lexical_classification("I did not like the dog.")
lexical_classification(DATA.SPLIT$state, DATA.SPLIT$person)
(out <- with(pres_debates2012, lexical_classification(dialogue, list(person, time))))
plot(out)
scores(out)
out2 <- preprocessed(out)
htruncdf(out2)
plot(out2)
plot(out[["content"]])
dev.new()
plot(out[["functional"]])
## cloud of functional vs. content
## Highlight Content Words
set.seed(10)
par(mar = c(0,0,0,0))
list(
content = out[["content"]],
functional = out[["functional"]]
) %>%
list_df2df("type") %>%
dplyr::mutate(colors = ifelse(type == "functional", "gray80", "blue")) %>%
with(., wordcloud::wordcloud(
word,
freq,
min.freq = 8,
random.order=FALSE,
ordered.colors = TRUE,
colors = colors
))
mtext("2012 Presidential Debates:\nFunctional vs. Content Word Use", padj=1.25)
legend(
.05, .12, bty = "n",
legend = c("functional", "content"),
fill = c("gray80", "blue"),
cex = .7
)
## Highlight Functional Words
set.seed(10)
par(mar = c(0,0,0,0))
list(
content = out[["content"]],
functional = out[["functional"]]
) %>%
list_df2df("type") %>%
dplyr::mutate(colors = ifelse(type == "functional", "red", "gray80")) %>%
with(., wordcloud::wordcloud(
word,
freq,
min.freq = 8,
random.order=FALSE,
ordered.colors = TRUE,
colors = colors
))
mtext("2012 Presidential Debates:\nFunctional vs. Content Word Use", padj=1.25)
legend(
.05, .12, bty = "n",
legend = c("functional", "content"),
fill = c("red", "gray80"),
cex = .7
)
#=============#
## ANIMATION ##
#=============#
## EXAMPLE 1
lex_ani <- lexical_classification(DATA.SPLIT$state, DATA.SPLIT$person)
lexa <- Animate(lex_ani, content="white", functional="blue",
current.color = "yellow", current.speaker.color="grey70")
bgb <- vertex_apply(lexa, label.color="grey80", size=20, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")
print(bgb, bg="black", net.legend.color ="white", pause=1)
## EXAMPLE 2
lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)
lexa2 <- Animate(lex_ani2, content="white", functional="blue",
current.color = "yellow", current.speaker.color="grey70")
bgb2 <- vertex_apply(lexa2, label.color="grey80", size=17, color="grey40")
bgb2 <- edge_apply(bgb2, label.color="yellow")
print(bgb2, bg="black", pause=.75, net.legend.color = "white")
## EXAMPLE 3 (bar plot)
Animate(lex_ani2, type="bar")
## EXAMPLE 4 (text plot)
Animate(lex_ani2, type="text")
#======================#
## Complex Animations ##
#======================#
## EXAMPLE 1: Network + Text + Bar
library(animation)
library(grid)
library(gridBase)
library(qdap)
library(reports)
library(igraph)
library(plotrix)
lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)
## Set up the network version
lex_net <- Animate(lex_ani2, contextual="white", lexal="blue",
current.color = "yellow", current.speaker.color="grey70")
bgb <- vertex_apply(lex_net, label.color="grey80", size=17, color="grey40")
bgb <- edge_apply(bgb, label.color="yellow")
## Set up the bar version
lex_bar <- Animate(lex_ani2, type="bar")
## Set up the text
lex_text <- Animate(lex_ani2, type="text", size = 3, width=125, color="white")
## Generate a folder
loc <- reports::folder(animation_lexical_classification)
setwd(loc)
## Set up the plotting function
oopt <- animation::ani.options(interval = 0.1)
lex_text_bar <- Map(function(x, y){
uns <- unit(c(-1.6,.5,-.2,.25), "cm")
x <- x +
theme(plot.margin = uns,
text=element_text(color="white"),
legend.text=element_text(color="white"),
legend.background = element_rect(fill = "black"),
panel.border = element_rect(color = "black"),
panel.background = element_rect(fill = "black"),
plot.background = element_rect(fill = "black",
color="black"))
uns2 <- unit(c(-.5,.5,-.45,.25), "cm")
y <- y +
theme(plot.margin = uns2,
text=element_text(color="white"),
legend.text=element_text(color="white"),
legend.background = element_rect(fill = "black"),
plot.background = element_rect(fill = "black",
color="black"))
gA <- ggplotGrob(x)
gB <- ggplotGrob(y)
maxWidth <- grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
out <- arrangeGrob(gA, gB, ncol=1, heights = c(.3, .70))
## grid.draw(out)
invisible(out)
}, lex_text, lex_bar)
FUN <- function(follow=FALSE, theseq = seq_along(bgb)) {
Title <- "Animated Content Rate: Romeo and Juliet Act 1"
Legend <- c(.2, -1, 1.5, -.95)
Legend.cex <- 1
lapply(theseq, function(i) {
if (follow) {
png(file=sprintf("%s/images/Rplot%s.png", loc, i),
width=750, height=875)
}
## Set up the layout
layout(matrix(c(rep(1, 7), rep(2, 6)), 13, 1, byrow = TRUE))
## Plot 1
par(mar=c(2, 0, 2, 0), bg="black")
#par(mar=c(2, 0, 2, 0))
set.seed(22)
plot.igraph(bgb[[i]], edge.curved=TRUE)
mtext(Title, side=3, col="white")
color.legend(Legend[1], Legend[2], Legend[3], Legend[4],
c("Functional", "Content"), attributes(bgb)[["legend"]],
cex = Legend.cex, col="white")
## Plot2
plot.new()
vps <- baseViewports()
print(lex_text_bar[[i]], vp = vpStack(vps$figure,vps$plot))
animation::ani.pause()
if (follow) {
dev.off()
}
})
}
FUN()
## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system
saveHTML(FUN(), autoplay = FALSE, loop = TRUE, verbose = FALSE,
ani.height = 1000, ani.width=750,
outdir = loc, single.opts =
"'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")
FUN(TRUE)
## EXAMPLE 2: Line + Text + Bar
## Generate a folder
loc2 <- reports::folder(animation_lexical_classification2)
setwd(loc2)
lex_ani2 <- lexical_classification(mraja1spl$dialogue, mraja1spl$person)
## Set up the bar version
lex_bar <- Animate(lex_ani2, type="bar")
cumline <- cumulative(lex_bar)
lex_line <- plot(cumline)
ylims <- range(cumline[[1]][-c(1:100)]) + c(-.1, .1)
## Set up the text
lex_text <- Animate(lex_ani2, type="text", size = 4, width = 80)
lex_line_text_bar <- Map(function(x, y, z){
mar <- theme(plot.margin = unit(c(0, .5, 0, .25), "cm"))
gA <- ggplotGrob(x + mar +
theme(panel.background = element_rect(fill = NA, colour = NA),
panel.border = element_rect(fill = NA, colour = NA),
plot.background = element_rect(fill = NA, colour = NA)))
gB <- ggplotGrob(y + mar)
gC <- ggplotGrob(z + mar + ylab("Average Content Rate") +
coord_cartesian(ylim = ylims) +
ggtitle("Average Content Rate: Romeo & Juliet Act 1"))
maxWidth <- grid::unit.pmax(gA$widths[2:5], gB$widths[2:5], gC$widths[2:5])
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
gC$widths[2:5] <- as.list(maxWidth)
out <- arrangeGrob(gC, gA, gB, ncol=1, heights = c(.38, .25, .37))
## grid.draw(out)
invisible(out)
}, lex_text, lex_bar, lex_line)
FUN2 <- function(follow=FALSE, theseq = seq_along(lex_line_text_bar)) {
lapply(theseq, function(i) {
if (follow) {
png(file=sprintf("%s/images/Rplot%s.png", loc2, i),
width=750, height=875)
}
print(lex_line_text_bar[[i]])
animation::ani.pause()
if (follow) {
dev.off()
}
})
}
FUN2()
## Detect OS
type <- if(.Platform$OS.type == "windows") shell else system
library(animation)
saveHTML(FUN2(), autoplay = FALSE, loop = TRUE, verbose = FALSE,
ani.height = 1000, ani.width=750,
outdir = loc2, single.opts =
"'controls': ['first', 'previous', 'play', 'next', 'last', 'loop', 'speed'], 'delayMin': 0")
FUN2(TRUE)
#==================#
## Static Network ##
#==================#
(lexdat <- with(sentSplit(DATA, 4), lexical_classification(state, person)))
m <- Network(lexdat)
m
print(m, bg="grey97", vertex.color="grey75")
print(m, title="Lexical Content Discourse Map", title.color="white",
bg="black", legend.text.color="white", vertex.label.color = "grey70",
edge.label.color="yellow")
## or use themes:
dev.off()
m + qtheme()
m + theme_nightheat
dev.off()
m + theme_nightheat(title="Lexical Content Discourse Map",
vertex.label.color = "grey50")
#==================================#
## Content Rate Over Time Example ##
#==================================#
lexpres <- lapply(with( pres_debates2012, split(dialogue, time)), function(x) {
lexical_classification(x)
})
lexplots <- lapply(seq_along(lexpres), function(i) {
dat <- cumulative(lexpres[[i]])
m <- plot(dat)
if (i != 2) m <- m + ylab("")
if (i == 2) m <- m + ylab("Average Content Rate")
if (i != 3) m <- m + xlab(NULL)
if (i != 1) m <- m + theme(plot.margin=unit(c(0, 1, 0, .5) + .1, "lines"))
m + ggtitle(paste("Debate", i)) +
coord_cartesian(xlim = c(300, length(dat[[1]])),
ylim = unlist(range(dat[[1]][-c(1:300)]) + c(-.25, .25)))
})
library(grid)
library(gridExtra)
do.call(grid.arrange, lexplots)Run the code above in your browser using DataLab