Reproducible research with R and RStudio / Christopher Gandrud
Tipo de material: TextoSeries Detalles de publicación: Boca Raton : CRC Press, Taylor & Francis Group, ©2014Edición: 1a ediciónDescripción: xxv, 288 páginas : ilustraciones; 23 x 16 centímetrosTipo de contenido:- texto
- sin medio
- volumen
- 9781466572843
- Reproducible research with R and R Studio [Título paralelo]
- Q 180.55 .S7 G19 2014
Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Signatura topográfica | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | |
---|---|---|---|---|---|---|---|---|---|---|---|
Libros para consulta en sala | Biblioteca Antonio Enriquez Savignac | Biblioteca Antonio Enriquez Savignac | COLECCIÓN RESERVA | Q 180.55 .S7 G19 2014 (Navegar estantería(Abre debajo)) | 1 | No para préstamo | Ing. Telematica | 036075 |
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Q180.55.M4 S3518 Métodos de investigación / | Q 180.55.M4 T33 2022 Cómo hacer un proyecto de investigación / | Q180 .55 .Q36 S74 2016 Algorithms for Data Science / | Q 180.55 .S7 G19 2014 Reproducible research with R and RStudio / | Q 180.55 .S7 I34 2014 Implementing reproducible research / | Q181 F669 La Formación del científico en México: Adquiriendo una nueva Identidad / | Q 182 .3 C3573 Catálogo de proyectos del fondo de cooperación internacional en ciencia y tecnología / |
"A Chapman & Hall book."
Incluye bibliografía: páginas 271-277
Getting Started. Introducing reproducible research -- Getting started with reproducible research -- Getting started with R, RStudio, and knitr -- Getting started with file management -- Data gathering and storage. Storing, collaborating, accessing files, and versioning -- Gathering data with R -- Preparing data for analysis -- Analysis and results. Statistical modeling and knitr -- Showing results with tables -- Showing results with figures -- Presentation documents. Presenting with LaTeX -- Large LaTeX documents: theses, books, and batch reports -- Presenting on the web with markdown
"Preface: This book has its genesis in my PhD research at the London School of Economics. I started the degree with questions about the 2008/09 financial crisis and planned to spend most of my time researching about capital adequacy requirements. But I quickly realized much of my time would actually be spent learning the day-to-day tasks of data gathering, analysis, and results presentation. After plodding through for awhile, the breaking point came while reentering results into a regression table after I had tweaked one of my statistical models, yet again. Surely there was a better way to do research that would allow me to spend more time answering my research questions. Making research reproducible for others also means making it better organized and efficient for yourself. So, my search for a better way led me straight to the tools for reproducible computational research. The reproducible research community is very active, knowledgeable and helpful. Nonetheless, I often encountered holes in this collective knowledge, or at least had no resource to bring it all together as a whole. That is my intention for this book: to bring together the skills I have picked up for actually doing and presenting computational research. Hopefully, the book along with making reproducible research more common, will save researchers hours of Googling, so they can spend more time addressing their research questions. I would not have been able to write this book without many people's advice and support. Foremost is John Kimmel, acquisitions editor at Chapman & Hall. He approached me with in Spring 2012 with the general idea and opportunity for this book" -- Del editor
Fondos PIFI Compra 17062014 944.33
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