In HumanitiesDataAnalysis/TEIdy: Work with TEI and other XML Data as Tidy Text.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Reading Paradise Lost from the EEBO version, and looking for the most distinguishing words in each book.

library(TEIdytext)
library(tidytext)
fname = system.file("extdata", "ParadiseLost.xml", package="TEIdytext")
data = TEIdy(fname)

data %>% select(plaintext, div.book.n) %>% 
  unnest_tokens(word, plaintext) %>% 
  mutate(div.book.n = as.numeric(div.book.n)) %>%
  group_by(div.book.n, word) %>% 
  summarize(count=n()) %>% 
  bind_tf_idf(word, div.book.n, count) %>%
  arrange(div.book.n, -tf_idf) %>%
  slice(1:6) %>%
  ggplot() + geom_point(aes(x=div.book.n, y=word, size=count)) + 
  scale_size_continuous(trans='sqrt') + facet_wrap(~div.book.n, scales='free_y')

data %>% filter(!is.na(author))

act1 %>% unnest_tokens(word, text) %>% group_by(`sp-who`) %>% summarize(count=n())

header = tree %>% xpl(1) %>% to_frame

colnas = . %>% apply(., 2, function(col) sum(is.na(col))) %>% order

reselect = function(frame) { neworder = frame %>% colnas; frame[neworder] }

header %>% filter(!is.na(`sex-value`)) %>% reselect()


macbeth = tree %>% to_frame

library(tidytext)
framed = d %>% to_frame %>% unnest_tokens(word, text)

library(ggplot2)
framed %>%
  group_by(stage, word) %>%
  summarize(count=n()) %>%
  arrange(-count) %>%
  ggplot() + geom_bar(aes(x=word, y = count, fill=stage), position="dodge", stat="identity") + coord_flip() +
  facet_wrap(~stage)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

HumanitiesDataAnalysis/TEIdy documentation built on May 11, 2019, 7:25 p.m.