Learn R Programming in 24 Hours Learn R Programming in 24 Hours

Learn R Programming in 24 Hours

    • $13.99
    • $13.99

Publisher Description

R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference.

R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple.

R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze.

Table of Contents

Chapter 1: What is R Programming Language? Introduction & Basics

Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows

Chapter 3: R Data Types, Arithmetic & Logical Operators with Example

Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice

Chapter 5: Factor in R: Categorical & Continuous Variables

Chapter 6: R Data Frame: Create, Append, Select, Subset

Chapter 7: List in R: Create, Select Elements with Example

Chapter 8: R Sort a Data Frame using Order()

Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread)

Chapter 10: Merge Data Frames in R: Full and Partial Match

Chapter 11: Functions in R Programming (with Example)

Chapter 12: IF, ELSE, ELSE IF Statement in R

Chapter 13: For Loop in R with Examples for List and Matrix

Chapter 14: While Loop in R with Example

Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples

Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files

Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm

Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File

Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example

Chapter 20: R Aggregate Function: Summarise & Group_by() Example

Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example

Chapter 22: Scatter Plot in R using ggplot2 (with Example)

Chapter 23: How to make Boxplot in R (with EXAMPLE)

Chapter 24: Bar Chart & Histogram in R (with Example)

Chapter 25: T Test in R: One Sample and Paired (with Example)

Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples)

Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example]

Chapter 28: Decision Tree in R with Example

Chapter 29: R Random Forest Tutorial with Example

Chapter 30: Generalized Linear Model (GLM) in R with Example

Chapter 31: K-means Clustering in R with Example

Chapter 32: R Vs Python: What's the Difference?

Chapter 33: SAS vs R: What's the Difference?

GENRE
Computers & Internet
RELEASED
2021
October 31
LANGUAGE
EN
English
LENGTH
450
Pages
PUBLISHER
PublishDrive
SELLER
PublishDrive Inc.
SIZE
5.6
MB

More Books Like This

R Cookbook R Cookbook
2019
R in a Nutshell R in a Nutshell
2012
25 Recipes for Getting Started with R 25 Recipes for Getting Started with R
2011
R for Everyone R for Everyone
2013
Data Science with Jupyter Data Science with Jupyter
2019
Practical Data Science with Jupyter: Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine Learning using Python and Jupyter (English Edition) Practical Data Science with Jupyter: Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine Learning using Python and Jupyter (English Edition)
2021

More Books by Alex Nordeen

Learn AngularJS in 24 Hours Learn AngularJS in 24 Hours
2020
Linux: Learn in 24 Hours Linux: Learn in 24 Hours
2021
Learn Hadoop in 24 Hours Learn Hadoop in 24 Hours
2021
Learn Operating System in 24 Hours Learn Operating System in 24 Hours
2022
C++ Learn in 24 Hours C++ Learn in 24 Hours
2022
Learn Design and Analysis of Algorithms in 24 Hours Learn Design and Analysis of Algorithms in 24 Hours
2022