Half the battle with R is getting your data imported and formatted.
This is especially true for string data and working with text.
ConversationAlign uses a series of sequential functions to
import, clean, and format your raw data. You MUST run
each of these functions. They append important variable names and
automatically reshape your data.
ConversationAlign works ONLY on dyadic
(i.e., two person) conversation transcripts.ConversationAlign contains an import function called
read_dyads() that will scan a target folder for text
samples.read_dyads() will import all of your transcripts into R
and concatenate them into a single dataframe.read_dyads() will append each transcript’s filename as
a unique identifier for that conversation. This is SUPER important to
remember when analyzing your data..csv, .txt, .ai) that you wish
to concatenate into a corpus in a folder. ConversationAlign
will search for a folder called my_transcripts in the same
directory as your script. However, feel free to name your folder
anything you like. You can specify a custom path as an argument to
read_dyads()read_dyads()Here are some exampples of read_dyads() in action. There
is only one argument to read_dyads(), and that is
my_path. This is for supplying a quoted directory path to
the folder where your transcripts live. Remember to treat this folder as
a staging area! Once you are finished with a set of transcripts and
don’t want them read into ConversationAlign move them out
of the folder, or specify a new folder. Language data tends to
proliferate quickly, and it is easy to forget what you are doing. Be a
CAREFUL secretary, and record your steps.
Arguments to read_dyads
include:
1. my_path: default is
‘my_transcripts’, change path to your folder name
read_1file()read_1file() to prep the Marc Maron and Terry Gross
transcript. Look at how the column headers have changed and the object
name (MaronGross_2013) is now the Event_ID (a document identifier),
Arguments to read_1file
include:
1. my_dat: object already in your
R environment containing text and speaker information.
MaryLittleLamb <- read_1file(MaronGross_2013)
#print first ten rows of header
knitr::kable(head(MaronGross_2013, 15), format = "pipe")| speaker | text |
|---|---|
| MARON | I’m a little nervous but I’ve prepared I’ve written things on a piece of paper |
| MARON | I don’t know how you prepare I could ask you that - maybe I will But this is how I prepare - I panic |
| MARON | For a while |
| GROSS | Yeah |
| MARON | And then I scramble and then I type some things up and then I handwrite things that are hard to read So I can you know challenge myself on that level during the interview |
| GROSS | Being self-defeating is always a good part of preparation |
| MARON | What is? |
| GROSS | Being self-defeating |
| MARON | Yes |
| GROSS | Self-sabotage |
| MARON | Yes |
| GROSS | Key |
| MARON | Right so you do that? |
| GROSS | I sometimes do that |
| MARON | How often? |