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Algo que me encanta de R como lenguaje para ciencia de datos, es la cantidad de recursos gratuitos y de calidad que hay para aprenderlo.
Con el tiempo he ido guardando un montón de libros interesantes. Hice una lista de algunos para compartir, que cumplieran con: 1) estar en versión web (es decir, los autores los actualizan y corrigen constantemente) y 2) haber sido actualizados a más tardar el 2020.
Hay de todo: para principiantes, expertos y también específicos para algunas disciplinas como medicina, ciencias sociales, educación, etc.
¡Espero les sirva!
Para comenzar
- R for Data Science (https://r4ds.had.co.nz/)
- R para Ciencia de Datos (https://es.r4ds.hadley.nz/)
Disciplinas específicas
Medicina, salud y psicología
- Reproducible Medical Research with R (https://bookdown.org/pdr_higgins/rmrwr/)
- The Epidemiologist R Handbook (https://epirhandbook.com/)
- Data Science for Psychologists (https://bookdown.org/hneth/ds4psy/)
- R for Psychological Science (https://psyr.djnavarro.net/index.html)
Ciencias sociales
- Handbook of Regression Modeling in People Analytics (http://peopleanalytics-regression-book.org/index.html)
- Data Science in Education Using R (https://datascienceineducation.com/)
- *Curso: Program Evaluation for Public Service (https://evalf20.classes.andrewheiss.com/)
- Using R in Social Work Research (https://bookdown.org/bean_jerry/bookdown_r_for_social_workers/)
- Introducción a la ciencia de datos con R, una guía rápida para cientistas sociales. (https://bookdown.org/gaston_becerra/curso-intro-r/)
- Handbook of Graphs and Networks in People Analytics: With Examples in R and Python (https://ona-book.org/databases.html)
Ciencia
- Geocomputation with R (https://geocompr.robinlovelace.net/)
- Introduction to Environmental Data Science (https://bookdown.org/igisc/EnvDataSci/)
- Meta-analysis of Ecological Data in R (https://bookdown.org/robcrystalornelas/meta-analysis_of_ecological_data/)
Estadística y econometría
- Introduction to Econometrics with R (https://www.econometrics-with-r.org/index.html)
- Statistical Inference via Data Science (https://moderndive.com/index.html)
- Applied Statistics with R (https://daviddalpiaz.github.io/appliedstats/)
- Forecasting: Principles and Practice (https://otexts.com/fpp2/)
- Bayes Rules! An Introduction to Bayesian Modeling with R (https://www.bayesrulesbook.com/)
- Modern Statistic with R (http://www.modernstatisticswithr.com/)
Visualización, reportería y documentación
- R Graphics Cookbook (https://r-graphics.org/index.html)
- Fundamentals of Data Visualization (https://clauswilke.com/dataviz/)
- ggplot2: Elegant Graphics for Data Analysis (https://ggplot2-book.org/)
- R Markdown: The Definitive Guide (https://bookdown.org/yihui/rmarkdown/)
- Mastering Shiny for creating web applications using R (https://mastering-shiny.org/index.html)
- bookdown: Authoring Books and Technical Documents with R Markdown (https://bookdown.org/yihui/bookdown/)
- Interactive web-based data visualization with R, plotly, and shiny (https://plotly-r.com/index.html)
- *Curso: Data Visualization: Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data (https://datavizm20.classes.andrewheiss.com/)
- Outstanding User Interfaces with Shiny (https://unleash-shiny.rinterface.com/index.html)
- Data Visualization with R (https://rkabacoff.github.io/datavis/)
Machine Learning
- An Introduction to Statistical Learning with Applications in R(https://web.stanford.edu/~hastie/ISLRv2_website.pdf)
- Tidy Modeling with R (https://www.tmwr.org/)
- Data Analysis and Prediction Algorithms with R (https://rafalab.github.io/dsbook/)
- Hands-On Machine Learning with R (https://bradleyboehmke.github.io/HOML/)
- Text Mining with R (https://www.tidytextmining.com/index.html)
- Interpretable Machine Learning (https://christophm.github.io/interpretable-ml-book/global.html)
- Supervised Machine Learning for Text Analysis in R (https://smltar.com/)
- R for Statistical Learning (https://daviddalpiaz.github.io/r4sl/)
- Supervised Machine Learning for Text Analysis in R (https://smltar.com/)
- Feature Engineering and Selection: A Practical Approach for Predictive Models (http://www.feat.engineering/)
Para profundizar
- Advanced R (https://adv-r.hadley.nz/)
- Advanced R Solutions (https://advanced-r-solutions.rbind.io/index.html)
- What They Forgot to Teach You About R (https://rstats.wtf/)
- JavaScript for R (https://book.javascript-for-r.com/)
- Tidyverse Skills for Data Science (https://jhudatascience.org/tidyversecourse/)
- Engineering Production-Grade Shiny Apps (https://engineering-shiny.org/)
- Efficient R programming (https://csgillespie.github.io/efficientR/)
- Data Science at the Command Line (https://www.datascienceatthecommandline.com/1e/)
- Hands-On Programming with R (https://rstudio-education.github.io/hopr/)
- Happy Git and GitHub for the useR (https://happygitwithr.com/)
- The Shiny AWS Book (https://business-science.github.io/shiny-production-with-aws-book/)
- Analyzing US Census Data: Methods, Maps, and Models in R (https://walker-data.com/census-r/index.html)
- Doing Meta-Analysis in R: A Hands-on Guide (https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/)
Otros recursos interesantes
- The Big Book of R (compilado de libros, mucho más grande que este) (https://www.bigbookofr.com/bookdown.html#a-minimal-book-example)
- Lista de empresas y organizaciones que usan R (https://github.com/ThinkR-open/companies-using-r)
- Visualizaciones bacanes hechas en Tidy Tuesday (https://nsgrantham.shinyapps.io/tidytuesdayrocks/)
- Extensiones bacanes de Shiny - Awesome Shiny Extensions (https://github.com/nanxstats/awesome-shiny-extensions)