Bundle Multi (2-in-1) - R Programming

Uplatz offers this comprehensive 2-in-1 multi bundle course on R Programming. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language. R is a language and environment for statistical computing and graphics.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular matrices,
a large, coherent, integrated collection of intermediate tools for data analysis,
graphical facilities for data analysis and display either on-screen or on hardcopy, and
a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

In this R Programming course by Uplatz you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

• R Programming - all lectures 8:40:12

  • 1: Introduction to R Language 14:34
  • 2: R Installation Preview 08:50
  • 3: R Data Structures 07:10
  • 4: R Data Types - part 1 04:41
  • 5: R Data Types - part 2 19:10
  • 6: R Packages Preview 11:25
  • 7: Simple Calculator using R 22:33
  • 8: R as a Calculator 18:48
  • 9: Condition Statements in R - part 1 28:48
  • 10: Condition Statements in R - part 2 16:15
  • 11: Looping in R 25:02
  • 12: Repeat Statement in R 15:32
  • 13: Sum of N Natural Numbers 07:17
  • 14: Episode 12 - Sum Recursion 05:52
  • 15: Switch Statement in R 33:57
  • 16: Data Pre-processing 1:01:55
  • 17: Functions in R 37:44
  • 18: Factors in R 37:01
  • 19: Data Frames in R 37:47
  • 20: Merging Data Frame in R 20:37
  • 21: Data Reshaping - part 1 25:10
  • 22: Data Reshaping - part 2 23:49
  • 23: Data Reshaping - part 3 10:08
  • 24: R Math built-in Functions 14:32
  • 25: Melting and Casting in R 11:35

• R Programming (basic to advanced) - all lectures 20:45:23

  • 26: Introduction to R Programming 16:58
  • 27: Setup of R Language 24:13
  • 28: Variables and Data Types - part 1 31:36
  • 29: Variables and Data Types - part 2 29:16
  • 30: Input-Output Features - part 1 38:05
  • 31: Input-Output Features - part 2 25:43
  • 32: Operators in R - part 1 34:16
  • 33: Operators in R - part 2 29:19
  • 34: Vectors - Data Structure - part 1 32:29
  • 35: Vectors - Data Structure - part 2 30:29
  • 36: List - Data Structure - part 1 33:47
  • 37: List - Data Structure - part 2 29:10
  • 38: Matrix - Data Structure - part 1 43:57
  • 39: Matrix - Data Structure - part 2 34:52
  • 40: Arrays - Data Structure - part 1 31:56
  • 41: Arrays - Data Structure - part 2 38:35
  • 42: Data Frame - Data Structure - part 1 43:06
  • 43: Data Frame - Data Structure - part 2 33:59
  • 44: Data Frame - Data Structure - part 3 50:54
  • 45: Factors - Data Structure - part 1 34:09
  • 46: Factors - Data Structure - part 2 17:14
  • 47: Decision Making in R - part 1 32:52
  • 48: Decision Making in R - part 2 45:55
  • 49: Loops in R - part 1 28:28
  • 50: Loops in R - part 2 32:03
  • 51: Loops in R - part 3 25:29
  • 52: Functions in R - part 1 37:42
  • 53: Functions in R - part 2 34:11
  • 54: Strings in R - part 1 25:18
  • 55: Strings in R - part 2 26:51
  • 56: Packages in R 35:17
  • 57: Data and File Management in R - part 1 32:45
  • 58: Data and File Management in R - part 2 23:18
  • 59: Line chart in R 33:06
  • 60: Scatter plot in R Preview 26:38
  • 61: Pie chart in R 33:32
  • 62: Bar chart in R 39:32
  • 63: Histogram in R 26:34
  • 64: Boxplots in R 21:49

R programming is a language and environment for statistical computing and graphics. It was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and was first released in 1995. R provides a wide variety of statistical and graphical techniques, and it is widely used by statisticians, data scientists, and researchers for data analysis, visualization, and modeling.

Here are some key features of R programming:

1. Data Manipulation: R provides extensive tools for data manipulation, including functions for filtering, sorting, aggregating, merging, and reshaping data.

2. Statistical Analysis: R offers a rich set of statistical functions and packages for performing various statistical analyses, such as hypothesis testing, regression analysis, time series analysis, and clustering.

3. Data Visualization: R has powerful graphical capabilities, allowing users to create a wide range of high-quality data visualizations, including scatter plots, bar plots, histograms, line plots, and more. The "ggplot2" package is particularly popular for creating sophisticated and customizable plots.

4. Extensibility: R is highly extensible, allowing users to create their own functions and packages to extend its capabilities. There is a vast collection of packages available on the Comprehensive R Archive Network (CRAN) and other repositories, covering diverse domains and analysis techniques.

5. Reproducible Research: R facilitates reproducible research by providing tools for documenting code, generating reports, and creating interactive documents. Packages like R Markdown and knitr enable the integration of code, analysis, and results in a single document.

6. Integration: R can be easily integrated with other programming languages like Python, Java, and C++, allowing users to leverage existing libraries and tools for specific tasks. Additionally, R has connectors to various databases, making it convenient for data extraction and manipulation.

Everyone

Passion & determination to achieve big goals in life!

• Software Engineer
• R Programmer
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Data Consultant
• Software Developer
• Business Analyst
• Financial Analyst

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