Nothing
R/read_dyads.R
In ConversationAlign: Process Text and Compute Linguistic Alignment in Conversation Transcripts
Defines functions
read_dyads
Documented in
read_dyads
#' read_dyads
#'
#' Reads pre-formatted dyadic (2 interlocutor) conversation transcripts from your machine. Transcripts must be either csv or txt format. IF you are supplying a txt file, your transcript must be formatted as an otter.ai txt file export. Your options for using csv files are more flexible. ConversationAlign minimally requires a csv file with two columns, denoting interlocutor and text. Each separate conversation transcript should be saved as a separate file. ConversationAlign will use the file names as a document ID. Within the read dyads function, set the my_path argument as the directory path to the local folder containing your transcripts on your machine (e.g., "my_transcripts"). Please see our github page for examples of properly formatted transcripts: https://github.com/Reilly-ConceptsCognitionLab/ConversationAlign
#'
#' @name read_dyads
#' @param my_path folder of conversation transcripts in csv or txt format
#' @returns
#' a dataframe where each individual conversation transcript in a user's directory has been concatenated.
#' read_dyads appends a unique document identifier to each conversation transcript appending its unique filename as a factor level to 'Event_ID'.
#' @importFrom magrittr %>%
#' @importFrom dplyr bind_rows
#' @importFrom utils read.csv
#' @export
read_dyads <- function(my_path = "my_transcripts") {
read_otter_transcript <- function(file_path) {
lines <- readLines(file_path) #read otter ai file
#removes otter ai watermark if it is present
if (any(grepl("otter.ai", lines)) == TRUE) {
lines <- as.character(lines[-(grep("otter.ai", lines))])}
num_lines <- length(lines) #create a var for number of lines
speaker <- character()
time <- character()
text <- character()
#process lines of dialogue
current_line <- 1
while (current_line <= num_lines) {
first_line_vec <- strsplit(lines[current_line], " ")
speaker_time <- first_line_vec[[1]]
speaker <- c(speaker, speaker_time[1]) #select speaker
# check if time is included in mm:ss format and if so add it to the time column
if (any(grepl(":", speaker_time)) == TRUE) {
timeadd <- grep(":", speaker_time)
}
else {timeadd <- NA}
time <- append(time, timeadd)
#select lines of speech
speech_lines <- character()
line_counter <- current_line + 1
while (line_counter <= num_lines && lines[line_counter] != "") { #if not max line/empty line
speech_lines <- c(speech_lines, lines[line_counter]) #add text on line to speech text vector
line_counter <- line_counter + 1
}
text <- append(text, paste(speech_lines, collapse = " "))
current_line <- line_counter + 1 #move to next speaker
}
#create df
transcript_df <- data.frame(Participant_ID = speaker,
Time = time,
Text_Raw = text,
stringsAsFactors = FALSE)
return(transcript_df)
}
#END DEFINE OTTER READ TRANSCRIPT .TXT FILE FUNCTION
#DEFINE READ ME TXT FILE FUNCTION
read_dyads_txt <- function(my_path){
if (any(grepl("*.txt$", list.files(path = my_path, pattern = ".", full.names = TRUE, recursive = TRUE))) == TRUE) {
file_list_txt <- list.files(path = my_path, pattern = "*.txt$", full.names = TRUE, recursive = TRUE) #list files with .txt ending
file_names_txt <- list.files(path = my_path, pattern = ".txt$", full.names = FALSE, recursive = TRUE)
file_names_txt <- gsub('.*/ ?(\\w+)', '\\1', file_names_txt)
file_names_txt <- gsub(".txt$", "", file_names_txt)
txtdata <- lapply(file_list_txt, function(x) {
#runs txt files names through otter reading function
xorf <- read_otter_transcript(x)
#selects those that were properly transcribed by otter, still need to add other parameters
if (ncol(xorf) == 3) {
x <- xorf
}
else {
# Other function for reading in non-otter txt transcripts can go here later
stop(paste("Unable to read transcript ", as.character(match(x, file_list_txt)), " as an otter transcript. Please refer to the ConversationAlign GitHub page for examples of properly formatted transcripts.", sep = ""), call. = FALSE)
}
})
#adds a doc id column to each transcript based on its name attribute
txtdata <- lapply(file_names_txt, function(x){
txtdata[[match(x, file_names_txt)]] <- cbind(Event_ID = rep(x, nrow(txtdata[[match(x, file_names_txt)]])), txtdata[[match(x, file_names_txt)]])})
return(txtdata)
}}
#END DEFINE READ ME TXT FILE FUNCITON
#DEFINE READ ME CSV FILE FUNCTION
read_dyads_csv <- function(my_path) {
if (any(grepl("*.csv$", list.files(path = my_path, pattern = ".",
full.names = TRUE, recursive = TRUE))) == TRUE) {
file_list_csv <- list.files(path = my_path, pattern = "*.csv$",
full.names = TRUE, recursive = TRUE)
file_names_csv <- list.files(path = my_path, pattern = ".csv$",
full.names = FALSE, recursive = TRUE)
file_names_csv<- gsub('.*/ ?(\\w+)', '\\1', file_names_csv)
file_names_csv <- gsub(".csv$", "", file_names_csv)
#creates a list of read in csv data frames
csvdata <- lapply(file_list_csv, function(x){
x_read_csv <- read.csv(x, header = TRUE)
#identify and remove all columns that are entirely NA or empty strings
sum_nas_es <- apply(x_read_csv, 2, function(y){sum(is.na(y) | y == "")})
x_read_csv <- x_read_csv[sum_nas_es < nrow(x_read_csv)]
x_read_csv <- data.frame(x_read_csv)
#check that the column names are correct
if ((any(grepl("^speaker$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^speaker_names_raw$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Participant$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Interlocutor$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Patient$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Person$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Partner$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Source$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^PID$", colnames(x_read_csv), ignore.case = T)) == TRUE) &
(any(grepl("^Text$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Turn$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^talker$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^MyText$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Utterance$", colnames(x_read_csv), ignore.case = T)) == TRUE)) {
#correct the speaker and text names to our conventions
colnames(x_read_csv)[which(grepl("speaker", colnames(x_read_csv), ignore.case = T) |
grepl("PID", colnames(x_read_csv), ignore.case = T) |
grepl("interlocutor", colnames(x_read_csv), ignore.case = T) |
grepl("patient", colnames(x_read_csv), ignore.case = T) |
grepl("person", colnames(x_read_csv), ignore.case = T) |
grepl("partner", colnames(x_read_csv), ignore.case = T) |
grepl("source", colnames(x_read_csv), ignore.case = T) |
grepl("talker", colnames(x_read_csv), ignore.case = T) |
grepl("participant", colnames(x_read_csv), ignore.case = T))] <- "Participant_ID"
colnames(x_read_csv)[which(grepl("Text", colnames(x_read_csv), ignore.case = T) |
grepl("utterance", colnames(x_read_csv), ignore.case = T))] <- "Text_Raw"
x_read_csv <- data.frame(x_read_csv)
x_final <- x_read_csv
}
col_check <- sum(colnames(x_read_csv) %in% c("Participant_ID", "Text_Raw"))
if (col_check != 2) { #if there are less than two columns
stop(paste("Function is unable to process csv transcript ", #error stating missing column
as.character(match(x, file_list_csv)), #also states the transcript
" correctly. Make sure that each transcript includes a column marking who is producing text in each row.
When you are preparing your language transcripts, please make sure this column is named one of the following: 'interlocutor', 'person', 'partner', 'source', 'speaker', 'participant', 'PID', or 'speaker_names_raw'.
Please make sure your a column containing raw language transcriptions is named 'utterance', 'turn', or 'text'.", sep = ""), call. = FALSE)
}
x_final
})
csvdata <- lapply(csvdata, data.frame)
csvdata <- lapply(file_names_csv, function(x){
csvdata[[match(x, file_names_csv)]] <- cbind(Event_ID = rep(x, nrow(csvdata[[match(x, file_names_csv)]])), csvdata[[match(x, file_names_csv)]])})
csvdata
}}
#END DEFINE READ ME CSV FILE FUNCTION
#calls two functions to read in txt and csv file transcripts, returning a list.
txtlist <- read_dyads_txt(my_path)
csvlist <- read_dyads_csv(my_path)
all_list <- append(txtlist, csvlist) #append the two lists into one list
#throws an error if no files are found
if (length(all_list) == 0) {
stop("No files found. Please make sure you are providing the local or absolute file path to the desired folder as a character vector. At least one .csv or .txt file must be present.")
}
alldf <- dplyr::bind_rows(all_list) #binds the rows of each list into one data frame
alldf$Event_ID <- as.factor(alldf$Event_ID)
alldf$Participant_ID <- as.factor(alldf$Participant_ID)
dat_read <- alldf
# NEW VALIDATION STEP: Check for exactly two participants per event
participant_check <- dat_read %>%
dplyr::group_by(Event_ID) %>%
dplyr::summarize(
n_participants = n_distinct(Participant_ID),
.groups = "drop"
)
# Identify events that don't have exactly 2 participants
invalid_events <- participant_check %>%
dplyr::filter(n_participants != 2) %>%
dplyr::pull(Event_ID)
if (length(invalid_events) > 0) {
error_message <- paste(
"Check your data! One or more conversations has more or less than two conversation partners.\n",
"ConversationAlign only works on dyadic transcripts. Each conversation transcript must have ONLY two participants.\n",
"Problematic Event_ID(s):", paste(invalid_events, collapse = ", ")
)
stop(error_message, call. = FALSE)
}
return(dat_read)
}
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ConversationAlign documentation built on Aug. 8, 2025, 7:22 p.m.
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Defines functions read_dyads
Documented in read_dyads
#' read_dyads
#'
#' Reads pre-formatted dyadic (2 interlocutor) conversation transcripts from your machine. Transcripts must be either csv or txt format. IF you are supplying a txt file, your transcript must be formatted as an otter.ai txt file export. Your options for using csv files are more flexible. ConversationAlign minimally requires a csv file with two columns, denoting interlocutor and text. Each separate conversation transcript should be saved as a separate file. ConversationAlign will use the file names as a document ID. Within the read dyads function, set the my_path argument as the directory path to the local folder containing your transcripts on your machine (e.g., "my_transcripts"). Please see our github page for examples of properly formatted transcripts: https://github.com/Reilly-ConceptsCognitionLab/ConversationAlign
#'
#' @name read_dyads
#' @param my_path folder of conversation transcripts in csv or txt format
#' @returns
#' a dataframe where each individual conversation transcript in a user's directory has been concatenated.
#' read_dyads appends a unique document identifier to each conversation transcript appending its unique filename as a factor level to 'Event_ID'.
#' @importFrom magrittr %>%
#' @importFrom dplyr bind_rows
#' @importFrom utils read.csv
#' @export
read_dyads <- function(my_path = "my_transcripts") {
read_otter_transcript <- function(file_path) {
lines <- readLines(file_path) #read otter ai file
#removes otter ai watermark if it is present
if (any(grepl("otter.ai", lines)) == TRUE) {
lines <- as.character(lines[-(grep("otter.ai", lines))])}
num_lines <- length(lines) #create a var for number of lines
speaker <- character()
time <- character()
text <- character()
#process lines of dialogue
current_line <- 1
while (current_line <= num_lines) {
first_line_vec <- strsplit(lines[current_line], " ")
speaker_time <- first_line_vec[[1]]
speaker <- c(speaker, speaker_time[1]) #select speaker
# check if time is included in mm:ss format and if so add it to the time column
if (any(grepl(":", speaker_time)) == TRUE) {
timeadd <- grep(":", speaker_time)
}
else {timeadd <- NA}
time <- append(time, timeadd)
#select lines of speech
speech_lines <- character()
line_counter <- current_line + 1
while (line_counter <= num_lines && lines[line_counter] != "") { #if not max line/empty line
speech_lines <- c(speech_lines, lines[line_counter]) #add text on line to speech text vector
line_counter <- line_counter + 1
}
text <- append(text, paste(speech_lines, collapse = " "))
current_line <- line_counter + 1 #move to next speaker
}
#create df
transcript_df <- data.frame(Participant_ID = speaker,
Time = time,
Text_Raw = text,
stringsAsFactors = FALSE)
return(transcript_df)
}
#END DEFINE OTTER READ TRANSCRIPT .TXT FILE FUNCTION
#DEFINE READ ME TXT FILE FUNCTION
read_dyads_txt <- function(my_path){
if (any(grepl("*.txt$", list.files(path = my_path, pattern = ".", full.names = TRUE, recursive = TRUE))) == TRUE) {
file_list_txt <- list.files(path = my_path, pattern = "*.txt$", full.names = TRUE, recursive = TRUE) #list files with .txt ending
file_names_txt <- list.files(path = my_path, pattern = ".txt$", full.names = FALSE, recursive = TRUE)
file_names_txt <- gsub('.*/ ?(\\w+)', '\\1', file_names_txt)
file_names_txt <- gsub(".txt$", "", file_names_txt)
txtdata <- lapply(file_list_txt, function(x) {
#runs txt files names through otter reading function
xorf <- read_otter_transcript(x)
#selects those that were properly transcribed by otter, still need to add other parameters
if (ncol(xorf) == 3) {
x <- xorf
}
else {
# Other function for reading in non-otter txt transcripts can go here later
stop(paste("Unable to read transcript ", as.character(match(x, file_list_txt)), " as an otter transcript. Please refer to the ConversationAlign GitHub page for examples of properly formatted transcripts.", sep = ""), call. = FALSE)
}
})
#adds a doc id column to each transcript based on its name attribute
txtdata <- lapply(file_names_txt, function(x){
txtdata[[match(x, file_names_txt)]] <- cbind(Event_ID = rep(x, nrow(txtdata[[match(x, file_names_txt)]])), txtdata[[match(x, file_names_txt)]])})
return(txtdata)
}}
#END DEFINE READ ME TXT FILE FUNCITON
#DEFINE READ ME CSV FILE FUNCTION
read_dyads_csv <- function(my_path) {
if (any(grepl("*.csv$", list.files(path = my_path, pattern = ".",
full.names = TRUE, recursive = TRUE))) == TRUE) {
file_list_csv <- list.files(path = my_path, pattern = "*.csv$",
full.names = TRUE, recursive = TRUE)
file_names_csv <- list.files(path = my_path, pattern = ".csv$",
full.names = FALSE, recursive = TRUE)
file_names_csv<- gsub('.*/ ?(\\w+)', '\\1', file_names_csv)
file_names_csv <- gsub(".csv$", "", file_names_csv)
#creates a list of read in csv data frames
csvdata <- lapply(file_list_csv, function(x){
x_read_csv <- read.csv(x, header = TRUE)
#identify and remove all columns that are entirely NA or empty strings
sum_nas_es <- apply(x_read_csv, 2, function(y){sum(is.na(y) | y == "")})
x_read_csv <- x_read_csv[sum_nas_es < nrow(x_read_csv)]
x_read_csv <- data.frame(x_read_csv)
#check that the column names are correct
if ((any(grepl("^speaker$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^speaker_names_raw$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Participant$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Interlocutor$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Patient$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Person$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Partner$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Source$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^PID$", colnames(x_read_csv), ignore.case = T)) == TRUE) &
(any(grepl("^Text$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Turn$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^talker$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^MyText$", colnames(x_read_csv), ignore.case = T)) == TRUE |
any(grepl("^Utterance$", colnames(x_read_csv), ignore.case = T)) == TRUE)) {
#correct the speaker and text names to our conventions
colnames(x_read_csv)[which(grepl("speaker", colnames(x_read_csv), ignore.case = T) |
grepl("PID", colnames(x_read_csv), ignore.case = T) |
grepl("interlocutor", colnames(x_read_csv), ignore.case = T) |
grepl("patient", colnames(x_read_csv), ignore.case = T) |
grepl("person", colnames(x_read_csv), ignore.case = T) |
grepl("partner", colnames(x_read_csv), ignore.case = T) |
grepl("source", colnames(x_read_csv), ignore.case = T) |
grepl("talker", colnames(x_read_csv), ignore.case = T) |
grepl("participant", colnames(x_read_csv), ignore.case = T))] <- "Participant_ID"
colnames(x_read_csv)[which(grepl("Text", colnames(x_read_csv), ignore.case = T) |
grepl("utterance", colnames(x_read_csv), ignore.case = T))] <- "Text_Raw"
x_read_csv <- data.frame(x_read_csv)
x_final <- x_read_csv
}
col_check <- sum(colnames(x_read_csv) %in% c("Participant_ID", "Text_Raw"))
if (col_check != 2) { #if there are less than two columns
stop(paste("Function is unable to process csv transcript ", #error stating missing column
as.character(match(x, file_list_csv)), #also states the transcript
" correctly. Make sure that each transcript includes a column marking who is producing text in each row.
When you are preparing your language transcripts, please make sure this column is named one of the following: 'interlocutor', 'person', 'partner', 'source', 'speaker', 'participant', 'PID', or 'speaker_names_raw'.
Please make sure your a column containing raw language transcriptions is named 'utterance', 'turn', or 'text'.", sep = ""), call. = FALSE)
}
x_final
})
csvdata <- lapply(csvdata, data.frame)
csvdata <- lapply(file_names_csv, function(x){
csvdata[[match(x, file_names_csv)]] <- cbind(Event_ID = rep(x, nrow(csvdata[[match(x, file_names_csv)]])), csvdata[[match(x, file_names_csv)]])})
csvdata
}}
#END DEFINE READ ME CSV FILE FUNCTION
#calls two functions to read in txt and csv file transcripts, returning a list.
txtlist <- read_dyads_txt(my_path)
csvlist <- read_dyads_csv(my_path)
all_list <- append(txtlist, csvlist) #append the two lists into one list
#throws an error if no files are found
if (length(all_list) == 0) {
stop("No files found. Please make sure you are providing the local or absolute file path to the desired folder as a character vector. At least one .csv or .txt file must be present.")
}
alldf <- dplyr::bind_rows(all_list) #binds the rows of each list into one data frame
alldf$Event_ID <- as.factor(alldf$Event_ID)
alldf$Participant_ID <- as.factor(alldf$Participant_ID)
dat_read <- alldf
# NEW VALIDATION STEP: Check for exactly two participants per event
participant_check <- dat_read %>%
dplyr::group_by(Event_ID) %>%
dplyr::summarize(
n_participants = n_distinct(Participant_ID),
.groups = "drop"
)
# Identify events that don't have exactly 2 participants
invalid_events <- participant_check %>%
dplyr::filter(n_participants != 2) %>%
dplyr::pull(Event_ID)
if (length(invalid_events) > 0) {
error_message <- paste(
"Check your data! One or more conversations has more or less than two conversation partners.\n",
"ConversationAlign only works on dyadic transcripts. Each conversation transcript must have ONLY two participants.\n",
"Problematic Event_ID(s):", paste(invalid_events, collapse = ", ")
)
stop(error_message, call. = FALSE)
}
return(dat_read)
}
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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