# ConversationAlign_Step1_Read

### Read and Format Data for ConversationAlign

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

#### 2025-11-12

# 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?