolsas
filter(TP_NIVEL_ACADEMICO == 1) %>%
group_by(NU_ANO_CENSO, CO_IES) %>%
dplyr::summarise(QTD_ALUNO_PRESENCIAL = sum(QT_MAT[TP_MODALIDADE_ENSINO == 1]),
QTD_ALUNO_EAD = sum(QT_MAT[TP_MODALIDADE_ENSINO == 2]),
QTD_ALUNO_ATIVO = sum(QT_MAT),
QTD_ALUNO_EVADIDO = sum(QT_SIT_TRANCADA+QT_SIT_DESVINCULADO+QT_SIT_FALECIDO),
QTD_ALUNO_FINANC = sum(QT_MAT_FINANC),
QTD_ALUNO_BOLSA = sum(QT_MAT_APOIO_SOCIAL),
QTD_ALUNOS = QTD_ALUNO_ATIVO + QTD_ALUNO_EVADIDO)## `summarise()` has grouped output by 'NU_ANO_CENSO'. You can override using the
## `.groups` argument.ies_2020_ext = ies_2020_ext %>% dplyr::select(everything()) %>%
left_join(alunos_2020_consolidado_IES, dplyr::select(QTD_ALUNO_PRESENCIAL,
QTD_ALUNO_EAD,
QTD_ALUNO_ATIVO,
QTD_ALUNO_EVADIDO,
QTD_ALUNO_FINANC,
QTD_ALUNO_BOLSA,
QTD_ALUNOS),
by = c("NU_ANO_CENSO" = "NU_ANO_CENSO", "CO_IES" = "CO_IES"))
ies_2020_ext[is.na(ies_2020_ext)] = 0
saveRDS(ies_2020_ext,"./data/ies_consolidado_2020_data.rds")
rm(alunos_2020_consolidado_IES)
rm(cursos_2020_full)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3714586 198.4 10144577 541.8 18747399 1001.3
## Vcells 19914535 152.0 258380526 1971.3 1726045989 13168.7Total de registros e dimensões:
dim(ies_2020_ext)## [1] 2457 30rm (ies_2020_ext)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3713512 198.4 10144577 541.8 18747399 1001.3
## Vcells 19865477 151.6 206704421 1577.1 1726045989 13168.76.2.2 Consolidação dos dados nas mantenedoras - 2019
Nesta etapa consolidaremos os dados das IES nas suas mantenedoras, agraupando os valores financeiros de acordo com as regras e criando as váriaveis de suporte
#*** TRATANDO, EXTRAINDO E CONVERTENDO 2019
#*carga dos dados tratados por ies_convert_data
ies_2020_ext = readRDS("./data/ies_consolidado_2020_data.rds")
# deixando somente as privadas
ies_2020_ext = ies_2020_ext %>% filter(NO_CATEGORIA_ADMINISTRATIVA %in% c("PRIVADA COM FINS LUCRATIVOS", "PRIVADA SEM FINS LUCRATIVOS"))
#primeiro somar todos os campos agrupando por mantenedora
temp_2020 = ies_2020_ext %>% dplyr::select(c(1,4,5,7,10:30)) %>%
group_by(NU_ANO_CENSO, CO_MANTENEDORA,NO_MANTENEDORA) %>%
dplyr::summarise(QT_TEC_TOTAL = sum(QT_TEC_TOTAL),
QT_TEC_D = sum(QT_TEC_D),
QT_TEC_M = sum(QT_TEC_M),
QT_TEC_E = sum(QT_TEC_E),
QT_TEC_G = sum(QT_TEC_G),
QT_TEC_MD = sum(QT_TEC_MD),
QT_TEC_F = sum(QT_TEC_F),
QT_DOC = sum(QT_DOC),
QT_DOC_D = sum(QT_DOC_D),
QT_DOC_M = sum(QT_DOC_M),
QT_DOC_E = sum(QT_DOC_E),
QT_DOC_G = sum(QT_DOC_G),
QT_DOC_SG = sum(QT_DOC_SG),
TVAGAS = sum(TVAGAS),
TINGRESSOS = sum(TINGRESSOS),
QTD_ALUNO_PRESENCIAL = sum(QTD_ALUNO_PRESENCIAL),
QTD_ALUNO_EAD = sum(QTD_ALUNO_EAD),
QTD_ALUNO_ATIVO = sum(QTD_ALUNO_ATIVO),
QTD_ALUNO_EVADIDO = sum(QTD_ALUNO_EVADIDO),
QTD_ALUNO_FINANC = sum(QTD_ALUNO_FINANC),
QTD_ALUNO_BOLSA = sum(QTD_ALUNO_BOLSA),
QTD_ALUNOS = sum(QTD_ALUNOS))## `summarise()` has grouped output by 'NU_ANO_CENSO', 'CO_MANTENEDORA'. You can
## override using the `.groups` argument.#join com sedap
sedap_2020_mant = readRDS("./data/sedap_mant_2020_data.rds")
mant_2020_agrupado = temp_2020 %>%
left_join(sedap_2020_mant, by = c("CO_MANTENEDORA" = "CO_MANTENEDORA"))
mant_2020_agrupado[is.na(mant_2020_agrupado)] = 0
mant_2020_agrupado = mant_2020_agrupado %>% dplyr::select(c(1,2,3,26:35,4:10,11:16,17,18,44,19:25))
colnames(mant_2020_agrupado)[14] = "QT_TEC_TOTAL"
#validação com dados de matriculas divulgado em graduação privada (6.724.002)
total_2020 = sum(mant_2020_agrupado$QTD_ALUNO_ATIVO)Total de registros e dimensões. Também compara a quantidade de matriculas ativas em comparação com a sinopse estatística oficial 2019, para fins de validação:
Total de Matrículas ativas divulgada na sinopse = 6.724.002
total_2020## [1] 6724002dim(mant_2020_agrupado)## [1] 1469 36#salvando dataset
saveRDS(mant_2020_agrupado,"./data/mant_2020_agrupado_data.rds")
rm(mant_2020_agrupado)
rm(ies_2020_ext)
rm (sedap_2020_mant)
rm (temp_2020)
rm (total_2020)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3713703 198.4 10144577 541.8 18747399 1001.3
## Vcells 19866463 151.6 165363537 1261.7 1726045989 13168.76.3 Exploração e Análise de Dados
Esta seção se dedica a explorar as informações de acordo com cada visualização elaborada.
6.3.1 Análise de fatores que podem se correlacionar com o resultado do ponto de equilíbrio das mantenedoras
MONTAGEM E ANÁLISE
# MONTAGEM DO DATASET
mant_2020_agrupado = readRDS("./data/mant_2020_agrupado_data.rds")
ds_PEQ_2020 = mant_2020_agrupado %>% select_all() %>%
mutate(VDPEQ = ((VL_RECEITA_PROPRIA - #somente receitas com mensalidades
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA))/
(VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+
VL_DESPESA_PESSOAL_ENCARGO+VL_DESPESA_CUSTEIO+
VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)), #variavel distancia ponto de equilíbrio
VRAD = QTD_ALUNO_ATIVO/QT_DOC,
VRAF = QTD_ALUNO_ATIVO/QT_TEC_TOTAL, #alterado para alunos/funcionarios
VCFPA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO)/QTD_ALUNO_ATIVO,
VCTTA = (VL_DESPESA_PESSOAL_DOCENTE+VL_DESPESA_PESSOAL_TECNICO+VL_DESPESA_PESSOAL_ENCARGO+
VL_DESPESA_CUSTEIO+VL_DESPESA_PESQUISA+VL_DESPESA_OUTRA)/QTD_ALUNO_ATIVO,
VTMA = (VL_RECEITA_PROPRIA/QTD_ALUNO_ATIVO),
VOCIO = TINGRESSOS/TVAGAS,
VEAD = QTD_ALUNO_EAD/QTD_ALUNO_ATIVO,
VPOL = TMATR_FSEDE/QTD_ALUNO_ATIVO,
IQCTA = ((5*QT_TEC_D+3*QT_TEC_M+2*QT_TEC_E+QT_TEC_G+0.75*QT_TEC_MD+0.5*QT_TEC_F)/
QT_TEC_TOTAL),
IQCDO = ((5*QT_DOC_D+3*QT_DOC_M+2*QT_DOC_E+1*QT_DOC_G)/QT_DOC),
VFINE = QTD_ALUNO_FINANC / QTD_ALUNO_ATIVO,
VBOLS = QTD_ALUNO_BOLSA / QTD_ALUNO_ATIVO,
VEVAD = QTD_ALUNO_EVADIDO / QTD_ALUNOS)
ds_PEQ_2020[is.na(ds_PEQ_2020)] = 0
ds_PEQ_2020 = ds_PEQ_2020 %>% dplyr::select(2,37:50)## Adding missing grouping variables: `NU_ANO_CENSO`ds_PEQ_2020 = (ds_PEQ_2020[,2:16])
rm(mant_2020_agrupado)ANÁLISE E REMOÇÃO DE OUTLIERS
Sumário antes da remoção:
tbl_summary(ds_PEQ_2020) #usando gtsummary| Characteristic | N = 1,4691 |
|---|---|
| CO_MANTENEDORA | 1,963 (855, 15,669) |
| VDPEQ | 0.07 (-0.15, 0.41) |
| VRAD | 13 (7, 21) |
| VRAF | 20 (11, 35) |
| VCFPA | 5,106 (2,776, 9,743) |
| VCTTA | 8,243 (4,602, 16,117) |
| VTMA | 8,850 (4,869, 16,319) |
| VOCIO | 0.20 (0.09, 0.35) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 0.00 (0.00, 0.14) |
| IQCTA | 1.19 (1.00, 1.47) |
| IQCDO | 3.16 (2.86, 3.49) |
| VFINE | 0.20 (0.00, 0.47) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.26 (0.18, 0.36) |
| 1 Median (IQR) | |
Boxplot antes do tratamento:
ds_boxplot_all = melt(ds_PEQ_2020, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2020, id.vars = "CO_MANTENEDORA", measure.vars =
## c("VCFPA", : The melt generic in data.table has been passed a grouped_df and
## will attempt to redirect to the relevant reshape2 method; please note that
## reshape2 is deprecated, and this redirection is now deprecated as well. To
## continue using melt methods from reshape2 while both libraries are attached,
## e.g. melt.list, you can prepend the namespace like reshape2::melt(ds_PEQ_2020).
## In the next version, this warning will become an error.ggplot(data = ds_boxplot_all, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning: Removed 10 rows containing non-finite values (stat_boxplot).## Warning: Removed 10 rows containing missing values (geom_point).## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Tratamento e remoção dos extremos, analisando VTMA e VCFPA 5% de cada lado = 95,9% / Perda total simultânea em VTMA e VCFPA = 13,3%
#Remoção de Registros inválidos - Outliers
q_vtma = quantile(ds_PEQ_2020$VTMA, probs = c(.05, .95), na.rm = F)
q_vcfpa = quantile(ds_PEQ_2020$VCFPA, probs = c(.05, .95), na.rm = F)
#dataset sem os outliers
ds_PEQ_2020_noOutliers = subset(ds_PEQ_2020, (ds_PEQ_2020$VTMA>=(q_vtma[1])&ds_PEQ_2020$VCFPA>=(q_vcfpa[1])) &
(ds_PEQ_2020$VTMA<=(q_vtma[2]) & ds_PEQ_2020$VCFPA<=(q_vcfpa[2])))
#dataset com os outliers excluidos
ds_PEQ_2020_Outliers = subset(ds_PEQ_2020, !(CO_MANTENEDORA %in% ds_PEQ_2020_noOutliers$CO_MANTENEDORA))Sumário após remoção:
tbl_summary(ds_PEQ_2020_noOutliers) #usando gtsummary| Characteristic | N = 1,2631 |
|---|---|
| CO_MANTENEDORA | 1,638 (790, 14,490) |
| VDPEQ | 0.07 (-0.13, 0.37) |
| VRAD | 13 (8, 21) |
| VRAF | 22 (12, 36) |
| VCFPA | 5,053 (2,939, 8,751) |
| VCTTA | 8,224 (4,963, 14,224) |
| VTMA | 8,960 (5,453, 14,899) |
| VOCIO | 0.21 (0.10, 0.35) |
| VEAD | 0.00 (0.00, 0.00) |
| VPOL | 0.00 (0.00, 0.16) |
| IQCTA | 1.19 (1.00, 1.45) |
| IQCDO | 3.16 (2.88, 3.48) |
| VFINE | 0.22 (0.01, 0.50) |
| VBOLS | 0.00 (0.00, 0.00) |
| VEVAD | 0.26 (0.19, 0.36) |
| 1 Median (IQR) | |
Boxplot após tratamento:
ds_boxplot_NoOutliers = melt(ds_PEQ_2020_noOutliers, id.vars = 'CO_MANTENEDORA', measure.vars = c('VCFPA', 'VTMA'))## Warning in melt(ds_PEQ_2020_noOutliers, id.vars = "CO_MANTENEDORA",
## measure.vars = c("VCFPA", : The melt generic in data.table has been passed
## a grouped_df and will attempt to redirect to the relevant reshape2 method;
## please note that reshape2 is deprecated, and this redirection is now
## deprecated as well. To continue using melt methods from reshape2 while both
## libraries are attached, e.g. melt.list, you can prepend the namespace like
## reshape2::melt(ds_PEQ_2020_noOutliers). In the next version, this warning will
## become an error.ggplot(data = ds_boxplot_NoOutliers, aes(x=variable, y=value, fill=variable)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("Análise de Variáveis Delimitadoras") +
xlab("")## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Perda Total dos registros (em %)
perda = 1-(dim(ds_PEQ_2020_noOutliers)[1]/dim(ds_PEQ_2020)[1])
paste(round(100*perda, 2), "%", sep="")## [1] "14.02%"ANÁLISE DOS RESÍDUOS OUTLIERS
#falta fazer....ANÁLISE DE COMPONENTES PRINCIPAIS - PCA
# analise PCA - Componentes Principais
# Filtrando variáveis númericas
ds_pca = ds_PEQ_2020_noOutliers[,3:15]
# Calculando o desvio padrão das variáveis
sapply(ds_pca, sd)## VRAD VRAF VCFPA VCTTA
## 1.87256128467e+01 2.99640804037e+01 5.36925131721e+03 1.68350647653e+04
## VTMA VOCIO VEAD VPOL
## 9.78894385100e+03 2.18446762904e-01 1.71360874250e-01 2.95113720420e-01
## IQCTA IQCDO VFINE VBOLS
## 3.75649929933e-01 4.67910585067e-01 3.08115091981e-01 1.66872187403e-01
## VEVAD
## 1.40823928359e-01# PCA com a matriz de covariâncias
pca_cov = prcomp(ds_pca)
summary(pca_cov)## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Standard deviation 17895.05408 8672.89693 3552.42419 30.4252062 13.8016526
## Proportion of Variance 0.78474 0.18433 0.03093 0.0000000 0.0000000
## Cumulative Proportion 0.78474 0.96907 1.00000 1.0000000 1.0000000
## PC6 PC7 PC8 PC9
## Standard deviation 0.434470513 0.371336466 0.305204578 0.289245931
## Proportion of Variance 0.000000000 0.000000000 0.000000000 0.000000000
## Cumulative Proportion 1.000000000 1.000000000 1.000000000 1.000000000
## PC10 PC11 PC12 PC13
## Standard deviation 0.209113501 0.165282978 0.144654369 0.131797562
## Proportion of Variance 0.000000000 0.000000000 0.000000000 0.000000000
## Cumulative Proportion 1.000000000 1.000000000 1.000000000 1.000000000# PCA com a matriz de correlação (variáveis padronizadas)
pca_corr = prcomp(ds_pca, center = T, scale = T)
summary(pca_corr)$importance## PC1 PC2 PC3 PC4
## Standard deviation 1.61453096379 1.37924448054 1.09332239492 1.04587319529
## Proportion of Variance 0.20052000000 0.14633000000 0.09195000000 0.08414000000
## Cumulative Proportion 0.20052000000 0.34685000000 0.43880000000 0.52294000000
## PC5 PC6 PC7
## Standard deviation 1.02438447766 0.970219916782 0.942910862876
## Proportion of Variance 0.08072000000 0.072410000000 0.068390000000
## Cumulative Proportion 0.60366000000 0.676070000000 0.744460000000
## PC8 PC9 PC10
## Standard deviation 0.923640912263 0.858339102969 0.813752120221
## Proportion of Variance 0.065620000000 0.056670000000 0.050940000000
## Cumulative Proportion 0.810090000000 0.866760000000 0.917700000000
## PC11 PC12 PC13
## Standard deviation 0.701340561728 0.556375881135 0.518184254296
## Proportion of Variance 0.037840000000 0.023810000000 0.020650000000
## Cumulative Proportion 0.955530000000 0.979350000000 1.000000000000# Gráfico das variáveis
fviz_pca_var(pca_corr,
col.var = "contrib", # Cor por contribuições para o PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE,
legend.title = "Contribuição"
)
Processo de análise de regressão múltipla
# Modelo de Regressão Linear Múltipla
ds_RLM = ds_PEQ_2020_noOutliers
summary(ds_RLM)## CO_MANTENEDORA VDPEQ VRAD
## Min. : 10.00000 Min. :-0.9613302530 Min. : 0.50000000
## 1st Qu.: 789.50000 1st Qu.:-0.1280697737 1st Qu.: 8.01428571
## Median : 1638.00000 Median : 0.0740342538 Median : 13.39416058
## Mean : 5677.25257 Mean : 0.2194022880 Mean : 17.83155025
## 3rd Qu.:14490.50000 3rd Qu.: 0.3670266695 3rd Qu.: 21.15757576
## Max. :17982.00000 Max. : 8.3057810099 Max. :262.04597701
## VRAF VCFPA VCTTA
## Min. : 0.365853659 Min. : 838.039856 Min. : 918.548387
## 1st Qu.: 11.955274225 1st Qu.: 2938.667112 1st Qu.: 4963.357550
## Median : 21.550000000 Median : 5052.518154 Median : 8223.577299
## Mean : 29.371582008 Mean : 6724.675495 Mean : 11578.883309
## 3rd Qu.: 36.140196078 3rd Qu.: 8750.540355 3rd Qu.: 14224.374628
## Max. :391.265116279 Max. :32636.134815 Max. :509693.279024
## VTMA VOCIO VEAD
## Min. : 1331.80865 Min. :0.0000000000 Min. :0.0000000000
## 1st Qu.: 5453.15831 1st Qu.:0.0990590565 1st Qu.:0.0000000000
## Median : 8960.26594 Median :0.2069759789 Median :0.0000000000
## Mean :11859.12391 Mean :0.2550740125 Mean :0.0615666184
## 3rd Qu.:14898.91310 3rd Qu.:0.3458036002 3rd Qu.:0.0000000000
## Max. :70458.19273 Max. :1.2903703704 Max. :1.0000000000
## VPOL IQCTA IQCDO
## Min. :0.000000000 Min. :0.50000000 Min. :1.90909091
## 1st Qu.:0.000000000 1st Qu.:1.00000000 1st Qu.:2.87679874
## Median :0.000000000 Median :1.18750000 Median :3.16161616
## Mean :0.153896046 Mean :1.26987633 Mean :3.18802171
## 3rd Qu.:0.162030081 3rd Qu.:1.45197234 3rd Qu.:3.47826087
## Max. :1.000000000 Max. :3.07500000 Max. :5.00000000
## VFINE VBOLS VEVAD
## Min. :0.0000000000 Min. :0.0000000000 Min. :0.000000000
## 1st Qu.:0.0112789619 1st Qu.:0.0000000000 1st Qu.:0.186259342
## Median :0.2222980660 Median :0.0000000000 Median :0.263656633
## Mean :0.3097371058 Mean :0.0397418567 Mean :0.281839932
## 3rd Qu.:0.4984472050 3rd Qu.:0.0000000000 3rd Qu.:0.356481601
## Max. :1.0000000000 Max. :1.0000000000 Max. :0.940972222# Correlograma
corrgram(ds_RLM[,c(2:15)],
#upper.panel=panel.cor,
upper.panel=panel.conf,
cex.labels=1.5, cex=1.2)## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
## Warning in par(usr): argument 1 does not name a graphical parameter
corrplot(cor(ds_RLM[,c(2:15)]), method="circle", type="lower", order="hclust", addCoef.col = "black")
# Modelo de regressão
modelo_RLM = lm(VDPEQ ~ ., data = ds_RLM[,2:15])
summary(modelo_RLM)##
## Call:
## lm(formula = VDPEQ ~ ., data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.386348297 -0.268567105 -0.095168757 0.132905446 7.498457351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.87352940e-01 1.34932567e-01 1.38849 0.1652345
## VRAD 5.39963031e-04 1.29074728e-03 0.41833 0.6757751
## VRAF 1.05771056e-03 6.92096904e-04 1.52827 0.1266988
## VCFPA -8.36968284e-05 4.64455199e-06 -18.02043 < 2.22e-16 ***
## VCTTA -7.38146172e-06 1.17372055e-06 -6.28894 4.4109e-10 ***
## VTMA 5.30875087e-05 2.37250521e-06 22.37614 < 2.22e-16 ***
## VOCIO -2.52477911e-01 7.72145717e-02 -3.26982 0.0011055 **
## VEAD -9.85863823e-02 1.14373533e-01 -0.86197 0.3888703
## VPOL 1.09395457e-02 5.53281142e-02 0.19772 0.8432953
## IQCTA 7.30479876e-02 4.37103875e-02 1.67118 0.0949364 .
## IQCDO 3.77294025e-03 3.79230302e-02 0.09949 0.9207657
## VFINE 1.70718474e-02 5.44218995e-02 0.31369 0.7538055
## VBOLS -1.88938502e-02 9.88453056e-02 -0.19115 0.8484425
## VEVAD -1.08055355e-01 1.18642390e-01 -0.91077 0.3625949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.574158335 on 1249 degrees of freedom
## Multiple R-squared: 0.354758486, Adjusted R-squared: 0.348042601
## F-statistic: 52.8237924 on 13 and 1249 DF, p-value: < 2.220446e-16tbl_regression(modelo_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.68 |
| VRAF | 0.00 | 0.00, 0.00 | 0.13 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VOCIO | -0.25 | -0.40, -0.10 | 0.001 |
| VEAD | -0.10 | -0.32, 0.13 | 0.39 |
| VPOL | 0.01 | -0.10, 0.12 | 0.84 |
| IQCTA | 0.07 | -0.01, 0.16 | 0.095 |
| IQCDO | 0.00 | -0.07, 0.08 | 0.92 |
| VFINE | 0.02 | -0.09, 0.12 | 0.75 |
| VBOLS | -0.02 | -0.21, 0.18 | 0.85 |
| VEVAD | -0.11 | -0.34, 0.12 | 0.36 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.187352939753 | 0.134932567406 | 1.388493107001 | 0.165234509757 | -0.077366559358 | 0.452072438864 |
| VRAD | 0.000539963031 | 0.001290747275 | 0.418333659621 | 0.675775099453 | -0.001992309041 | 0.003072235104 |
| VRAF | 0.001057710560 | 0.000692096904 | 1.528269457316 | 0.126698846625 | -0.000300090224 | 0.002415511344 |
| VCFPA | -0.000083696828 | 0.000004644552 | -18.020430952283 | 0.000000000000 | -0.000092808813 | -0.000074584844 |
| VCTTA | -0.000007381462 | 0.000001173721 | -6.288943092196 | 0.000000000441 | -0.000009684143 | -0.000005078780 |
| VTMA | 0.000053087509 | 0.000002372505 | 22.376139960020 | 0.000000000000 | 0.000048432973 | 0.000057742044 |
| VOCIO | -0.252477911119 | 0.077214571701 | -3.269822075752 | 0.001105494951 | -0.403962486991 | -0.100993335247 |
| VEAD | -0.098586382252 | 0.114373532739 | -0.861968498227 | 0.388870311186 | -0.322971828110 | 0.125799063606 |
| VPOL | 0.010939545669 | 0.055328114220 | 0.197721281909 | 0.843295310893 | -0.097606752417 | 0.119485843756 |
| IQCTA | 0.073047987582 | 0.043710387470 | 1.671181424151 | 0.094936357393 | -0.012705897484 | 0.158801872648 |
| IQCDO | 0.003772940246 | 0.037923030216 | 0.099489419092 | 0.920765653930 | -0.070626930418 | 0.078172810911 |
| VFINE | 0.017071847361 | 0.054421899522 | 0.313694441225 | 0.753805503807 | -0.089696579706 | 0.123840274427 |
| VBOLS | -0.018893850236 | 0.098845305595 | -0.191145650496 | 0.848442549224 | -0.212815008705 | 0.175027308232 |
| VEVAD | -0.108055355242 | 0.118642390153 | -0.910765158243 | 0.362594856369 | -0.340815723612 | 0.124705013128 |
kable(broom::glance(modelo_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.35475848591 | 0.348042601456 | 0.574158335031 | 52.8237923589 | 0 | 13 | -1084.30463621 | 2198.60927242 | 2275.72794925 | 411.742584313 | 1249 | 1263 |
plot(modelo_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo_RLM, order.by = ~., data = ds_RLM[,3:15], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo_RLM
## GQ = 1.575106766, df1 = 614, df2 = 614, p-value = 1.05555169e-08
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo_RLM
## BP = 37.67198282, df = 13, p-value = 0.00032482561plot(modelo_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 -0.0108000970796 2.0213357933 0.748
## Alternative hypothesis: rho != 0car::vif(modelo_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 2.23640534288 1.64638624021 2.38073539375 1.49470333599 2.06481921511
## VOCIO VEAD VPOL IQCTA IQCDO
## 1.08914516456 1.47051824402 1.02062580802 1.03212416005 1.20539063001
## VFINE VBOLS VEVAD
## 1.07638922610 1.04154016357 1.06863327228plot(modelo_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo_RLM$residuals
## W = 0.6963659422, p-value < 2.220446e-16#modelo 2 - somente variáveis mais significativas - PCA
modelo2_RLM = lm(VDPEQ ~ VRAD+VRAF+VCFPA+VCTTA+VTMA+VEAD+VOCIO+VPOL+VEVAD, data = ds_RLM[,2:15])
summary(modelo2_RLM)##
## Call:
## lm(formula = VDPEQ ~ VRAD + VRAF + VCFPA + VCTTA + VTMA + VEAD +
## VOCIO + VPOL + VEVAD, data = ds_RLM[, 2:15])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.396144001 -0.269511873 -0.092700380 0.131874945 7.491614520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.99123151e-01 5.41564641e-02 5.52331 4.0413e-08 ***
## VRAD 4.20430880e-04 1.26572899e-03 0.33217 0.7398202
## VRAF 1.03157914e-03 6.90641249e-04 1.49365 0.1355178
## VCFPA -8.33985101e-05 4.53455537e-06 -18.39177 < 2.22e-16 ***
## VCTTA -7.30205440e-06 1.14903420e-06 -6.35495 2.9125e-10 ***
## VTMA 5.29191713e-05 2.33650679e-06 22.64884 < 2.22e-16 ***
## VEAD -9.09941800e-02 1.13433977e-01 -0.80218 0.4226024
## VOCIO -2.51020420e-01 7.65960152e-02 -3.27720 0.0010772 **
## VPOL 7.40501912e-03 5.51226482e-02 0.13434 0.8931576
## VEVAD -1.10782663e-01 1.17729624e-01 -0.94099 0.3468902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.57390264 on 1253 degrees of freedom
## Multiple R-squared: 0.353268474, Adjusted R-squared: 0.348623156
## F-statistic: 76.0482828 on 9 and 1253 DF, p-value: < 2.220446e-16anova(modelo_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 8.9643817 8.96438172 27.19299 2.1532e-07 ***
## VRAF 1 3.3016569 3.30165693 10.01541 0.0015898 **
## VCFPA 1 30.1551532 30.15515323 91.47411 < 2.22e-16 ***
## VCTTA 1 2.8623807 2.86238070 8.68288 0.0032717 **
## VTMA 1 176.1453788 176.14537877 534.32797 < 2.22e-16 ***
## VOCIO 1 3.4351121 3.43511206 10.42024 0.0012788 **
## VEAD 1 0.2696725 0.26967249 0.81804 0.3659290
## VPOL 1 0.0028872 0.00288719 0.00876 0.9254538
## IQCTA 1 0.9096139 0.90961385 2.75927 0.0969435 .
## IQCDO 1 0.0048257 0.00482574 0.01464 0.9037182
## VFINE 1 0.0503857 0.05038570 0.15284 0.6959004
## VBOLS 1 0.0041757 0.00417569 0.01267 0.9104081
## VEVAD 1 0.2734489 0.27344889 0.82949 0.3625949
## Residuals 1249 411.7425843 0.32965779
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tbl_regression(modelo2_RLM, pvalue_fun = ~ style_pvalue(.x , digits = 2)) %>%
modify_header(update =list(label ~ "**Variáveis**" ,
p.value ~ "**Valor-p**")) %>% bold_labels() %>% #grifa a variavel
bold_p(t = 0.05) %>% #grifa os p valores menores que t
modify_header(update =list(label ~ "**Variáveis**")) | Variáveis | Beta | 95% CI1 | Valor-p |
|---|---|---|---|
| VRAD | 0.00 | 0.00, 0.00 | 0.74 |
| VRAF | 0.00 | 0.00, 0.00 | 0.14 |
| VCFPA | 0.00 | 0.00, 0.00 | <0.001 |
| VCTTA | 0.00 | 0.00, 0.00 | <0.001 |
| VTMA | 0.00 | 0.00, 0.00 | <0.001 |
| VEAD | -0.09 | -0.31, 0.13 | 0.42 |
| VOCIO | -0.25 | -0.40, -0.10 | 0.001 |
| VPOL | 0.01 | -0.10, 0.12 | 0.89 |
| VEVAD | -0.11 | -0.34, 0.12 | 0.35 |
| 1 CI = Confidence Interval | |||
kable(broom::tidy(modelo2_RLM, conf.int = T))| term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|
| (Intercept) | 0.299123151149 | 0.054156464118 | 5.523313901997 | 0.000000040413 | 0.192875801537 | 0.405370500760 |
| VRAD | 0.000420430880 | 0.001265728986 | 0.332165009350 | 0.739820219575 | -0.002062750993 | 0.002903612754 |
| VRAF | 0.001031579135 | 0.000690641249 | 1.493654102570 | 0.135517812627 | -0.000323361654 | 0.002386519925 |
| VCFPA | -0.000083398510 | 0.000004534555 | -18.391772363308 | 0.000000000000 | -0.000092294669 | -0.000074502352 |
| VCTTA | -0.000007302054 | 0.000001149034 | -6.354949588430 | 0.000000000291 | -0.000009556298 | -0.000005047811 |
| VTMA | 0.000052919171 | 0.000002336507 | 22.648841225545 | 0.000000000000 | 0.000048335274 | 0.000057503068 |
| VEAD | -0.090994180029 | 0.113433977239 | -0.802177462553 | 0.422602422558 | -0.313535655615 | 0.131547295558 |
| VOCIO | -0.251020419721 | 0.076596015168 | -3.277199462256 | 0.001077159034 | -0.401291005788 | -0.100749833653 |
| VPOL | 0.007405019117 | 0.055122648163 | 0.134337143871 | 0.893157556832 | -0.100737847413 | 0.115547885647 |
| VEVAD | -0.110782663260 | 0.117729623633 | -0.940992248526 | 0.346890205197 | -0.341751591630 | 0.120186265109 |
kable(broom::glance(modelo2_RLM, conf.int = T))| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.353268473945 | 0.348623155721 | 0.573902640172 | 76.0482827916 | 0 | 9 | -1085.76123467 | 2193.52246935 | 2250.07616569 | 412.693393217 | 1253 | 1263 |
plot(modelo2_RLM,1) #O ideal é que os pontos estejam distribuídos em torno de uma média 0.
lmtest::gqtest(modelo2_RLM, order.by = ~., data = ds_RLM[,3:12], fraction = 7)##
## Goldfeld-Quandt test
##
## data: modelo2_RLM
## GQ = 1.281162554, df1 = 618, df2 = 618, p-value = 0.00105414188
## alternative hypothesis: variance increases from segment 1 to 2lmtest::bptest(modelo2_RLM)##
## studentized Breusch-Pagan test
##
## data: modelo2_RLM
## BP = 36.23968639, df = 9, p-value = 3.59456103e-05plot(modelo2_RLM$residuals) #espera-se que os pontos estejam distribuídos de forma aleatória.
car::durbinWatsonTest(modelo2_RLM)## lag Autocorrelation D-W Statistic p-value
## 1 -0.00633292414126 2.01241370969 0.868
## Alternative hypothesis: rho != 0car::vif(modelo2_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 2.15246669875 1.64092920262 2.27132767602 1.43376641038 2.00441977492
## VEAD VOCIO VPOL VEVAD
## 1.44774664513 1.07272027563 1.01396241909 1.05319146485plot(modelo2_RLM, 2) #O ideal é que os pontos estejam próximos da linha tracejada.
shapiro.test(modelo2_RLM$residuals)##
## Shapiro-Wilk normality test
##
## data: modelo2_RLM$residuals
## W = 0.6965761234, p-value < 2.220446e-16faraway::vif(modelo2_RLM)## VRAD VRAF VCFPA VCTTA VTMA
## 2.15246669875 1.64092920262 2.27132767602 1.43376641038 2.00441977492
## VEAD VOCIO VPOL VEVAD
## 1.44774664513 1.07272027563 1.01396241909 1.05319146485anova(modelo2_RLM)## Analysis of Variance Table
##
## Response: VDPEQ
## Df Sum Sq Mean Sq F value Pr(>F)
## VRAD 1 8.9643817 8.96438172 27.21723 2.1259e-07 ***
## VRAF 1 3.3016569 3.30165693 10.02433 0.0015821 **
## VCFPA 1 30.1551532 30.15515323 91.55564 < 2.22e-16 ***
## VCTTA 1 2.8623807 2.86238070 8.69062 0.0032578 **
## VTMA 1 176.1453788 176.14537877 534.80420 < 2.22e-16 ***
## VEAD 1 0.2288010 0.22880095 0.69467 0.4047378
## VOCIO 1 3.4759836 3.47598359 10.55362 0.0011904 **
## VPOL 1 0.0028872 0.00288719 0.00877 0.9254206
## VEVAD 1 0.2916410 0.29164097 0.88547 0.3468902
## Residuals 1253 412.6933932 0.32936424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#algoritmos de seleção de variáveis
ts2 = ds_PEQ_2020_noOutliers[,-c(1)]
modelo = lm(data = ts2, VDPEQ ~ .)
summary(modelo)##
## Call:
## lm(formula = VDPEQ ~ ., data = ts2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.386348297 -0.268567105 -0.095168757 0.132905446 7.498457351
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.87352940e-01 1.34932567e-01 1.38849 0.1652345
## VRAD 5.39963031e-04 1.29074728e-03 0.41833 0.6757751
## VRAF 1.05771056e-03 6.92096904e-04 1.52827 0.1266988
## VCFPA -8.36968284e-05 4.64455199e-06 -18.02043 < 2.22e-16 ***
## VCTTA -7.38146172e-06 1.17372055e-06 -6.28894 4.4109e-10 ***
## VTMA 5.30875087e-05 2.37250521e-06 22.37614 < 2.22e-16 ***
## VOCIO -2.52477911e-01 7.72145717e-02 -3.26982 0.0011055 **
## VEAD -9.85863823e-02 1.14373533e-01 -0.86197 0.3888703
## VPOL 1.09395457e-02 5.53281142e-02 0.19772 0.8432953
## IQCTA 7.30479876e-02 4.37103875e-02 1.67118 0.0949364 .
## IQCDO 3.77294025e-03 3.79230302e-02 0.09949 0.9207657
## VFINE 1.70718474e-02 5.44218995e-02 0.31369 0.7538055
## VBOLS -1.88938502e-02 9.88453056e-02 -0.19115 0.8484425
## VEVAD -1.08055355e-01 1.18642390e-01 -0.91077 0.3625949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.574158335 on 1249 degrees of freedom
## Multiple R-squared: 0.354758486, Adjusted R-squared: 0.348042601
## F-statistic: 52.8237924 on 13 and 1249 DF, p-value: < 2.220446e-16stats::step(modelo, direction = "both", trace = F)##
## Call:
## lm(formula = VDPEQ ~ VRAF + VCFPA + VCTTA + VTMA + VOCIO + IQCTA,
## data = ts2)
##
## Coefficients:
## (Intercept) VRAF VCFPA VCTTA
## 1.77130976e-01 1.12272900e-03 -8.43232845e-05 -7.47001388e-06
## VTMA VOCIO IQCTA
## 5.37660174e-05 -2.46908865e-01 6.94542448e-02#modelo 2 recomendado
par(mfrow = c(2,2))
plot(modelo2_RLM, which = c(1:4), pch = 20)
Processo de análise XAI - IA Explicada
# analise XAI
# Feature Importance
#carrega arquivo
ds_xai = ds_PEQ_2020_noOutliers[2:15]
head(ds_xai)## # A tibble: 6 × 14
## VDPEQ VRAD VRAF VCFPA VCTTA VTMA VOCIO VEAD VPOL IQCTA IQCDO VFINE
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.113 17.3 19.1 20402. 26025. 28966. 0.365 0.198 0.163 1.16 3.98 0.284
## 2 -0.258 19.4 23.5 16481. 21818. 16180. 0.330 0.00636 0 1.23 3.93 0.534
## 3 -0.0133 20.7 18.5 10145. 18166. 17925. 0.228 0.147 0.302 1.10 3.69 0.172
## 4 0.0748 19.1 19.9 11449. 19202. 20640. 0.191 0.254 0.113 1.12 4.18 0.503
## 5 0.0609 13.1 17.1 11798. 29957. 31781. 0.113 0.309 0 1.30 3.34 0.325
## 6 0.135 17.6 18.4 14887. 20257. 22993. 0.421 0 0.238 0.982 3.98 0.268
## # … with 2 more variables: VBOLS <dbl>, VEVAD <dbl>
## # ℹ Use `colnames()` to see all variable names#criamos modelo randomForest
modelo_rf = randomForest(VDPEQ ~ ., data = ds_xai, ntree = 500, prox = T)
summary(modelo_rf)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 1263 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 1263 -none- numeric
## importance 13 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 1595169 -none- numeric
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 1263 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms call#previsor, modelo, dados, e previsor
previsor = Predictor$new(modelo_rf, ds_xai[,-1] , y = ds_xai$VDPEQ )
#feature importance
importancia = FeatureImp$new(previsor, loss = "mae")
#dataframe
importancia$results## feature importance.05 importance importance.95 permutation.error
## 1 VTMA 6.88154713615 7.15803043905 7.26579975980 0.6166936847076
## 2 VCTTA 5.86312271819 5.93432273669 6.04678761081 0.5112662464761
## 3 VCFPA 2.47756574218 2.52848995128 2.58411075708 0.2178397812190
## 4 VRAD 1.40714695389 1.43616539741 1.45691751081 0.1237315401664
## 5 VEVAD 1.40343122080 1.41506704573 1.42646062254 0.1219138306233
## 6 VRAF 1.33166746799 1.33760780519 1.37490858095 0.1152403993111
## 7 IQCDO 1.27458116032 1.28114685443 1.29205520241 0.1103760567994
## 8 VFINE 1.23721281646 1.25081200409 1.25447985756 0.1077625850085
## 9 VOCIO 1.19198516226 1.20100715063 1.21325082925 0.1034716925819
## 10 IQCTA 1.18388603593 1.19926243075 1.20581218842 0.1033213778072
## 11 VPOL 1.07377286239 1.07639641246 1.08083452145 0.0927359663334
## 12 VEAD 1.05804540382 1.06933070574 1.07457293994 0.0921272267157
## 13 VBOLS 1.04703392098 1.04776273325 1.05099963027 0.0902690574131plot(importancia)
#Feature Interaction
interacao = Interaction$new(previsor)
plot(interacao)
interacao$results## .feature .interaction
## 1: VRAD 0.0891128888792
## 2: VRAF 0.0636715802806
## 3: VCFPA 0.2786890087295
## 4: VCTTA 0.3558194457475
## 5: VTMA 0.4104880808778
## 6: VOCIO 0.0443071687761
## 7: VEAD 0.0245503408334
## 8: VPOL 0.0234779043606
## 9: IQCTA 0.0404747137395
## 10: IQCDO 0.0607081584084
## 11: VFINE 0.0577437811111
## 12: VBOLS 0.0266438546326
## 13: VEVAD 0.0631544921125#cria modelo substituto
arvore = TreeSurrogate$new(previsor, maxdepth = 3)
#plot a árvore
plot(arvore$tree)
7 Estudo de Porte das Mantenedoras
Análise da quantidade de mantenedoras pelo porte das instituições
7.1 Montagem do Dataset
#carregando as informações das matenedoras
mant_2017_agrupado = readRDS("./data/mant_2017_agrupado_data.rds")
mant_2018_agrupado = readRDS("./data/mant_2018_agrupado_data.rds")
mant_2019_agrupado = readRDS("./data/mant_2019_agrupado_data.rds")
mant_2020_agrupado = readRDS("./data/mant_2020_agrupado_data.rds")
#2017
m2017_alunos = mant_2017_agrupado %>% dplyr::select(2,3,32) %>%
mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2018#porte por semesp
m2018_alunos = mant_2018_agrupado %>% dplyr::select(2,3,32) %>%
mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2019
m2019_alunos = mant_2019_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2020
m2020_alunos = mant_2020_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#junção dos dados
temp2017 = m2017_alunos %>% group_by (Porte_S) %>% dplyr::summarise("2017" = n())
temp2018 = m2018_alunos %>% group_by (Porte_S) %>% dplyr::summarise("2018" = n())
temp2019 = m2019_alunos %>% group_by (Porte_S) %>% dplyr::summarise("2019" = n())
temp2020 = m2020_alunos %>% group_by (Porte_S) %>% dplyr::summarise("2020" = n())
Mant_Porte = temp2017 %>% left_join(temp2018) %>% left_join(temp2019)## Joining, by = "Porte_S"
## Joining, by = "Porte_S"Mant_Porte = gather(Mant_Porte, key = "Ano", value = "Qtd", "2017", "2018", "2019")
Mant_Porte## # A tibble: 12 × 3
## Porte_S Ano Qtd
## <chr> <chr> <int>
## 1 G 2017 99
## 2 GG 2017 51
## 3 M 2017 165
## 4 P 2017 1177
## 5 G 2018 94
## 6 GG 2018 48
## 7 M 2018 176
## 8 P 2018 1240
## 9 G 2019 92
## 10 GG 2019 48
## 11 M 2019 174
## 12 P 2019 12487.2 Visualização dos dados
ggplot(data=Mant_Porte, aes(x=Porte_S, y=Qtd, fill=Ano)) +
geom_bar(stat="identity", position = position_dodge())+
geom_text(aes(label=Qtd), vjust=0.5, hjust = -0.1, color="black",
position = position_dodge(0.9), size=3.5)+
scale_x_discrete(limits = c("P", "M", "G", "GG"), labels = c("P (<3)", "M (3<->7)", "G (7<->20)", "GG (>20)"))+
scale_y_continuous(breaks = NULL)+
labs(title = "Mantenedoras por Porte", x = "Porte(em mil matrículas)", y = "")+
scale_fill_brewer(palette="Blues")+
theme(legend.position="left")+
coord_flip()+
theme_minimal()
rm(m2017_alunos)
rm(m2018_alunos)
rm(m2019_alunos)
rm(m2020_alunos)
rm(mant_2017_agrupado)
rm(mant_2018_agrupado)
rm(mant_2019_agrupado)
rm(mant_2020_agrupado)
rm (Mant_Porte)
rm (temp2017)
rm (temp2018)
rm (temp2019)
rm (temp2020)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3780152 201.9 10144577 541.8 18747399 1001.3
## Vcells 19941818 152.2 132290830 1009.3 1726045989 13168.7Análise da quantidade de matrículas pelo porte das instituições
7.2.1 Montagem do Dataset
#carregando as informações das matenedoras
mant_2017_agrupado = readRDS("./data/mant_2017_agrupado_data.rds")
mant_2018_agrupado = readRDS("./data/mant_2018_agrupado_data.rds")
mant_2019_agrupado = readRDS("./data/mant_2019_agrupado_data.rds")
mant_2020_agrupado = readRDS("./data/mant_2020_agrupado_data.rds")
#2017
m2017_alunos = mant_2017_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2018
m2018_alunos = mant_2018_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2019
m2019_alunos = mant_2019_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#2020
m2020_alunos = mant_2020_agrupado %>% dplyr::select(2,3,32) %>% mutate(Porte_S = case_when(QTD_ALUNO_ATIVO<=3000 ~ "P",
QTD_ALUNO_ATIVO>3000 & QTD_ALUNO_ATIVO<=7000 ~ "M",
QTD_ALUNO_ATIVO>7000 & QTD_ALUNO_ATIVO<=20000 ~ "G",
QTD_ALUNO_ATIVO>20000 ~ "GG"))## Adding missing grouping variables: `NU_ANO_CENSO`#junção dos dados
temp2017 = m2017_alunos %>% group_by (Porte_S) %>% dplyr::summarise(c2017 = sum(QTD_ALUNO_ATIVO)) %>% mutate("2017" = c2017/sum(c2017)*100)
temp2018 = m2018_alunos %>% group_by (Porte_S) %>% dplyr::summarise(c2018 = sum(QTD_ALUNO_ATIVO)) %>% mutate("2018" = c2018/sum(c2018)*100)
temp2019 = m2019_alunos %>% group_by (Porte_S) %>% dplyr::summarise(c2019 = sum(QTD_ALUNO_ATIVO)) %>% mutate("2019" = c2019/sum(c2019)*100)
temp2020 = m2020_alunos %>% group_by (Porte_S) %>% dplyr::summarise(c2020 = sum(QTD_ALUNO_ATIVO)) %>% mutate("2020" = c2020/sum(c2020)*100)
temp2017 = dplyr::select(temp2017, c(1,3))
temp2018 = dplyr::select(temp2018, c(1,3))
temp2019 = dplyr::select(temp2019, c(1,3))
temp2020 = dplyr::select(temp2020, c(1,3))
Matr_Porte = temp2017 %>% left_join(temp2018) %>% left_join(temp2019) %>% left_join(temp2020)## Joining, by = "Porte_S"
## Joining, by = "Porte_S"
## Joining, by = "Porte_S"Matr_Porte = gather(Matr_Porte, key = "Ano", value = "Perc","2017", "2018", "2019", "2020")
Matr_Porte$Porte_S = factor(as.character(Matr_Porte$Porte_S), levels = c("P", "M", "G", "GG"))7.2.2 Visualização dos dados
#barras empilhadas por %
ggplot(data=Matr_Porte, aes(x=reorder(Ano, desc(Ano)), y=Perc, fill=Porte_S, label = round(Perc, 1))) +
geom_col()+
#scale_fill_manual(values = c("GG", "G", "M", "P"))+
scale_fill_brewer(palette = 3, type = "qual", guide = guide_legend(reverse = TRUE))+
geom_text(aes(label = paste0(round(Perc,1), "%")), colour = "black", position = position_stack(vjust = 0.5)) +
coord_flip()+
theme_minimal()+
theme(legend.position="top",
legend.title = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank())
rm(m2017_alunos)
rm(m2018_alunos)
rm(m2019_alunos)
rm(m2020_alunos)
rm(mant_2017_agrupado)
rm(mant_2018_agrupado)
rm(mant_2019_agrupado)
rm(mant_2020_agrupado)
rm (Matr_Porte)
rm (temp2017)
rm (temp2018)
rm (temp2019)
rm (temp2020)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 3755947 200.6 10144577 541.8 18747399 1001.3
## Vcells 19657509 150.0 105832664 807.5 1726045989 13168.7