Title:	Monitoring and Analyzing Dedoose Qualitative Data Exports
Version:	2.0.0.2
Description:	Streamlines analysis of qualitative data exported from 'Dedoose' https://www.dedoose.com. Supports monitoring thematic saturation, calculating code frequencies, organizing excerpts, generating dynamic codebooks, and producing code network maps within 'R'.
License:	Apache License (≥ 2)
URL:	https://abiraahmi.github.io/DedooseR/ https://github.com/abiraahmi/DedooseR
BugReports:	https://github.com/abiraahmi/DedooseR/issues
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	dplyr, tidyr, ggplot2, knitr, DT, tibble, labelled, kableExtra, ggraph, igraph, wordcloud2, tidytext, purrr, haven, openxlsx
VignetteBuilder:	knitr
Config/testthat/edition:	3
Suggests:	rmarkdown, testthat (≥ 3.0.0)
NeedsCompilation:	no
Packaged:	2026-01-08 16:31:32 UTC; abishankar
Author:	Abiraahmi Shankar [aut, cre], Catalina Cañizares [aut], Francisco Cardozo [aut], Sabrina Stockmans [aut], Pamela Morris-Perez [aut]
Maintainer:	Abiraahmi Shankar <abiraahmi.shankar@nyu.edu>
Repository:	CRAN
Date/Publication:	2026-01-09 00:20:07 UTC

Clean and prepare qualitative excerpts for export

Description

This function standardizes and cleans a dataset of qualitative excerpts coded by multiple coders. It standardizes column names, filters excerpts by a preferred coder hierarchy, converts code columns to logical (TRUE/FALSE), assigns descriptive variable labels, and optionally exports the cleaned data to Excel (.xlsx) or Stata (.dta) format. The function also returns a codebook containing variable names, labels, and data types.

Usage

clean_data(
  excerpts,
  preferred_coders,
  rename_vars = NULL,
  relabel_vars = NULL,
  output_path = NULL,
  output_type = c("none", "xlsx", "dta")
)

Arguments

Details

The function performs the following steps:

1. Standardizes variable names (lowercase, underscores instead of spaces).

2. Renames excerpt_copy to excerpt if present.

3. Removes columns ending with "range" or "weight".

4. Detects code columns matching the pattern "^code.*applied$" and converts them to logicals.

5. Renames code columns with a c_ prefix and assigns human-readable variable labels.

6. Filters to the preferred coder per media_title.

7. Applies default labels to key metadata variables (e.g., excerpt_creator, media_title).

8. Optionally renames or relabels variables via user-supplied arguments.

9. Drops columns that are entirely NA.

10. Generates a codebook summarizing variables, labels, and types.

When exporting to .dta, logicals remain stored as TRUE/FALSE rather than being coerced to 0/1. Variable labels are preserved in Stata format using the labelled and haven packages.

Value

A list with two elements:

data

A cleaned data frame with standardized names, filtered coders, and labelled variables.

codebook

A data frame with columns: variable, label, and type.

Examples

## Not run: 
result <- clean_data(
  excerpts = excerpts_raw,
  preferred_coders = c("CoderA", "CoderB"),
  rename_vars = list(new_name = "old_name"),
  relabel_vars = list(old_name = "new variable label"),
  output_path = "cleaned_excerpts.dta",
  output_type = "dta"
)

# Access cleaned data and codebook
head(result$data)
head(result$codebook)

## End(Not run)

Compare Code Saturation Across Threshold Sets

Description

This function compares code saturation results from a code summary table (typically generated by create_code_summary()) against one or more threshold sets that define what constitutes "saturation" based on the number of excerpts (count) and proportion of media titles (prop_media_titles) where each code appears.

Optionally, the function can produce a faceted bar plot showing which codes meet each set of thresholds, with metrics plotted as counts, proportions, or both.

Usage

compare_saturation(
  code_summary,
  thresholds_list,
  output_type = c("tibble", "kable"),
  plot = FALSE,
  plot_metric = c("count", "prop", "both")
)

Arguments

Details

Each threshold set is applied independently. A code is considered to meet a given threshold set if both its excerpt count and proportion of media titles are greater than or equal to the respective thresholds.

Value

If plot = FALSE, returns either a tibble or kable table of results. If plot = TRUE, returns a list with:

• results: the tibble or kable table with logical columns indicating which codes meet each threshold set.

• plot: a ggplot2 object visualizing saturation across threshold sets.

Examples

# Example data: excerpts with coded logical columns
set.seed(123)
excerpts <- data.frame(
  media_title = rep(paste0("Interview_", 1:5), each = 3),
  code_A = sample(c(TRUE, FALSE), 15, replace = TRUE),
  code_B = sample(c(TRUE, FALSE), 15, replace = TRUE),
  code_C = sample(c(TRUE, FALSE), 15, replace = TRUE)
)

# Create a code summary table (from your package function)
code_summary <- create_code_summary(excerpts, output_type = "tibble")

# Define two saturation threshold sets
thresholds_list <- list(
  Liberal = list(code_count = 3, prop_media_title = 0.2),
  Strict  = list(code_count = 5, prop_media_title = 0.5)
)

# Compare saturation (table only)
compare_saturation(code_summary, thresholds_list)

# Compare and plot using proportions
res <- compare_saturation(code_summary, thresholds_list, plot = TRUE, plot_metric = "prop")
res$plot

# Compare and plot both metrics with dual y-axes
res2 <- compare_saturation(code_summary, thresholds_list, plot = TRUE, plot_metric = "both")
res2$plot

Create a Code Co-occurrence Matrix and Network Plot

Description

Builds a co-occurrence matrix showing how often qualitative codes appear together within the same unit (e.g., transcript, document, or media title). The function expects a coded dataset (excerpts) and returns both a formatted matrix and (optionally) a network visualization. The returned matrix can be displayed as raw counts or column-wise proportions, whereas the network plot always reflects the underlying raw counts.

Usage

cooccur(
  excerpts = NULL,
  min_bold = 10,
  scale = c("count", "prop"),
  output = c("kable", "tibble", "data.frame"),
  plot = TRUE,
  edge_min = 10,
  layout = "circle",
  edge_color_low = "lightgray",
  edge_color_high = "purple",
  node_color = "lightblue",
  use_labels = FALSE,
  codebook = NULL
)

Arguments

Details

The function identifies columns beginning with "c_" as code variables. It computes co-occurrences by summing pairwise intersections of codes across all unique media_title units. The diagonal represents the marginal frequencies (the number of transcripts where each code appears).

The resulting matrix can be output as a tibble, a simple data frame, or a formatted HTML table via knitr::kable. If plot = TRUE, the function also returns a network visualization of code co-occurrences using ggraph and igraph. Edges are filtered via the edge_min threshold, and nodes without any remaining connections are removed from the plot.

Value

A named list with two elements:

matrix

A tibble, data frame, or formatted HTML table of the co-occurrence matrix.

plot

A ggplot object visualizing the co-occurrence network (if plot = TRUE).

Examples

# Example 1: Basic co-occurrence matrix from excerpts
df <- data.frame(
  media_title = c("Doc1", "Doc2", "Doc3"),
  c_hope = c(1, 0, 1),
  c_family = c(1, 1, 0),
  c_school = c(0, 1, 1)
)

result <- cooccur(
  excerpts = df,
  scale = "count",
  output = "tibble",
  plot = TRUE
)

result$matrix  # Co-occurrence matrix
result$plot    # Network plot

# Example 2: Use descriptive labels from a codebook and proportions in the table
codebook <- data.frame(
  variable = c("c_hope", "c_family", "c_school"),
  label = c("Hope & Optimism", "Family Connectedness", "School Belonging")
)

labeled_result <- cooccur(
  excerpts = df,
  use_labels = TRUE,
  codebook = codebook,
  scale = "prop",
  output = "kable",
  plot = TRUE
)

labeled_result$matrix
labeled_result$plot

Create a Summary Table and Plot of Code Frequencies

Description

Summarizes how often each qualitative code (represented by logical 0/1 variables) appears across excerpts or media titles. Optionally produces a frequency table and visualization of code distributions.

This function automatically handles Stata-labelled (haven_labelled) or numeric 0/1 variables by converting them to logicals. You can also pass in a custom codebook to apply human-readable code labels.

Usage

create_code_summary(
  excerpts,
  table_min_count = 1,
  table_min_prop = NULL,
  plot = FALSE,
  plot_min_count = NULL,
  plot_min_prop = NULL,
  output_type = c("tibble", "kable", "datatable"),
  exclude = NULL,
  plot_metric = c("count", "prop", "both"),
  fill_color = "steelblue",
  use_labels = FALSE,
  codebook = NULL
)

Arguments

Details

The function first identifies all logical (or 0/1 numeric) columns in excerpts and calculates:

• count: total number of excerpts where the code is applied

• n_media_titles: number of distinct media titles containing the code

• prop_media_titles: proportion of media titles containing the code (relative to max)

The table can be output as a tibble, formatted table (knitr::kable), or interactive data table (DT::datatable).

When plot = TRUE, the function generates a ggplot2 bar chart showing either code counts, proportions, or both (dual-axis view).

Value

If plot = FALSE, returns a table in the selected output_type format. If plot = TRUE, invisibly returns a list with two elements:

table

A table of summarized code frequencies.

plot

A ggplot object visualizing the results.

Examples

# Example 1: Basic usage without a codebook
df <- data.frame(
  media_title = c("Doc1", "Doc2", "Doc3", "Doc4"),
  code_a = c(TRUE, FALSE, TRUE, TRUE),
  code_b = c(FALSE, TRUE, TRUE, FALSE)
)

create_code_summary(df, plot = TRUE)

# Example 2: Using a codebook for readable labels
codebook <- data.frame(
  variable = c("code_a", "code_b"),
  label = c("Community Engagement", "Policy Support")
)

create_code_summary(
  df,
  use_labels = TRUE,
  codebook = codebook,
  plot = TRUE,
  plot_metric = "both"
)

# Example 3: Excluding a code and outputting as datatable
create_code_summary(
  df,
  exclude = "code_b",
  output_type = "datatable"
)

Recode logical code variables and optionally relabel them

Description

recode_themes() combines multiple logical (TRUE/FALSE) code variables into new composite variables. For each new variable, the function computes a logical OR across the specified source variables—meaning the new variable is TRUE when any source variable is TRUE. Optionally, descriptive labels can be supplied for the newly created variables, and a codebook summarizing the resulting dataset is returned.

Usage

recode_themes(data, recodes, relabel_vars = NULL)

Arguments

Details

The function first verifies that the specified source variables exist in the dataset. It then creates the new logical variables defined by recodes, assigns user-specified or default labels, removes the original source variables (unless one overlaps with a new variable name), and builds a codebook summarizing the recoded dataset.

Value

A list with four elements:

data_recode

A data frame containing the updated dataset with recoded logical code variables.

codebook_recode

A data frame summarizing variable names, labels (if available), and data types.

data_merged

Alias for data_recode retained for backward compatibility.

codebook_merged

Alias for codebook_recode retained for backward compatibility.

Examples

# Example dataset
df <- data.frame(
c_support = c(TRUE, FALSE, TRUE),
c_assist = c(FALSE, TRUE, TRUE),
c_anxiety = c(TRUE, FALSE, FALSE),
c_pressure = c(FALSE, TRUE, FALSE)
)

# Define recodes
recode_plan <- list(
c_help = c("c_support", "c_assist"),
c_stress = c("c_anxiety", "c_pressure")
)

# Run recode_themes() with new labels
result <- recode_themes(
data = df,
recodes = recode_plan,
relabel_vars = list(
c_help = "Mentions of helping or supporting others",
c_stress = "Mentions of stress or pressure"
)
)

# Extract recoded data and codebook
data_recode <- result$data_recode
codebook_recode <- result$codebook_recode

# View recoded dataset
head(data_recode)

# View codebook
head(codebook_recode)

Compute and Visualize Code Saturation

Description

This function summarizes code counts and their proportional representation across media titles (e.g., interviews, focus groups, or other qualitative data sources). It can optionally produce a formatted table and/or a ggplot visualization showing saturation by code frequency or proportion.

Usage

set_saturation(
  code_counts,
  total_media_titles = NULL,
  table_min_count = 1,
  table_min_prop = NULL,
  output_type = c("tibble", "kable"),
  plot = FALSE,
  plot_min_count = NULL,
  plot_min_prop = NULL,
  plot_metric = c("prop", "count", "both"),
  fill_color = "steelblue"
)

Arguments

Value

• If plot = FALSE: returns a tibble (or kable table) summarizing code frequencies and proportions.

• If plot = TRUE: returns a list with two elements:

  • table: the filtered tibble

  • plot: a ggplot2 object.

Examples

# Example dataset
code_counts <- tibble::tibble(
  code = c("Belonging", "Resilience", "Stress", "Hope"),
  count = c(15, 10, 8, 5),
  n_media_titles = c(8, 6, 5, 3)
)

# Basic usage (returns a tibble)
set_saturation(code_counts)

# Apply count and proportion filters, return a kable table
set_saturation(
  code_counts,
  total_media_titles = 10,
  table_min_count = 5,
  table_min_prop = 0.3,
  output_type = "kable"
)

# Generate a plot of proportions
res <- set_saturation(
  code_counts,
  total_media_titles = 10,
  plot = TRUE,
  plot_metric = "prop"
)
res$plot

# Plot both count and proportion using dual y-axes
res <- set_saturation(
  code_counts,
  total_media_titles = 10,
  plot = TRUE,
  plot_metric = "both",
  fill_color = "darkgreen"
)
res$plot

View Qualitative Excerpts by Code

Description

Displays qualitative excerpts interactively in a searchable, filterable data table. Each row represents an excerpt associated with one or more qualitative codes. Code columns are automatically detected as those starting with "c_", and their variable labels (if available) are used as readable code names.

This function is primarily designed for exploring and reviewing coded qualitative data, allowing users to filter by code and quickly browse the corresponding excerpts.

Usage

view_excerpts(data)

Arguments

Details

• Variable labels are extracted from the "label" attribute of each code column (e.g., assigned via haven::labelled or attr(x, "label") <- "Label").

• Only excerpts where a code is marked as TRUE are displayed.

• The table uses custom styling with a purple header and automatic text wrapping.

Value

A DT::datatable() object that displays:

• Code: readable code label or variable name

• Excerpt: associated qualitative text

The output table includes:

• A dropdown filter for selecting specific codes

• Search boxes for column-wise filtering

• Responsive column widths and formatted text wrapping

Examples

library(dplyr)

df <- tibble::tibble(
  excerpt = c(
    "I felt supported by my peers.",
    "Teachers really listened to us.",
    "I learned a lot about myself."
  ),
  c_support = c(TRUE, TRUE, FALSE),
  c_growth = c(FALSE, FALSE, TRUE)
)
attr(df$c_support, "label") <- "Peer/Teacher Support"
attr(df$c_growth, "label") <- "Personal Growth"

# View excerpts interactively
if (interactive()) view_excerpts(df)

Generate a word cloud for excerpts by code

Description

Creates a word cloud of words from all excerpts where a given code is applied. Common English stop words, user-supplied stop words, and punctuation are removed.

Usage

wordcloud(data, code, max_words = 100, custom_stopwords = NULL)

Arguments

Value

An interactive word cloud (from wordcloud2).

Examples

library(dplyr)
df <- tibble::tibble(
  excerpt = c(
    "I felt connected to peers and friends.",
    "We should normalize conversations about mental health.",
    "My teachers helped me belong at school.",
    "I am comfortable talking about suicide prevention."
  ),
  c_belonging = c(TRUE, FALSE, TRUE, FALSE),
  c_destigmatization = c(FALSE, TRUE, FALSE, FALSE)
)

# Word cloud for belonging excerpts
wordcloud(df, "c_belonging")

# With custom stop words
wordcloud(df, "c_belonging", custom_stopwords = c("connected", "school"))