Learn R Programming in 24 Hours

Alex Nordeen

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Beschreibung zu „Learn R Programming in 24 Hours“

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?


Verlag:

PublishDrive

Veröffentlicht:

2021

Druckseiten:

ca. 231

Sprache:

English

Medientyp:

eBook


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