├── .DS_Store ├── Rplot.pdf ├── .gitignore ├── img ├── 2022-08-16 17_02_41_NL.pdf ├── 2022-08-17 12_14_54_NL.pdf ├── 2022-08-18 18_49_44_NL.pdf ├── 2022-08-19 15_20_48_NL.pdf ├── 2022-08-19 15_21_52_NL.pdf ├── 2022-08-22 17_33_23_NL.pdf ├── 2022-08-22 17_45_29_NL.pdf ├── 2022-08-24 09_57_09_NL.pdf ├── 2022-08-25 14_23_30_NL.pdf ├── 2022-08-29 09_54_44_NL.pdf ├── 2022-08-29 16_28_17_NL.pdf ├── 2022-08-29 16_28_50_NL.pdf ├── 2022-08-29 16_29_04_NL.pdf ├── 2022-08-29 20_38_33_NL.pdf ├── 2022-08-29 21_58_01_NL.pdf ├── 2022-08-29 21_58_18_NL.pdf ├── 2022-08-29 21_59_15_NL.pdf ├── 2022-08-30 15_20_30_NL.pdf ├── 2022-08-31 12_41_39_NL.pdf ├── 2022-08-31 12_44_08_NL.pdf ├── 2022-10-10 18_33_39_NL.svg ├── 2022-10-14 18_13_38_NL.svg ├── 2022-10-10 18_28_58_NL.svg └── 2022-10-21 14_16_13_NL.svg ├── pesquisas-presidenciais-2022.Rproj ├── download.R ├── CODE_2T.R ├── CODE_NL.R ├── README.md ├── CODE_NL_com_Tebet.R ├── pesquisas_2t.csv └── pesquisas_1t.csv /.DS_Store: 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-------------------------------------------------------------------------------- 1 | # download 2 | library(googlesheets4) 3 | library(tidyverse) 4 | 5 | # url (LINK NAO É PUBLICO) 6 | url = "https://docs.google.com/spreadsheets/d/1VBOhutLq4geLNQ7UzK5wUfx2TKaFVstsvr0rJEuGtik/edit#gid=0" 7 | 8 | # download 9 | pesq <- read_sheet(url, sheet="2-turno") %>% 10 | mutate(Data = as.Date(Data)) 11 | 12 | write_csv(pesq, file="pesquisas_2t.csv") -------------------------------------------------------------------------------- /CODE_2T.R: -------------------------------------------------------------------------------- 1 | 2 | # Configurar -------------------------------------------------------------- 3 | 4 | #-- bibliotecas 5 | library(readxl) 6 | library(tidyverse) 7 | library(lubridate) 8 | 9 | #-- lista de pesquisas 10 | pesq <- read_csv("pesquisas_2t.csv") %>% 11 | select(-`Data Divulgação`, -VV13, -VV22) %>% 12 | filter(Data>=dmy("01-09-2022")) 13 | 14 | exp = T 15 | 16 | # Grafico ----------------------------------------------------------------- 17 | 18 | #-- argumento que controla o quão suave é a linah 19 | span=.50 20 | 21 | #-- gerar estimativas 22 | results <- pesq %>% 23 | drop_na() %>% 24 | mutate(Data = as.Date(Data)) %>% 25 | pivot_longer(cols=c(Lula:BNI)) %>% 26 | mutate(value = value/100) %>% 27 | # retirar institutos de pesquisa 28 | filter(!(Instituto%in%c("Sigma", "Brasmarket", 29 | "Verita", "IFP"))) %>% 30 | mutate(Data=as.numeric(Data)) %>% 31 | nest(-name) %>% 32 | mutate( 33 | # loess calculation 34 | m = purrr::map(data, loess, 35 | formula = value ~ Data, span = span), 36 | # valores previstos 37 | fitted = purrr::map(m, `[[`, "fitted")) %>% 38 | select(-m) %>% 39 | unnest() 40 | 41 | 42 | 43 | # Cores dos candidatos 44 | col=c( "#fd6166", "#6973ad", 45 | "#8E8E8E","#000000","#FECE43", "#c05b5e") 46 | 47 | results %>% 48 | mutate(Data = as.Date(Data, origin="1970/1/1"), 49 | name = fct_relevel(name, "Lula", "Bolsonaro", 50 | "Outros", "BNI", "Tebet", "Ciro")) %>% 51 | ggplot(aes(x = Data, y = value, group = name, color = name)) + 52 | geom_point(alpha=.66, size=.85) + 53 | #-- line e smooth geram a mesma linha aqui 54 | #-- smooth até gerar o s.e. automaticamente 55 | #geom_line(aes(y = fitted), size=1) + 56 | geom_smooth(se=T, alpha=.33, span=span, aes(fill=name)) + 57 | scale_fill_manual( 58 | values=col) + 59 | scale_color_manual(values=col) + 60 | geom_vline(xintercept = dmy("02/10/2022"), linetype="dashed") + 61 | scale_y_continuous(limits=c(0,.60), labels=scales::percent) + 62 | scale_x_date(date_breaks = "1 month", date_labels = "%m/%y") + 63 | theme_minimal() 64 | 65 | if(exp) { 66 | ggsave(paste0("./img/", 67 | str_replace_all(Sys.time(), ":", "_"), "_NL.svg"), 68 | width=400*10, height=167*10, unit="px") 69 | } 70 | 71 | #-- validos 72 | results %>% 73 | filter(Data==max(Data)) %>% 74 | mutate(Data = as.Date(Data, origin="1970/1/1")) %>% 75 | mutate(name = fct_relevel(name, "Lula", "Bolsonaro", "BNI")) %>% 76 | group_by(name) %>% 77 | summarise(mean=mean(fitted)) %>% 78 | filter(name!="BNI") %>% 79 | mutate(mean = mean/sum(mean)) %>% 80 | mutate(mean=paste0(round(mean*100,1), "%")) 81 | 82 | #-- totais 83 | results %>% 84 | filter(Data==max(Data)) %>% 85 | mutate(Data = as.Date(Data, origin="1970/1/1")) %>% 86 | mutate(name = fct_relevel(name, "Lula", "Bolsonaro", 87 | "BNI")) %>% 88 | group_by(name) %>% 89 | summarise(mean=mean(fitted)) %>% 90 | mutate(mean = mean/sum(mean)) %>% 91 | mutate(mean=paste0(round(mean*100,1), "%")) 92 | 93 | 94 | 95 | -------------------------------------------------------------------------------- /CODE_NL.R: -------------------------------------------------------------------------------- 1 | 2 | # Configurar -------------------------------------------------------------- 3 | 4 | #-- bibliotecas 5 | library(readxl) 6 | library(tidyverse) 7 | library(lubridate) 8 | 9 | #-- lista de pesquisas 10 | pesq <- read_csv("pesquisas_1t.csv") 11 | 12 | exp = T 13 | 14 | # Grafico ----------------------------------------------------------------- 15 | 16 | #-- argumento que controla o quão suave é a linah 17 | span=.90 18 | 19 | #-- gerar estimativas 20 | results <- pesq %>% 21 | drop_na() %>% 22 | mutate(Data = as.Date(Data), 23 | Outros = Tebet+Outros) %>% 24 | select(-Tebet) %>% 25 | pivot_longer(cols=c(Lula:Ciro, Outros:BNI)) %>% 26 | mutate(value = value/100) %>% 27 | # retirar institutos de pesquisa 28 | filter(!(Instituto%in%c("Sigma", "Brasmarket", 29 | "Futura", "Gerp"))) %>% 30 | mutate(Data=as.numeric(Data)) %>% 31 | nest(-name) %>% 32 | mutate( 33 | # loess calculation 34 | m = purrr::map(data, loess, 35 | formula = value ~ Data, span = span), 36 | # valores previstos 37 | fitted = purrr::map(m, `[[`, "fitted")) %>% 38 | select(-m) %>% 39 | unnest() 40 | 41 | # Cores dos candidatos 42 | col=c( "#fd6166", "#6973ad", 43 | "#8E8E8E","#000000","#c05b5e") 44 | 45 | results %>% 46 | mutate(Data = as.Date(Data, origin="1970/1/1"), 47 | name = fct_relevel(name, "Lula", "Bolsonaro", 48 | "Outros", "BNI", "Ciro")) %>% 49 | ggplot(aes(x = Data, y = value, group = name, color = name)) + 50 | geom_point(alpha=.66, size=.85) + 51 | # geom_point(data=pesq %>% 52 | # mutate(Data = as.Date(Data), 53 | # Outros = Tebet+Outros) %>% 54 | # select(-Tebet) %>% 55 | # pivot_longer(cols=c(Lula:Ciro, Outros:BNI)) %>% 56 | # mutate(value = value/100) %>% 57 | # mutate(name=fct_relevel(name, "Lula", "Bolsonaro", 58 | # "Outros", "BNI", "Ciro")), 59 | # aes(x=Data,y=value, group=name, color=name), 60 | # alpha=.66, size=.85) + 61 | #-- line e smooth geram a mesma linha aqui 62 | 63 | 64 | #-- smooth até gerar o s.e. automaticamente 65 | #geom_line(aes(y = fitted), size=1) + 66 | geom_smooth(se=T, alpha=.33, span=span, aes(fill=name)) + 67 | scale_fill_manual( 68 | values=col) + 69 | scale_color_manual(values=col) + 70 | scale_y_continuous(limits=c(0,.5), labels=scales::percent) + 71 | scale_x_date(date_breaks = "1 month", date_labels = "%m/%y") + 72 | theme_minimal() 73 | 74 | if(exp) { 75 | ggsave(paste0("./img/", 76 | str_replace_all(Sys.time(), ":", "_"), "_NL.svg"), 77 | width=400*10, height=167*10, unit="px") 78 | } 79 | 80 | #-- resultados gerais 81 | results %>% 82 | filter(Data==max(Data)) %>% 83 | mutate(Data = as.Date(Data, origin="1970/1/1")) %>% 84 | mutate(name = fct_relevel(name, "Lula", "Bolsonaro", 85 | "Ciro", "BNI", "Outros")) %>% 86 | group_by(name) %>% 87 | summarise(mean=mean(fitted)) %>% 88 | mutate(mean=paste0(round(mean*100,1), "%")) 89 | 90 | #-- mais recentes 91 | pesq %>% 92 | mutate(r = rank(-as.numeric(Data))) %>% 93 | filter(r <10) %>% 94 | arrange(r) %>% 95 | select(Data, Instituto, Registro, Lula:BNI) %>% 96 | mutate(text = paste(Data, Instituto, Registro, Lula, Bolsonaro, Ciro, Tebet, Outros, BNI, sep=" | ")) %>% 97 | select(text) 98 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pesquisas para presidente - 2022 2 | 3 | Repositório para o gráfico de pesquisas eleitorais da newsletter 'Durma com essa', do Nexo Jornal. 4 | 5 | ## Dados 6 | 7 | 8 | ### Dados completos 9 | 10 | Acesse todos os registros no arquivo [pesquisas_1t.csv](https://github.com/Nexo-Dados/pesquisas-presidenciais-2022/blob/main/pesquisas_1t.csv). 11 | 12 | ## Metodologia 13 | 14 | A metodologia de coleta e agregação é detalhada aqui. Qualquer alteração no procedimento também será registrada nesta página. 15 | 16 | ### Linha de tendência 17 | 18 | A linha de tendência foi gerada a partir do método de regressão local. Como o objetivo é apresentar tendências mais gerais, o argumento span, que determina o quão suave será a curva, foi de .90 (que tende a dar bastante suavidade para a linha). 19 | 20 | O intervalo de confiança se refere ao intervalo do erro padrão em torno de linha, gerado pelo método de regressão local. 21 | 22 | ### Pesquisas consideradas 23 | 24 | Foram consideradas as pesquisas, com registro no TSE, publicadas ao longo do ano de divulgação. 25 | 26 | A princípio, todos os institutos foram considerados, independente do tamanho amostral e método de entrevista. Algumas considerações: 27 | 28 | * A pesquisa do Sigma e Brasmarket está na tabela mas não é utilizada nos gráficos. Os resultados deste instituto são completamente divergentes dos demais. Os testes realizados pelo **Nexo** de viés dos institutos justifica a exclusão dessa pesquisa. 29 | 30 | * As pesquisas dos institutos Gerp e Futura apresentam o candidato Jair Bolsonaro (PL) consistentemente mais bem posicionado nas pesquisas do que os demais institutos. No entanto, um critério objetivo para retirar essas pesquisas iria remover as pesquisas de institutos, como o Datafolha, que apontam Lula (PT) melhor do que nos demais institutos. Por ora, essas pesquisas foram todas mantidas. 31 | 32 | * Quando havia mais de um cenário pesquisado (isso ocorreu sobretudo nas pesquisas do primeiro semestre), foi considerado apenas o cenário A ou cenário 1, por ser entendido como o cenário principal. 33 | 34 | ### Padronização dos dados 35 | 36 | A maioria dos institutos disponibiliza o resultado da pesquisa sem nenhuma casa decimal. Para padronizar os valores na tabela, os dados dos que disponibilizam os percentuais com decimal foram arredondados para o número inteiro mais próximo. Quando o decimal terminava em 5, foi arrendodado para cima (ex: 1,5% ficou 2%). 37 | 38 | A data na tabela se refere ao último dia de coleta das entrevistas. 39 | 40 | ### Candidatos considerados 41 | 42 | Os quatro primeiros candidatos (Lula, Bolsonaro, Ciro e Tebet) estão inclusos no gráfico. 43 | 44 | Os dados BNI se referem à soma de brancos, nulos e indeciso. A categoria Outros se refere à soma dos candidatos que não são exibidos no gráfico (guardada a consideração acima exposta sobre o percentual de Tebet). 45 | 46 | A soma foi aferida pela subtração de todos os outros valores de 100 nas pesquisas coletadas até setembro. Exemplo: 47 | 48 | * Lula 40% 49 | * Bolsonaro 30% 50 | * Ciro 5% 51 | * Tebet 3% 52 | * BNI 15% 53 | * Outros = 100% - (40%+30%+5%+3%+15%) = 7% 54 | 55 | ### Alterações na metodologia 56 | 57 | - 31/08/2022: As pesquisas da Futura/Modalmais foram retiradas do gráfico e do resultado agregado. Isso foi feito por conta da decisão de uma decisão do TSE (PJe 0600876-28.2022.6.00.000) que põe em dúvida a metodologia do instituto. Os dados de todos os institutos, como sempre, continuam disponíveis na planilha csv dispinibilizada pelo **Nexo**. 58 | 59 | ## Uso e contato 60 | 61 | Os dados originais são dos institutos de pesquisa. A coleta deles é feita sob responsabilidade da equipe de Gráficos do **Nexo Jornal**. O uso deles é autorizado, desde que seja citado o **Nexo**. 62 | 63 | Em caso de dúvidas ou sugestões, contatar: 64 | 65 | * Equipe de Gráficos: 66 | dados@nexojornal.com.br 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | -------------------------------------------------------------------------------- /CODE_NL_com_Tebet.R: -------------------------------------------------------------------------------- 1 | 2 | # Configurar -------------------------------------------------------------- 3 | 4 | #-- bibliotecas 5 | library(readxl) 6 | library(tidyverse) 7 | library(lubridate) 8 | 9 | #-- lista de pesquisas 10 | pesq <- read_csv("pesquisas_1t.csv") %>% 11 | select(-`Data divulgação`) 12 | exp = T 13 | 14 | # Grafico ----------------------------------------------------------------- 15 | 16 | #-- argumento que controla o quão suave é a linah 17 | span=.50 18 | 19 | #-- gerar estimativas 20 | results <- pesq %>% 21 | drop_na() %>% 22 | mutate(Data = as.Date(Data)) %>% 23 | pivot_longer(cols=c(Lula:BNI)) %>% 24 | mutate(value = value/100) %>% 25 | # retirar institutos de pesquisa 26 | filter(!(Instituto%in%c("Sigma", "Brasmarket"))) %>% 27 | mutate(Data=as.numeric(Data)) %>% 28 | nest(-name) %>% 29 | mutate( 30 | # loess calculation 31 | m = purrr::map(data, loess, 32 | formula = value ~ Data, span = span), 33 | # valores previstos 34 | fitted = purrr::map(m, `[[`, "fitted")) %>% 35 | select(-m) %>% 36 | unnest() 37 | 38 | 39 | 40 | # Cores dos candidatos 41 | col=c( "#fd6166", "#6973ad", 42 | "#8E8E8E","#000000","#FECE43", "#c05b5e") 43 | 44 | results %>% 45 | mutate(Data = as.Date(Data, origin="1970/1/1"), 46 | name = fct_relevel(name, "Lula", "Bolsonaro", 47 | "BNI")) %>% 48 | ggplot(aes(x = Data, y = value, group = name, color = name)) + 49 | geom_point(alpha=.66, size=.85) + 50 | geom_smooth(se=T, alpha=.33, span=span, aes(fill=name)) + 51 | scale_fill_manual( 52 | values=col) + 53 | scale_color_manual(values=col) + 54 | scale_y_continuous(limits=c(0,.5), labels=scales::percent) + 55 | scale_x_date(date_breaks = "1 month", date_labels = "%m/%y") + 56 | theme_minimal() 57 | 58 | if(exp) { 59 | ggsave(paste0("./img/", 60 | str_replace_all(Sys.time(), ":", "_"), "_NL.svg"), 61 | width=400*10, height=167*10, unit="px") 62 | } 63 | 64 | #-- resultados gerais 65 | results %>% 66 | filter(Data==max(Data)) %>% 67 | mutate(Data = as.Date(Data, origin="1970/1/1")) %>% 68 | mutate(name = fct_relevel(name, "Lula", "Bolsonaro", 69 | "Ciro", "BNI", "Outros")) %>% 70 | group_by(name) %>% 71 | summarise(mean=mean(fitted)) %>% 72 | filter(name!="BNI") %>% 73 | mutate(mean = mean/sum(mean)) %>% 74 | mutate(mean=paste0(round(mean*100,1), "%")) 75 | 76 | span=.60 77 | results %>% 78 | mutate(Data = as.Date(Data, origin="1970/1/1"), 79 | name = fct_relevel(name, "Lula", "Bolsonaro", 80 | "Outros", "BNI", "Tebet", "Ciro")) %>% 81 | filter(name%in%c("Ciro", "Tebet")) %>% 82 | ggplot(aes(x = Data, y = value, group = name, color = name)) + 83 | geom_point(alpha=.66, size=.85) + 84 | geom_smooth(se=T, alpha=.33, span=span, aes(fill=name)) + 85 | scale_fill_manual( 86 | values=c("#f15a24", "#662d91")) + 87 | scale_color_manual(values=c("#f15a24", "#662d91")) + 88 | scale_y_continuous(limits=c(0,.12), labels=scales::percent) + 89 | scale_x_date(date_breaks = "1 month", date_labels = "%m/%y") + 90 | theme_minimal() 91 | 92 | pesq %>% 93 | mutate(week = week(Data)) %>% 94 | filter(Data>=dmy("15-08-2022")) %>% 95 | group_by(week, Ciro) %>% 96 | #summarise(Ciro = mean(Ciro, na.rm=T)) %>% 97 | summarise(Cirop = n()) %>% 98 | ggplot(aes(x=week, y=Ciro, fill=Cirop)) + 99 | geom_tile() + 100 | scale_fill_distiller(palette="Purples", direction=1) 101 | 102 | 103 | pesq %>% 104 | mutate(week = week(Data)) %>% 105 | filter(Data>=dmy("15-08-2022")) %>% 106 | group_by(week, Tebet) %>% 107 | #summarise(Ciro = mean(Ciro, na.rm=T)) %>% 108 | summarise(Cirop = n()) %>% 109 | ggplot(aes(x=week, y=Tebet, fill=Cirop)) + 110 | geom_tile() + 111 | scale_fill_distiller(palette="Purples", direction=1) 112 | 113 | 114 | 115 | 116 | 117 | 118 | -------------------------------------------------------------------------------- /pesquisas_2t.csv: -------------------------------------------------------------------------------- 1 | Instituto,Data,Data Divulgação,Registro,Lula,Bolsonaro,BNI,VV13,VV22 2 | Datafolha,2022-10-27,2022-10-27T00:00:00Z,BR-04208/2022,49,44,7,0.5268817204301075,0.4731182795698925 3 | Atlas,2022-10-25,2022-10-27T00:00:00Z,BR-01560/2022,52,46,2,0.5306122448979592,0.46938775510204084 4 | Gerp,2022-10-26,2022-10-27T00:00:00Z,BR-05418/2022,43,47,10,0.4777777777777778,0.5222222222222223 5 | Quaest,2022-10-25,2022-10-26T00:00:00Z,BR-00470/2022,48,42,10,0.5333333333333333,0.4666666666666667 6 | PoderData,2022-10-25,2022-10-26T00:00:00Z,BR-01159/2022,49,44,7,0.5268817204301075,0.4731182795698925 7 | Brasmarket,2022-10-25,2022-10-26T00:00:00Z,BR-08584/2022,42,48,10,0.4666666666666667,0.5333333333333333 8 | Ipespe,2022-10-24,2022-10-25T00:00:00Z,BR-08044/2022,50,44,6,0.5319148936170213,0.46808510638297873 9 | Paraná Pesquisas,2022-10-24,2022-10-25T00:00:00Z,BR-00525/2022,46,46,8,0.5,0.5 10 | Brasmarket,2022-10-23,2022-10-24T00:00:00Z,BR-08487/2022,42,48,10,0.4666666666666667,0.5333333333333333 11 | Ipec,2022-10-24,2022-10-24T00:00:00Z,BR-06043/2022,50,43,7,0.54,0.46 12 | IFP,2022-10-21,2022-10-23T00:00:00Z,BR-01775/2022,44,47,9,0.4835164835164835,0.5164835164835165 13 | Atlas,2022-10-22,2022-10-23T00:00:00Z,BR-06415/2022,52,46,2,0.53,0.47 14 | Futura,2022-10-19,2022-10-21T00:00:00Z,BR-08523/2022,46,47,7,0.4946236559139785,0.5053763440860215 15 | Ideia,2022-10-19,2022-10-20T00:00:00Z,BR-00053/2022,50,46,4,0.5208333333333334,0.4791666666666667 16 | Brasmarket,2022-10-19,2022-10-20T00:00:00Z,BR-05389/2022,41,46,13,0.47126436781609193,0.5287356321839081 17 | Paraná Pesquisas,2022-10-19,2022-10-20T00:00:00Z,BR-02276/2022,47,45,8,0.5108695652173914,0.4891304347826087 18 | Verita,2022-10-20,2022-10-20T00:00:00Z,BR-04043/2022,47,49,4,0.4895833333333333,0.5104166666666666 19 | Datafolha,2022-10-19,2022-10-19T00:00:00Z,BR-07340/2022,49,45,5,0.52,0.48 20 | PoderData,2022-10-18,2022-10-19T00:00:00Z,BR-08917/2022,48,44,8,0.52,0.48 21 | Quaest,2022-10-18,2022-10-19T00:00:00Z,BR-04387/2022,47,42,11,0.53,0.47 22 | Ipespe,2022-10-18,2022-10-18T00:00:00Z,BR-06307/2022,49,43,8,0.53,0.47 23 | Ipec,2022-10-17,2022-10-17T00:00:00Z,BR-02707/2022,50,43,7,0.54,0.46 24 | MDA,2022-10-16,2022-10-17T00:00:00Z,BR-05514/2022,48,42,10,0.54,0.4666666666666667 25 | Verita,2022-10-15,2022-10-16T00:00:00Z,BR-04850/2022,46,49,5,0.49,0.51 26 | Datafolha,2022-10-14,2022-10-14T00:00:00Z,BR-01682/2022,49,44,7,0.5268817204301075,0.4731182795698925 27 | Ipespe,2022-10-12,2022-10-14T00:00:00Z,BR-07942/2022,49,43,8,0.532608695652174,0.4673913043478261 28 | Futura,2022-10-12,2022-10-13T00:00:00Z,BR-06280/2022,47,47,6,0.5,0.5 29 | Quaest,2022-10-12,2022-10-13T00:00:00Z,BR-07106/2022,49,41,10,0.5444444444444444,0.45555555555555555 30 | Atlas,2022-10-12,2022-10-13T00:00:00Z,BR-06012/2022,51,47,2,0.52,0.48 31 | Paraná Pesquisas,2022-10-12,2022-10-13T00:00:00Z,BR-08438/2022,48,44,8,0.5217391304347826,0.4782608695652174 32 | PoderData,2022-10-11,2022-10-12T00:00:00Z,BR-09241/2022,48,44,8,0.5217391304347826,0.4782608695652174 33 | Gerp,2022-10-11,2022-10-11T00:00:00Z,BR-02322/2022,48,46,6,0.5106382978723404,0.48936170212765956 34 | Ipespe,2022-10-11,2022-10-11T00:00:00Z,BR-01120/2022,50,43,7,0.5376344086021505,0.46236559139784944 35 | Ipec,2022-10-10,2022-10-10T00:00:00Z,BR-02853/2022,51,42,7,0.5483870967741935,0.45161290322580644 36 | Datafolha,2022-10-07,2022-10-07T00:00:00Z,BR-02012/2022,49,44,7,0.5268817204301075,0.4731182795698925 37 | PoderData,2022-10-05,2022-10-06T00:00:00Z,BR-08253/2022,48,44,8,0.5217391304347826,0.4782608695652174 38 | Quaest,2022-10-05,2022-10-06T00:00:00Z,BR-07940/2022,48,41,11,0.55,0.45 39 | Futura,2022-09-30,2022-10-06T00:00:00Z,BR-08263/2022,49,46,5,0.5157894736842106,0.4842105263157895 40 | Ipec,2022-10-05,2022-10-05T00:00:00Z,BR-02736/2022,51,43,6,0.55,0.45 41 | Ipec,2022-10-01,NA,BR-00999/2022,52,37,11,0.5842696629213483,0.4157303370786517 42 | Atlas,2022-09-30,NA,BR-07338/2022,53,43,4,0.5520833333333334,0.4479166666666667 43 | Ipespe,2022-09-30,NA,BR-05007/2022,55,38,7,0.5913978494623656,0.40860215053763443 44 | MDA,2022-09-30,NA,BR-02944/2022,50,41,9,0.5494505494505495,0.45054945054945056 45 | Datafolha,2022-09-29,NA,BR-09479/2022,54,39,7,0.5806451612903226,0.41935483870967744 46 | Atlas,2022-09-28,NA,BR-01318/2022,52,42,6,0.5531914893617021,0.44680851063829785 47 | Futura,2022-09-28,NA,BR-06743/2022,48,43,9,0.5274725274725275,0.4725274725274725 48 | Ideia Big Data,2022-09-28,NA,BR-09782/2022,52,41,7,0.5591397849462365,0.44086021505376344 49 | PoderData,2022-09-27,NA,BR-01426/2022,51,39,10,0.5666666666666667,0.43333333333333335 50 | Quaest,2022-09-27,NA,BR-04371/2022,52,38,10,0.5777777777777777,0.4222222222222222 51 | Atlas,2022-09-26,NA,BR-02714/2022,51,44,5,0.5368421052631579,0.4631578947368421 52 | Ipec,2022-09-26,NA,BR-01640/2022,54,35,11,0.6067415730337079,0.39325842696629215 53 | FSB,2022-09-25,NA,BR-08123/2022,52,40,8,0.5652173913043478,0.43478260869565216 54 | Ipespe,2022-09-23,NA,BR-01897/2022,54,38,8,0.5869565217391305,0.41304347826086957 55 | Datafolha,2022-09-22,NA,BR-04180/2022,54,38,8,0.5869565217391305,0.41304347826086957 56 | Ipespe,2022-09-21,NA,BR-08425/2022,54,37,9,0.5934065934065934,0.4065934065934066 57 | Futura,2022-09-21,NA,BR-02650/2022,47,44,9,0.5164835164835165,0.4835164835164835 58 | Opinião,2022-09-20,NA,BR-09430/2022,51,37,12,0.5795454545454546,0.42045454545454547 59 | Atlas,2022-09-20,NA,BR-01204/2022,53,41,6,0.5638297872340425,0.43617021276595747 60 | Quaest,2022-09-20,NA,BR-04459/2022,50,40,10,0.5555555555555556,0.4444444444444444 61 | Ipec,2022-09-19,NA,BR-00073/2022,54,35,11,0.6067415730337079,0.39325842696629215 62 | FSB,2022-09-18,NA,BR-07560/2022,52,39,9,0.5714285714285714,0.42857142857142855 63 | Ipespe,2022-09-16,NA,BR-08883/2022,53,38,9,0.5824175824175825,0.4175824175824176 64 | Datafolha,2022-09-15,NA,BR-04099/2022,54,38,8,0.5869565217391305,0.41304347826086957 65 | Futura,2022-09-14,NA,BR-00745/2022,45,46,9,0.4945054945054945,0.5054945054945055 66 | MDA,2022-09-14,NA,BR-06984/2022,49,31,20,0.6125,0.3875 67 | PoderData,2022-09-13,NA,BR-02955/2022,51,42,7,0.5483870967741935,0.45161290322580644 68 | Quaest,2022-09-13,NA,BR-03420/2022,48,40,12,0.5454545454545454,0.45454545454545453 69 | Ipec,2022-09-12,NA,BR-01390/2022,53,36,11,0.5955056179775281,0.4044943820224719 70 | Paraná Pesquisas,2022-09-12,NA,BR-05388/2022,47,41,12,0.5340909090909091,0.4659090909090909 71 | FSB,2022-09-11,NA,BR-06321/2022,51,38,11,0.5730337078651685,0.42696629213483145 72 | Datafolha,2022-09-09,NA,BR-07422/2022,53,39,8,0.5760869565217391,0.42391304347826086 73 | Ipespe,2022-09-09,NA,BR-07606/2022,52,39,9,0.5714285714285714,0.42857142857142855 74 | Futura,2022-09-07,NA,BR-02618/2022,45,46,9,0.4945054945054945,0.5054945054945055 75 | PoderData,2022-09-06,NA,BR-03760/2022,52,40,8,0.5652173913043478,0.43478260869565216 76 | Ipec,2022-09-05,NA,BR-00922/2022,52,36,12,0.5909090909090909,0.4090909090909091 77 | Paraná Pesquisas,2022-09-05,NA,BR-09446/2022,47,40,13,0.5402298850574713,0.45977011494252873 78 | FSB,2022-09-04,NA,BR-01786/2022,53,40,7,0.5698924731182796,0.43010752688172044 79 | Quaest,2022-09-04,NA,BR-00807/2022,51,39,10,0.5666666666666667,0.43333333333333335 80 | Datafolha,2022-09-01,NA,BR-00433/2022,55,38,7,0.5913978494623656,0.40860215053763443 81 | Ipespe,2022-09-01,NA,BR-09344/2022,53,38,9,0.5824175824175825,0.4175824175824176 82 | Paraná Pesquisas,2022-08-30,NA,BR-03492/2022,48,41,11,0.5393258426966292,0.4606741573033708 83 | PoderData,2022-08-30,NA,BR-06922/2022,50,41,9,0.5494505494505495,0.45054945054945056 84 | Ipec,2022-08-29,NA,BR-01979/2022,50,37,13,0.5747126436781609,0.42528735632183906 85 | Ipespe,2022-08-29,NA,BR-04347/2022,53,38,9,0.5824175824175825,0.4175824175824176 86 | FSB,2022-08-28,NA,BR-08934/2022,52,39,9,0.5714285714285714,0.42857142857142855 87 | Quaest,2022-08-28,NA,BR-00585/2022,51,37,12,0.5795454545454546,0.42045454545454547 88 | MDA,2022-08-28,NA,BR-00950/2022,50,39,11,0.5617977528089888,0.43820224719101125 89 | Futura,2022-08-25,NA,BR-07568/2022,45,44,11,0.5056179775280899,0.4943820224719101 90 | Ideia Big Data,2022-08-24,NA,BR-02405/2022,52,38,10,0.5777777777777777,0.4222222222222222 91 | Atlas,2022-08-24,NA,BR-00848/2022,52,41,7,0.5591397849462365,0.44086021505376344 92 | Paraná Pesquisas,2022-08-23,NA,BR-03138/2022,48,40,12,0.5454545454545454,0.45454545454545453 93 | FSB,2022-08-21,NA,BR-00244/2022,52,39,9,0.5714285714285714,0.42857142857142855 94 | PoderData,2022-08-16,NA,BR-02548/2022,52,38,10,0.5777777777777777,0.4222222222222222 95 | Datafolha,2022-08-16,NA,BR-09404/2022,54,37,9,0.5934065934065934,0.4065934065934066 96 | Ipec,2022-08-15,NA,BR-03980/2022,51,35,14,0.5930232558139535,0.4069767441860465 97 | Quaest,2022-08-14,NA,BR-01167/2022,51,38,11,0.5730337078651685,0.42696629213483145 98 | -------------------------------------------------------------------------------- /pesquisas_1t.csv: -------------------------------------------------------------------------------- 1 | "Instituto","Data","Data divulgação","Registro","Lula","Bolsonaro","Ciro","Tebet","Outros","BNI" 2 | "Datafolha",2022-10-01,2022-10-01,"BR-00245/2022",48,34,6,5,2,5 3 | "Ipec",2022-10-01,2022-10-01,"BR-00999/2022",47,34,5,5,2,7 4 | "Quaest",2022-10-01,2022-10-01,"BR-02444/2022",49,38,6,5,2,NA 5 | "Verita",2022-10-01,2022-10-01,"BR-05793/2022",NA,NA,NA,NA,NA,NA 6 | "Atlas",2022-09-30,2022-10-01,"BR-07338/2022",50,41,4,3,1,1 7 | "Ipespe",2022-09-30,2022-10-01,"BR-05007/2022",46,33,7,6,4,4 8 | "MDA",2022-09-30,2022-10-01,"BR-02944/2022",44,36,5,4,3,8 9 | "Datafolha",2022-09-29,2022-09-29,"BR-09479/2022",48,34,6,5,2,5 10 | "Paraná Pesquisas",2022-09-29,2022-09-30,"BR-07917/2022",44,37,5,6,1,7 11 | "Verita",2022-09-29,2022-09-30,"BR-05980/2022",42,45,4,4,3,2 12 | "Atlas",2022-09-28,2022-09-30,"BR-01318/2022",49,40,4,3,1,3 13 | "Brasmarket",2022-09-28,2022-09-30,"BR-08847/2022",31,45,6,5,2,11 14 | "Futura",2022-09-28,2022-09-29,"BR-06743/2022",41,38,6,7,1,7 15 | "Ideia",2022-09-28,2022-09-29,"BR-09782/2022",47,37,6,5,1,4 16 | "PoderData",2022-09-27,2022-09-28,"BR-01426/2022",45,36,6,4,4,5 17 | "Quaest",2022-09-27,2022-09-28,"BR-04371/2022",46,33,6,5,1,9 18 | "Atlas",2022-09-26,2022-09-26,"BR-02714/2022",48,41,4,2,3,2 19 | "Ipec",2022-09-26,2022-09-26,"BR-01640/2022",48,31,6,5,2,8 20 | "Paraná Pesquisas",2022-09-26,2022-09-27,"BR-03928/2022",43,36,6,5,1,9 21 | "FSB",2022-09-25,2022-09-26,"BR-08123/2022",45,35,7,4,3,6 22 | "Ipespe",2022-09-23,2022-09-24,"BR-01897/2022",46,35,6,4,0,9 23 | "Verita",2022-09-23,2022-09-18,"BR-06580/2022",42,43,5,4,3,3 24 | "Datafolha",2022-09-22,2022-09-22,"BR-04180/2022",47,33,7,5,2,6 25 | "Futura",2022-09-21,NA,"BR-02650/2022",39,39,7,6,2,7 26 | "Ipespe",2022-09-21,2022-09-23,"BR-08425/2022",46,35,7,4,1,7 27 | "Atlas",2022-09-20,2022-09-20,"BR-01204/2022",48,39,6,4,2,1 28 | "Brasmarket",2022-09-20,NA,"BR-00580/2022",31,45,7,5,0,12 29 | "Opinião",2022-09-20,2022-09-21,"BR-09430/2022",45,33,7,5,2,8 30 | "PoderData",2022-09-20,2022-09-21,"BR-00407/2022",44,37,7,4,3,5 31 | "Quaest",2022-09-20,2022-09-21,"BR-04459/2022",44,34,6,5,2,10 32 | "Ipec",2022-09-19,2022-09-19,"BR-00073/2022",47,31,7,5,1,9 33 | "Paraná Pesquisas",2022-09-19,2022-09-20,"BR-09417/2022",40,36,7,5,2,10 34 | "FSB",2022-09-18,2022-09-19,"BR-07560/2022",44,35,7,5,1,7 35 | "Ipespe",2022-09-16,2022-09-17,"BR-08883/2022",45,35,7,5,2,5 36 | "Datafolha",2022-09-15,2022-09-15,"BR-04099/2022",45,33,8,5,2,6 37 | "Brasmarket",2022-09-14,2022-09-15,"BR-01527/2022",31,44,8,5,1,12 38 | "Futura",2022-09-14,2022-09-15,"BR-00745/2022",39,42,4,3,1,12 39 | "MDA",2022-09-14,2022-09-16,"BR-06984/2022",43,35,6,5,1,10 40 | "PoderData",2022-09-13,2022-09-14,"BR-02955/2022",43,37,8,5,2,5 41 | "Quaest",2022-09-13,2022-09-14,"BR-03420/2022",42,34,7,4,2,11 42 | "FSB",2022-09-12,2022-09-12,"BR-06321/2022",41,35,9,7,2,6 43 | "Ipec",2022-09-12,2022-09-12,"BR-01390/2022",46,31,7,4,2,10 44 | "Paraná Pesquisas",2022-09-12,2022-09-13,"BR-05388/2022",40,37,7,5,1,10 45 | "Ipespe",2022-09-10,2022-09-10,"BR-07606/2022",44,36,8,5,2,5 46 | "Datafolha",2022-09-09,2022-09-09,"BR-07422/2022",45,34,7,5,2,7 47 | "Futura",2022-09-08,2022-09-08,"BR-02618/2022",36,42,8,5,2,7 48 | "PoderData",2022-09-06,2022-09-07,"BR-03760/2022",43,37,8,5,2,5 49 | "Ipec",2022-09-05,2022-09-05,"BR-00922/2022",44,31,8,4,2,11 50 | "Paraná Pesquisas",2022-09-05,2022-09-06,"BR-09446/2022",40,36,7,4,3,10 51 | "FSB",2022-09-04,2022-09-05,"BR-01786/2022",42,34,8,6,3,7 52 | "Quaest",2022-09-04,2022-09-07,"BR-00807/2022",44,34,7,4,2,9 53 | "Datafolha",2022-09-01,2022-09-01,"BR-00433/2022",45,32,9,5,3,6 54 | "Gerp",2022-09-01,2022-09-04,"BR-09102/2022",38,39,11,6,1,5 55 | "Ipespe",2022-09-01,2022-09-03,"BR-09344/2022",44,35,9,5,2,5 56 | "Paraná Pesquisas",2022-08-30,2022-08-31,"BR-03492/2022",41,37,8,2,2,10 57 | "PoderData",2022-08-30,2022-08-31,"BR-06922/2022",44,36,8,4,3,5 58 | "Ipec",2022-08-29,2022-08-29,"BR-01979/2022",44,32,7,3,1,13 59 | "Ipespe",2022-08-29,2022-08-31,"BR-04347/2022",43,35,9,5,2,6 60 | "FSB",2022-08-28,2022-08-29,"BR-08934/2022",43,36,9,4,1,7 61 | "MDA",2022-08-28,2022-08-30,"BR-00950/2022",42,34,7,2,2,13 62 | "Quaest",2022-08-28,2022-08-31,"BR-00585/2022",44,32,8,3,2,11 63 | "Futura",2022-08-25,2022-08-31,"BR-07568/2022",37,40,10,2,2,9 64 | "Atlas",2022-08-24,2022-08-25,"BR-00848/2022",47,38,6,4,3,2 65 | "Ideia Big Data",2022-08-24,2022-08-25,"BR-02405/2022",44,36,9,4,2,5 66 | "Paraná Pesquisas",2022-08-23,NA,"BR-03138/2022",42,37,7,3,1,10 67 | "Brasmarket",2022-08-22,NA,"BR-07901/2022",33,41,4,3,2,17 68 | "FSB",2022-08-21,NA,"BR-00244/2022",45,36,6,3,5,5 69 | "Datafolha",2022-08-16,NA,"BR-09404/2022",47,32,7,2,4,8 70 | "PoderData",2022-08-16,NA,"BR-02548/2022",44,37,6,4,2,7 71 | "Ipec",2022-08-15,NA,"BR-03980/2022",44,32,6,2,1,15 72 | "FSB",2022-08-14,NA,"BR-00603/2022",45,34,8,2,3,8 73 | "Quaest",2022-08-14,NA,"BR-01167/2022",45,33,6,2,2,12 74 | "FSB",2022-08-07,NA,"BR-08028/2022",41,34,7,3,4,11 75 | "Gerp",2022-08-05,NA,"BR-009327/2022",38,38,10,3,4,7 76 | "Sigma",2022-08-05,NA,"BR-04937/2022",28,38,8,3,2,21 77 | "PoderData",2022-08-02,NA,"BR-08398/2022",43,35,5,4,7,6 78 | "Paraná Pesquisas",2022-08-01,NA,"BR-05251/2022",41,36,8,2,3,10 79 | "Quaest",2022-07-31,NA,"BR-02546/2022",44,32,5,2,5,12 80 | "Datafolha",2022-07-28,NA,"BR-01192/2022",47,29,8,2,5,9 81 | "Futura",2022-07-25,NA,"BR-07639/2022",40,38,7,1,3,11 82 | "FSB",2022-07-24,NA,"BR-05938/2022",44,31,9,2,4,10 83 | "Ipespe",2022-07-22,NA,"BR-08220/2022",44,35,9,4,0,8 84 | "Ideia Big Data",2022-07-20,NA,"BR-09608-2022",44,33,8,4,4,7 85 | "PoderData",2022-07-19,NA,"BR-07122/2022",43,37,6,3,3,8 86 | "FSB",2022-07-10,NA,"BR-09292/2022",41,32,9,4,6,8 87 | "Paraná Pesquisas",2022-07-05,NA,"BR-09408/2022",41,35,7,2,5,10 88 | "PoderData",2022-07-05,NA,"BR-06550/2022",44,36,6,1,4,9 89 | "Quaest",2022-07-02,NA,"BR-01763/2022",45,31,6,2,4,12 90 | "FSB",2022-06-26,NA,"BR-05022/2022",43,33,8,3,5,8 91 | "Datafolha",2022-06-23,NA,"BR-09088/2022",47,28,8,1,5,11 92 | "Ideia Big Data",2022-06-22,NA,"BR-02845/2022",45,36,7,3,2,7 93 | "PoderData",2022-06-21,NA,"BR-07003/2022",44,34,5,1,7,9 94 | "FSB",2022-06-12,NA,"BR-03958/2022",44,32,9,2,4,9 95 | "PoderData",2022-06-07,NA,"BR-01975/2022",43,35,6,1,5,10 96 | "Quaest",2022-06-05,NA,"BR-03552/2022",46,30,7,1,4,12 97 | "Gerp",2022-06-03,NA,"BR-00123/2022",39,37,7,2,6,9 98 | "Ipespe",2022-06-01,NA,"BR-02893/2022",45,34,9,3,0,9 99 | "Paraná Pesquisas",2022-05-30,NA,"BR-04618/2022",41,35,8,1,5,10 100 | "FSB",2022-05-29,NA,"BR-03196/2022",46,32,9,2,3,8 101 | "Datafolha",2022-05-26,NA,"BR-05166/2022",48,27,7,2,5,11 102 | "Ipespe",2022-05-25,NA,"BR-07856/2022",45,34,8,3,5,5 103 | "PoderData",2022-05-24,NA,"BR-05638/2022",43,35,7,1,4,10 104 | "Real Time Big Data",2022-05-24,NA,"BR-07451/2022",39,31,8,2,6,14 105 | "Futura",2022-05-19,NA,"BR-05658/2022",41,36,6,1,5,11 106 | "Ideia Big Data",2022-05-19,NA,"BR-01734/2022",41,32,9,1,5,12 107 | "Ipespe",2022-05-18,NA,"BR-08011/2022",44,32,8,2,6,8 108 | "Ranking",2022-05-14,NA,"BR-06764/2022",39,34,6,1,5,15 109 | "Ipespe",2022-05-11,NA,"BR-02603/2022",44,32,8,1,5,10 110 | "PoderData",2022-05-10,NA,"BR-08423/2022",42,35,5,0,9,9 111 | "Quaest",2022-05-08,NA,"BR-01603/2022",46,29,7,1,8,9 112 | "MDA",2022-05-07,NA,"BR-05757/2022",41,32,7,2,6,12 113 | "Ipespe",2022-05-04,NA,"BR-03473/2022",44,31,8,1,6,10 114 | "Paraná Pesquisas",2022-05-03,NA,"BR-09280/2022",40,35,7,1,6,11 115 | "PoderData",2022-04-26,NA,"BR-07167/2022",41,36,6,1,9,7 116 | "Futura",2022-04-25,NA,"BR-08858/2022",41,35,7,3,4,10 117 | "FSB",2022-04-24,NA,"BR-04676/2022",41,32,9,1,8,9 118 | "Ideia Big Data",2022-04-20,NA,"BR-02495/2022",42,33,10,1,7,7 119 | "Ipespe",2022-04-20,NA,"BR-05747/2022",45,31,8,2,4,10 120 | "PoderData",2022-04-12,NA,"BR-00368/2022",40,35,5,2,7,11 121 | "Sensus",2022-04-11,NA,"BR-01631/2022",43,29,6,1,6,15 122 | "Gerp",2022-04-05,NA,"BR-02346/2022",37,35,5,0,9,14 123 | "Ipespe",2022-04-05,NA,"BR-03874/2022",44,30,9,2,3,12 124 | "Paraná Pesquisas",2022-04-05,NA,"BR-08065/2022",40,33,5,1,10,11 125 | "Quaest",2022-04-03,NA,"BR-00372/2022",44,29,5,1,11,10 126 | "PoderData",2022-03-29,NA,"BR-06661/2022",41,32,7,1,12,7 127 | "Futura",2022-03-25,NA,"BR-00758/2022",39,36,5,1,10,9 128 | "Datafolha",2022-03-23,NA,"BR-08967/2022",43,26,6,1,16,8 129 | "Ideia Big Data",2022-03-23,NA,"BR-04244/2022",40,29,9,1,15,6 130 | "Ipespe",2022-03-23,NA,"BR-04222/2022",44,26,7,1,13,9 131 | "FSB",2022-03-20,NA,"BR-09630/2022",43,29,9,1,13,5 132 | "PoderData",2022-03-15,NA,"BR-00835/2022",40,30,7,2,13,8 133 | "Quaest",2022-03-13,NA,"BR-06693/2022",44,26,7,1,11,11 134 | "Ranking",2022-03-12,NA,"BR-04724/2022",38,31,6,1,12,12 135 | "Gerp",2022-03-10,NA,"BR-07402/2022",38,31,5,0,10,16 136 | "Ipespe",2022-03-09,NA,"BR-03573/2022",43,28,8,1,11,9 137 | "Paraná Pesquisas",2022-03-08,NA,"BR-06682/2022",39,30,7,0,13,11 138 | "Ipespe",2022-02-23,NA,"BR-05015/2022",43,26,7,1,14,9 139 | "Ideia Big Data",2022-02-22,NA,"BR-05955/2022",42,27,8,0,16,7 140 | "MDA",2022-02-19,NA,"BR-09751/2022",42,28,7,1,10,12 141 | "Futura",2022-02-17,NA,"BR-03014/2022",35,35,6,1,12,11 142 | "PoderData",2022-02-15,NA,"BR-06942/2022",40,32,7,0,13,8 143 | "Ipespe",2022-02-09,NA,"BR-03828/2022",43,25,8,1,11,12 144 | "Quaest",2022-02-06,NA,"BR-08857/2022",45,23,7,1,11,13 145 | "Paraná Pesquisas",2022-02-01,NA,"BR-09055/2022",40,29,6,1,14,10 146 | "PoderData",2022-02-01,NA,"BR-09445/2022",41,30,7,1,13,8 147 | "Ipespe",2022-01-25,NA,"BR-06408/2022",44,24,8,1,11,12 148 | "Futura",2022-01-21,NA,"BR-08869/2022",37,31,6,1,14,11 149 | "PoderData",2022-01-18,NA,"BR-02137/2022",42,28,3,1,14,12 150 | "Ideia Big Data",2022-01-13,NA,"BR-03460/2022",40,30,7,0,12,11 151 | "Ipespe",2022-01-12,NA,"BR-09080/2022",44,24,7,1,12,12 152 | "Quaest",2022-01-09,NA,"BR-00075/2022",45,23,5,1,13,13 153 | -------------------------------------------------------------------------------- /img/2022-10-10 18_33_39_NL.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 0% 183 | 20% 184 | 40% 185 | 60% 186 | 09/22 187 | 10/22 188 | Data 189 | value 190 | name 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | Lula 201 | Bolsonaro 202 | BNI 203 | 204 | 205 | -------------------------------------------------------------------------------- /img/2022-10-14 18_13_38_NL.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 0% 208 | 20% 209 | 40% 210 | 60% 211 | 09/22 212 | 10/22 213 | Data 214 | value 215 | name 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | Lula 226 | Bolsonaro 227 | BNI 228 | 229 | 230 | -------------------------------------------------------------------------------- /img/2022-10-10 18_28_58_NL.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 0% 229 | 20% 230 | 40% 231 | 60% 232 | 09/22 233 | 10/22 234 | Data 235 | value 236 | name 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | Lula 247 | Bolsonaro 248 | BNI 249 | 250 | 251 | -------------------------------------------------------------------------------- /img/2022-10-21 14_16_13_NL.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 0% 232 | 20% 233 | 40% 234 | 60% 235 | 09/22 236 | 10/22 237 | Data 238 | value 239 | name 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | Lula 250 | Bolsonaro 251 | BNI 252 | 253 | 254 | --------------------------------------------------------------------------------