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drkamarul
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datamanagement

Manages and transforms R data using tidyverse ecosystem tools.

Updated May 21, 2026
Search Tags:#data management#functional programming#tidyverse#R#dplyr#data wrangling
One-click install
npx skills add https://github.com/drkamarul/claude-skills --skill datamanagement
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datamanagement
Source: https://github.com/drkamarul/claude-skills/tree/main/datamanagement
Command: npx skills add https://github.com/drkamarul/claude-skills --skill datamanagement

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tidyverse, janitor, lubridate, readxl, haven, gtsummary, labelled, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenges of data management, wrangling, and transformation in R, providing a comprehensive solution for data scientists and analysts.

Core Features & Use Cases

  • Data Import: Handles CSV, Excel, SPSS, Stata, and SAS files with support for multiple files and sheets.
  • Data Cleaning: Renames columns, converts data types, handles missing data, and recodes variables.
  • Data Transformation: Utilizes dplyr for row and column operations, summarizing data, and reshaping datasets.
  • Joining/Merging: Performs various join operations like left, inner, semi, and anti joins.
  • Functional Programming: Implements purrr for list manipulation and functional programming patterns.
  • Epidemiological Data: Provides patterns for WHO NCD STEPS data preparation and surveillance/line-list cleaning.
  • Data Validation: Includes assertion checks and summary validation for data quality assurance.
  • Shiny Integration: Combines with shinyskill for data processing in Shiny apps.

Quick Start

Use the datamanagement skill to clean and transform your dataset named 'data.csv'.

Frequently Asked Questions about datamanagement

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I import and clean SPSS and Stata files in R for data wrangling?▼

Data wrangling in R supports importing and cleaning SPSS and Stata files using the haven package, converting data types, renaming columns, and recoding variables. It handles CSV, Excel, and SAS formats for comprehensive dataset preparation.

What is the best way to join and reshape epidemiological datasets using tidyverse?▼

Joining and reshaping epidemiological datasets using tidyverse is best handled with dplyr for row and column operations. It performs left, inner, semi, and anti joins, and implements purrr for functional programming patterns in data transformation.

Can I use functional programming with purrr to manipulate data lists in R?▼

Functional programming with purrr manipulates data lists in R by applying functions across list elements. This Skill implements purrr patterns alongside tidyverse tools to automate data transformation, cleaning, and summarizing tasks efficiently.

Does this R data management approach work with WHO NCD STEPS surveillance data?▼

This R data management approach works with WHO NCD STEPS surveillance data by providing specific patterns for preparation and line-list cleaning. It uses labelled and gtsummary packages to handle variable labelling and summary validation for public health datasets.

How do I validate data quality and handle missing values during data transformation?▼

Validating data quality and handling missing values during data transformation uses assertion checks and summary validation. The Skill cleans data by converting types, recoding variables, and applying janitor functions to ensure dataset integrity before analysis.

What tidyverse packages are required to run this data management workflow?▼

Running this data management workflow requires tidyverse, janitor, lubridate, readxl, haven, gtsummary, and labelled packages. These dependencies enable file import, date parsing, variable labelling, and summary generation for epidemiological data processing.

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