This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Roger Peng. Also By Roger D. Peng R Programming for Data Science The Art of Data Science Report Writing for Data Science in R Contents 1. Stay in Touch! . . . . .
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
Literate programming never became a huge success for writing programs, but for doing data science, it is having a comeback. The results of a data analysis project is typically a report describing models and analysis results, ...
"This book describes the process of analyzing data.
That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual.
In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ...
Data Analysis and Prediction Algorithms with R Rafael A. Irizarry ... See Productivity tools Data analysis project organization with Unix, See Unix Data analysis project report writing, See R markdown Data analysis with large datasets, ...
"This book is about the fundamentals of R programming.
What you will learnUse basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow controlImport data to R from various formats such as CSV, Excel, and SQLClean data by handling missing values ...
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks.