rtweet pkg

• Load packages

## load rtweet dplyr and ggplot2
library(rtweet)
library(dplyr)
library(ggplot2)

• Stream tweets with R tweet package

## stream
rt <- stream_tweets(timeout = 30, verbose = FALSE)
## group by english and non-english
rt$eng <- ifelse(
    rt$lang == "en" & !is.na(rt$lang), "English", "Non-English"
  )
rt <- group_by(rt, eng)

Plot time series of tweets

• ts_data and trimts are useful rtweet functions

ts_data(rt, "5 secs") %>%
  trimts(1, 1) %>%
  ggplot(aes(x = time, y = n)) +
  geom_smooth(method = "loess", aes(group = eng), colour = "#aa00cc", alpha = .3) +
  geom_point(aes(fill = eng), shape = 21, size = 3.5, alpha = .7) +
  labs(title = "Filtering Twitter's stream API using stop words",
       subtitle = "Tweets collected and aggregated in 10 second
       intervals in R using rtweet") +
  ggthemes::theme_fivethirtyeight(base_size = 18) +
  theme(legend.title = element_blank()) +
  scale_color_manual(values = c("#5577fa", "#df6666")) +
  scale_fill_manual(values = c("#5577fa", "#df6666"))

• The plot looks nice, I promise.

R code example

• Basic R code with output

foo <- function(n) {
  data.frame(
    var1 = rnorm(n),
    var2 = rnorm(n)
  )
}
df <- foo(10)
print(df)

##          var1       var2
## 1   1.2550940 -2.1614949
## 2   0.8774562  0.9635271
## 3   1.6292473 -0.8959539
## 4   0.6926544 -0.6766164
## 5   1.5077352  1.9477026
## 6  -0.2652300  0.8850373
## 7   1.0913394  1.6788624
## 8  -0.1731676  0.1782695
## 9  -0.5003689 -1.3293232
## 10 -0.0732295 -1.1541722

Content

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