2VA

VADeaths

colors <- c("blue", "green", "yellow", "orange", "red")
categories = c("Rural Male", "Rural Female", "Urban Male", "Urban Female")
leg = c("50-54", "55-59", "60-64", "65-69", "70-74")

barplot(VADeaths,
        main="Death Rates in Virginia",
        names.arg=categories,
        xlab="Population Group",ylab="Rate",
        col=colors,
        beside = T)
legend("topright", pch=c(15,15,15,15), col=colors, legend=leg)

ClassificaçãoDoença

estagios = c("moderado", "leve", "leve", "severo", "leve", "moderado",
             "moderado", "moderado", "leve", "leve", "severo","leve",
             "moderado", "moderado", "leve", "severo", "moderado",
             "moderado", "moderado","leve")
count <- table(estagios)
labels <- c("leve", "moderado", "severo")
percent <- c(count[1], count[2], count[3])/sum(count)
percent_label <- paste(percent * 100, "%", sep="")

pie(percent,
    percent_label,
    main="Porcentagem de estágios da doença",
    col=c("blue", "yellow", "red"))

legend("topleft",
       legend=labels,
       pch=15,
       col=c("blue", "yellow", "red"))

Twitters

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

setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

## [1] "Using direct authentication"

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

tweets <- twListToDF(tweets)
texts <- paste(tweets$text, collapse = " ")

corpus <- Corpus(VectorSource(texts))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords("portuguese"))

wordcloud(corpus, min.freq=1, max.words=60, random.order=F,
          rot.per=0.35, colors=brewer.pal(8, "Dark2"))

library(syuzhet)
s <- get_nrc_sentiment(tweets$text)
barplot(colSums(s), las=2,col = rainbow(10), ylab = "Quantidade",main = "Sentimento dos tweets")

Teorema

flu <- read_csv("flu.csv")
hist(flu$age)

n <- 200
tam <- 35
xbar <- rep(NA, n)
count <- table(flu)

for(i in 1:n){
    amostra <- sample(count, size=tam)
    xbar[i] <- mean(amostra)
}

hist(xbar, probability = T)
dens <- density(xbar)
lines(dens)