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ntluong95
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Data Management

Import, wrangle, and transform data in R using readr, dplyr, and data.table.

Updated Mar 7, 2026
Search Tags:#data management#r#missing data#dplyr#data.table#lubridate
One-click install
npx skills add https://github.com/ntluong95/agent-skills-statistics --skill data-management-ntluong95
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Data Management
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/r-language/data-management
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill data-management-ntluong95

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of importing, cleaning, transforming, and managing data within R, ensuring data integrity and efficient analysis.

Core Features & Use Cases

  • Data Import/Export: Efficiently read and write various file formats (CSV, Stata, SAS, SPSS, R objects).
  • Data Wrangling: Perform complex data manipulations using dplyr and data.table.
  • Date/Time Handling: Parse, manipulate, and manage date and time data accurately, crucial for time-series analysis.
  • Missing Data Management: Diagnose, document, and strategize handling of missing values.
  • Use Case: Load a large CSV file, join it with another dataset based on user IDs and dates, parse the date column into a proper date format, and then filter for records within a specific date range.

Quick Start

Use the data management skill to read the csv file named 'sales_data.csv' into a data.table and then filter for all rows where the 'region' column is equal to 'North'.

Frequently Asked Questions about Data Management

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

FAQPage Schema
How do I handle missing data and diagnose gaps in an R dataset?▼

To handle missing data in R, use this Skill to diagnose, document, and strategize imputation for missing values, ensuring data integrity for statistical analysis and reproducible pipelines.

What is the best way to parse dates and manage time zones using lubridate in R?▼

Parsing dates with lubridate involves using this Skill's standards to accurately parse, manipulate, and manage date and time data, which is crucial for time-series analysis and complex transformations.

How do I efficiently import and read large CSV files in R?▼

Efficiently reading large CSV files in R is supported through this Skill's use of readr and data.table, enabling fast data import and export for formats like CSV, Stata, and SAS.

Does this approach support complex joins and data wrangling with dplyr and data.table?▼

Yes, complex joins and data wrangling are supported by this Skill through standards for using dplyr and data.table, allowing you to perform complex manipulations and merge datasets based on specific columns.

Can I use this Skill to build reproducible data pipelines for statistical analysis?▼

You can build reproducible data pipelines for statistical analysis by applying this Skill's standards for data import, wrangling, transformation, and missing data management within R.

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