markdown-in-r.Rmd

---
title: "Digital Tutorial 5: Text Analysis with R Markdown"
author: Christopher Ohge
output: html_document
toc: true
toc_float: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
chooseCRANmirror(graphics=FALSE, ind=1)
```

If you want to analyze the data of your edition, you then have to run more transformations of your data in order to optimize analysis. 

### Enter R Markdown 

What you're reading is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.

### Why use R?

You don't have to use R. You could Python; you could use Perl. You could also just use ... Markdown (which I generally prefer).

Think about what you will want to do in your project.

Describe the problem.

Create a program will analyze the text.

Model > Transform / Analyze > Visualise.

RMarkdown will show you what is possible for digital textual editing that goes beyond simply transcribing and annotating texts. With R you can build in other unique features, such as text analysis and visualizations. Here is an example. Download the "Cranch-notebook-1839.xml" file from our GitHub repo (in the "texts" subdirectory). That is an already-published TEI-XML file of a digital diplomatic edition. But what if I wanted to visualize the most frequent words in the edition? R can quickly handle that, and produce a visualization.

```{r}

```

# R Basics

Begin by downloading R Studio (<https://www.rstudio.com/products/rstudio/>). Once it is up and running, start with a simple mathematical calculation.

```{r}
134600 + 24078 / 5
```

For us, though, we are primarily concerned with text. The most basic data structure is the vector.

Now, let's create a small collection of words called a vector. Any object that contains data is called a data structure and numeric
vectors are the simplest type of data structure in R. In fact, even a single number or word is considered a vector of length one.

The easiest way to create a vector is with the c() function, which stands for 'concatenate' or 'combine'.

```{r}
c("Call", "me", "Ishmael")
```

In R it is very important to store your data in variables, as the code will often refer back to previous functions. Let's create a variable called "md.open" for the previous vector.

```{r}
md.open <- c("Call", "me", "Ishmael")
# To print the results of the variable, simply type it out:
md.open
```
With this variable, R can do quite a bit to analyze its contents and features

```{r}
# To find the length of the vector (i.e., word count):
length(md.open)
# To calculate the character length
nchar(md.open, type = "chars")
# To create a numbered list of each element in the vector:
strsplit(md.open, "characters", "\n")
# To create a table of the words and their frequencies:
table(md.open)
```
These are only a small sample of available text analysis functions.