R Programming Course

This R Programming course by Uplatz will help you to master the R programming language in great detail. This is self-paced video-based course. You will be awarded Course Completion Certificate at the end of the course.

R is a programming language and software environment used for statistical computing, data analysis, and graphical visualization. It was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.

R has become increasingly popular in recent years due to its ability to handle large and complex data sets and its extensive library of statistical and graphical tools. It is widely used by statisticians, data scientists, and researchers in various fields, including finance, biology, engineering, and social sciences.

R is an open-source software and is freely available under the GNU General Public License. It can be installed on various operating systems, including Windows, macOS, and Linux. R has a large and active community of users who contribute to its development, maintenance, and improvement.

To start working with R, users can download and install the R software, which includes the R programming language and several useful packages. Users can write and execute R code in the R console, a command-line interface that allows users to interact with R and view the output of their code. Alternatively, users can use an integrated development environment (IDE) like RStudio, which provides a more user-friendly interface and additional features for data analysis and visualization.

Some of the key features of R include:

• Data manipulation and cleaning: R provides various functions and packages for cleaning and transforming data, including filtering, sorting, merging, and reshaping data frames.
• Statistical analysis: R includes a vast library of functions and packages for statistical analysis, including regression analysis, hypothesis testing, and time series analysis.
• Data visualization: R provides powerful tools for creating visualizations of data, including bar charts, histograms, scatter plots, and heat maps.
• Machine learning: R includes several packages for machine learning, including classification, regression, and clustering algorithms.
• Reproducibility: R provides tools for creating reproducible research, which enables other researchers to reproduce and verify the results of a study.

At the end of this course, your will be able to:
- Explain the advantage of using a script vs point-and-click methods
- Understand basic programming concepts such as data types, data structures and
indexing, and use them in your work
- Apply basic functions
- Conceptualize and create if-else statements and loops to solve different types of
problems
- Create your own customized functions
- Create plots
- Perform basic exploratory data analysis with summary statistics and plots
- Demonstrate the use of selected libraries
- Understand new data sets and functions by yourself using R

• R Programming - all lectures 20:45:39

  • 1: Introduction to R Programming Preview 16:58
  • 2: Setup of R Language 24:14
  • 3: Variables and Data Types - part 1 31:37
  • 4: Variables and Data Types - part 2 29:17
  • 5: Input-Output Features - part 1 38:05
  • 6: Input-Output Features - part 2 25:43
  • 7: Operators in R - part 1 34:16
  • 8: Operators in R - part 2 29:20
  • 9: Vectors - Data Structure - part 1 32:30
  • 10: Vectors - Data Structure - part 2 30:30
  • 11: List - Data Structure - part 1 33:48
  • 12: List - Data Structure - part 2 29:10
  • 13: Matrix - Data Structure - part 1 43:57
  • 14: Matrix - Data Structure - part 2 34:52
  • 15: Arrays - Data Structure - part 1 31:56
  • 16: Arrays - Data Structure - part 2 38:36
  • 17: Data Frame - Data Structure - part 1 43:06
  • 18: Data Frame - Data Structure - part 2 33:59
  • 19: Data Frame - Data Structure - part 3 50:54
  • 20: Factors - Data Structure - part 1 34:10
  • 21: Factors - Data Structure - part 2 17:15
  • 22: Decision Making in R - part 1 32:53
  • 23: Decision Making in R - part 2 45:55
  • 24: Loops in R - part 1 28:29
  • 25: Loops in R - part 2 32:03
  • 26: Loops in R 25:29
  • 27: Functions in R - part 1 37:42
  • 28: Functions in R - part 2 34:12
  • 29: Strings in R - part 1 25:18
  • 30: Strings in R - part 2 26:52
  • 31: Packages in R 35:17
  • 32: Data and File Management in R - part 1 32:45
  • 33: Data and File Management in R - part 2 23:19
  • 34: Line chart in R 33:07
  • 35: Scatter plot in R 26:38
  • 36: Pie chart in R 33:32
  • 37: Bar chart in R 39:32
  • 38: Histogram in R 26:34
  • 39: Boxplots in R 21:49

R Programming - Course Curriculum

Module 1: Introduction to R

• What is R?
• Installing R and RStudio
• R basics: data types, arithmetic, variables, functions
• Introduction to R packages

Module 2: Data manipulation in R

• Reading and writing data in R
• Data frames and tibbles
• Subsetting and filtering data
• Manipulating data with dplyr and tidyr

Module 3: Data visualization in R

• Introduction to ggplot2
• Basic plots: scatter plots, histograms, box plots
• Customizing plots with themes and aesthetics

Module 4: Data analysis in R

• Introduction to statistical analysis in R
• Basic statistical tests: t-tests, ANOVA, correlation
• Linear regression in R

Module 5: Programming with R

• Control structures: if/else, for loops, while loops
• Functions in R
• Writing scripts and organizing code

Module 6: Advanced topics

• Working with dates and times
• RMarkdown: creating reports and documents with R
• Shiny: creating interactive web applications with R

There are various job roles associated with R programming. Some of the common job roles are:

1. Data Analyst: Data analysts use R programming to analyze large amounts of data, visualize data, and extract insights from data. They typically work with data from various sources such as business operations, financial transactions, and social media to help organizations make informed decisions.

2. Data Scientist: Data scientists use R programming to develop predictive models, machine learning algorithms, and other statistical methods to analyze complex data sets. They typically work with data that is both structured and unstructured to solve complex problems.

3. Statistician: Statisticians use R programming to perform statistical analysis on data sets, develop statistical models, and perform hypothesis testing. They typically work in fields such as healthcare, social sciences, and business to help organizations make data-driven decisions.

4. Quantitative Analyst: Quantitative analysts use R programming to develop mathematical models and algorithms to analyze financial data. They typically work in financial institutions such as banks, hedge funds, and investment firms.

5. Machine Learning Engineer: Machine learning engineers use R programming to develop machine learning algorithms and models. They typically work in industries such as healthcare, finance, and technology to build predictive models and solve complex problems.

6. Researcher: Researchers use R programming to analyze data in fields such as science, engineering, and social sciences. They typically work in academic institutions and research organizations to analyze data and publish research papers.

These are just a few examples of job roles associated with R programming. With its popularity among data analysts and data scientists, R programming has become an important skill in various industries.

• Data Scientist
• Software Engineer
• Data Analyst
• Machine Learning Engineer
• Data Visualization Developer
• Software Developer
• Data Consultant
• BI Developer
• Application Developer
• BI Consultant

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Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included