2VA

2VA

VADeaths

##Questão 1
colors = c("yellow","blue","green","orange","brown")
categorias <- c("Rural Male","Rural Female","Urban Male","Urban Female")
age <- c("50-54","55-59","60-64","65-69","70-74")
 # Criar a matriz dos valores
Values <- matrix(c(11.7,8.7,15.4,8.4,18.1,11.7,24.3,13.6,26.9,20.3,37.0,19.3,41.0,30.9,54.6,35.1,66.0,54.3,71.1,50.0),
                nrow = 5, ncol = 4, 
                 byrow = TRUE)
par(mar = c(5, 6, 2.5, 8),xpd=TRUE) 
barplot(Values, main = "Death Rates", names.arg = categorias, cex=0.8,
                        xlab = "Population group", ylab =
                          "Rates", space=0.6,width=3, cex.axis=0.8, col = colors)
legend("topright", pch=c(15,15,15,15,15), 
       col = colors,inset=c(-0.2,0),cex=0.8, legend=age)

ClassificaçãoDoença

##Questão 2
pacientes <- c("moderado","leve","leve","severo","leve","moderado","moderado","moderado", "leve", "leve","severo","leve", "moderado",
              "moderado", "leve", "severo", "moderado", "moderado",
              "moderado","leve")
estagios <- c("leve","moderado","severo")
qnt <-c(sum(pacientes=="leve"),sum(pacientes=="moderado"),sum(pacientes=="severo"))
pct<-round(qnt/length(pacientes)*100)
lbls<-paste(pct,"%",sep="")
pie(qnt,lbls,main="Estado dos Pacientes",col=rainbow(length(estagios)))

legend("topright",
       legend=estagios,
       cex=0.8, fill=rainbow(length(estagios)))

Twitters

library(twitteR)
library(tm)
library(wordcloud)
library(readr)
library(syuzhet)

setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

tweets_racismo <- searchTwitter("#racismo", n=500,lang="pt")

tweets_racismo <- twListToDF(tweets_racismo)

tweets_racismo_t <- paste(tweets_racismo$text, collapse = " ")
tweets_S <- VectorSource(tweets_racismo_t)
corpus2<-Corpus(tweets_S)

inspect(corpus2)

corpus2<-tm_map(corpus2,content_transformer(tolower))
corpus2<-tm_map(corpus2,removePunctuation)
corpus2<-tm_map(corpus2,stripWhitespace)
corpus2<-tm_map(corpus2,removeWords,stopwords('portuguese'))
removeURL <- function(x) gsub("http[^[:space:]]*", "", x)
corpus2 <- tm_map(corpus2, removeURL)
removeNumPunct <- function(x) gsub("[^[:alpha:][:space:]]*", "", x)
corpus2 <- tm_map(corpus2, content_transformer(removeNumPunct))
inspect(corpus2)

matriz2 <- TermDocumentMatrix(corpus2)
matriz2 <- as.matrix(matriz2)
fre2 <- sort(rowSums(matriz2),decreasing=TRUE)

wordcloud(corpus2,scale = c(2, 1), min.freq = 1, max.words=100,
  random.order=FALSE, rot.per=0.35,
  colors=brewer.pal(8, "Dark2"))

s<-get_nrc_sentiment(tweets_racismo_t)
s2<- sort(s,decreasing=TRUE)
barplot(colSums(s), las=2, col = rainbow(10),
  ylab = "Quantidade", main = "Pontuação de Sentimento para
  os Twittes sobre Racismo")

Teorema

flu<-read.csv("flu.csv")
hist(flu$age, main = "Histograma",xlab = "Idades")

hist(flu$age, probability=T, main = "Histograma - curva de densidade",xlab = "Idades")
lines(density(flu$age),col=10)

#Amostra
n<-200
tam<-35
xbar<-rep(NA,n)
for (i in 1:n){
  amostra<-sample(flu$age,size=tam)
  xbar[i]<-mean(amostra)
}
par(mar = c(5, 8, 2.5, 8),xpd=TRUE)
hist(xbar, probability=T, main = "Histograma - curva de densidade",xlab = "Médias de idade das amostras")
lines(density(xbar),col=10)