ConversationAlign Step1 Read

Read and Format Data for ConversationAlign

Jamie Reilly, Ben Sacks, Ginny Ulichney, Gus Cooney, Chelsea Helion

2025-07-21

Reading data into R for ConversationAlign

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.

Prepping your data for import

• ConversationAlign works ONLY on dyadic (i.e., two person) conversation transcripts.
• Each transcript must nominally contain two colummns, one column should delineate the interlocutor (person who produced the text), and another column should contain the text itself.
• 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.
• Store each of your individual conversation transcripts (.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()
• Each transcript must nominally contain two columns of data (Participant and Text). All other columns (e.g., meta-data) will be retained.

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

#will search for folder 'my_transcripts' in your current directory
MyConvos <- read_dyads()

#will scan custom folder called 'MyStuff' in your current directory, concatenating all files in that folder into a single dataframe
MyConvos2 <- read_dyads(my_path='/MyStuff')

read_1file()

• Read single transcript already in R environment. We will use 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?