This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. The introductory methods course has two primary aims. First, students will be introduced to quantitative methodology that researchers and policymakers use in answering social, political and economic questions. Second, the course will equip students to use one or more of the discussed techniques in their MSc dissertation.
By the end of the course, you should be able to understand basic research methods, apply them to real world problems and evaluate their use in published research. Students will also acquire competency in performing statistical analyses using a popular statistical program (R).
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
A wealth of tutorials, articles, and examples exist to help you learn R and its extensions. Scroll down or click a link below for a curated guide to learning R and its extensions.
This book aims to teach the following:
Getting started with your own R Markdown document
Improve workflow:
With rstudio projects
Using keyboard shortcuts
Export your R Markdown document to PDF, HTML, and Microsoft Word
Better manage figures and tables
Reference figures and tables in text so that they dynamically update
Create captions for figures and tables
Change the size and type of figures
Save the figures to disk when creating an rmarkdown document
Work with equations
inline and display
caption equations
reference equations
Manage bibliographies
Cite articles in text
generate bibliographies
Change bibliography styles
Debug and handle common errors with rmarkdown
Next steps in working with rmarkdown - how to extend yourself to other rmarkdown formats
Este texto ha sido editado en respusta a la aparente falta de un libro de texto introductorio al análisis cuantitativo y estadísticas acesible y moderno en castellano. Si bien fue concebido como material de cátedra para Métodos cuantitativos materia que dicta el autor en la Escuela de Humanidades de la Universidad Nacional San Martín, se adaptará fácilmente a cursos introductorios de estadísticas en general.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
Este texto te enseñará cómo hacer ciencia de datos con R: aprenderás a importar datos, llevarlos a la estructura más conveniente, transformarlos, visualizarlos y modelarlos. Con él podrás poner en pŕactica las habilidades necesarias para hacer ciencia de datos. Tal como los químicos aprenden a limpiar tubos de ensayo y ordenar un laboratorio, aprenderás a limpiar datos y crear gráficos— junto a muchas otras habilidades que permiten que la ciencia de datos tenga lugar. En este libro encontrarás las mejores prácticas para desarrollar dichas tareas usando R. También aprenderás a usar la gramática de gráficos, programación letrada e investigación reproducible para ahorrar tiempo. Además, aprenderás a manejar recursos cognitivos para facilitar el hacer descubrimientos al momento de manipular, visualizar y explorar datos.