Aula RMarkdown

APRESENTAÇÃO

Gomes et al. (2012)

Dos Santos et al. (2009)

Paredes et al. (2010)

Lima et al. (2011)

Christensen et al. (2013)

EQUAÇÕES MATEMATICAS

\(Y= \beta_0 + \beta_1 x+ \epsilon\)

\(f(x) = 4x^3 + 2x^2 +1\)

\(\sqrt{x}\)

\(\mathbb{N} = \{1, 2,\ldots\}\)

\(x=\frac{-b \pm \sqrt{bˆ2-4ac}}{2a}\)

I HAVE A DREAM

library(readr)
library(tm)

## Loading required package: NLP

discurso <- read_table("discurso.txt")

## Parsed with column specification:
## cols(
##   texto = col_character()
## )

discurso2 <- paste(discurso$texto, collapse = " ")
VS <- VectorSource(discurso2)
corpus <- Corpus(VS)
corpus <- tm_map(corpus, content_transformer(tolower))

## Warning in tm_map.SimpleCorpus(corpus, content_transformer(tolower)):
## transformation drops documents

corpus <- tm_map(corpus, removePunctuation)

## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents

corpus <- tm_map(corpus, stripWhitespace)

## Warning in tm_map.SimpleCorpus(corpus, stripWhitespace): transformation drops
## documents

corpus <- tm_map(corpus, removeNumbers)

## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documents

corpus <- tm_map(corpus, removeWords, stopwords('en'))

## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents

tdm <- as.matrix(TermDocumentMatrix(corpus))
fre <- sort(rowSums(tdm),decreasing = TRUE)
aux <- subset(fre, fre>4)
barplot(aux, las=2, col=rainbow(10))

Figure 1. Discurso I have a dream

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

## Loading required package: RColorBrewer

discurso <- read_table("discurso.txt")

## Parsed with column specification:
## cols(
##   texto = col_character()
## )

discurso2 <- paste(discurso$texto, collapse = " ")
VS <- VectorSource(discurso2)
corpus <- Corpus(VS)
corpus <- tm_map(corpus, content_transformer(tolower))

## Warning in tm_map.SimpleCorpus(corpus, content_transformer(tolower)):
## transformation drops documents

corpus <- tm_map(corpus, removePunctuation)

## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents

corpus <- tm_map(corpus, stripWhitespace)

## Warning in tm_map.SimpleCorpus(corpus, stripWhitespace): transformation drops
## documents

corpus <- tm_map(corpus, removeNumbers)

## Warning in tm_map.SimpleCorpus(corpus, removeNumbers): transformation drops
## documents

corpus <- tm_map(corpus, removeWords, stopwords('en'))

## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents

tdm <- as.matrix(TermDocumentMatrix(corpus))
fre <- sort(rowSums(tdm),decreasing = TRUE)
aux <- subset(fre, fre>4)
wordcloud(corpus, min.freq = 1, max.words = 60, random.order = FALSE, rot.per = 0.35, colors = brewer.pal(8, "Dark2"))

Figure 2. Wordcloud I have a dream

BLACK LIVE MATTERS

library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(syuzhet)
consumer_key <- '9OxAwudl99CaQkJOLo4OCTAXu'
consumer_secret <- 'VZM7ZFaMkKhhZsJBK4Hx3BpWC173zHQMieVfzK2wgfn2da8glo'
access_token <- '1282402530543247360-qs8GFFvufitQvCcbtAFW9h2G4yQwJH'
access_secret <- 'mgaB0ZZ6UzIoLIYzbMCYSeBpSyi7lbD479gsaBNjfroFq'
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

## [1] "Using direct authentication"

tweets <- searchTwitteR("Black Live Matters", n=500, lang = "en") # lang="en"
tweets <- twListToDF(tweets)
tweets_t <- paste(tweets$text, collapse = " ")
tweets_s <- VectorSource(tweets_t)
corpus <- Corpus(tweets_s)
corpus <- tm_map(corpus, tolower)

## Warning in tm_map.SimpleCorpus(corpus, tolower): transformation drops documents

corpus <- tm_map(corpus, removePunctuation)

## Warning in tm_map.SimpleCorpus(corpus, removePunctuation): transformation drops
## documents

corpus <- tm_map(corpus, stripWhitespace)

## Warning in tm_map.SimpleCorpus(corpus, stripWhitespace): transformation drops
## documents

corpus <- tm_map(corpus, removeWords, stopwords('en'))

## Warning in tm_map.SimpleCorpus(corpus, removeWords, stopwords("en")):
## transformation drops documents

removeURL <- function(x) gsub("http[^[:space:]]*", "", x)
corpus <- tm_map(corpus, removeURL)

## Warning in tm_map.SimpleCorpus(corpus, removeURL): transformation drops
## documents

removeNumPunct <- function(x) gsub("[^[alpha:][:space:]]*", "", x)
corpus <- tm_map(corpus, content_transformer(removeNumPunct))

## Warning in tm_map.SimpleCorpus(corpus, content_transformer(removeNumPunct)):
## transformation drops documents

dtm <- TermDocumentMatrix(corpus)
dtm <- as.matrix(dtm)
fre <- sort(rowSums(dtm), decreasing = TRUE)
wordcloud(corpus, min.freq = 3, max.words = Inf,
          random.order = FALSE, rot.per = 0.15,
          color=brewer.pal(8,"Dark2"), scale=c(9.4,.3))

Figure 3. Wordcloud Black Live Matters

library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(syuzhet)
consumer_key <- '9OxAwudl99CaQkJOLo4OCTAXu'
consumer_secret <- 'VZM7ZFaMkKhhZsJBK4Hx3BpWC173zHQMieVfzK2wgfn2da8glo'
access_token <- '1282402530543247360-qs8GFFvufitQvCcbtAFW9h2G4yQwJH'
access_secret <- 'mgaB0ZZ6UzIoLIYzbMCYSeBpSyi7lbD479gsaBNjfroFq'
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

## [1] "Using direct authentication"

tweets <- searchTwitteR("Black Live Matters", n=500, lang = "en") # lang="en"
tweets <- twListToDF(tweets)
tweets <- tweets$text
s <-get_nrc_sentiment(tweets)

## Warning: `filter_()` is deprecated as of dplyr 0.7.0.
## Please use `filter()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

## Warning: `data_frame()` is deprecated as of tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

barplot(colSums(s), las=2, col=rainbow(10),
        ylab = "Contagem", main = "Sentimentos com relação a Black Live Matters")

Figure 4. Sentimento Black Live Matters

FIGURES

Figure 5.

Figure 6.

REFERENCES

Christensen, Jens Hesselbjerg, Krishna Kumar Kanikicharla, Edvin Aldrian, Soon Il An, Iracema Fonseca Albuquerque Cavalcanti, Manuel de Castro, Wenjie Dong, et al. 2013. “Climate Phenomena and Their Relevance for Future Regional Climate Change.” In Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1217–1308. Cambridge University Press.

Dos Santos, Simone C, Maria da Conceição Moraes Batista, Ana Paula C Cavalcanti, Jones O Albuquerque, and Silvio RL Meira. 2009. “Applying Pbl in Software Engineering Education.” In 2009 22nd Conference on Software Engineering Education and Training, 182–89. IEEE.

Gomes, ElainneChristinedeSouza, Onicio Batista Leal-Neto, Jones Albuquerque, HernandePereira da Silva, and Constança Simões Barbosa. 2012. “Schistosomiasis Transmission and Environmental Change: A Spatio-Temporal Analysis in Porto de Galinhas, Pernambuco-Brazil.” International Journal of Health Geographics 11 (1). Springer: 51.

Lima, Michella de Albuquerque, Victor Freitas de Castro, Jones Batista Vidal, and Joaquim Enéas-Filho. 2011. “Aplicação de Silı'cio Em Milho E Feijão-de-Corda Sob Estresse Salino.” Revista Ciência Agronômica 42 (2). SciELO Brasil: 398–403.

Paredes, Helen, Reinaldo Souza-Santos, Ana Paula da Costa Resendes, Marco Antônio Andrade de Souza, Jones Albuquerque, Silvana Bocanegra, Elainne Christine de Souza Gomes, and Constança Simões Barbosa. 2010. “Spatial Pattern, Water Use and Risk Levels Associated with the Transmission of Schistosomiasis on the North Coast of Pernambuco, Brazil.” Cadernos de Saúde Pública 26 (5). SciELO Brasil: 1013–23.