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DedooseR

DedooseR is an R package that connects with Dedoose to support the analysis of qualitative data. It was built to help researchers streamline workflows, explore qualitative data flexibly, and conduct qualitative coding and analysis with rigor.

Key Features

DedooseR currently has 8 key functions that allow you to:

  • clean_data: standardizes column names, keeps the highest ranked coder per transcript, drops range/weight columns, prefixes code variables with c_, and returns both the cleaned data and a codebook.
  • recode: combines selected codes into a single logical column and updates the codebook
  • view_excerpts: create an interactive, filterable datatable to view the excerpts behind each code
  • wordcloud: filters excerpts for a selected code, removes common stop words, and renders the result into a beautiful word cloud
  • create_code_summary to summarize code counts and the proportion of transcripts/media objects they come from, set a min count or proportion for the summary output and plot counts, proportions or both!
  • set_saturation: uses the output of create_code_summary to filter and visualiz codes that meet minimum appearance targets
  • compare_saturation: builds on the same summary table to check multiple threshold sets at once - useful when you want a strict bar versus a more liberal bar. You can also plot these different bars against each other!
  • cooccurence: helps you see which codes travel together within the same transcript or media title, building both a matrix and a network plot

Why This Package?

Ongoing challenges in qualitative research include defining what constitutes high-quality data and demonstrating transparency in how saturation is reached (Small & Calarco, 2022). Informed by guidelines for high-quality qualitative research (Hennink & Kaiser, 2022), DedooseR allows you to better understand your data with quality tags in Dedoose like:

  • The concreteness of excerpts (priority code)
  • The heterogeneity within codes (heterogeneity code)

By tagging these indicators in Dedoose and exploring them in R, this allows for gain greater confidence in both the depth and diversity of datasets.

Installation

You can install the development version of DedooseR from GitHub with:

# install.packages("pak")
pak::pak("abiraahmi/DedooseR")

How do I use the package?

The vignettes walk you through how to use each of the functions, from cleaning to recoding to viewing excerpts to assessing saturation and creating code co-occurence network maps, so do check them out!

References

Hennink, M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social science & medicine, 292, 114523.

Small, M. L., & Calarco, J. M. (2022). Qualitative Literacy: A Guide to Evaluating Ethnographic and Interview Research (1st ed.). University of California Press. https://doi.org/10.2307/j.ctv2vr9c4x