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Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R´s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions.
Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations.
What You Will Learn

Discover R, statistics, data science, data mining, and big data

Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions

Work with descriptive statistics

Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots

Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions

Who This Book Is For
Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

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Chapter 1: Introduction Chapter Goal: To understand what is R, why use R, statistics in data mining and data science No of pages 15 Sub -Topics 1. What is R? 2. High Level and Low Level Language 3. What is Statistics? 4. What is Data Science? 5. What is Data Mining? 6. What is Text Mining? 7. Three Types of Analytics 8. Big Data 9. Why R? 10. Conclusion
Chapter 2: Getting Started Chapter Goal: To set up the computer for R Programming No of pages: 15 Sub - Topics 1. What is R and RStudio? 2. Installation of R and RStudio 3. Integrated Development Environment 4. RStudio - The IDE for R. 5. Conclusion Chapter 3: Basic Syntax Chapter Goal: To learn R programming basics No of pages : 30 Sub - Topics: 1. Writing in R Console 2. Using Code Editor 3. Variables and Data Types 4. Vectors 5. Lists 6. Data Frame 7. Logical Statements 8. Loops 9. Functions 10. Conclusion
Chapter 4: Descriptive Statistics Chapter Goal: To learn Descriptive Statistics in R No of pages: 20 Sub - Topics: 1. Reading Data Files 2. Mean, Median, Min, Max, ... 3. Percentile, Standard Deviations 4. The Summary() and Str() functions 5. Distributions 6. Conclusion


Chapter 5: Data Visualizations Chapter Goal: To learn Data Visualizations in R No of pages: 20 Sub - Topics: 1. What is Data Visualizations? 2. Bar Chart, Histogram 3. Line Chart, Pie Chart 4. Scatterplot and Box Plot 5. Scatterplot Matrix 6. Decision Trees 7. Conclusion

Chapter 6: Inferential Statistics and Regressions Chapter Goal: To learn inferential statistics and regressions in R No of pages: 20 Sub - Topics: 1. Correlations 2. T Test, Chi Square, ANOVA 3. Non Parametric Test 4. Linear Regressions 5. Multiple Linear Regressions

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