Data Management in R

via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

10 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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Data management is an essential skill for anyone working with modern datasets, and R provides a powerful environment for organizing, cleaning, transforming, and analyzing information efficiently. This course introduces practical techniques for working with data in R, covering data frames, tidyverse workflows, survey datasets, spatial information, text data, and time series. The course combines foundational concepts with practical applications, helping you build confidence while working with real-world datasets. You will explore common data management challenges and learn methods for importing, organizing, and manipulating data using widely adopted R tools and packages. This course is ideal for aspiring data analysts, researchers, statisticians, and professionals who want to strengthen their data management skills using R. A basic understanding of programming or data concepts will be helpful, but no prior experience with R is required. By the end of this course, you will be able to manage complex datasets in R, apply modern data transformation techniques, work with spatial and text data, and prepare structured datasets for efficient analysis and visualization. Copyright ©2021 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Introduction

This module introduces foundational concepts in R, focusing on efficient data loading, data structures, and the use of R scripts for automating repetitive tasks. Learners will compare R base functions with data.table and Tidyverse to enhance data management skills. By the end, you'll be equipped to streamline your workflow and prepare data for analysis.

Building Blocks of Data

This module introduces the fundamental data structures in R, including numeric, logical, and character vectors, as well as factors. Learners will gain hands-on experience creating, manipulating, and extracting data from these structures, and understand their importance in data analysis. Key functions and operators for basic data manipulation and organization are also covered.

Rectangles of Variables and Observations: Data Frames and Their Management

This module introduces the structure and management of data frames in R, emphasizing techniques for accessing, modifying, reshaping, and aggregating data. Learners will explore practical functions for handling variables, importing data from other statistical packages, and preparing data for analysis. By the end, participants will be equipped to efficiently manage tabular data in social science research contexts.

Data Tables and the Tidyverse

This module introduces learners to efficient data management in R using both the data.table package and the Tidyverse suite. You will compare their unique approaches, explore key packages like tibble and tidyr, and practice tidying real-world datasets for analysis.

Handling Data from Social Science Surveys

This module guides learners through the process of importing, recoding, and preparing social science survey data for analysis in R. You will learn how to handle data from formats like SPSS and Stata, interpret codebooks, and transform variables to suit analytical needs.

Managing Data from Complex Samples

This module introduces techniques for handling data from complex survey samples, focusing on the creation and management of survey design objects and the application of weighting adjustments such as post-stratification, raking, and calibration. Learners will gain practical skills in preparing data for accurate statistical inference in social science research.

Dates, Times, and Time Series

This module introduces learners to the fundamentals of working with temporal data in R, including the distinctions between regular and irregular time series. Learners will gain hands-on experience with date and time classes, and learn to manage irregular time series using the zoo package.

Spatial/Geographical Data

This module introduces the fundamentals of spatial and geographical data analysis in the social sciences using R's sf package. Learners will explore spatial data structures, coordinate systems, and key spatial relationships, as well as practical approaches to handling geographical data files and projections.

Text as Data

This module introduces key techniques for manipulating and analyzing textual data in R. Learners will explore essential string functions and discover how to manage text corpora using the tm package. By the end, you'll be equipped to process and analyze character vectors for text-based data analysis.

Bibliography

This module introduces learners to the key sources and citation practices used in political science research. You will explore how to properly reference major datasets and scholarly works, ensuring academic integrity and credibility in your own projects.