Programming using R
R programming language is popular among statisticians and data miners for developing statistical software and data analysis. R Training has picked up a lot of popularity in recent years and there is a lot of demand for R Language Training.
Objectives
- Master the use of the R and RStudio interactive environment
- Expand R by installing R packages
- Read Structured Data into R from various sources
- Understand the different data structures in R
- Understand how to create and manipulate dates in R
- Use the tidyverse collection of packages to manipulate dataframes
- Write user-defined R functions
- Write Loop constructs in R
- Use the apply family of functions to iterate functions across data
- Expand iteration and programming through the Purrr package
- Reshape data from long to wide and back to support different analyses
- Perform merge operations with R
- Understand split-apply-combine (group-wise operations) in R
- Identify and deal with missing data
- Manipulate strings in R
- Understand basic regular expressions in R
- Understand base R graphics
Target Audience
- Ideal for graduates with 0 – 3 years of experience & degrees in B. Tech, B.E and B.Sc. IT Or Any Computer-Related program
Prerequisites
- Knowledge of statistics theory in mathematics.
- You should have solid understanding of statistics in mathematics.
- Understanding of various type of graphs for data representation.
- Prior knowledge of any programming.
Duration
- If you have experience in any programming language, it takes 7 days to learn R programming spending at least 3 hours a day. If you are a beginner, it takes 3 weeks to learn R programming. In the second week, learn concepts like how to create, append, subset datasets, lists, join.
Course Outline
Module 1: Introduction to Data Analytics
Objectives:
- This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
- ou can also learn how R can play an important role in solving complex analytical problems.
- This module tells you what is R and how it is used by the giants like Google, Facebook etc.
- Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
Topics:
- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R
Module 2: Introduction to R programming
Objectives:
- This module starts from the basics of R programming like datatypes and functions.
- In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
- You will also learn how to apply the ‘join’ function in SQL.
Topics:
- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
- Factors
Module 3: Data Manipulation in R
Objectives:
- In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
- Thus using and exploring the popular functions required to clean data in R.
Topics:
- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions
Module 4: Data Import techniques in R
Objectives:
- This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
- This module teaches you various data importing techniques in R.
Topics:
- Reading Data
- Writing Data
- Basic SQL queries in R
- Web Scraping
Module 5: Exploratory Data Analysis
Objectives:
- In this module, you will learn that exploratory data analysis is an important step in the analysis.
- EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics:
- Box plot
- Histogram
- Pareto charts
- Pie graph
- Line chart
- Scatterplot
- Developing graphs
Module 6: Overview of Machine Learning techniques
Objectives:
- This module touches the base Statistics, Machine learning techniques used in the Industry and will cover case studies.
Topics:
- Standard deviation
- Outlier
- Linear regression
- Multiple regression
- Logistic regressions
- Correlation
Module 7: Project Work
- 1 Real-time project