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?
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?
Learn R Programming in 24 Hours
450
Learn R Programming in 24 Hours
450
Product Details
| BN ID: | 2940164706715 |
|---|---|
| Publisher: | PublishDrive |
| Publication date: | 10/31/2021 |
| Sold by: | PUBLISHDRIVE KFT |
| Format: | eBook |
| Pages: | 450 |
| File size: | 5 MB |