caminho = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" iris = read.table(caminho, header = FALSE, ",") head(iris) x = iris$V2 y = iris$V4 x y plot(x, y, type = "p", main = "Planta iris", xlab = "Largura Sépala", ylab = "Largura Pétala")

> Lojas = c(1,1,1,1,1,17,33,55,84,116,165,272,425,677,1015,1412,1886,2498,3501,4709,5886,7225,8569,10241,12440,15011,16680,16635,16858,17003,18066,19767,21366,23043,25085,28039,29865) > Anos = c(1971,1982,1983,1984,1985,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018) > > barplot(Lojas, names.arg = Anos, main = "Starbucks - Crescimento anual até 2018", xlab = "Anos", ylab = "Número Lojas", ylim = c(0,30000), sub = "Fonte: https://stories.starbucks.com/uploads/2019/01/AboutUs-Timeline-1.24.19.pdf")

Gráfico professores x escolas. > Professores = c(2250, 1248, 875, 37) > Alunos = c(28790, 22578, 19345, 347) > Escola = c("Privada", "Estadual", "Municipal", "Federal") > barplot(Professores, names.arg = Escola, main = "Professores Ens Fund", xlab = "Escolas", ylab = "No Docentes", sub = "Fonte: www.xxx.com.br")

Histograma frequencia no de voltas. > tempo = c(25,27,18,16,21,22,21,20,18,23,27,21,19,20,21,16) > hist(tempo, right = F, main = "Histograma Voltas", xlab = "min", ylab = "Frequencia", col = 13)

Exercício MBA. > frota = c(13377, 18754, 8058, 3201, 2154, 1895) > veiculo = c("Automoveis", "Motocicletas","Caminhonetes", "Motonetas", "Onibus", "Caminhoes") > pie(frota, veiculo)

Gráfico dispersão Sépala x Pétala > caminho = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" > iris = read.table(caminho, header=FALSE, sep=",") > head(iris) V1 V2 V3 V4 V5 1 5.1 3.5 1.4 0.2 Iris-setosa 2 4.9 3.0 1.4 0.2 Iris-setosa 3 4.7 3.2 1.3 0.2 Iris-setosa 4 4.6 3.1 1.5 0.2 Iris-setosa 5 5.0 3.6 1.4 0.2 Iris-setosa 6 5.4 3.9 1.7 0.4 Iris-setosa > x = iris[,"V2"] > y = iris[,"V4"] > plot(x, y, type = "p", main = "Planta Iris", xlab = "Largura Sépala", ylab = "Largura Pétala")

gdpPercap x lifeExp, Color continent, Size pop library(gapminder) library(dplyr) library(ggplot2) ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) + geom_point() + scale_x_log10() + facet_wrap(~ year)