Have you ever wanted to learn data mining? Data science is a very hot field now. This course provides fundamentals of R for applied statistics. Applied statistics can be used in data understanding and data exploration stage of the data mining process (CRISP DM). This course will teach: - Introduction - Getting Started - Basic R - Descriptive Statistics (Mean, Median, Variance, ..) - Data Visualization (bar, histogram, scatter matrix, ...) - Inferential statistics and regressions (ttest, chisq, linear regression, ...) This course is based on my ebooks at SVBook.

About R: R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years. In this course, you will learn: R Introduction Data analysis(creating dummy data and data manipulation and exploration) R Variables and Operators Data Structure Functions Flow Control R Packages Exploring Data With R Exception Handling Join and Remove Working with PHP Softwares used in Course R Studio

Learn Data Mining - Clustering Segmentation Using R,Tableau is designed to cover majority of the capabilities of R from Analytics & Data Science perspective, which includes the following: Learn about the usage of R for building Various models Learn about the K-Means clustering algorithm & how to use R to accomplish the same Learn about the science behind Clustering & accomplish the same using R and Tableau Course is structured to start with introduction to the tool & the principles behind Clustering Using R tool. From there R is explained thoroughly including analytical concepts behind applicable Data Mining Techniques. Finally course ends with explanation of clustering using Tableau and statistics behind clustering along with interview questions for job seekers

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.

R-Tutorials shows how to create convincing graphs in R Do you want to create insightful graphs? Do you want to show your data crystal clear? Do you want your data to be understood by everyone? Do you want a versatile graphics toolbox? Do you want powerful formatting skills? If you anwered YES to some of these questions - this course is for you! Data is useless if you do not have the right tools to build informative graphs. Plots need to be understood easily while being accurate at the same time. R-Tutorials gladly enlarges your data toolbox so that you can surmount in your career. R offers a variety of plotting devices, some of them (like ggplot2) are whole systems which need to be learned like a new language. R-Tutorials shows how to learn those languages. In this course you will learn about the most important plotting packages ggplot2, lattice and plotrix. According to the teaching principles of R Tutorials every section is enforced by exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own. The course starts with the base parameters which are needed to format and manipulate any basic graphs in R. After that you will learn about the most common types of graphs in R base and you will see some very useful graphical extensions of the plotrix package. Ggplot2 is a very famous graphs package and is viewed as the most powerful graphics device R has to offer. You will get an in depth tutorial on that package. At last you can see how Lattice offers some more very useful functions . With that knowledge you will have an extremely powerful toolbox to excel in your career and in your studies. What R you waiting for? Martin

R shiny allows you to present your data interactively – that means your app users can: Set filters and columns in tables Generate parameters for plots Zoom and focus on specific areas of plots Focus on selected portions of your data Provide or upload files, text and all sorts of data And much more App users can do all of this without any R knowledge. You do the coding, your users get the info they are looking for! In this course I will show you step by step how to master R Shiny. We will start out with the general shiny script – all scripts should have the same basic structure. You will then learn how to make your app interactive by using input widgets . These features take user inputs which can be used to generate or modify the app content. You will learn how to style your app for an appealing layout . I will also show you how to use HTML tags to integrate or embed standard web content like youtube videos, pdfs, text, pictures and much more. After that you will learn about advanced shiny apps that allow zooming and downloading And you will learn how to integrate tables. This course is not just pure theory. The last section is all about applying your new shiny skills. I prepared a course project which combines all the topics discussed in the course and more . This project is modeled after a real world financial app. You will get the project description and the raw data to test your skills. Note that this course requires basic R skills. If you are totally new to R, check out some R intro material first and then revisit the course. All the software downloads, add on packages as well as entry level hosting for shiny are totally free. I will show you what you need and where to get it. That includes hosting as well. Take a look at shiny – your boss, colleagues, students, customers will be astonished what modern day data visualization can do.

Comprehensive Graphics with R is a thorough, comprehensive overview of each of three major graphics approaches in R: base, lattice, and ggplot. The course also demonstrates the use of the R Commander interface to create a variety of 2D and 3D graphics. Most of the course is engaged in live, "hands-on" demonstrations of creating a wide range of 2D and 3D plots and graphs using extensive scripts and data sets, all provided with the course materials. Adequate documentation including slides, exercises and exercise solutions are also provided. The course demonstrates (and uses) two of the most popular ‘front-ends’ to the R Console: R Commander and RStudio. We begin by exploring the range of graphics output available using both the R Commander and RStudio GUI interfaces to the R Console. The course then follows with a more in-depth examination of the graphics capabilities for each of the three main graphics systems, base, lattice, and ggplot. This course is a ‘must see’ for anyone who will use R and wishes to get the most out of the stunning variety of graphical charts, plots, and even animations that are available. The R software was designed from the outset to be particularly strong in visualization and graphical capabilities. However, if you are unaware of the full range of these capabilities you are missing opportunities to apply this wide variety of rich, powerful graphics to your own work and research projects. Accordingly, this course is specifically designed to comprehensively demonstrate and explain the broad range of graphical outputs that are available with R.

'R Crash Course - a short and concise introduction to R and R Studio, R-programming for the Beginners' This is a R crash course for anyone who previously had no or very little contact with script-based programming in R. The main goal is to establish the basic understanding needed for more advanced courses that uses R language, RStudio and R-programming for example, for data science, machine learning or statistical analysis in R. This is also a baseline course that I will recommend to my students to take to refresh their knowledge on learn R-programming language for my upcoming data science courses in R. The best about this course that is a very concise manner (2 hours!) you will be able to learn all the fundamentals of R-programming that will enable you to get started with R! What will you learn in this course: § Package Management § Calculate with R § Variables § Vectors § Matrices § Lists § Data frames § Missing values § Functions § Control Structures § For loops All the R-scripts used in this course will be also provided to you. The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field. This course is NOT for you if you an intermediate or advanced user of R and don't need an introduction to R programming! Let’s get started!

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables. Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population. Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts. Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers. Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit. So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

If you've always been a bit curious about how R works (specifically, how to do data wrangling with it) this course is for you. I cover a nice example and demonstrate lots of nifty, very useful techniques for turning a collection of messy data sources into something that's tidy. Many of the essential components of your data wrangling toolkit will be covered. This is not exactly a theoretical style course. I'm not going to go be explaining all the different techniques available to you with R code. Rather, this is a brief-ish demonstration-style course.. where you can follow along.