The wizaRdry package provides a comprehensive data analysis framework specifically designed for NIH-funded computational psychiatry, neuroscience, and psychology research with built-in NIH Data Archive (NDA) integration.

You can install the latest published version from CRAN with:

install.packages("wizaRdry")

Alternativley, you can install the latest development version from GitHub:

remove.packages("wizaRdry")
rstudioapi::restartSession()
if(!require(devtools)) {install.packages("devtools")}; library(devtools)
devtools::install_github("belieflab/wizaRdry")

After installation, follow these steps to set up your project:

Use the scry() function to create the necessary directory structure:

library(wizaRdry)
scry()

This will create a standard directory structure that looks like this:

.
├── clean
│   ├── csv
│   ├── mongo
│   ├── qualtrics
│   ├── redcap
│   └── sql
├── nda
│   ├── csv
│   ├── mongo
│   ├── qualtrics
│   ├── redcap
│   └── sql
├── tmp
├── .gitignore
├── config.yml
├── main.R
├── project.Rproj
└── secrets.R

Each directory has a specific purpose in the wizaRdry workflow:

• clean/ - Scripts for cleaning and processing raw data
• nda/ - Scripts for preparing NDA submission templates
• tmp/ - Temporary output files
• Configuration files at the root level

Edit the generated secrets.R file to add your API credentials:

# REDCap
uri   <- "https://your-redcap-instance.edu/api/"
token <- "YOUR_TOKEN"

# Qualtrics
apiKeys <- c("API_KEY_1", "API_KEY_2")
baseUrls <- c("BASE_URL_1", "BASE_URL_2")

# MongoDB
connectionString <- "mongodb://your-connection-string"

# SQL
conn <- "BASE_URL"
uid <- "USERNAME"
pwd <- "PASSWORD"

Edit the generated config.yml file to specify your study settings:

default:
  study_alias: yourstudy
  identifier: src_subject_id
  mongo:
    database: ${study_alias}
  qualtrics:
    survey_ids:
      Institution1:
        survey_alias: "SV_QUALTRICS_ID"
  redcap:
    primary_key: record_id
    superkey: ndar_subject01
  sql:
    primary_key: 'sub_id'
    superkey: 'phi'
    schemas:
      - ${study_alias}
    pii_fields:
      - 'name_first'
      - 'name_middle'
      - 'name_last'

Additionally, edit the generated config.yml file to specify missing data codes if they are used in your data.

You may use multiple types of codes (skipped, refused, missing, undefined) and multiple codes for each.

These values will be replaced by the missing data codes associated with each NDA Data Structure automatically when using nda()

default:
  missing_data_codes:
    skipped:
      - -888   # Skip pattern/branching logic
    refused:
      - -9999  # Explicitly declined to answer (radio buttons)
      - -1     # Explicitly declined to answer (text boxes)
    missing:
      - -777    # Missing for unknown reasons
    undefined:
      - -555   # Otherwise undefined value

• Project scaffolding: Creates standard directory structures with scry()
• Cross-modal data access: Unified interface to REDCap, MongoDB, Qualtrics and SQL (beta)
• Memory-aware parallel processing: Automatically scales to available resources
• Field harmonization: Standardizes data fields across platforms
• NIH Data Archive integration: Prepares submissions for NDA compliance
• Collaborative workflow: Enables multiple researchers to work from the same data source

wizaRdry provides a suite of functions organized by their purpose in the data workflow:

# Initialize project structure
scry()

#  data from REDCap
demoses01 <- redcap("demoses01")

# Get data from Qualtrics
lshrs01 <- qualtrics("lshrs01")

# Get data from MongoDB
prl01 <- mongo("prl01")

# Data Cleaning Workflow - run cleaning scripts and validation
clean("demo", "rgpts", "overfitting", csv = TRUE)

Cleaning scripts are written inside the clean/ directory and called by their script name (e.g., "demo" for demographics) in clean()

# Filter data
filtered_data <- sift(df, rows = c("sub001","sub002"),
                          cols = c("src_subject_id", "phenotype"))

# Merge datasets
merged_data <- meld(demo_clean, rgpts_clean) 

# Parse multi-survey datasets
rune("overfitting")

# NDA Submission Workflow - prepare NDA templates
nda("demoses01", "lshrs01", "prl01")

# Create CSV output
to.csv(df, "data_export")

# Create R data file
to.rds(df, "data_export")

# Create SPSS file
to.sav(df, "data_export")

The wizaRdry package supports two distinct but complementary workflows:

This workflow focuses on accessing and cleaning raw data for analysis:

• Place cleaning scripts in the clean/ directory
• Scripts should be organized by data source: clean/qualtrics/, clean/redcap/, clean/mongo/
• Name your cleaned datasets with the _clean suffix (e.g., rgpts_clean)
• Access and process data with:

# Process data from multiple sources in one command
clean("rgpts", "wtar", "prl", csv = TRUE)

This runs your cleaning scripts, performs validation tests, and exports cleaned data.

This workflow prepares data for NIH Data Archive submission:

• Place NDA remediation scripts in the nda/ directory
• Scripts should follow NDA structure naming: nda/qualtrics/rgpts01.R
• NDA structure names typically end with a two-digit suffix (e.g., 01)
• Process and validate NDA structures with:

# Prepare NDA submission templates
nda("rgpts01", "wtar01", "prl01", csv = TRUE)

This creates properly formatted NDA submission templates in the .nda/tmp directory.

# Get raw data from Qualtrics
rgpts <- qualtrics("rgpts")

# Cleaning process
rgpts$interview_date <- as.Date(rgpts$interview_date, "%m/%d/%Y")
rgpts$src_subject_id <- as.numeric(rgpts$src_subject_id)

# Calculate scores
rgpts$rgpts_total <- rowSums(rgpts[,grep("^rgpts_q\\d+$", names(rgpts))], na.rm = TRUE)

# Final cleaned dataset
rgpts_clean <- rgpts

# Get data for NDA submission
rgpts01 <- qualtrics("rgpts")

# Apply NDA standards
rgpts01$src_subject_id <- as.character(rgpts01$src_subject_id)
rgpts01$interview_date <- format(as.Date(rgpts01$interview_date, "%m/%d/%Y"), "%m/%d/%Y")

# Ensure NDA structure compliance
if (!"visit" %in% names(rgpts01)) {
  rgpts01$visit <- "baseline"
}

# Additional NDA-specific processing...

If you use wizaRdry in your research, please cite it:

Kenney, J., Williams, T., Pappu, M., Spilka, M., Pratt, D., Pokorny, V., Castiello de Obeso, S., Suthaharan, P., & Horgan, C. (2025). 
wizaRdry: A Framework For Collaborative & Reproducible Data Analysis. 
R package version 0.1.0. https://github.com/belieflab/wizaRdry

MIT © Joshua Kenney