Decision making structures require the programmer to specify one or more conditions to be evaluated or tested by the program, along with a statement or statements to be executed if the condition is determined to be true, and optionally, other statements to be executed if the condition is determined to be false.

Following is the general form of a typical decision-making structure found in most of the programming languages −

R provides the following types of decision-making statements. Click the following links to check their detail.

Sr.No.	Statement & Description
1	if statement An if statement consists of a Boolean expression followed by one or more statements.
2	if…else statement An if statement can be followed by an optional else statement, which executes when the Boolean expression is false.
3	switch statement A switch statement allows a variable to be tested for equality against a list of values.

R – Loops

There may be a situation when you need to execute a block of code several number of times. In general, statements are executed sequentially. The first statement in a function is executed first, followed by the second, and so on.

Programming languages provide various control structures that allow for more complicated execution paths.

A loop statement allows us to execute a statement or group of statements multiple times and the following is the general form of a loop statement in most of the programming languages −

R programming language provides the following kinds of loop to handle looping requirements. Click the following links to check their detail.

Sr.No.	Loop Type & Description
1	repeat loop Executes a sequence of statements multiple times and abbreviates the code that manages the loop variable.
2	while loop Repeats a statement or group of statements while a given condition is true. It tests the condition before executing the loop body.
3	for loop Like a while statement, except that it tests the condition at the end of the loop body.

Loop Control Statements

Loop control statements change execution from its normal sequence. When execution leaves a scope, all automatic objects that were created in that scope are destroyed.

R supports the following control statements. Click the following links to check their detail.

Sr.No.

Control Statement & Description

break statement

Terminates the loop statement and transfers execution to the statement immediately following the loop.

Next statement

The next statement simulates the behavior of R switch.

So, this brings us to the end of blog. This Tecklearn ‘Decision Making and Loops in R Language’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/data-science-training-using-r-language/

Data Science using R Language Training

About the Course

Tecklearn’s Data Science using R Language Training develops knowledge and skills to visualize, transform, and model data in R language. It helps you to master the Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Data Science course certification training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more. This course will help you gain hands-on experience in deploying Recommender using R, Evaluation, Data Transformation etc.

Why Should you take Data Science Using R Training?

The Average salary of a Data Scientist in R is $123k per annum – Glassdoor.com
A recent market study shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.
IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using data science for their Data analysis.

What you will Learn in this Course?

Introduction to Data Science

Need for Data Science
What is Data Science
Life Cycle of Data Science
Applications of Data Science
Introduction to Big Data
Introduction to Machine Learning
Introduction to Deep Learning
Introduction to R&R-Studio
Project Based Data Science

Introduction to R

Introduction to R
Data Exploration
Operators in R
Inbuilt Functions in R
Flow Control Statements & User Defined Functions
Data Structures in R

Data Manipulation

Need for Data Manipulation
Introduction to dplyr package
Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
Getting summarized results with the summarise() function,
Combining different functions with the pipe operator
Implementing sql like operations with sqldf()

Visualization of Data

Loading different types of datasets in R
Arranging the data
Plotting the graphs

Introduction to Statistics

Types of Data
Probability
Correlation and Co-variance
Hypothesis Testing
Standardization and Normalization

Introduction to Machine Learning

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Supervised Learning algorithm: Linear Regression and Logistic Regression

Logistic Regression

Intro to Logistic Regression
Simple Logistic Regression in R
Multiple Logistic Regression in R
Confusion Matrix
ROC Curve

Classification Techniques

What are classification and its use cases?
What is Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?
What is Naive Bayes?
Support Vector Machine: Classification

Decision Tree

Decision Tree in R
Information Gain
Gini Index
Pruning

Recommender Engines

What is Association Rules & its use cases?
What is Recommendation Engine & it’s working?
Types of Recommendations
User-Based Recommendation
Item-Based Recommendation
Difference: User-Based and Item-Based Recommendation
Recommendation use cases

Time Series Analysis

What is Time Series data?
Time Series variables
Different components of Time Series data
Visualize the data to identify Time Series Components
Implement ARIMA model for forecasting
Exponential smoothing models
Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Got a question for us? Please mention it in the comments section and we will get back to you.

482