 Welcome to dealing with materials data, in this course we are going to learn about collecting analyzing and interpreting materials data. This is the first module, this is an introduction to R module, so in this module we are learning how to use R for data analysis and interpretation, some preliminaries of the R programming language. We are going to learn more about the data analysis and interpretation parts as we move along in this course. So this is the last session of this module, so this is a summary session and so if you look at a book like R for beginners by Emmanuel Paradis there are about 5 sections in the book. The first one is getting started, the second is called data, how to read data, how to generate data, how to save data and how to manipulate data, third one is plotting, how to as well as some packages, for example we have used dgplot2, fourth is statistical analysis and that will be covered in great detail with specific reference to materials data in the rest of this course and the fifth is related to programming, so R is both a software and data programming language and like I said we are giving just a tutorial introduction to R, we are not going to cover lots of programming aspects but I do believe that this course will prepare you for programming and if you are so inclined then this should be a nice starting point for you to start. So if you look at the material that we have gone through of course we have learned how to get started with R, we have also seen how to read or generate and save or manipulate data and we have looked at plotting in little bit of a detail and statistical analysis is something that we have not started but the very next module we are going to start for example is on descriptive data analysis and so but there are some more aspects that has been left out and so I am just going to mention them here and I recommend that you read more about this with the available material online. So R works with objects as you have noticed and each of these objects have a name and content for example when we have this variable name element or x and it has some content so it is 15 observations and 4 variables etc. And the objects have intrinsic attributes like the length and mode so if you can ask for length of an object and it will tell you what is its size and the mode is like whether it is numeric or character or complex or it is a logical like true false etc. And of course you can use both the mode and the length commands to probe the intrinsic attributes of any given object and objects representing data can be of many different types vector, factor, array, matrix, data frame etc. We have seen tables and matrices and data frames etc so there could be much more detailed information and this might become very important if you get into our programming and reading data we have used read.csv but it is also possible to use read.table and scan to read data from text files so this might become important in some cases so you can learn about them and similarly you can use write.table to write data for example. So to move forward we are not going to spend too much of time doing these other aspects so this introduction to R is a very very tutorial introduction so it is more focused towards what we want to do for the rest of the course so sometimes all the details are not given and also when you are programming there is not just one way of doing things there could be more than one way of doing things and there could be gradation among these different ways of doing things maybe some are more efficient computationally or more optimal than others so those aspects we have really not looked into so sometimes we have done very circuitous things maybe because that is just the one way of doing it or just straightforward way of doing it maybe there are better ways of doing it so all these aspects we have not discussed and however there are several references that I have given so far and I want to add this Paradis book introduction to R also because it is a very short book running into less than 100 pages but it discusses all these 5 aspects like I mentioned earlier and so it can add to what you have already learnt and it can prepare you for programming or if you are so inclined but we will continue with our tutorial mode and we will start using R for doing some statistics and data analysis for the rest of this course so thank you.