R is one of the best programming languages specifically designed for statistics and graphics. Programming in R is a fast and effective way to perform advanced data analyses and manipulations. In this course, you will learn how to use R and utilize the many data analysis techniques, methods, and functions it has to offer to the professional data scientist.
00:54 Downloading and installing R and RStudio
04:10 Quick guide to the RStudio user interface
11:40 Changing the appearance of RStudio
13:19 Installing packages and using the library
18:24 Creating an object in R
23:39 Data types in R (Integers and doubles)
28:19 Data types in R (Characters and logicals)
31:35 Coercion rules in R
34:06 Functions in R
37:24 Functions and arguments
39:55 Building a function in R
48:00 Using the script vs. using the console
👉 This video will introduce you to the R programming language, and will give you a hands-on overview of the statistical programming language R, one of the most important programming tools in data science…
Enjoy the video!
Link to download R:
Link to Download RStudio:
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Data types are kept easy.
Data types of R are quite different when we compare with other programming languages. Here, we’ll outline the data types of R.
Integers are numbers without a decimal point. Unlike other programming languages, R represents all integers as a “double” data type. But the main difference is, you need to write “L” to represent integers in arithmetic operations. For example 9L.
Instead of floats, R has a double data type to represent both for making arithmetic calculations simpler. And when you look for integers and floats in programming languages, you’ll see double after writing the function to specify the data type. As an example: 9 and 3,4.
The most basic data type of R that is the lifeblood of its operations because R works with vectorized operations. When we define vectors in R, they’re a sequence of the data element of the same basic time. It only contains the element of the same data type. If not, R will try to convert it into the most dominant data type.
The complex data type is only used to represent complex (imaginary) numbers in R. Unlike most data types, the complex data type is not used commonly in R. We can give a+bi as an example.
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In this R Tutorial For Beginners video, you will learn r programming language from scratch to advance concepts. This R training video also covers hands-on demo and interview questions. This R Programming Course is a must-watch video for everyone who wishes to learn the R language and make a career in the data science domain.
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Learn R Programming for Beginners in our 3-hour video releasing today! R is a programming language software environment used for statistical computing and graphics. Owing to its computational power, the R programming language is extremely favoured by statisticians and data analysts and data miners for the purpose of carrying out data analysis and developing statistical software. These factors have made R to be one of the most favoured programming languages with data science, which is exactly why so many people want to learn it today.
Great Learning brings you this tutorial on R Programming for Beginners to help you understand everything you need to know about this topic and getting started on the journey to learn about it well. This video starts with an introduction to R and its installation, followed by understanding the concepts of variables, data types and operators, etc. in R. Following this, we will understand data manipulation with dplyr and data visualization with ggplot2. Finally, we learn about neural networks! This video teaches R programming and its key functions and concepts with a case study, demonstrations & examples to help you get started on the right foot.
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R is most popular and the leading open source language language in data science and statistics. Today, R language is the choice for most data science professionals in every industry and academics.
This course is thoroughly described R programming, Statistics and Data Science for beginners using real life examples.
No Basic knowledge is required
What will you learn
Let’s parse that.
This course does not require a prior quantitative or mathematics background. It starts fundamental concepts of R programming, introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings.
This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary.
Course material in the form for articles include in this program
Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames.
Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
Inferential Statistics: Hypothesis testing, Test statistic, Test of significance.
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