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Data Pre-Processing is the *very first step in data analytics*. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find.

With this course I will change this!

Data Pre-Processing as taught in this course has the following steps:

1. Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing .CSV files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle.

2. Selecting the object class: a standard data.frame might be fine for easy standard tasks, but there are more advanced classes out there like the data.table. Especially with those huge datasets nowadays, a data.frame might not do it anymore. Alternatives will be demonstrated in this course.

3. Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format.

4. Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data.table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section.

5. Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time.

6. Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R.* *

*How do you best prepare yourself for this course?*

You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier.

The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course.

Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier!

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*Do you want to create overwhelming plots?*

*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?*

*Do you want to add R functionality to Tableau?*

**If you answered 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 and tables (called views in Tableau). Plots need to be understood easily while being accurate at the same time. We gladly enlarge your data toolbox so that you can thrive in your career.

Tableau is a prime platform for all sorts of data visualization. By adding R analytics power to the software you can tremendously enhance the functionality of Tableau 9.

*In this course you will learn*

- which Tableau product to choose
- how to load/connect to different file types
- how to store Tableau work
- plotting different types of charts
- creating tables
- you will learn how to embed R code into Tableau and how to use R calculations in charts
- and much more…

Once you finished the course, you will be a skilled Tableau data scientist. You will be able to use Tableau to optimally visualize your data. This knowledge can be applied in sales, management, science, finance, online business and much more.

Just take the course and explore the magic of Tableau 9!

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It allows you to

- see patterns in time series data
- model this data
- finally make forecasts based on those models

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in *time series analysis and forecasting* will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and can* boost your career*!

- What will you learn in this course and how is it structured?

You will learn about different ways in how you can **handle date and time data** in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.

After that you will learn about **statistical methods** used for time series. You will hear about autocorrelation, stationarity and unit root tests.

Then you will see how different **models** work, how they are set up in R and how you can use them for **forecasting**. Of course all of this is accompanied with plenty **of exercises**.

- Where are those methods applied?

In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

- Is it hard to understand and learn those methods?

Unfortunately learning material on* Time Series Analysis Programming in R* is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make understanding modeling and forecasting as** intuitive** and **simple** as possible for you.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.

- How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (courses *R Basics* and *R Level 1*).

- Over 28 lectures and 2.5 hours of content!
- use R to perform calculations with time and date based data
- create models for time series data
- use models for forecasting
- identify which models are suitable for a given dataset
- visualize time series data
- transform standard data into time series format

- this course is for people working with time series data
- this course is for people interested in the R statistical software
- this course is for people with beginner knowledge in both R programming and statistics
- this course is for people working in various fields like (and not limited to): academia, marketing, business, econometrics, finance, medicine, engineering and science

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**Learn Excel 2016 from scratch or improve your skills and learn new tricks to speed up your work**

**Do you want to make your work with Excel faster, more efficient and fun?****Do you want to learn more about hidden tricks that make Excel easier to use?****Are you looking for a structured way to get really good with Excel?**

If your answer was **YES** to some of the questions, this course might be of interest to you.

When I created this course I put full emphasis on a structured and didactically sound way to gently show you how Excel 2016 and 2013 works.This course is especially suited towards a beginners and intermediate user group.

The course was shot with Excel 2016, which basically means that you can apply the knowledge from this course also to Excel 2013 (and 2010) since those versions are quite similar.

Excel is such a huge and powerful software so that learning it can be confusing. It is advisable to learn it in a structured way so that you can improvise and find proper solutions to new problems. Many users tend to only know the parts of Excel that are needed for their daily routine. Once a totally new problem arises, they are stuck. Having structured training combined with practice is key to finding solutions faster and without frustration.

In this course you will learn about the general interface, the capabilities and limitations, popular and efficient functions, useful tools, data visualization, database handling, getting help and much more. Take a look at the course content overview to see how extensive the whole product is.

At the end of this beginners and intermediate course you should be confident in using for your daily tasks. You will know

*how to use Excel’s functions and other tools**what the capabilities of Excel are**where to find the appropriate features**how to get help**and how to work more efficiently with Excel.*

Excel is one of the key software products of today’s professional world. You will use it extensively in your work life, and of course your colleagues will be thankful if you can show them some tricks they did not know before!

- you will need Excel 2016 (or 2013) installed on your machine
- you will need a general understanding of maths
- interest in learning and solving the exercises provided

- Over 116 lectures and 11 hours of content!
- know how to get help in Excel
- know how to use the many features of Excel 2016 or earlier
- know how to use Excel in order to solve new problems that arise
- help out your colleagues in general questions concerning Excel
- make your work faster, better and more efficient

- people already using Excel but wanting to get more out of the software
- beginners to Excel 2016 or earlier versions
- users of earlier versions looking to update their skills
- everybody interested in the Excel software

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- Are you thinking about working in data science?
- Do you already work in a data analytics job, but you do not know how to get your career to the next level?
- Are you interested in getting add-on credentials in data science, but you do not yet know which way to go?
- Do you want to know about
*salaries*in data science? - Are you searching for ways to improve your CV?
- Do you want to know which
*countries are best*for a data analytics career? *Simply spoken: Do you want to get your career right from the start?*

If you answered YES to some of these questions, this course might be interesting for you.

Your work takes the biggest chunk of your time, it should be a fun and challenging thing to do – so you better plan for a successful and meaningful work life. I will show you how.

By only investing 3 hours of your time you will get a clear idea about how to plan your career in data science. I assume you do not want to be one of those people who do a job that makes them unhappy and that leads to nowhere. Proper planning on education, industry, location and salary expectation will enable you to avoid the pitfalls of our modern job market. In this course you will learn about all of these and much more.

I have worked for several multinational corporations as well as an independent consultant and did my fair share of statistical programming and analytics jobs. In this course I will tell you anything you need to know in order to even start or improve your career.

This course is designed for anybody interested in a data analytics based career. No matter if you are just starting your stats, physics, math, business or science education, or if you are already doing an analytics job, you will get valuable info to bring your career to the next step.

This course gives you an idea about where to work with your quantitative education. If you want to work in data analytics, this course will show you how to get there. Information about all the opportunities that are available is hard to find. Students often only have a limited view about their possibilities. In this course I will show alternative fields of employment which you did never think about.

At the end of this course you should be able to plan your career. If you are already in a data driven field, you should be able to plan for your next steps. Maybe it is an add-on education you need, maybe you need to change industries or employers. All of those topics are covered and explained in detail, so that you can make your career a success story.

I will give you valuable info about how to find jobs, how to improve your CV and how to even position yourself without job experience. I know that it is tough to get a foot in the door without experience, therefore I provide some little known techniques in how to use social media to start your career.

There is a lot of hype around data science careers and job opportunities. While some of it is clearly justified, data science is a **key technology** for the years to come, it is also important to manage your expectations. Salaries are above average, but you need to get the job first. Interviews and assessment centers can be quite tough and the competition is not sleeping. I will show you how to stay ahead of the competition with some creative social media techniques.

Do yourself a favor, save time and energy for a prosperous career, and subscribe to this course.

- have an interest in data science
- a desire to work in analytics / quant / data science
- you will not need any specific software

- Over 30 lectures and 2.5 hours of content!
- plan your career in data science
- identify possible employers
- improve your CV to get more job interviews
- identify the best locations for data scientists
- evaluate if a particular add-on education is of any value for you

- Take this course if you are planing a career in an analytics/quantitative field
- Take this course if you already work in data science but you want to learn about alternative ways to get to the next level
- No matter if you just started your data science education, or you are already done with it, you will still get info on how to get a foot in the door with attractive companies

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Due to modern technology and the internet, the amount of available data grows substantially from day to day. Successful companies know that. And they also know that seeing the patterns in the data gives them an edge on increasingly competitive markets. Proper understanding and training in **Machine Learning** **and Statistical Modeling** will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can* boost your career*!

*Marketing companies* use Machine Learning to identify potential customers and how to best present products.

*Scientists* use Machine Learning to capture new insights in nearly any given field ranging from psychology to physics and computer sciences.

*IT companies* use Machine Learning to create new search tools or cutting edge mobile apps.

*Insurance companies, banks and investment funds* use Machine Learning to make the right financial decisions or even use it for algorithmic trading.

*Consulting companies* use Machine Learning to help their customers on decision making.

Artificial intelligence would not be possible without those modeling tools.

Basically we already live in a world that is heavily influenced by Machine Learning algorithms.

Machine learning is a collection of modern statistical methods for various applications. Those methods have one thing in common: they try to create a model based on underlying (training) data to predict outcomes on new data you feed into the model. A test dataset is used to see how accurate the model works. Basically Machine learning is the same as **Statistical Modeling**.

Unfortunately the learning materials about Machine Learning tend to be quite technical and need tons of prior knowledge to be understood.

With this course it is my main goal to make understanding those tools as** intuitive** and **simple** as possible.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with data on a regular basis can benefit from this course.

For a better learning success, each section has a* theory part*, a* practice part* where I will show you an example in R and at last every section is enforced with ** exercises.** You can download the

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (courses *R Basics* and *R Level 1*). You should also know the basics of modeling and statistics and how to implement that in R (*Statistics in R* course).

- You need a solid foundation in R
- You need a good understanding of general statistics
- You should be interested in machine learning and modeling

- Over 23 lectures and 2.5 hours of content!
- understand the most common principles of machine learning and statistical modeling
- perform machine learning tasks in R
- understand which machine learning tool is suitable for a given problem
- know what machine learning can do
- implement machine learning and statistical modeling in your work

- You should take this course if you are interested in statistics and analytics
- You should take this course if you want to use R to solve modeling problems
- You should take this course if you encounter problems that need more complex statistical solutions
- You should take this course if you want to enlarge your analytics toolbox

*“I’ve been looking for several days to get a hold on a useful and comprehensive intro into machine learning. Now I found one that is not too hard, and has some hands on work – nice job, thank you so much!”*

*“For me, Machine Learning sounds a big word as a business background students but Got general overview but still feel need to know a more of it.”*

*“I found this course and was curious about getting the basics about machine learning right. As a novice to Machine Learning it was time to eventually also tackle this topic. As a quantitative marketing guy, some things I do in my work can be handled with tools outlined here. Fortunately as one of the first students in the course I got a course release offer, so that even adds up to the postive experience. I am happy with the time invested and feel more confident about machine learning.”*

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Do you plan on** trading biotech stocks**?

Do you want to learn how to **identify promising healthcare stocks**?

Do you want to professionally **read a company pipeline**?

Do you want to know where to **get all the relevant info **for biotech/healthcare company evaluation?

Do you want to **tailor your investment strategy** towards a healthcare portfolio?

Those are just some of the question you will learn in this tutorial!

The biotech industry can be highly rewarding if you, as an investor, know how to read the signs. Healthcare is a highly relevant topic to all of us. In various stages of our lives we get costumers of healthcare companies. Ranging from simple over the counter (OTC) pain killers to high tech antibodies, biotech does all the development work to finally provide us with the medical support we need.

*The whole drug market will reach 1 TRILLION USD of sales in the next years.* It is a gigantic market, which is highly influencing our economy. So far it is mainly the sphere of investment funds, banks and insurances who have highly educated staff to properly read the signs.

It is a risky industry to invest in. Often times we see hit or miss events, with either huge stock price gains or losses. To protect yourself against clueless trading and the superiority of institutional traders, it is important to get proper training first.

Biotech training material and education for the private investor is hard to find. With this *unique course*** **you will learn the principles that lead the whole healthcare sector. If you understand how drug development works, you will be able to read the signs and make sound decisions.

If you want to earn more money with healthcare and biotech stocks, I would highly recommend to take this course. Understand the principles of healthcare first, so that you have the power to make your own decisions and not rely on external sources!

- You should have an understanding of the stock markets
- You should have a strong interest in the healthcare and biotech market
- You should have a willingness to implement the principles of this course into your trading strategy

- Over 16 lectures and 1.5 hours of content!
- understand the drug development process
- identify interesting healthcare companies
- identify red flags and pitfalls with some stocks
- know where to get in depth information to make clever decisions
- tailor your investment strategy towards the healthcare sector

- This course is for you, if you already made a few trades and know about the financial and emotiaonal hurdles that come with the stock markets.
- This course is for you, if you are willing to implement this information into your own trading strategy.
- This course is NOT for you, if you are looking for a get rich quick solution!
- This course is for you, if you have a genuine interest in the healthcare sector and the opportunities that come with it.
- This course is for you, if you want to create a trading strategy in a field that only very few people have figured out yet.

*“This course was very helpful to actually feel more confident in selecting biotech and pharma stocks. So far I was very sceptical about that sort of stocks – they are very risky. This course helped me a lot to develop a biotech stock strategy. Nice work!”*

This video is from our R Basics course. If you want to have a structured video curse (+2hrs) on the basics of R, click here to get it for free without limitations.

- lifetime access
- instructor support
- +20.000 students learning together

Also check out this article on datasets.

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- Are you an
**advanced R**user, looking to expand your R toolbox? - Are you interested in
**social media sentiment analysis**? - Do you want to learn how you can get and use Twitter data for your R analysis?
- Do you want to learn how you can systematically find related words (keywords) to a search term using Twitter and R?
- Are you interested in creating visualizations like
**wordclouds**out of text data? - Do you want to learn which R packages you can use for web scraping and text analysis purposes?

*If YES came to your mind to some of those points – this course might be tailored towards your needs!*

This course will teach you anything you need to know about how to handle social media data in R. We will use Twitter data as our example dataset.

During this course we will take a walk through the whole text analysis process of Twitter data.

At first you will learn which packages are available for social media analysis.

You will learn how to scrape social media (Twitter) data and get it into your R session.

After that we will filter, clean and structure our text corpus.

The next step is the visualization of the text data via wordclouds and dendrograms.

And in the last section we will do a whole sentiment analysis by using a common word lexicon.

All of those steps are accompanied by exercise sessions so that you can check if you can put the information to work.

According to the teaching principles of R Tutorials every section is enforced with **exercises** for a better learning experience. You can download the **code pdf** of every section to try the presented code on your own.

If you are not yet on an advanced level in R programming, we would highly recommend our R Level 1 course. The

Level 1 coursecovers all the basic coding strategies that are essential for your day to day programming. R features like functions and apply are covered in the Level 1 course and are also needed for this course.

- intermediate R knowledge is required (R Level 1 course)
- R program ready on your computer
- basic understanding of social media and web technologies

- Over 34 lectures and 3 hours of content!
- use R for social media mining
- get data from Twitter to do text analysis
- perform web scraping tasks using the twitteR package
- know which packages to use for web scraping
- get R and Twitter connected
- know how to perform a sentiment analysis in R
- plot text data visualizations

- everybody interested in social media analysis
- everybody interested in using R for web scraping
- everybody interested in sentiment analysis
- everybody interested in text analysis
- everybody interested in enlarging their R toolbox

*“This was pretty good. I knew very little about the scraping of Tweets so I found this valuable. I already knew a lot about Text Mining. I was very interested in sentiment analysis. The instructor’s presentation of it was good but I would have liked to have learned other methods of sentiment analysis such as the ones in the qdap and syuzhet packages.”*

*“This is a highly focused and well-designed class around Twitter and Sentiment analysis. The construction of the Breen sentiment scoring function is a nice takeaway. If you have never done Twitter or analysis of words, this is a good course in R to start with and build from.”*

*“Martin does a great job of teaching by example. While R is not the most complicated language out here, it has plenty of packages that are built for it. Martin has taken the topic of text mining, scraping, and sentiment analysis and shown how to accomplish these tasks through use of R. This is a growing area of study and this course will definitely show you what can be done. If you have any interest in this topic, the course will whet your appetite for more!”*

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- Do you work with Excel charts but struggle to identify which chart to use?
- Are you new to data analysis?
- Do you want to learn about the charting options Excel 2013/2010 offers?
- Are you looking for a structured way to learn about the Excel charting tools?

*When I created this course I put full emphasis on a structured and didactically sound way to gently show you how data visualization in Excel 2013 works. ***This course is especially suited towards a beginners audience to data visualization with Excel.**

Most of the concepts of this course also apply to Excel 2010, so you might also consider this course if you are on 2010.

Beginners to data analysis often need a clear understanding of which chart type is suitable for a given dataset. I will give you this important knowledge right at the beginning so that you know from the start what you are dealing with and how you can utilize that knowledge.

I will also show you step by step all you need to know about the basic chart formatting tools. You will learn how to add elements, format axes, change colors and so on.

There is also a section about the frequently used types of charts and how you can easily create them in Excel.

At the end of this beginners course you should be confident in using Excel for standard charts. You will be also able to format already existing charts and tailor them in accordance with your needs.

I highly recommend to take a close look at the introductory videos on which type of chart to use for a given circumstance. Only if you know which chart type is suitable for a dataset will you be able to make full use of the many options Excel has to offer.

- Excel 2013 is required – but the previous Excel version has similar features so you might also use your knowledge with the 2010 version
- interest in data visualization
- a basic understanding of charts and graphs

- Over 30 lectures and 3 hours of content!
- create the most common type of charts in Excel
- know which chart to use for a given dataset
- perform standard format changes in charts
- use Excel 2013 confidently for charts and graphs

- Everybody interested in data visualization
- Everybody willing to try out the material at home and solve the exercises
- Everybody starting out to work with Excel
- Everybody needing charts for work or study purposes

*“The course presents many aspects of charting and graphing. There are many ways to represent the data. Martin has been able to show these various styles and then show why some don’t work and others shine light on previous dark areas of your data. Good course and the price is right.”*

*“I purchased this course for a friend who’s starting out with Excel. I’ll stream this to her TV through Udemy’s mobile app and Google Chromecast. A great way to learn.”*

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- Are you a R user?
- Do you want to learn more about statistical programming?
- Are you in a quantitative field?
- You want to know how to perform statistical tests and regressions?
- Do you want to hack the learning curve and stay ahead of your competition?

This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R.

While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions.

After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R.

Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.

Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming.

Since data visualization is such an important part within data science you can also take a look at our Graphs in R course. This course gives you in depth knowledge about R plotting devices. Nowhere else on the web can you find such an extended course about this crucial topic.

- solid understanding of R programming – up to my “R Level 1” course
- R and R Studio ready on your computer
- basic understanding of statistics (descriptive, inferential, regression)
- high interest in data analysis

- Over 30 lectures and 3 hours of content!
- know which statistical test to use for a given question
- know how to perform the most important statistical tests in R
- know how to perform regression modeling in R
- have a very good understand of statistical testing and regressions

- students who need data analysis in their work
- data analysts
- entrepreneurs with quantitative interests
- everybody interested in statistics

*“The instructor does a great job of showing how to get things set up and running in R. I took an R course on Coursera and came here looking for a real tutorial where you could see someone type things into R. This class is excellent, I learned how to actually do things in R.”*

*“I liked the course as a result of preceding 3 consecutive courses of R by the same instructor, who developed the courses very systematic and step by step process and made me comfortable with the learning process. Thanks a lot, Martin.”*

*“1 month ago, I had never used R. I have statistics knowledge, so I wanted to learn a new tool to perform analysis with. I took R Basics as my first course and enjoyed it so much that I decided try R Statistics. Both courses are unbelievably well organized, and all of the videos flow seamlessly and logically from one topic to the next topic. The instructor is very clear and gives great explanations, so I never felt overwhelmed. Not to mention, the course covers a lot of very important and useful analytical techniques. By the end, I was confident enough to upload my own datasets and start working on them. I’m strongly considering taking R Level 1 next to continue to build my foundation.”*

*“The four courses viz., R Basics, R Level 1, Statistics in R and Graphics in R together form a comprehensive set of courses on R. Structured, logical and systematic approach provides the confidence that the learner desires very quickly. Statistics in R is very unique course not found elsewhere. Graphics in R provides overall and almost complete picture of various plotting packages in R and their relative significance. Good way to learn R. Martin, thanks a lot for creating these courses.”*

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**Udemy** is an online tutoring platform where tutors upload videos and learning materials. * R Tutorials* cooperates with Udemy and offers its R and Excel tutorials via this platform.

R Tutorials offers several full teaching tutorials at Udemy. Among those videos you may also find the 2+hr free ‘R Basics’ course including exercises and code scripts. A great way to start learning R! Along with many hours of videos you also get access to the user discussion forum where you can post questions you may encounter during your journey to R mastery.

The usage of the tutoring platform requires registration, which is free of charge. Once you are registered, you can join free courses or you can buy high quality video courses.

Once you get an R tutorial via Udemy you have an unlimited, life-time access to it.If you are not happy with our product, you can get your money back within the first 30 days!

you can enjoy our tutorials and learn R not only on your computer or laptop, but also on your smartphone and tablet since all of the R tutorials have a 100% responsive design.Do not forget:

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*Do you want to create overwhelming plots?**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.

This is one of the most extensive tutorials on R graphs on the web! If you need graphical skills for your career, this is your chance.

In case you also want to gain a very solid understanding of R`s alternative functionalities including loops and functions, you can check out the Level 1 course.

- Basic statistics knowledge
- Basic understanding of graph creation
- Good understanding of R (up to R Level 1 course content)
- Interest in data science
- R and RStudio ready on your computer

- Over 46 lectures and 4.5 hours of content!
- you will learn all about the graphical parameters in R
- you will see how the plotrix library can enlarge your data toolbox
- you will learn how you can create histograms, barplots, scatterplots, lineplots, stepplots and many more by using the R base functionalities
- you will learn about the whole different system of ggplot2 plots
- you will learn about the lattice package, which is widely used in academic settings

- Statisticians and data scientists
- Students working with data
- Entrepreneurs working with R
- Web Developers

*“Without any exaggeration, I should say it is well organised with examples and exercises. When I started my R Basic, had a bit difficult but later on I watched again – More clear. So, my personal suggestion as a novice, if it is expected to take over the fear of R, it is better to review the every Module at least twice with focus. Thanks Martin with the steady and consistent process of Lecture presentation.”*

*“The instructor does a great job not only in his presentation, but takes the time to recap the lessons.”*

*“I was searching for a comprehensive R Graphs course and I finally found one. I need to create approx 10-20 graphs for my PhD thesis. Of course I want them to be of perfect quality. This course is helping me a lot in learning that. Very extensive, good exercises, and I like the teaching style! And the best part is the quick and helpful answer to my pm.”*

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*Are you new to R?**Do you want to learn more about statistical programming?**Are you in a quantitative field?**You just started learning R but you do not want to struggle with all the free but unorganized material available elsewhere?**Do you want to hack the learning curve and stay ahead of your competition?*

**If YES came to your mind to some of those points – read on!**

All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered.

While planing this course we were using the Pareto 80/20 principle. We filtered for the most useful items in the R language which will give you a **quick and efficient learning experience.**

Learning R will help you conduct your projects and on the long run it is an invaluable skill which will **enhance your career.**

Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used to do loops. According to the teaching principles of R Tutorials **every section is enforced with exercises **for a better learning experience. You can **download the code pdf** of every section to try the presented code on your own.

In the graphics section you will learn how you can create and tailor your graphs. As an example we are creating boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation.

This tutorial is your first step to benefit from this open source software.

Since data visualization is such an important part within data science you can also take a look at our **Graphs in R** course. This course gives you in depth knowledge about R plotting devices. Nowhere else on the web can you find such an extended course about this crucial topic.

- interest in statistical programming
- R and RStudio ready on your computer
- basic understanding of statistics and data structure

- Over 69 lectures and 5.5 hours of content!
- this course will show you how the most common types of graphs can be produced with R base
- you will get a good understanding of functions and loops in R which are very useful programming skills to have
- you will get the necessary theoretical background for R
- you will learn how to create and handle different types of objects
- you will get fluent in the R programming language to master your specific quantitative tasks

- scientists
- data analysts
- entrepreneurs
- web developers
- anybody interested in statistical programming

*“Content is Good. Very Comprehensive and informative for Beginners. Instructor delivery is very much engaging and motivating to learn R”*

*“I’m a new comer in R. This video lead me into R by the clear and systematic steps. This is helpful. This is also the first time I raised a discussion through the Udemy system. Thanks Martin for the nice lesson and engaging.”*

*“I thought this course did a great job in explaining all of the functions and arguments and so forth. I thought the exercises were great; challenging, but not too hard. I also really appreciated the fact there were ‘cheat sheets’ you could download after each section to keep for future reference.”*

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The R Basics course was created by R Tutorials. It is meant to give you an introductory understanding of the R language. It takes about 2 hr (+ the time you need to solve the exercises) to complete this course. This is just enough time for a brief introduction.

R programming becomes more and more popular since it is fully open source and reacts very dynamic to new developments.

Nowadays it is vital in many scientific or other analytical fields to have a good understanding of the R language. With the R Basics course you can build a very solid foundation to later on branch out to the various applications R has to offer.

You will learn about basic commands like “paste”, “seq”, “rep” and you will also see how graphs are created in R.

We use RStudio as our user interface. You will quickly see that this software makes using R much easier.

**This course is totally free to you – it is the perfect chance to get familiar with R programming.**

- Genuine Interest in statistical programming
- Computer ready to run R and RStudio
- Basic understanding of statistics and data structure

- Over 29 lectures and 3 hours of content!
- You will learn how to navigate in the RStudio interface
- You will learn how to make basic graphs
- You will learn about the basic structure of R including packages
- You will learn how to perform basic commands in the R programming language

- Students who need R for their courses
- Web developers who want to implement data analysis features in their webpage
- Everybody interested in statistics and data sciences
- Researchers who perform data analysis including graphs

*“This is a great introduction to R. I was able to easily follow the lessons. I specially appreciate the short time format!”*

*“I loved the course. I can’t wait to start another one by the instructor! Presented the material in an easy to understand and logical manner. Great work! Thank you.”*

*“This was an excellent course for beginners. It is presented well with a good explanation of each step.”*

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What do You think about the R Basics course? Check it out and share you valuable thoughts with us