R programming for beginners

Introduction to Programming Language R. Tools for Data Science, Data Analysis and Statistical Analysis. RStudio.

Created by : Rated :4.3 83843 views

Last Updated: 2023-03-22 10:03:37 English Course Type: Self Learning

Install R and RStudio and create R script and be able to save your work in R project
Be able to differentiate between different R data structures such as: string, number, vector, matrix, data frame, factor, date and time object, and many more
Be able to access elements from R objects, and be able to reshape R objects
Write R program for executing repetitive tasks using loops and vectorized code
Write your own user defined functions and create simulations inside R environment

Are you one of the people that would like to start a data science career or are you just fond of using data for data analysis in your spare time or for your job? Do you use spreadsheets for data cleaning, wrangling, visualization, and data analysis? I think it is time to enhance your hobby or your career path with learning adequate skills such as R.

R is s a programming language that enables all essential steps when you are dealing with data like:

importing,
exporting,
cleaning,
merging,
transforming,
analyzing,
visualizing,
and extracting insights from the data.

Originally R began as a free software environment for statistical computing with graphics supported. Over the years with the rapid development of computing power and the need for tools used for mining and analyzing tons of data that are being generated on every step of our lives, R has emerged into something much greater than its original laid path. Nowadays the R community is vast, every day thousands of people start learning R, and every day new R's libraries are being made and released to the world. These libraries solve different users' needs because they provide different functions for dealing with all kinds of data.

If you are still not convinced to join me on a journey where foundations for your R skills will be laid, please bear with me a bit more. In this R for Beginners course, you will dive into essential aspects of the language that will help you escalate your learning curve. Course first gently touches the basics like:

how to install R and how to install R's Integrated Development Environment (IDE) RStudio,
then you will learn how to create your first R script and R project folder,
R project folder will be your baseline folder where all your scripts and assignments will be saved,
you will learn how to install different R packages and how to use functions provided with each package.

After these first steps, you will dive into sections where all major R data structures are presented. You will be able to:

differentiate among each data structure,
use built-in functions to manipulate data structures,
reshape, access elements, and convert R objects,
import data from many different sources into R's workspace and
export R objects to different data sources.

When you will have a grasp of what R is capable of, a section devoted to programming elements will guide you through essential steps for writing a programming code that can execute repetitive tasks. Here you will master:

your first loops,
conditional statements,
your custom made functions,
and you will be able to optimize your code using vectorization.

It is said that a picture can tell an observer a powerful story and holds a stronger message than a thousand words combined. In the final section of this course, the greatest R's power is revealed, the power to tell the story by using data visualization. Here you will master how to build:

scatterplots,
line charts,
histograms,
box plots,
bar charts,
mosaic plots,
how to alter R's default graphical parameters to make beautiful figures,
and how to export a figure from R to a proper format for further sharing with your colleagues.

If you are still not convinced to start learning R, I will share with you how the course is structured:

Each section holds separate exercises covering learning material that is related to the section's topic.
Normally each exercise begins with a short intro that provides a basic understanding of the topic, then a coding exercise is presented.
During coding exercise, you will write the R code for executing given tasks.
At the end of each section, an assignment is presented.
Each assignment tests the skills you have learned during a given section.
In the last two assignments, you will write a code to build a simulation environment where you will execute the simulation and present the results with proper visualization techniques.

Do not lose more time and please enroll in the course today. I guarantee you will learn a lot and you will enjoy the learning process.

No prior knowledge is required. You need only a desire to learn new things and an access to a computer.

Anyone whose hobby or career is related to data analysis
Data science or statistics enthusiasts
Anyone who does data analysis with spreadsheets and would like to enhance his skills and deliverance
Anyone with a desire to learn a new programming language for statistics and data science
Business analyst and researchers who would like to enhance skills of data visualization
Beginner R developers starting career in data science

Section 1 : Getting Started 10 Lectures 01:05:11
Lecture 1 :
Section intro Preview
Lecture 2 :
Install R and RStudio
Lecture 3 :
RStudio
Lecture 4 :
R basics
Lecture 5 :
Basic mathematical operations
Lecture 6 :
R scripts and RStudio projects
Lecture 7 :
R packages
Lecture 8 :
Basic built-in functions
Lecture 9 :
Save and load workspace
Lecture 10 :
Section summary and assignment 1
Section 2 : Basic R Data Structures 12 Lectures 01:40:17
Lecture 1 :
Integers
Lecture 2 :
Doubles
Lecture 3 :
Complex numbers
Lecture 4 :
Logicals
Lecture 5 :
Strings introduction
Lecture 6 :
Strings manipulation
Lecture 7 :
Strings matching, replacement and regular expressions (part 1)
Lecture 8 :
Strings matching, replacement and regular expressions (part 2)
Lecture 9 :
Special R values and data type conversion
Lecture 10 :
Section summary and assignment 2
Lecture 11 :
Assignment 2 walk-through
Lecture 12 :
Section intro
Section 3 : Multidimensional R Data Structures 12 Lectures 02:10:31
Lecture 1 :
Section intro
Lecture 2 :
Vectors - part 1
Lecture 3 :
Vectors - part 2
Lecture 4 :
Matrices - part 1
Lecture 5 :
Matrices - part 2
Lecture 6 :
Arrays - part 1
Lecture 7 :
Arrays - part 2
Lecture 8 :
Lists - part 1
Lecture 9 :
Lists - part 2
Lecture 10 :
Section summary and assignment 3
Lecture 11 :
Assignment 3 walk-through - part 1
Lecture 12 :
Assignment 3 walk-through - part 2
Section 4 : Special R data structures & dealing with import / export 13 Lectures 02:45:06
Lecture 1 :
Section intro
Lecture 2 :
Factors - part 1
Lecture 3 :
Factors - part 2
Lecture 4 :
Date and time - part 1
Lecture 5 :
Date and time - part 2
Lecture 6 :
Data frames - part 1
Lecture 7 :
Data frames - part 2
Lecture 8 :
Import data from a file - part 1
Lecture 9 :
Import data from a file - part 2
Lecture 10 :
Export data to a file - part 1
Lecture 11 :
Export data to a file - part 2
Lecture 12 :
Section summary and assignment 4
Lecture 13 :
Assignment 4 walk-through
Section 5 : Programming with R 17 Lectures 02:57:11
Lecture 1 :
Section intro
Lecture 2 :
Logical statements - part 1
Lecture 3 :
Logical statements - part 2
Lecture 4 :
for loop - part 1
Lecture 5 :
for loop - part 2
Lecture 6 :
next & break statement - part 1
Lecture 7 :
next & break statement - part 2
Lecture 8 :
while loop - part 1
Lecture 9 :
while loop - part 2
Lecture 10 :
Nested loops - part 1
Lecture 11 :
Nested loops - part 2
Lecture 12 :
User defined functions - part 1
Lecture 13 :
User defined functions - part 2
Lecture 14 :
Vectorized code - part 1
Lecture 15 :
Vectorized code - part 2
Lecture 16 :
Section summary and assignment 5
Lecture 17 :
Assignment 5 walk-through
Section 6 : Visualize data with R base graphics 14 Lectures 02:48:03
Lecture 1 :
Section intro
Lecture 2 :
Scatter plots
Lecture 3 :
Line charts
Lecture 4 :
Histograms & density plots
Lecture 5 :
Box plots
Lecture 6 :
Bar charts
Lecture 7 :
Mosaic plots
Lecture 8 :
Graphical parameters - part 1
Lecture 9 :
Graphical parameters - part 2
Lecture 10 :
Multi-plots
Lecture 11 :
Section summary and assignment 6
Lecture 12 :
Assignment 6 walk-through - part 1
Lecture 13 :
Assignment 6 walk-through - part 2
Lecture 14 :
Assignment 6 walk-through - part 3

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Marko Intihar,

83843 Course Views

1 Courses

Hi, my name is Marko Intihar. I have a Ph.D. from Logistics from the University of Maribor, and years of professional experience as an instructor and an expert in the fields of applied Statistics, Data science and programming. During my active academic career, I have published several scientific papers in the domain of time series modeling. I began my professional career as a researcher and a teaching assistant at the university. Core teaching and research fields were statistics, data analysis, and operations research. After several years of working for the university, I have decided to test my theoretical knowledge and apply my skills for solving practical problems from the industry. I employed as a data scientist for the biggest Slovenian retail company. In retail tons of data are generated daily, therefore I was able to enhance my data mining and machine learning skills to deliver practical solutions for the company. After 4 years of working in retail, I decided to move to the financial sector. Currently, I am working as a data scientist in a bank. The banking industry carries different kinds of challenges as the retail industry, therefore work enables lots of substance for professional and personal growth. I am an enthusiastic data scientist is my private life, therefore my career and hobby go with one and another perfectly. It would be my pleasure to share some of my knowledge with you.