Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)

Quiz

  1. Quiz-tion: What is the output of the command lapply from the apply family of functions
    1. List
    2. Matrix
    3. A list and a matrix
  2. Quiz-tion: Which of the following options best describes the functionality of fread in data.table?
    1. To load functions from a file
    2. To load a delimited file
    3. To load comma-separated (csv) files with embedded R functions
  1. Quiz-tion: Many data.table functions (set functions) are applied in-place, that is, the functions modify the object without creating a duplicate copy. How can we ensure that our original data.table remains unchanged when applying such operations?
    1. Create a copy of the data.table using the make.data.table command
    2. Create a copy using the copy command in data.table
    3. This is not possible due to restrictions in data.table. Use data.frame instead

Answers:
Q1 - 1, Q2 - 2, Q3 - 2