Books I have read

Books I have read

I started reading books from month of April, 2018. Which is very useful for my life as a believer of self-learning. I hope to share these books with you so that it will be useful. There is no meaning in numbers except that they are just for ordering from 1 to further in no hierarchy. Genre specificity is not my thing therefore these books are not divided into groups.

April 2018 - December 2018

  1. Rise the Dark by Michael Koryta
  2. The Bourne Enigma by Eric Van LustBader
  3. Jack Reacher thriller, A Wanted Man by Lee Child
  4. Digital Fortress by Dan Brown
  5. The Selfish Gene by Richard Dawkins
  6. How Democracies Die by Steven Levitsky and Daniel Ziblatt
  7. Elon Musk by Ashlee Vance
  8. Roll of Thunder Hear My Cry by Mildred Taylor
  9. Boy by Roald Dohl
  10. Animal Farm by George Orwell
  11. The power of Habit by Charles Duhigg

October 2019

  1. The Lost Symbol by Dan Brown
  2. Jack Reacher thriller, Die Trying by Lee Child
  3. Like a Virgin by Richard Branson
  4. Crash Bang Wallop

November 2019

  1. The Gambler by Fyodor Dostoevsky

Books Related to R, Data Science and Statistics

  1. Text Mining with R : A Tidy Approach by Julia Silge and David Robinson
  2. R for Journalists by Andrew Ba Tran
  3. Interpretable Machine Learning by Christoph Molnar
  4. Advanced R by Hadley Wickham
  5. Modern R with the tidyverse by Bruno Rodrigues
  6. What They Forgot to Teach You About R by Jennifer Bryan, Jim Hester
  7. bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie
  8. blogdown: Creating Websites with R Markdown by Yihui Xie, Amber Thomas, Alison Presmanes Hill
  9. Happy Git and GitHub for the UseR by Jenny Bryan, the STAT 545 TAs, Jim Hester
  10. Tidy Evaluation
  11. Building Big Shiny Apps - A Workflow
  12. Hands-On Programming with R
  13. Mastering Shiny
  14. ggplot2: Elegant Graphics for Data Analysis
  15. The ggplot2 Tutor
  16. R Markdown: The Definitive Guide
  17. Machine Learning with rtemis
  18. Feature Engineering and Selection: A Practical Approach for Predictive Models
  19. R for Data Science
  20. Introduction to Data Science
  21. Agile Data Science with R
  22. STAT 545
  23. UC Business Analytics R Programming Guide

Podcasts to Listen

  1. The Bad Take
  2. Not So Standard Deviations
  3. Bayes Factor
  4. Stats and Stories

Websites to Keep up with for R

  1. R Views
  2. R-bloggers
  3. R Weekly
  4. Rshiny Application for R related Books
  5. ggplot2 gallery

THANK YOU

Books