In BrunoDaleffi/CrawlerCarf: A web scraper for the CARF system
Essa pesquisa faz o levantamento de todos os conselheiros que aparecerem como
relatores em processos do CARF baixados usando a ferramenta de pesquisa de
jurisprudência.
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(magrittr)
library(ggplot2)
Relatores nos processos
files <- carf::download_decision_year(2014, dir, min_page = 1, max_page = 50)
parsed <- carf::parse_decision(files)
decisions <- parsed$decicions
decisions <- readRDS("example-data/decisions_2014.rds")
relatores_raw <- decisions %>%
dplyr::mutate(
rapporteur = abjutils::rm_accent(rapporteur),
rapporteur = stringr::str_to_upper(rapporteur)) %>%
dplyr::count(rapporteur)
relatores <- decisions %>%
dplyr::mutate(
rapporteur = rapporteur %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace("rapporteurA? ", "") %>%
stringr::str_replace("- ?(REDATORA? )?(DESIGNADO?A? )?AD?S? HOC", "") %>%
stringr::str_replace(" REDATOR DESIGNADO|DESIGNADA?O?$", "") %>%
stringr::str_replace(" ?-? ?rapporteurA?F?(KJH)?$", "") %>%
stringr::str_replace(", rapporteur\\.$", "") %>%
stringr::str_replace("^CONSELHEIRO?A? ", "") %>%
stringr::str_trim() %>%
stringr::str_replace("NAO SE APLICA|rapporteur|^$|^MAR$", "VAZIO/NAO SE APLICA")) %>%
dplyr::count(rapporteur, sort = TRUE)
knitr::kable(head(relatores, 20))
Conselheiros do CARF
conselheiros_carf <- carf::rapporteur_data %>%
dplyr::filter(rapporteur != "VAGO") %>%
dplyr::mutate(
rapporteur = ifelse(rapporteur == "MERCIA HELENA TRAJANO D AMORIM", "MERCIA HELENA TRAJANO DAMORIM", rapporteur),
rapporteur = ifelse(rapporteur == "KLEBER FERREIRA ARAUJO", "KLEBER FERREIRA DE ARAUJO", rapporteur),
rapporteur = ifelse(rapporteur == "ANDRE LUIS MARISCO LOMBARDI", "ANDRE LUIS MARSICO LOMBARDI", rapporteur))
pander::pander(head(conselheiros_carf, 20))
Join das duas bases (faltantes)
nomes_faltantes <- relatores %>%
dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>%
dplyr::anti_join(conselheiros_carf, "rapporteur") %>%
dplyr::arrange(dplyr::desc(n))
knitr::kable(head(nomes_faltantes, 20))
Incluindo nomes faltantes
aux <- relatores %>%
dplyr::select(-n) %>%
dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>%
dplyr::inner_join(carf::rapporteur_goliva, "rapporteur") %>%
dplyr::select(-n)
relatores_final <- relatores %>%
dplyr::select(-n) %>%
dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>%
dplyr::inner_join(conselheiros_carf, "rapporteur") %>%
dplyr::bind_rows(aux) %>%
dplyr::mutate(type = ifelse(type == "TRABALHADORES", "CONTRIBUINTES", type)) %>%
dplyr::filter(!section %in% c("CSRF", "CSFR")) %>%
dplyr::group_by(rapporteur) %>%
dplyr::mutate(n = length(title)) %>%
dplyr::ungroup() %>%
dplyr::mutate(
chamber = ifelse(n>1, NA, chamber),
section = ifelse(n>1, NA, section)) %>%
dplyr::select(-n) %>%
dplyr::distinct(rapporteur, .keep_all = TRUE)
d_relatores <- decisions %>%
dplyr::mutate(
rapporteur = rapporteur %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace("rapporteurA? ", "") %>%
stringr::str_replace("- ?(REDATORA? )?(DESIGNADO?A? )?AD?S? HOC", "") %>%
stringr::str_replace(" REDATOR DESIGNADO|DESIGNADA?O?$", "") %>%
stringr::str_replace(" ?-? ?rapporteurA?F?(KJH)?$", "") %>%
stringr::str_replace(", rapporteur\\.$", "") %>%
stringr::str_replace("^CONSELHEIRO?A? ", "") %>%
stringr::str_trim() %>%
stringr::str_replace("NAO SE APLICA|rapporteur|^$|^MAR$", "VAZIO/NAO SE APLICA")) %>%
dplyr::filter(rapporteur != "VAZIO/NAO SE APLICA") %>%
dplyr::inner_join(relatores_final, "rapporteur")
knitr::kable(head(d_relatores, 20))
Análise dos resultados
Resultados dos processos
negar_provimento <- "negar?(do)? (o )?provimento|negou se (o )?provimento|recurso nao provido"
dar_provimento <- "dar?(do)? (o )?provimento|deu se (o )?provimento|recurso provido"
em_parte <- "em parte|parcial"
diligencia <- "diligencia"
nao_conhecer <- "conhec"
anular <- "nul(a|o|i)"
d_decisoes <- d_relatores %>%
dplyr::distinct(id_lawsuit, .keep_all = TRUE) %>%
dplyr::filter(type_appeal %in% c("RECURSO VOLUNTARIO", "RECURSO DE OFÍCIO")) %>%
dplyr::mutate(
decision2 = decision %>%
stringr::str_to_lower() %>%
stringr::str_replace_all("[[:punct:]]|\n\r|\r\n", " ") %>%
stringr::str_replace_all("\n|\r", " ") %>%
stringr::str_replace_all(" +", " ") %>%
abjutils::rm_accent(),
negar_provimento = stringr::str_detect(decision2, negar_provimento),
dar_provimento = stringr::str_detect(decision2, dar_provimento),
em_parte = stringr::str_detect(decision2, em_parte),
diligencia = stringr::str_detect(decision2, diligencia),
nao_conhecer = stringr::str_detect(decision2, nao_conhecer),
anular = stringr::str_detect(decision2, anular)) %>%
dplyr::mutate(
sum = negar_provimento + (dar_provimento|em_parte) + diligencia + nao_conhecer + anular) %>%
dplyr::mutate(
result = ifelse(em_parte, "Parcialmente favorável ao contribuinte",
ifelse(dar_provimento & type_appeal=="RECURSO VOLUNTARIO", "Favorável ao contribuinte",
ifelse(dar_provimento & type_appeal=="RECURSO DE OFÍCIO", "Desfavorável ao contribuinte",
ifelse(negar_provimento & type_appeal=="RECURSO VOLUNTARIO", "Desfavorável ao contribuinte",
ifelse(negar_provimento & type_appeal=="RECURSO DE OFÍCIO", "Favorável ao contribuinte",
ifelse(diligencia|nao_conhecer|anular, "Não conhecido, anulado ou convertido em diligência",
"Outro / vazio"))))))) %>%
dplyr::mutate(
result = ifelse(dar_provimento & negar_provimento, "Parcialmente favorável ao contribuinte", result))
Proporçao de decisoes favoráveis por tipo de relator
d_decisoes %>%
dplyr::count(type_appeal) %>%
knitr::kable()
d_decisoes %>%
dplyr::count(type_appeal, result) %>%
dplyr::mutate(perc = scales::percent(n / sum(n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(type_appeal = ifelse(is.na(type_appeal), "VAZIO", type_appeal)) %>%
dplyr::mutate(n_perc = sprintf("%04d (%5s)", n, perc)) %>%
dplyr::select(-n, -perc) %>%
tidyr::spread(type_appeal, n_perc) %>%
dplyr::mutate_all(stringr::str_replace_all, "^0", " ") %>%
dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>%
dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>%
dplyr::mutate_all(dplyr::funs(ifelse(is.na(.), " 0 ( 0.0%)", .))) %>%
knitr::kable()
Mesma informacao no grafico
d_decisoes %>%
dplyr::count(type_appeal, result) %>%
dplyr::mutate(perc = scales::percent(n / sum(n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(type_appeal = ifelse(is.na(type_appeal), "VAZIO", type_appeal)) %>%
dplyr::filter(type_appeal != "NÃO IDENTIFICADO") %>%
ggplot(aes(x = result, y = perc, fill = type_appeal)) +
geom_bar(stat = "identity", position = "dodge") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14))
Relatores com maior proporçao de processos favoráveis ao contribuinte
d_decisoes %>%
dplyr::group_by(rapporteur) %>%
dplyr::mutate(n_rapporteur = length(taxpayer)) %>%
dplyr::ungroup() %>%
dplyr::filter(n_rapporteur > 5) %>%
dplyr::select(-n_rapporteur) %>%
dplyr::count(rapporteur, type_appeal, result) %>%
dplyr::mutate(
perc_num = n / sum(n),
perc = scales::percent(perc_num)) %>%
dplyr::ungroup() %>%
dplyr::arrange(dplyr::desc(perc)) %>%
dplyr::select(-perc_num) %>%
dplyr::mutate(n_perc = sprintf("%04d (%5s)", n, perc)) %>%
dplyr::select(-n, -perc) %>%
tidyr::spread(result, n_perc) %>%
purrr::set_names(
c("rapporteur", "type_appeal", "unfavorable", "favorable", "unknown", "other", "partially")) %>%
dplyr::mutate_all(dplyr::funs(ifelse(is.na(.), "0000 ( 0.0%)", .))) %>%
tidyr::separate(favorable, c("lixo", "num"), sep = " \\(", remove = FALSE) %>%
dplyr::select(-lixo) %>%
dplyr::mutate(num = as.numeric(stringr::str_replace_all("%|\\)", "", num))) %>%
dplyr::arrange(dplyr::desc(num)) %>%
dplyr::select(-num) %>%
dplyr::mutate_all(stringr::str_replace_all, "^0", " ") %>%
dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>%
dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>%
pander::pander()
Resultados por seção, camara e turma
Primeira seção
rs <- d_decisoes %>% dplyr::count(result) %>% dplyr::pull(result) %>% sort()
rs_ord <- c(rs[1], rs[5], rs[2], rs[3], rs[4])
aux <- d_decisoes %>%
dplyr::mutate(
section = section %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all("\\?", "a"),
camara = chamber %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all(" - ", "\n") %>%
stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"),
turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>%
dplyr::mutate(
turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma),
camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>%
dplyr::filter(
title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section),
section != "NAO IDENTIFICADO", camara %in% c("1a CAMARA")) %>%
dplyr::count(section, camara, turma, type_appeal, result) %>%
dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>%
tidyr::unite(chamber, section, camara, turma, sep = "\n")
aux %>%
ggplot(aes(x = type_appeal, y = perc, fill = result)) +
geom_bar(stat = "identity", position = "stack", alpha = .9) +
geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) +
theme_bw() +
facet_wrap(~ chamber) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) +
scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
Segunda seção
aux <- d_decisoes %>%
dplyr::mutate(
section = section %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all("\\?", "a"),
camara = chamber %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all(" - ", "\n") %>%
stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"),
turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>%
dplyr::mutate(
turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma),
camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>%
dplyr::filter(
title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section),
section != "NAO IDENTIFICADO", camara %in% c("2a CAMARA"),
chamber != "SEGUNDA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)") %>%
dplyr::count(section, camara, turma, type_appeal, result) %>%
dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>%
tidyr::unite(chamber, section, camara, turma, sep = "\n")
aux %>%
ggplot(aes(x = type_appeal, y = perc, fill = result)) +
geom_bar(stat = "identity", position = "stack", alpha = .9) +
geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) +
theme_bw() +
facet_wrap(~ chamber) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) +
scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
Terceira seção
aux <- d_decisoes %>%
dplyr::mutate(
section = section %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all("\\?", "a"),
camara = chamber %>%
abjutils::rm_accent() %>%
stringr::str_to_upper() %>%
stringr::str_replace_all(" - ", "\n") %>%
stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"),
turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>%
dplyr::mutate(
turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma),
turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma),
camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara),
camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>%
dplyr::filter(
title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section),
section != "NAO IDENTIFICADO", camara %in% c("3a CAMARA"),
!chamber %in% c("TERCEITA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)",
"SEGUNDA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)",
"PRIMEIRA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)")) %>%
dplyr::count(section, camara, turma, type_appeal, result) %>%
dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>%
dplyr::ungroup() %>%
dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>%
tidyr::unite(chamber, section, camara, turma, sep = "\n")
aux %>%
ggplot(aes(x = type_appeal, y = perc, fill = result)) +
geom_bar(stat = "identity", position = "stack", alpha = .9) +
geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) +
theme_bw() +
facet_wrap(~ chamber) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) +
scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
BrunoDaleffi/CrawlerCarf documentation built on May 7, 2019, 4:36 p.m.
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Essa pesquisa faz o levantamento de todos os conselheiros que aparecerem como relatores em processos do CARF baixados usando a ferramenta de pesquisa de jurisprudência.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(magrittr) library(ggplot2)
Relatores nos processos
files <- carf::download_decision_year(2014, dir, min_page = 1, max_page = 50) parsed <- carf::parse_decision(files) decisions <- parsed$decicions
decisions <- readRDS("example-data/decisions_2014.rds")
relatores_raw <- decisions %>% dplyr::mutate( rapporteur = abjutils::rm_accent(rapporteur), rapporteur = stringr::str_to_upper(rapporteur)) %>% dplyr::count(rapporteur) relatores <- decisions %>% dplyr::mutate( rapporteur = rapporteur %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace("rapporteurA? ", "") %>% stringr::str_replace("- ?(REDATORA? )?(DESIGNADO?A? )?AD?S? HOC", "") %>% stringr::str_replace(" REDATOR DESIGNADO|DESIGNADA?O?$", "") %>% stringr::str_replace(" ?-? ?rapporteurA?F?(KJH)?$", "") %>% stringr::str_replace(", rapporteur\\.$", "") %>% stringr::str_replace("^CONSELHEIRO?A? ", "") %>% stringr::str_trim() %>% stringr::str_replace("NAO SE APLICA|rapporteur|^$|^MAR$", "VAZIO/NAO SE APLICA")) %>% dplyr::count(rapporteur, sort = TRUE) knitr::kable(head(relatores, 20))
Conselheiros do CARF
conselheiros_carf <- carf::rapporteur_data %>% dplyr::filter(rapporteur != "VAGO") %>% dplyr::mutate( rapporteur = ifelse(rapporteur == "MERCIA HELENA TRAJANO D AMORIM", "MERCIA HELENA TRAJANO DAMORIM", rapporteur), rapporteur = ifelse(rapporteur == "KLEBER FERREIRA ARAUJO", "KLEBER FERREIRA DE ARAUJO", rapporteur), rapporteur = ifelse(rapporteur == "ANDRE LUIS MARISCO LOMBARDI", "ANDRE LUIS MARSICO LOMBARDI", rapporteur)) pander::pander(head(conselheiros_carf, 20))
Join das duas bases (faltantes)
nomes_faltantes <- relatores %>% dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>% dplyr::anti_join(conselheiros_carf, "rapporteur") %>% dplyr::arrange(dplyr::desc(n)) knitr::kable(head(nomes_faltantes, 20))
Incluindo nomes faltantes
aux <- relatores %>% dplyr::select(-n) %>% dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>% dplyr::inner_join(carf::rapporteur_goliva, "rapporteur") %>% dplyr::select(-n) relatores_final <- relatores %>% dplyr::select(-n) %>% dplyr::filter(!stringr::str_detect(rapporteur, "VAZIO")) %>% dplyr::inner_join(conselheiros_carf, "rapporteur") %>% dplyr::bind_rows(aux) %>% dplyr::mutate(type = ifelse(type == "TRABALHADORES", "CONTRIBUINTES", type)) %>% dplyr::filter(!section %in% c("CSRF", "CSFR")) %>% dplyr::group_by(rapporteur) %>% dplyr::mutate(n = length(title)) %>% dplyr::ungroup() %>% dplyr::mutate( chamber = ifelse(n>1, NA, chamber), section = ifelse(n>1, NA, section)) %>% dplyr::select(-n) %>% dplyr::distinct(rapporteur, .keep_all = TRUE) d_relatores <- decisions %>% dplyr::mutate( rapporteur = rapporteur %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace("rapporteurA? ", "") %>% stringr::str_replace("- ?(REDATORA? )?(DESIGNADO?A? )?AD?S? HOC", "") %>% stringr::str_replace(" REDATOR DESIGNADO|DESIGNADA?O?$", "") %>% stringr::str_replace(" ?-? ?rapporteurA?F?(KJH)?$", "") %>% stringr::str_replace(", rapporteur\\.$", "") %>% stringr::str_replace("^CONSELHEIRO?A? ", "") %>% stringr::str_trim() %>% stringr::str_replace("NAO SE APLICA|rapporteur|^$|^MAR$", "VAZIO/NAO SE APLICA")) %>% dplyr::filter(rapporteur != "VAZIO/NAO SE APLICA") %>% dplyr::inner_join(relatores_final, "rapporteur") knitr::kable(head(d_relatores, 20))
Análise dos resultados
Resultados dos processos
negar_provimento <- "negar?(do)? (o )?provimento|negou se (o )?provimento|recurso nao provido" dar_provimento <- "dar?(do)? (o )?provimento|deu se (o )?provimento|recurso provido" em_parte <- "em parte|parcial" diligencia <- "diligencia" nao_conhecer <- "conhec" anular <- "nul(a|o|i)" d_decisoes <- d_relatores %>% dplyr::distinct(id_lawsuit, .keep_all = TRUE) %>% dplyr::filter(type_appeal %in% c("RECURSO VOLUNTARIO", "RECURSO DE OFÍCIO")) %>% dplyr::mutate( decision2 = decision %>% stringr::str_to_lower() %>% stringr::str_replace_all("[[:punct:]]|\n\r|\r\n", " ") %>% stringr::str_replace_all("\n|\r", " ") %>% stringr::str_replace_all(" +", " ") %>% abjutils::rm_accent(), negar_provimento = stringr::str_detect(decision2, negar_provimento), dar_provimento = stringr::str_detect(decision2, dar_provimento), em_parte = stringr::str_detect(decision2, em_parte), diligencia = stringr::str_detect(decision2, diligencia), nao_conhecer = stringr::str_detect(decision2, nao_conhecer), anular = stringr::str_detect(decision2, anular)) %>% dplyr::mutate( sum = negar_provimento + (dar_provimento|em_parte) + diligencia + nao_conhecer + anular) %>% dplyr::mutate( result = ifelse(em_parte, "Parcialmente favorável ao contribuinte", ifelse(dar_provimento & type_appeal=="RECURSO VOLUNTARIO", "Favorável ao contribuinte", ifelse(dar_provimento & type_appeal=="RECURSO DE OFÍCIO", "Desfavorável ao contribuinte", ifelse(negar_provimento & type_appeal=="RECURSO VOLUNTARIO", "Desfavorável ao contribuinte", ifelse(negar_provimento & type_appeal=="RECURSO DE OFÍCIO", "Favorável ao contribuinte", ifelse(diligencia|nao_conhecer|anular, "Não conhecido, anulado ou convertido em diligência", "Outro / vazio"))))))) %>% dplyr::mutate( result = ifelse(dar_provimento & negar_provimento, "Parcialmente favorável ao contribuinte", result))
Proporçao de decisoes favoráveis por tipo de relator
d_decisoes %>% dplyr::count(type_appeal) %>% knitr::kable()
d_decisoes %>% dplyr::count(type_appeal, result) %>% dplyr::mutate(perc = scales::percent(n / sum(n))) %>% dplyr::ungroup() %>% dplyr::mutate(type_appeal = ifelse(is.na(type_appeal), "VAZIO", type_appeal)) %>% dplyr::mutate(n_perc = sprintf("%04d (%5s)", n, perc)) %>% dplyr::select(-n, -perc) %>% tidyr::spread(type_appeal, n_perc) %>% dplyr::mutate_all(stringr::str_replace_all, "^0", " ") %>% dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>% dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>% dplyr::mutate_all(dplyr::funs(ifelse(is.na(.), " 0 ( 0.0%)", .))) %>% knitr::kable()
Mesma informacao no grafico
d_decisoes %>% dplyr::count(type_appeal, result) %>% dplyr::mutate(perc = scales::percent(n / sum(n))) %>% dplyr::ungroup() %>% dplyr::mutate(type_appeal = ifelse(is.na(type_appeal), "VAZIO", type_appeal)) %>% dplyr::filter(type_appeal != "NÃO IDENTIFICADO") %>% ggplot(aes(x = result, y = perc, fill = type_appeal)) + geom_bar(stat = "identity", position = "dodge") + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14))
Relatores com maior proporçao de processos favoráveis ao contribuinte
d_decisoes %>% dplyr::group_by(rapporteur) %>% dplyr::mutate(n_rapporteur = length(taxpayer)) %>% dplyr::ungroup() %>% dplyr::filter(n_rapporteur > 5) %>% dplyr::select(-n_rapporteur) %>% dplyr::count(rapporteur, type_appeal, result) %>% dplyr::mutate( perc_num = n / sum(n), perc = scales::percent(perc_num)) %>% dplyr::ungroup() %>% dplyr::arrange(dplyr::desc(perc)) %>% dplyr::select(-perc_num) %>% dplyr::mutate(n_perc = sprintf("%04d (%5s)", n, perc)) %>% dplyr::select(-n, -perc) %>% tidyr::spread(result, n_perc) %>% purrr::set_names( c("rapporteur", "type_appeal", "unfavorable", "favorable", "unknown", "other", "partially")) %>% dplyr::mutate_all(dplyr::funs(ifelse(is.na(.), "0000 ( 0.0%)", .))) %>% tidyr::separate(favorable, c("lixo", "num"), sep = " \\(", remove = FALSE) %>% dplyr::select(-lixo) %>% dplyr::mutate(num = as.numeric(stringr::str_replace_all("%|\\)", "", num))) %>% dplyr::arrange(dplyr::desc(num)) %>% dplyr::select(-num) %>% dplyr::mutate_all(stringr::str_replace_all, "^0", " ") %>% dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>% dplyr::mutate_all(stringr::str_replace_all, "^( 0)", " ") %>% pander::pander()
Resultados por seção, camara e turma
Primeira seção
rs <- d_decisoes %>% dplyr::count(result) %>% dplyr::pull(result) %>% sort() rs_ord <- c(rs[1], rs[5], rs[2], rs[3], rs[4])
aux <- d_decisoes %>% dplyr::mutate( section = section %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all("\\?", "a"), camara = chamber %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all(" - ", "\n") %>% stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"), turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>% dplyr::mutate( turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma), camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>% dplyr::filter( title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section), section != "NAO IDENTIFICADO", camara %in% c("1a CAMARA")) %>% dplyr::count(section, camara, turma, type_appeal, result) %>% dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>% dplyr::ungroup() %>% dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>% tidyr::unite(chamber, section, camara, turma, sep = "\n") aux %>% ggplot(aes(x = type_appeal, y = perc, fill = result)) + geom_bar(stat = "identity", position = "stack", alpha = .9) + geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) + theme_bw() + facet_wrap(~ chamber) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) + scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
Segunda seção
aux <- d_decisoes %>% dplyr::mutate( section = section %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all("\\?", "a"), camara = chamber %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all(" - ", "\n") %>% stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"), turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>% dplyr::mutate( turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma), camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>% dplyr::filter( title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section), section != "NAO IDENTIFICADO", camara %in% c("2a CAMARA"), chamber != "SEGUNDA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)") %>% dplyr::count(section, camara, turma, type_appeal, result) %>% dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>% dplyr::ungroup() %>% dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>% tidyr::unite(chamber, section, camara, turma, sep = "\n") aux %>% ggplot(aes(x = type_appeal, y = perc, fill = result)) + geom_bar(stat = "identity", position = "stack", alpha = .9) + geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) + theme_bw() + facet_wrap(~ chamber) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) + scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
Terceira seção
aux <- d_decisoes %>% dplyr::mutate( section = section %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all("\\?", "a"), camara = chamber %>% abjutils::rm_accent() %>% stringr::str_to_upper() %>% stringr::str_replace_all(" - ", "\n") %>% stringr::str_extract("(PRIMEIRA|SEGUNDA|TERCEIRA|QUARTA)(?= CAMARA)"), turma = ifelse(stringr::str_detect(chamber, "ESPECIAL"), "ESPECIAL", camara)) %>% dplyr::mutate( turma = ifelse(stringr::str_detect(turma, "PRIMEIRA"), "1a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "SEGUNDA"), "2a TURMA", turma), turma = ifelse(stringr::str_detect(turma, "TERCEIRA"), "3a TURMA", turma), camara = ifelse(stringr::str_detect(camara, "PRIMEIRA"), "1a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "SEGUNDA"), "2a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "TERCEIRA"), "3a CAMARA", camara), camara = ifelse(stringr::str_detect(camara, "QUARTA"), "4a CAMARA", camara)) %>% dplyr::filter( title %in% c("PRO TEMPORE", "TITULAR"), !is.na(section), section != "NAO IDENTIFICADO", camara %in% c("3a CAMARA"), !chamber %in% c("TERCEITA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)", "SEGUNDA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)", "PRIMEIRA TURMA ESPECIAL\n(VINCULADA A QUARTA CAMARA)")) %>% dplyr::count(section, camara, turma, type_appeal, result) %>% dplyr::mutate(perc = n / sum(n), n_tot = paste0("n = ", sum(n))) %>% dplyr::ungroup() %>% dplyr::mutate(resultado = factor(result, levels = rs_ord, ordered = TRUE)) %>% tidyr::unite(chamber, section, camara, turma, sep = "\n") aux %>% ggplot(aes(x = type_appeal, y = perc, fill = result)) + geom_bar(stat = "identity", position = "stack", alpha = .9) + geom_text(aes(x = as.numeric(as.factor(type_appeal)), y = 1.1, label = n_tot), alpha=.9, size = 4) + theme_bw() + facet_wrap(~ chamber) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 10)) + scale_y_continuous(labels = scales::percent, breaks = 0:10/10, limits = c(0, 1.2))
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