benmarwick

```{r table-simple, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
require(pander)
panderOptions('table.split.table', Inf)
set.caption("My great data")
my.data <- " 
  Tables       | Are           | Cool
  col 3 is     | right-aligned | $1600 
  col 2 is     | centered      |   $12 
  zebra stripe | are neat      |    $1"
df <- read.delim(textConnection(my.data),header=FALSE,sep="|",strip.white=TRUE,stringsAsFactors=FALSE)

benmarwick

# Basic analysis of UW Archaeology PhD theses

There are no clear guidelines about the length or structure of a PhD thesis in 
archaeology at UW. To answer these questions, we decided to make a quick study
of the norms evident in PhD theses produced in the last ten years. 

## Methods

We made counts of the total number of pages for each theses and the number of pages per chapter. We also made a note of the year the thesis was passed to examine trends over time. We entered the data into a google sheet.

benmarwick

# read data in, three columns Name = lab code, Date = radiocarbon age, Uncertainty = error
dates <- read.csv('F:/My Documents/My UW/Research/1308 Sulawesi/Dates/TalimbueDatesForOxcal-2.csv', stringsAsFactors = FALSE)

# construct OxCal format
oxcal_format <- paste0('R_Date(\"',  gsub("^\\s+|\\s+$", "", dates$Name), '\",', dates$Date, ',', dates$Uncertainty, ');')
# inspect
cat(oxcal_format)
  
 # write formatted dates to text file
write.table(oxcal_format, file = 'oxcal_format.txt', row.names = FALSE, col.names = FALSE, quote = FALSE)

benmarwick

```{r}
# bring data into R
require(gdata) # must have Perl installed first: http://strawberryperl.com/
data <- read.xls("F:/My Documents/My UW/Teaching/Graduate Students/Amy Jordan/shell vs grit charts.xls", sheet = 'main', stringsAsFactors = FALSE)
```


```{r}
# check variables
unique(data$Level)

benmarwick

#Title: An example of the correlation of x and y for various distributions of (x,y) pairs
#Tags: Mathematics; Statistics; Correlation
#Author: Denis Boigelot
#Packets needed : mvtnorm (rmvnorm), #RSVGTipsDevice (devSVGTips)
#How to use: output()
#
#This is an translated version in R of an Matematica 6 code by Imagecreator.
# from http://en.wikipedia.org/wiki/File:Correlation_examples2.svg

library(mvtnorm)

benmarwick

Quick and dirtly look at Shakespeare's plays
====

Introduction
----
I was recently inpsired by the recent posts of Andrew Collier ([1](http://www.exegetic.biz/blog/2013/09/text-mining-the-complete-works-of-william-shakespeare/) and [2](http://www.exegetic.biz/blog/2013/09/clustering-the-words-of-william-shakespeare/)) and an earlier post by [Matt Jockers](http://www.matthewjockers.net/2009/02/13/machine-classifying-novels-and-plays-by-genre/) to take a recreational look at the plays of Shakespeare. 

Motivated by Jockers, the specific topic I was interested in is the genres of the plays. For example, are the genres discrete or is there lots of overlap? Are the genres equal in variation or is one genre very focused and other very diverse? What are the key attributes that define the genres? And can I reproduce Jockers' use of high frequency words to identify genres? Related to Jockers' work on high frequency words is an earlier study by [Brainerd (1979)](http://www.jstor.org/stable/30207229) who used pronouns 

benmarwick

Which documents belong to each topic?

Documents don't belong to a single topic, there is a distribution of topics
over each document. 

But we can Find the topic with the highest proportion for each document.
That top-ranking topic might be called the 'topic' for the document, but note
that all docs have all topics to varying proportions

Assume that we start with `topic_docs` from the output of the mallet package

benmarwick

## Shell:

git clone --recursive https://github.com/cscheid/rcloud.git

sudo apt-get install libxt-dev libcurl4-openssl-dev libcairo2-dev libreadline-dev git

Create github app according to instructions here: https://github.com/att/rcloud

Edit conf/rcloud.conf according to instructions here: https://github.com/att/rcloud

benmarwick

# test it by creating some small text files to run the function on 

txt <- c("here is", "some text", "to test", "this function with", "'including a leading quote", '"and another leading quote')
# make text files
dir.create("testdir")
for(i in 1:length(txt)){
  writeLines(txt[i], paste0("testdir/outfile-", i, ".txt"))
}

# run the function and then look in the CSV file that is produced.

benmarwick

#' Making several text files from a single CSV file
#' 
#' Convert a single CSV file (one text per row) into
#' separate text files. A function in R.
#' 
#' To use this function for the first time run:
#' install.packages("devtools")
#' then thereafter you just need to load the function 
#' fom github like so:
#' library(devtools) # windows users need Rtools installed, mac users need XCode installed