class: center, middle, inverse, title-slide .title[ # <img 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width="30%" alt="SESC Logo"></br> ] .subtitle[ ## Introdução à Estatística usando R </br> Por Haydée Svab ] .author[ ### 06/11/2022 ] --- layout: true <div class="my-footer"><span> <a href="https://www.linkedin.com/in/hsvab/">LinkdIn</a> | <a href="https://twitter.com/hsvab">Twitter</a> | <a href="https://www.instagram.com/haydeesvab/">Instagram</a> | <a href="https://www.facebook.com/haydee.svab/">Facebook</a> | <a href="https://github.com/hsvab">GitHub</a> <a href="#indice">[Voltar à agenda de hoje]</a> </span></div> --- class: center, middle ## Apresentações & Agenda --- class: split-two with-border .column.bg-main1[.content[ <BR><BR> - Cientista de Dados e Pesquisadora em Mobilidade Urbana e Cidades Inteligentes - CEO da ASK-AR Consultoria em análise de dados - Já trabalhei como consultora pra BID, Banco Mundial, IDEC, entre outros - Mestra em Engenharia e Planejamento de Transportes (Poli-USP) - Engenheira Civil (Poli-USP) - Email: <a href="mailto:hsvab@hsvab.eng.br">hsvab@hsvab.eng.br</a> ]] .column.bg-main2[.content[ <br> ## Haydée Svab <img src="img/foto-linkedin.jpeg" width="30%" /> ]] -- ## E vocês? --- name: indice # Agenda de hoje - [Atalhos](#atalhos), [pacotes](#pacotes) e [importação de dados](#importacao) - Intervalo dos dados: [mínimo e máximo](#intervalo) - Medidas de posição: [média](#media) e [mediana](#mediana) - [Medidas de dispersão](#dispersao): - [Absoluta: variância e desvio padrão](#absoluta) - Relativa: [coeficiente de variação](#cv) - Medida de associação: [correlação](#cor) - Identificando [outliers](#outlier) - [Frequências](#freq) e [Tabela de contingência](#contingencia) - [Histograma](#hist) e [Distribuições](#distr) - [Quarteto de Anscombe](#anscombe) --- class: center, middle ## Pré-requisitos --- class: # Pré-requisitos ## - `R` e `RStudio` instalados no seu notebook <img src="img/rlogos/rstudio-r.jpg" width="25%" style="display: block; margin: auto;" /> ## <center><b>OU</b></center> ## - `RStudio` Cloud <img src="img/rstudiocloud.PNG" width="30%" style="display: block; margin: auto;" /> Link do workshop: [https://bit.ly/coda2022-workshop-haydee](https://bit.ly/coda2022-workshop-haydee) Não esqueça de fazer uma cópia do projeto --- class: center, middle ## Atalhos, pacotes e importação de dados --- name: atalhos class: # Atalhos `CTRL + ENTER`: executa a linha selecionada no script `ALT` + **`-`** gera o operador **<-** `CTRL` + `SHIFT` + `M` gera o operador pipe **%>%** <center><img src="img/S5e27_T.V._typing.gif"></center> --- name: pacotes class: # Pacotes ### Instalar - Via CRAN: install.packages("nome-do-pacote"). ```r install.packages("tidyverse") install.packages("janitor") install.packages("inspectdf") install.packages("data.table") ``` ### Carregar ```r library(tidyverse) library(janitor) library(inspectdf) library(data.table) ``` ### Lembrete - Você só precisa instalar o pacote uma vez, mas precisa carregá-lo sempre que começar uma nova sessão --- name: importacao # Importação ## Base de dados: Trabalharemos com dados referentes às eleições 2022. Dados obtidos a partir de API do TSE de 30/out/2022. Base preparada agregando patrimonio, despesas contratadas e votos computados à base de candidaturas, a patir do número sequencial do candidato (identificador). Para fins das nossas análises, foram excluídos registros com UF ZZ (exterior) e BR (para evitar multiplicidade de registros). ### Leitura de arquivos csv ```r eleicoes_2022 <- fread("data/eleicoes_2022.csv", sep = ";", dec = ",") %>% clean_names(case = "snake") ``` --- class: middle ## Intervalo dos dados Medidas de ordenação (posição relativa) --- name: intervalo # Intervalo dos dados ## valor mínimo: `min()` ## valor máximo: `max()` Qual a idade da pessoa mais jovem e da mais velha a se candidatar nas eleições de 2022? ```r # Idade mínima min(eleicoes_2022$nr_idade_data_posse) ``` ``` ## [1] 20 ``` ```r #Idade máxima max(eleicoes_2022$nr_idade_data_posse) ``` ``` ## [1] 92 ``` --- # Intervalo dos dados E se quisermos saber as idades mínima e máxima, por sexo, de candidatos(as) nas eleições de 2022? ```r eleicoes_2022 %>% group_by(ds_genero) %>% summarise( idade_minima = min(nr_idade_data_posse), idade_maxima = max(nr_idade_data_posse) ) ``` ``` ## # A tibble: 2 × 3 ## ds_genero idade_minima idade_maxima ## <chr> <int> <int> ## 1 FEMININO 20 89 ## 2 MASCULINO 21 92 ``` --- # Intervalo dos dados E se quisermos saber as quantidades mínima e máxima de votos, por sexo, de candidatos(as)a deputado(a) federal nas eleições de 2022? ```r eleicoes_2022 %>% filter(ds_cargo == "DEPUTADO FEDERAL") %>% group_by(ds_genero) %>% summarise( qtde_min_votos = min(votos_computados, na.rm = TRUE), qtde_max_votos = max(votos_computados, na.rm = TRUE) ) ``` ``` ## # A tibble: 2 × 3 ## ds_genero qtde_min_votos qtde_max_votos ## <chr> <int> <int> ## 1 FEMININO 0 946244 ## 2 MASCULINO 0 1492047 ``` --- class: middle ## Medidas de posição Medidas de concentração ou tendência central --- name: media # Medidas de posição ## média: `mean()` É definida somando-se todos os números do conjunto de dados e então dividindo o resultado pelo número de valores do conjunto. Definição formal: <img src="img/media.png" width="50%" style="display: block; margin: auto;" /> *Indicação: Indicada quando os dados têm distribuição normal e poucos valores discrepantes (outliers).* --- name: mediana # Medidas de posição ## mediana: `median()` É o **valor do meio** quando o conjunto de dados está ordenado do menor para o maior (se for um número par de dados, corresponde à média dos 2 elementos do meio), de forma a separar a amostra ou população em duas metades. <img src="img/mediana.png" width="30%" style="display: block; margin: auto;" /> *Indicação: Indicada quando a distribuição é distorcida em relação à normal ou quando a forte influência de outliers.* --- # Medidas de posição Exemplo: Cálculo da média e da mediana dos patrimonios declarados de candidatos(as) ```r patrimonio_cadidatura <- eleicoes_2022 %>% # retirando os NAs filter(!is.na(patrimonio)) %>% # selecioando só as variáveis de interesse para ficar mais leve e rápido select(sq_candidato, patrimonio, ds_cargo, ds_genero) %>% # no caso de presidente, há votos por UF e a informação de patrimônio se repete # com distinct tiramos essa repetição distinct() %>% # agrupando por cargo e sexo do(a) candidato(a) group_by(ds_cargo, ds_genero) %>% # calculando média e mediana summarise(patrimonio_media = mean(patrimonio), patrimonio_mediana = median(patrimonio) ) %>% # ordenando do maior para o menor valor de patrimônio arrange(-patrimonio_mediana) ``` --- # Medidas de posição Exemplo: Cálculo da média e da mediana dos patrimonios declarados de candidatos(as) ``` ## # A tibble: 12 × 4 ## # Groups: ds_cargo [6] ## ds_cargo ds_genero patrimonio_media patrimonio_mediana ## <chr> <chr> <dbl> <dbl> ## 1 PRESIDENTE MASCULINO 6283177. 3039762. ## 2 GOVERNADOR MASCULINO 11542755. 981234. ## 3 SENADOR MASCULINO 2922741. 911258. ## 4 PRESIDENTE FEMININO 903385. 640500 ## 5 SENADOR FEMININO 1268932. 491229. ## 6 DEPUTADO FEDERAL MASCULINO 1389690. 384298. ## 7 DEPUTADO DISTRITAL MASCULINO 785567. 329000 ## 8 DEPUTADO ESTADUAL MASCULINO 1000548. 282736 ## 9 GOVERNADOR FEMININO 525617. 257118. ## 10 DEPUTADO DISTRITAL FEMININO 544095. 256589. ## 11 DEPUTADO FEDERAL FEMININO 577480. 185000 ## 12 DEPUTADO ESTADUAL FEMININO 515300. 165564. ``` --- class: middle ## Medidas de dispersão --- name: dispersao class: center # Medidas de dispersão Olhar apenas as medidas de posição de uma variável **ignora** a sua variabilidade, o que pode revelar informações importantes. <img src="img/exemplo-dispersao.png" width="50%" style="display: block; margin: auto;" /> <small> *Histograma indicando a profundidade de covas para transplantes de mudas de laranja, antes e depois o treinamento sobre cultivo* </small> <small> Fonte: [DEST UFPR - Medidas de dispersão, forma e associação](https://www.youtube.com/watch?v=ZvUhRikhNEk&list=PLQcLb-PUD9WNZnVBYDKEonioyJw3nEaOM&index=10) </small> --- name: # Medidas de dispersão ### Absolutas - variância - desvio-padrão - amplitude (amplitude inter-quartil) ### Relativa - coeficiente de variação --- name: absoluta # Medidas de dispersão absolutas ## variância: `var()` Mostra o quão próximo / distante um conjunto de dados está de um valor central, neste caso, a média desses valores. ## desvio padrão: `sd()` Medida que expressa o grau de dispersão de um conjunto de dados (quanto mais próximo de 0, mais homogêneo é o conjunto de dados) em relação a uma medida de posição centra - por padrão essa medida é a média. *Observação: a vantagem do desvio padrão é que ele encontra-se na mesma unidade que a variável medida.* --- # Medidas de dispersão absolutas Exemplo: Cálculo de média, desvio padrão e variância das despesas contratadas, por UF, das candidaturas ao legislativo ```r resumo_estados <- eleicoes_2022 %>% # retirando os NAs e cargos executivos (presidente e governador) filter(!is.na(despesa_contratada), ds_cargo!="PRESIDENTE", ds_cargo!="GOVERNADOR") %>% select(sq_candidato, despesa_contratada, ds_cargo, sg_uf) %>% distinct() %>% # agrupando por estado group_by(sg_uf) %>% # dividindo por 1000 apenas para melhorar a escala mutate(despesa_contratada = despesa_contratada/1000) %>% # calculando média, desvio padrão e variância summarise(despesa_contratada_media = mean(despesa_contratada), despesa_contratada_desvio = sd(despesa_contratada), despesa_contratada_variancia = var(despesa_contratada)) %>% # ordenando da maior média da despesa contratada para a menor arrange(-despesa_contratada_media) ``` --- # Medidas de dispersão absolutas Exemplo: Cálculo de média, desvio padrão e variância das despesas contratadas, por UF, das candidaturas ao legislativo ``` ## # A tibble: 27 × 4 ## sg_uf despesa_contratada_media despesa_contratada_desvio despesa_contratada…¹ ## <chr> <dbl> <dbl> <dbl> ## 1 PI 329. 599. 358448. ## 2 MT 281. 478. 228065. ## 3 PE 276. 545. 297025. ## 4 AL 270. 567. 321035. ## 5 SP 249. 496. 245591. ## 6 BA 242. 526. 277054. ## 7 TO 235. 459. 211114. ## 8 SE 231. 490. 240497. ## 9 MA 226. 493. 243230. ## 10 RN 220. 482. 232540. ## # … with 17 more rows, and abbreviated variable name ## # ¹​despesa_contratada_variancia ``` --- class: middle ## Medidas de dispersão relativas --- name: cv # Medidas de dispersão relativas ## coeficiente de variação: `goeveg::cv()` O coeficiente de variação é Definido como a razão do desvio padrão pela média e é usado para expressar a variabilidade dos dados estatísticos excluindo a influência da ordem de grandeza da variável. Definição formal: <img src="img/cv.png" width="12%" style="display: block; margin: auto;" /> Exemplo: Cálculo do coeficiente de variação das despesas contratadas, por estado ```r resumo_estados <- resumo_estados %>% mutate(cv = despesa_contratada_desvio / despesa_contratada_media) ``` <small>Quanto menor for o valor do cv, mais homogêneos serão os dados.</small> --- # Medidas de dispersão relativas ## coeficiente de variação Exemplo: Cálculo do coeficiente de variação das despesas contratadas, por estado ``` ## # A tibble: 27 × 5 ## sg_uf despesa_contratada_media despesa_contratada_desvio despesa_cont…¹ cv ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 PI 329. 599. 358448. 1.82 ## 2 MT 281. 478. 228065. 1.70 ## 3 PE 276. 545. 297025. 1.98 ## 4 AL 270. 567. 321035. 2.10 ## 5 SP 249. 496. 245591. 1.99 ## 6 BA 242. 526. 277054. 2.17 ## 7 TO 235. 459. 211114. 1.96 ## 8 SE 231. 490. 240497. 2.12 ## 9 MA 226. 493. 243230. 2.18 ## 10 RN 220. 482. 232540. 2.19 ## # … with 17 more rows, and abbreviated variable name ## # ¹​despesa_contratada_variancia ``` --- class: middle ## Medidas de associação: correlação --- name: cor # Medidas de associação ## correlação A correlação trata da medida da direção e do grau com que duas variáveis se associam linearmente (em amostra ou em população). Definição formal do coeficiente de correlação de pearson: <img src="img/corr.png" width="40%" style="display: block; margin: auto;" /> <img src="img/coef-corr-pearson.png" width="95%" style="display: block; margin: auto;" /> --- # Medidas de associação ## correlação Exemplo: Cálculo do coeficiente de correlação de Pearson entre despesas contratadas e votos computados, para presidência ```r # selecionando apenas candidaturas à presidência cor_presidente <- eleicoes_2022 %>% filter(ds_cargo=="PRESIDENTE") # fazendo o teste de correlação de pearson cor_presidente <- cor.test( cor_presidente$despesa_contratada, cor_presidente$votos_computados, # tomando somente os pares de pontos em que as duas observações têm valores use="pairwise.complete.obs") # se quisermos somente o r cor_presidente$estimate ``` ``` ## cor ## 0.2674356 ``` --- # Medidas de associação ## correlação Exemplo: Cálculo do coeficiente de correlação de Pearson entre despesas contratadas e votos computados, para governos estaduais ```r # selecionando apenas candidaturas a governo de estado cor_gov <- eleicoes_2022 %>% filter(ds_cargo=="GOVERNADOR") # fazendo o teste de correlação de pearson cor_gov <- cor.test( cor_gov$despesa_contratada, cor_gov$votos_computados, # tomando somente os pares de pontos em que as duas observações têm valores use="pairwise.complete.obs") # se quisermos somente o r cor_gov$estimate ``` ``` ## cor ## 0.8454288 ``` --- # Medidas de associação ## correlações espúrias <img src="img/corr-espuria.png" width="83%" style="display: block; margin: auto;" /> Fonte: [Spurious correlations](https://www.tylervigen.com/spurious-correlations) -- <small>**Importante! Correlação é sinônimo de associação / dependência, mas não de causalidade.**</small> --- class: middle ## Outliers --- name: outlier # Outliers São dados que se diferenciam drasticamente de todos os outros e que podem causar anomalias nos resultados obtidos por meio de algoritmos e sistemas de análise (distorção na média, por exemplo). -- Há dois **tipos de outliers**: naturais e artificiais. Os outliers naturais representam as diferenças de dados a que todas as situações estão sujeitas, por exemplo uma idade mais avançada ao avaliar expetativa de vida. Já os artificiais podem decorrer de: erro de input, erro de amostragem, erro de medida, erro ao procesar dados e erro intencional. -- Há algumas **formas de lidar** com outliers: - excluir o valor - tratar separadamente - etc (transformar, clusterizar, ...) --- class: # Outliers É possível reconhecer outliers graficamente através do diagrama de caixa (ou boxplot): <img src="img/boxplot.png" width="60%" style="display: block; margin: auto;" /> ```r outlier < 3/2*Q1 OU outlier > 3/2*Q3 ``` --- class: # Outliers Exemplo: Avaliar se existem outliers na variável idade dos(as) candidatos(as) -- ```r boxplot <- (eleicoes_2022$nr_idade_data_posse) boxplot ``` <img src="img/boxplot-idade.png" width="80%" style="display: block; margin: auto;" /> --- class: # Outliers Plotando os valores no boxplot ```r # cálculo do boxplot boxplot <- boxplot(eleicoes_2022$nr_idade_data_posse) # texto referente aos quartis text(1.3, boxplot$stats, boxplot$stats) # Cálculo do intervalo interquartil (IQR) IQR = boxplot$stats[4] - boxplot$stats[2] # Construção das flechinhas segments(0.5, c(boxplot$stats[2], boxplot$stats[4]), 0.7, c(boxplot$stats[2], boxplot$stats[4])) text(0.6, boxplot$stats[3], IQR) arrows(0.6, boxplot$stats[3]+1.5, 0.6, boxplot$stats[4]-1.5, 0.08) arrows(0.6, boxplot$stats[3]-1.5, 0.6, boxplot$stats[2]+1.5, 0.08) ``` --- class: # Outliers Plotando os valores no boxplot <img src="index_files/figure-html/unnamed-chunk-30-1.png" style="display: block; margin: auto;" /> --- class: center, middle ## Frequências --- name: freq # Frequências Uma primeira aproximação comum com algum conjunto dados é compreender a sua frequência, absoluta e relativa. Exemplo: Quantidade de candidaturas por sexo -- ```r eleicoes_2022 %>% tabyl(ds_genero) ``` ``` ## ds_genero n percent ## FEMININO 8960 0.3385604 ## MASCULINO 17505 0.6614396 ``` -- A mesma tabela de frequências, num formato melhor: ```r eleicoes_2022 %>% tabyl(ds_genero) %>% adorn_totals() %>% adorn_pct_formatting() ``` ``` ## ds_genero n percent ## FEMININO 8960 33.9% ## MASCULINO 17505 66.1% ## Total 26465 100.0% ``` --- name: contingencia # Tabela de contigência É usada para registrar observações de duas ou mais variáveis, em geral qualitativas. Exemplo: Quantidade de pessoas, por sexo e por resultado eleitoral (situacao) ```r # Valores absolutos eleicoes_2022 %>% tabyl(ds_genero, situacao) %>% adorn_totals(c("row", "col")) ``` ``` ## ds_genero 2 turno eleito não eleito suplente Total ## FEMININO 2 287 3455 5216 8960 ## MASCULINO 80 1366 6642 9417 17505 ## Total 82 1653 10097 14633 26465 ``` ```r # Valores relativos eleicoes_2022 %>% tabyl(ds_genero, situacao) %>% adorn_totals(c("row", "col")) %>% adorn_percentages("row") %>% adorn_pct_formatting() ``` ``` ## ds_genero 2 turno eleito não eleito suplente Total ## FEMININO 0.0% 3.2% 38.6% 58.2% 100.0% ## MASCULINO 0.5% 7.8% 37.9% 53.8% 100.0% ## Total 0.3% 6.2% 38.2% 55.3% 100.0% ``` --- name: hist # Histograma Trata-se de um gráfico de barras que mostra a distribuição das frequências. O eixo horizontal representa as classes e o eixo vertical representa as quantidades (frequência absoluta). **Por que fazer um histograma?** - Ajuda a ilustrar como uma determinada amostra de dados ou população está distribuída - É uma forma de resumir visualmente grandes conjuntos de dados (tabelas são mais difíceis de ler) - Para comparar resultados (posso fazer distribuição de várias variáveis ou da mesma variável em momentos diferentes) --- # Histograma O que observar num histograma: - centralidade (maior concentraçao de observações) - amplitude (dá pistas de mínimo e máximo) - simetria <img src="img/hist-simetrias.png" width="100%" style="display: block; margin: auto;" /> *Dica: um histograma funciona melhor quando o tamanho de amostra for superior a 20 (se a amostra for muito pequena, cada barra no histograma pode não conter pontos de dados suficientes para demonstrar precisamente a distribuição)* --- # Histograma Exemplo: Distribuição de frequência (histograma) da idade na data da posse ```r hist(eleicoes_2022$nr_idade_data_posse, main = ("Histograma da Idade na Data de Posse"), xlab = ("Idade na data de posse"), ylab = ("Frequência")) ``` <img src="index_files/figure-html/unnamed-chunk-36-1.png" width="45%" style="display: block; margin: auto;" /> --- name: distr # Distribuições Uma distribuição estatística, ou distribuição de probabilidade, descreve como os valores estão distribuídos. Observar e compreender as distribuições nos permite começar a buscar respostas apra perguntas como: - Os dados são todos iguais? - Em sendo diferentes, são muito diferentes uns dos outros? E de que modo são diferentes? - Existe alguma estrutura subjacente ou alguma tendência? - Existem alguns agrupamentos especiais? --- # Distribuições Existem muitos tipos de distribuições estatísticas, incluindo a **distribuição normal**, a mais fundamental e conhecida. <img src="img/normal.svg" width="60%" style="display: block; margin: auto;" /> --- # Distribuições ## Distribução Normal É definida por 2 parâmetros: média e desvio padrão. A área sob a curva representa 100% da probabilidade de ocorrência de um fenômeno. Isso quer dizer que a probabilidade de uma observação assumir um valor entre dois pontos quaisquer é igual à área compreendida entre esses dois pontos. <img src="img/normal.png" width="50%" style="display: block; margin: auto;" /> --- name: anscombe # Quarteto de Ascombe Considere eses quatro conjuntos de dados: <img src="img/anscombe1.png" width="90%" style="display: block; margin: auto;" /> --- # Quarteto de Ascombe São quatro conjuntos de dados que têm estatísticas descritivas quase idênticas (como a média e a variância): <img src="img/anscombe2.png" width="80%" style="display: block; margin: auto;" /> Fonte: [Wikipedia](https://pt.wikipedia.org/wiki/Quarteto_de_Anscombe) --- # Quarteto de Ascombe Mas quando analisamos além das estatísticas básicas, precebemos que os conjuntos variam bastante! <img src="img/anscombe3.svg" width="65%" style="display: block; margin: auto;" /> <small> **Tente sempre usar algum tipo de visualização (além das estatísticas básicas) ao analisar seus dados ;-)** </small> --- class: # Para aprender mais & Referências: - [Estatística Descritiva para leigos - Escola de Dados](https://escoladedados.org/tutoriais/analise-com-estatistica-descritiva-para-leigos/) <br></br> - [Estatística prática para cientistas de dados: 50 conceitos essenciais](https://www.amazon.com.br/Estatística-Prática-Para-Cientistas-Dados/dp/855080603X) <br></br> - [Do You Understand the Variance In Your Data? - Harvard Business Review](https://hbr.org/2019/08/do-you-understand-the-variance-in-your-data) <br></br> - [Boxplot: um recurso gráfico para a análise e interpretação de dados quantitativos](https://www.robrac.org.br/seer/index.php/ROBRAC/article/download/1132/897/) <br></br> - [Livro `R` for Data Science](https://r4ds.had.co.nz) <br></br> - [Repositório RLadies São Paulo](https://github.com/rladies/meetup-presentations_sao-paulo) <br></br> --- class:center # Agradecimentos À Bea Milz À Nathalia Demétrio e ao Diego Rabatone Oliveira <img src="img/diego-rabatone.jpeg" width="15%" style="display: block; margin: auto;" /> Capítulo RLadies São Paulo <img src="img/rlogos/r-ladies-sp.png" width="15%" style="display: block; margin: auto;" /> Apresentação feita com [RMarkdown](https://rmarkdown.rstudio.com/) e [Xaringan](https://github.com/yihui/xaringan), <br> com o tema `metropolis` modificado por Bea Milz e Haydee Svab --- class: middle <center><img src="img/if_you_want_to_go_fast_go_alone.jpg" style="width: 50%"></center>