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003_clean_fulltext.r
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003_clean_fulltext.r
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#' clean out non-ASCII characters and formatting
# remove non-ASCII characters
Encoding(blogtext[,1]) <- "latin1"
iconv(blogtext[,1], "latin1", "ASCII", sub="")
# remove newline character
blogtext[,1] <- gsub("\n","", blogtext[,1])
# save as CSV so others can use it
write.csv(blogtext, 'dayofarchaeology.csv')
# a few quick summary statistics
# How many posts in total?
nrow(blogtext)
# How many words in total?
length(unlist(lapply(blogtext$text, function(i) strsplit(i, " ")[[1]])))
# how many authors in total?
length(unique(blogtext$author))
# How many posts in each year?
table(blogtext$year)
# how many words per year?
length(unlist(lapply(blogtext[blogtext$year == 2012,]$text, function(i) strsplit(i, " ")[[1]])))
length(unlist(lapply(blogtext[blogtext$year == 2013,]$text, function(i) strsplit(i, " ")[[1]])))
# how many authors per year?
length(unique(blogtext[blogtext$year == 2012,]$author))
length(unique(blogtext[blogtext$year == 2013,]$author))
# words per post per year
length(unlist(lapply(blogtext[blogtext$year == 2012,]$text, function(i) strsplit(i, " ")[[1]])))/table(blogtext$year)[[1]]
length(unlist(lapply(blogtext[blogtext$year == 2013,]$text, function(i) strsplit(i, " ")[[1]])))/table(blogtext$year)[[2]]
# plot distribution of words
# how many words per post?
wpp <- data.frame(
words = unlist(lapply(blogtext$text, function(i) length(strsplit(i, " ")[[1]]))),
year = blogtext$year, stringsAsFactors = FALSE)
# only look at 2012 and 2013
wpp <- wpp[wpp$year %in% c(2012, 2013),]
# function for labels
nlabels <- table(wpp$year)
# To create the median labels, you can use by
meds <- round(c(by(wpp$words, wpp$year, mean)),0)
# make the plot
require(ggplot2)
ggplot(wpp, aes(as.factor(year), words, label=rownames(wpp))) +
geom_violin() +
geom_text(data = data.frame(), aes(x = names(meds) , y = meds,
label = paste("mean =", meds))) +
xlab("Year")
# check if a certain word is present at all
require(tm)
# create corpus
corp <- Corpus(VectorSource(blogtext[,1]))
# process text
skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
corp <- tm_map(corp, FUN = tm_reduce, tmFuns = funcs)
# create document term matrix
dtm <- DocumentTermMatrix(corp, control =
# limit word lengths
list(wordLengths = c(2,10))) # ,
## A few other options for text mining
# control weighting
# weighting = weightTfIdf,
# keep words in more than 1%
# and less than 95% of docs
# bounds = list(global = c(
# length(corp)*0.01,length(corp)*0.95))))
# how many times does the word 'pyramid' occur in this document term matrix?
dtmdf <- data.frame(inspect(dtm))
sum(dtmdf[, names(dtmdf) == 'pyramid'])
# Indiana Jones comparison
# list words of interest - things archys use
IJ1 <- c('trowel', 'shovel', 'spade', 'gun', 'whip', 'fedora', 'computer', 'pen')
# get word counts in document term matrix
IJ2 <- dtmdf[, intersect(names(dtmdf), IJ1)]
# find words that don't occur in dtm at all
notin <- setdiff(IJ1, names(dtmdf) )
# append of cols of zeros for these words not in the dtm
IJ3 <- cbind(IJ2, replicate(length(notin), rep(0,nrow(IJ2))) )
# edit col names
names(IJ3) <- c(names(IJ2), notin)
# reshape for plotting
require(reshape2)
IJ4 <- melt(IJ3)
require(ggplot2)
ggplot(IJ4, aes(reorder(variable,-value), value)) +
geom_bar(stat="identity") +
xlab("Things archaeologists use in the field") +
ylab("Term Frequency") + theme(axis.text.x = element_text(colour="grey20",size=17,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="grey20",size=15,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="grey20",size=15,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="grey20",size=15,angle=90,hjust=.5,vjust=.5,face="plain"))
# list words of interest - things archys fear
IJ1 <- c('tunnels', 'cliffs', 'heat', 'cold', 'insects', 'snakes', 'bears', 'nazis', 'aliens')
# get word counts in document term matrix
IJ2 <- dtmdf[, intersect(names(dtmdf), IJ1)]
# find words that don't occur in dtm at all
notin <- setdiff(IJ1, names(dtmdf) )
# append of cols of zeros for these words not in the dtm
IJ3 <- cbind(IJ2, replicate(length(notin), rep(0,nrow(IJ2))) )
# edit col names
names(IJ3) <- c(names(IJ2), notin)
# reshape for plotting
require(reshape2)
IJ4 <- melt(IJ3)
require(ggplot2)
ggplot(IJ4, aes(reorder(variable,-value), value)) +
geom_bar(stat="identity") +
xlab("Dangers faced by archaeologists") +
ylab("Term Frequency") + theme(axis.text.x = element_text(colour="grey20",size=17,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="grey20",size=15,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="grey20",size=15,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="grey20",size=15,angle=90,hjust=.5,vjust=.5,face="plain"))
# what kinds of artefacts do archys study?
# list words of interest - things archys study
IJ1 <- c('pottery', 'bones', 'pollen', 'stones', 'bricks', 'wood', 'metal', 'treasure', 'grail')
# get word counts in document term matrix
IJ2 <- dtmdf[, intersect(names(dtmdf), IJ1)]
# find words that don't occur in dtm at all
notin <- setdiff(IJ1, names(dtmdf) )
# append of cols of zeros for these words not in the dtm
ifelse(length(notin) == 0,
IJ3 <- IJ2,
IJ3 <- cbind(IJ2, replicate(length(notin), rep(0,nrow(IJ2))) )
)
# edit col names
names(IJ3) <- c(names(IJ2), notin)
# reshape for plotting
require(reshape2)
IJ4 <- melt(IJ3)
require(ggplot2)
ggplot(IJ4, aes(reorder(variable,-value), value)) +
geom_bar(stat="identity") +
xlab("Things archaeologists study") +
ylab("Term Frequency") + theme(axis.text.x = element_text(colour="grey20",size=12,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="grey20",size=15,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="grey20",size=15,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="grey20",size=15,angle=90,hjust=.5,vjust=.5,face="plain"))
# what about gender?
# list words of interest - things archys study
IJ1 <- c('he', 'him', 'his', 'she', 'her', 'hers')
# get word counts in document term matrix
IJ2 <- dtmdf[, intersect(names(dtmdf), IJ1)]
# find words that don't occur in dtm at all
notin <- setdiff(IJ1, names(dtmdf) )
# append of cols of zeros for these words not in the dtm
ifelse(length(notin) == 0,
IJ3 <- IJ2,
IJ3 <- cbind(IJ2, replicate(length(notin), rep(0,nrow(IJ2))) )
)
# edit col names
names(IJ3) <- c(names(IJ2), notin)
# reshape for plotting
require(reshape2)
IJ4 <- melt(IJ3)
require(ggplot2)
ggplot(IJ4, aes(reorder(variable,-value), value)) +
geom_bar(stat="identity") +
xlab("Use of gender-specific pronouns by archaeologists") +
ylab("Term Frequency") + theme(axis.text.x = element_text(colour="grey20",size=12,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="grey20",size=15,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="grey20",size=15,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="grey20",size=15,angle=90,hjust=.5,vjust=.5,face="plain"))
# aggregate male and female pronouns
IJ5 <- data.frame(male = rowSums(cbind(IJ3$he, IJ3$him, IJ3$his)),
female = rowSums(cbind(IJ3$she, IJ3$her, IJ3$hers)))
IJ6 <- melt(IJ5)
require(ggplot2)
ggplot(IJ6, aes(reorder(variable,-value), value, fill = variable))+
geom_bar(stat="identity") +
xlab("Use of gender-specific pronouns by archaeologists") +
ylab("Term Frequency") + theme(axis.text.x = element_text(colour="grey20",size=12,angle=0,hjust=.5,vjust=.5,face="plain"),
axis.text.y = element_text(colour="grey20",size=15,angle=0,hjust=1,vjust=0,face="plain"),
axis.title.x = element_text(colour="grey20",size=15,angle=0,hjust=.5,vjust=0,face="plain"),
axis.title.y = element_text(colour="grey20",size=15,angle=90,hjust=.5,vjust=.5,face="plain")) +
scale_fill_manual(values=c("blue", "#FF0066"))