Programming in R: Getting Started with RStudio

1. Welcome to the Course!: Hi, I'm Emmanuel soggy and welcome to my course Programming in our getting started with RStudio. So this is the first in a series of data analysis with R. And there are three courses here, three short courses. So this course is 46 min long, long, and there are seven lessons. The first lesson is installing RStudio on your desktop. Then how to use RStudio Cloud. There's a practice practice quiz as the third lesson. Then the most important features about RStudio. How do we install and load our packages using RStudio Cloud and creating interactive graphs, maps, and tables in just one knife code. The last lesson is a practice activity. And then you're going to have, of course, a project, like all my skill share projects, courses. They have a small Capstone Project you to do. It's for this particular project is for tasks for you to go in your RStudio Cloud and perform some operations change and options, and look around navigate so you can be comfortable using RStudio to store programming in R. So welcome to the course and let's dive in. 2. Intalling Rstudio On Your Desktop: Okay, so there is two ways to use RStudio. First, we're going to download R and RStudio on the computer. That's gonna be the first task and the second lecture or the second task will be to use RStudio on the Cloud. That RStudio themselves released in July of August, I believe, 2020. And it's been it's been a lifesaver for me when I forget my computer, my laptop, I can just go on any Any computer and I can just login in the Cloud. But the first task here is to download R and RStudio on the laptop or on your desktop computer. For this, you're going to, there's two steps actually to do that. First of all, you're going to download R, the programming language on your computer. And second, after that, you're going to download the IDE or integrated development interface, RStudio IDE. That's gonna be on top of the art programming language. And then we both, with both on your computer, you're gonna be able to develop applications in R using RStudio. So you first download R. So here I'm on Google download R. Here I can see the first link is go to cran dot r-project dot org. So I click on it. Here, I have a link, so I just follow the link, right? Download R for a Windows. And then Danny, downloads for me are on my computer here. We are in September 2022. So the version is 4.2, 0.11. Once it's downloaded, I'm going to pause the video and once it's downloaded, I'm going to install it on my computer. And then I'm going to come back and we are going to download the IDE, RStudio IDE. Okay, So I started the install here. So double-click on the installer, just follow the prompts here. And I install R on my computer. So I'm going to pause the video and then we're going to install RStudio. Here. I'm on rstudio.com. Here I click on products and RStudio, the first one, the premier IDE for R. I take on this. Here. I have two options, or desktop or RStudio server. I'm going to click on RStudio Desktop. That's the one I want to to download. And I click on Download RStudio Desktop. I choose my version. There's different versions here. The first is the RStudio Desktop free that I want to download. So I click on Download. And again, it detected that I have windows. So here I haven't been download RStudio for Windows. Again, it's downloading here. And once it's downloaded, I'm going to double-click on it to install it on my computer. So I'm going to pause the video while it's downloading. Okay, so I double-clicked on the RStudio icon. And here the RStudio setup software is starting. So I click on Next. And then RStudio is starting to install on my computer. Okay, so are using installed here, I'll click Finish. Then I go to my Start button here, and I click on RStudio. I click Open. And here are the RStudio IDE has just started with the version are our version 4.2, 0.1, which I downloaded here. Funny-looking kid isn't name of the version and year. The carrot sign signifies that you can start coding in R. So that's it for this video. In the next video, you're going to learn how to go on the RStudio Cloud installed using the RStudio Cloud interface. Thank you. 3. How to Use RStudio on the Cloud: So the second way to use RStudio is in the Cloud. So RStudio, the company in July or August 2020, released a Cloud version of RStudio. So that's what we're going to learn how to use right now. It's very simple. First you go to RStudio, dotCloud, RStudio dot cloud. Then you click on get started for free. Here you can see four different packages. Cloud free, club, Premium, Cloud instructor, and Cloud organizations. All the packages are really very affordable to five to maybe $10 a month for instructor or $15 a month. Of course, what we're going to use in class is cloud free. So you can upgrade your account for $5 a month for 75 projects. We're going to sign up for free. Of course for free you have 25 projects that you can, you can use and 25 h per month that you can use. So what you do is you click on Sign Up here. And here you have different ways of signing up your three ways you can use your email and enter a password of firstName and lastName and sign up. Or you can use your Google account here or sign up with a GitHub account. If you have a GitHub account, I already have an account here. So what I'm gonna do is I'm going to login and I'm going to login with Google. And here I am in RStudio Cloud. So what you have here on the left, you have the spaces here. So what you can do is you can, you have your own workspace. You can create another workspace, but that's it. For your free version and your workspace, you can create up to 25 projects. Here you have learned section, you have cheat sheets of domain packages in R like deep lawyer and the Tidyverse. Here you have primers, any or you have what's new. Here. You can have different help links and information about Plans and Pricing and Terms and Conditions. Okay, so I'm going to close it out. We're going to work out of our workspace. So your workspace here, and on the top here you have the three tabs Projects. So here you can have a list of your projects That's going to be listed here. And then you have an About section that explain, that explains a personal workspace, what you can do with your personal workspace. You can share projects that you're doing. Okay? And here, the second tab here is the usage. So how many project hours do you have available and still available on your on your account for the month? Here on the right. When I click on my name, I can see some information about my personal account. So the plan is cloud free. The period is September 12th, October 12th. I can have up to 50 project and have to 25 project hours. So so far, I use point for project 2 h and on October 12th, everything is going to be reset as far as project hours are concerned. Okay. So if I click on Account here, I have more description about my account. I have one space 50 projects available, 25 h and currently I have zero Project and 0.4 h here I can upgrade. Okay, here you can see my usage again. What we're gonna do right now is we are going to go in your account and we are going to start creating a project. So for this, you're going to need to go on the right hand side here. I'm going to create on the arrow here. And you're going to click on the arrow here. New projects. You can create a new RStudio project that's going to launch an instance of RStudio with hopefully the latest are available. You could create a Jupiter Notebook project. Now, not on the free Cloud though, so we can't do that. The free plan. But if you have a graded your plan to a premium plan, e.g. which is just ten or $15 a month. And you want to try and you can afford that. You can create a Jupyter project here, but you can create a new project from a Git repository if you have one. So what we're gonna do here, we're going to create a brand new RStudio projects. And when I click on this, RStudio is deploying a brand new project with is launching an R session and is launching an RStudio instance for you. So here we have RStudio here, just like we've been downloaded in the previous task, we've been downloaded RStudio on the computer. Well here you have the same thing. It's the exact same layout with the version 4.24, 0.1. So it's the latest version and it's ready to code here on the top here you have your workspace and a space to create a project so we can name your project, I mean, so we can write test project, e.g. enter. So here we have our project and then test project. And here we have an instance of the RStudio. If we go back to our workspace, we can see under my projects here we have a list of our projects that we created. So we just created in one project. Here, test project. If I click on it, then I go back to a resume, my R session and my RStudio session. If I come back again, here we have again the project. And on the right, we can either put the project in the trash can, trash bin or download and export projects or here we can archived project. If we click on Settings, we have a few different settings that we can change. And who can have access to this project. So me or everyone in the cloud, if I click on everyone in the Cloud, then if I give the link, everyone can have access to that project. So that's the basic four RStudio Cloud. And as I said before, it's been a lifesaver for me because I don't always have my laptop or computer with me. And here I can access RStudio from anywhere. That's it for this lecture. And next lecture, we're going to use again or your Cloud. We're gonna use us to use Cloud for this entire project. And I'm going to show you the most important features of RStudio IDE within the RStudio Cloud. Thank you very much. 4. Practice Quiz: Here is the first practice quiz for the RStudio course, getting started with our studio. So according to the first two videos that you watched, what are some advantages of RStudio Cloud versus RStudio Desktop? Hey, we always use RStudio Desktop because RStudio cloud does not allow for many are functions be using RStudio Cloud because I'm on the road a lot and accessing RStudio using an Internet browser is very convenient. I don't need to use my own laptop all the time. See, RStudio Cloud makes it very easy to share projects with my team and my students. D. Rstudio Cloud is very easy to use because there is nothing to configure and no software installation to do. I just sign up to us to do dotCloud and I can use RStudio in my browser. So what are all the possible answers here? A, we always use RStudio Desktop because RStudio cloud does not allow for many are functions. This is incorrect. Rstudio Cloud provides all functionalities that are found in the Earth's to you free version. And you can use all the functions provided by the R programming language. B. I use RStudio Cloud because I'm on the road a lot and accessing or using an Internet browser is very convenient. This is correct in this particular use case, if you don't want to bring your laptop, just use RStudio Cloud on trips. Just create projects with RStudio Cloud. Then you can use are on any browser. See, RStudio Cloud makes it very easy to share projects with my team or my students. This is also correct. With RStudio Cloud, it is easier to work with a team or students to share projects than it is with RStudio Desktop. D. Rstudio Cloud is very easy to use because there's nothing to configure and no software installation to do? This is correct. There is nothing to configure, no software to install. Just sign up to RStudio Cloud and you can create projects and start using the R programming language on any browser. 5. Most Important Features About RStudio: Okay, so this activity is about looking at RStudio, IDE, the layout, and talking about best practices and how to go into the global options to change some options to make our life easier really, as an artist, your developer. So here I'm in my RStudio Cloud instance, right? And I have a project, so I click on Project here. Our instance is starting or steed you instances starting with The through. All the files here on the bottom right corner, all the files of our projects. What we're gonna do first of all, is looking at our global options in tools. So go to Tools, Global Options. A best practice in any IDE is to save the source, but not the workspace. In R and RStudio. It means two things. Always start with a blank state, okay, a blank slate. So to do that, we are going to the general options. And here you can see under workspace, you have restored our data into workspace at the startup. So what we wanna do is we want to uncheck this and save workspace to our data on exit, we want to choose never. The oral data is a file that is used to save all your data object when you work on a project. And you don't want to save that. What you wanna do is that whenever you start a project or the next day you come back to your project, your RStudio session, you want to start fresh. You want to always start our with a blank slate. So you don't want to save your data file every time you exit RStudio. Since we are in the global options here, we're going to change the appearance here, unlike a dark theme. So here you have RStudio theme, modern or sky. So I choose modern 12 and then tomorrow night, 80s for a dark theme. And then here under Pane Layout, you can see here when you come to RStudio, There's four panes, two on the left and to underwrite. The first on the left, top-left is a source. Top right is a console. Bottom-left environment with your variables. And on the bottom right you have your files, your packages, your plots, et cetera. Now you can change it however you want. Okay? I personally like that idea of having the source on the left and the console on the right. And all my extra things at the bottom here. So here I click Apply and click OK. Here. I can collapse here if I wanted to. And you can see that you have your four panes here. You have the code that is in here. You have the console on the right. With a terminal. You can use a terminal or background jobs. So console terminal are here on the right or the man right here you have all your files, you have the plots, you have your packages that are installed already. And we're going to use the Install button package is a lot to install different packages for our project. We have a help, we have the viewer when you create e.g. an interactive plots or tables. And you have your tab for presentation, e.g. if you create a PowerPoint presentation with R. And at the bottom here, you have your environment with all your variables that are going to be placed here. The history, your connections. Here. If you have database connections, e.g. and here you have access to several tutorials about Shiny, about our programming in general. Now, let's go over the different tabs here. First of all, on the File option, the most important for me option here is to import the data set. So we're going to see in importing are the section number to module number two in this course, how to import text, Excel files, SPSS, etc. So it's gonna be here under edit. The most important for me is to clear the console. Sometimes it will console, you have a lot of code. Control L, just clear, clear the console or this option to Find in Files can be very useful. If you don't know, if you have multiple files and you want to find a certain word in all the files of your projects. Right? Under code here, you have several options here. Under View, you can change the layout of your panes here. So I use this sometimes here for your plots sessions. Let's talk about a minute about restarting R. When you develop in R, It's a very good idea to restart our very often. And sometimes beginner students, they use this command that they've learned or they looked online. They looked up online. And it's to remove basically all the objects in your workspace, right? And you hit Enter and removes all the objects, the variables that you would have created that are placed here on the bottom left of RStudio. The problem when you do this, it doesn't really give you a blank slate. Because when you work with R and RStudio in shiny, if you have a Shiny application, there's a lot of things going on in the background. And the best way to really restart fresh is to click on restore R. It's going to start a new session and it's going to empty all your variables and MT, really everything that is in the background that you don't see, you completely restored a new R session. And it is really best practice that when you develop, when you debug just to restart are often hear when you build e.g. packages. Here a section where to use when you are debugging here to start profiling. Here's the time you're going to use to install all your packages. This is also the same thing if you go to package here and you click on Install. So same thing. Here, you have different tools, the Global Options, Project Options, and here you have helped. You're on the right. You have the version of R that you're working with. If you want to work with different versions, you click on the arrow here and you have a list of the previous versions, and you click, you can click on any version you want. If you want to test e.g. we created a package and you want to test if your package works well in different versions of R, you can use that function. So we went through the RStudio interface. Again. For me, the most important thing, the best practice is really to save the code and not to save the workspace. To start with a blank slate when you start an RStudio. And to do that, you renounce the, our data file. So we went in the beginning to Global Options and we unclicked the restore our data. And also to restore our very often, especially when you debug. So that's it for this video. In other video, we're gonna learn how to install and load packages. 6. Install and Load R Packages: Okay, So in the last video, I showed you the most common options to be able to navigate RStudio IDE on the Cloud, on RStudio dot cloud. Now we're going to talk about downloading and activating our packages. So what are our packages to start with? Well, they are free libraries of code written by the R users in our community. And many functions that we use every single day come in packages such as functions to manipulate data, create maps, develop web applications, build statistical models, create datasets, create interactive graphs and tables, and scrape the web, use Web API, etc, etc. So there are a lot of packages out there, but usually they are found in two places. The first place that is the default in RStudio is the crane. And a crane stands for Comprehensive R Archive Network. You can say that it is the public clearinghouse for our packages. So you're not going to download the packages directly from the Internet. You are going to use the RStudio interface to be able to download those packages from the Quran. So first I want to show you how you can manage all these repositories of packages. So you go to Tools, Global Options here. And under Packages, you can see there's a tab for package management. Here you have already a repository, which is the primary repository, is the CRAN, like you couldn't change it if you want, but it's usually by default the primary repository you can add or remove other repository as such as GitHub or other repositories that you find on the web. Okay, So the primary repository is CRAN. We're going to exit from here. And what we're gonna do is use the RStudio interface to download packages on our stdio library. So first of all, I wanted to show you under packages, you have all these packages here that are pre-installed, that come with the installation of RStudio. So when you install RStudio on your computer or you start an osteo instance. You have already all these packages installed, pre-installed for you. There's a lot of packages that are not installed that you're going to use every single day to manipulate data, e.g. so what we're going to use over gonna do right now is to download one of those packages. In one of those packages, it called D player. So under Packages, you have been called Install. Here you can choose where you want to install those packages from. Here you have the primary repository from your global options here. Right here you enter the name of the package. So here we're going to download deep layer. As I type here you can see an auto completion of all the packages that starts with D P, L course in the Qur'an. So here we're going to choose supplier. It's going to install the plan. You're into our account, into this library. We're installing the dependencies always. And we'll click on Install. You can see here the command install dot packages. Dplyr installed all the dependencies and installed the player. We can go under Packages here. On the right. We're going to enter deep layer. And what you see here that the player has been installed, the name here, the description of grammar of data manipulation, and the version 1.0, 0.10. If we click on D player, we have a description or documentation of this package. Grandma data manipulation. We have the documentation of this version here with a description of all the available functions in this package. So let's say we want to see a function here. We click on the function and we have a description of the function with the arguments, the value, etcetera, etcetera. We can of course, find documentation on the web. We can go to Google and type in deep liar and the name of the function. And we're gonna go through the supplier website and find a description of that function. So now we have this dplyr package installed in our account. So now we need to activate it. And to activate it, you simply click the checkbox here. And the command is library, the supplier. In here. All the functions available in that package are now available in our, our session. And we can use them to manipulate data, created, etc. Now the second most common way to download and use packages or through GitHub. So to download and activate packages from GitHub, we first need to download another package, night, and this package is called DevTools. It's a package that has a lot of tools to develop other package. And this package has a function called install Git Hub that we will need to be able to download packages from GitHub. So we'll need to download this one first. So DevTools, and we'll click on Install. Okay, so depending on your connection, you can take a while. Here it has downloaded DevTools, so we need to activate it. Dev tools. You see here the version 2.4, 0.4, dev tools here, we need to activate it. So all the functions within dev tools are available for us and one of the functions is installed Git Hub. We're going to install a package from GitHub, a package called bromine. So this is specific syntax for this Install GitHub function. So install. Then you can see the auto-completion here. Get hub. I clicked on tab and then quote the author of the package for its slash, the name of the package. And then we press Enter. So here it's downloading here. And depending on the conductivity of your, your computer, you can take a while. So I'm going to pause the video and come back. When the functions and packages installed. The package is installed and now you're going to use the command library to just activate the library and you can use all the functions available in that package. So that's the end of the video that was about R packages, how to download the packages, the two most common places to download packages and how to activate packages. And activate all the functions within the package. Within your R session. 7. Create Interactive Graphs Maps Tables: So in this video, we will download the more libraries, more packages to create tables, maps and graphics. And we'll use the functions within those packages to create interactive tables. Actually interactive HTML widgets for tables and graphs and maps with just one line of code. So first what I'm gonna do is I'm going to show how to get data because we need a dataset to work with these graphs and these maps and all that. So here I'm in my RStudio account. So I click here on the little broom here to clear my console. So already done that. And here I click on the collapse icon. So we get more space here. And this line of code will show us the data set that are preloaded when you start an R session. So here I'm going to collapse this to. And you can see here all the data sets that are included in a package called that I said that are already, that is already included when you start an R session. So you have all these datasets to play with basically, right? So e.g. here I have a data set called Titanic. Here, description of the dataset survival of passengers on the Titanic. Here you have a tooth growth. Us arrests which has violent crime rates by US state. So I'm just going to pick a data set here, US arrests e.g. and I press Enter. So here the columns represent the four variables, four columns, and the rows represent each state. We can get another data set, swiss e.g. here's another dataset with 123456 column or six variables. And each row represents a county in Switzerland. And for each county, the data set is representing the fertility, percentage, agriculture examination, education, catholic, and the mortality of infants in each county in Switzerland. So we're going to clear all this and that has set we're going to use is called empty cars. So first we're gonna use a package called DT to create an interactive table. First, we need to download this package. We are going to the tab package and then install. And we are entering DT. Dt is the first option here. That's the name of the package. And we're clicking on Install. So here you have install.packages. D t is the command. Now DT is installed, we need to load it. So the easiest way is to go here type D T, and click the check mark here. Alternatively, you can use the command library d t to load the packages in memory. So now you're going to use the functions that are within the library within the R package to create an interactive table function we're going to use, it's called data table. Data table. And then we're going to pass the first argument, which is the data set will want to work with. So the data set we are using is empty cars. And we'll press Enter. Here in one line of code, we have a nice interactive table here with different options. We can show several entries of the several options, 102550100 entries. We can search for a certain car brand if we want. Here, you have an interactive pagination option. And of course this function as many, many different options that you can change that better suit your needs. D. D is a package that is used a lot when creating shiny applications, e.g. or web applications using the shiny package. And what you can do to look at all the options that are possible. You go to Google, type in our package d t. And then the first link will bring you to the website. That's gonna give you all the options and functions available within the package. So now I'm going back to RStudio. I'm going to click on the broom here to the console. And then we're going to use another package called high charter. A charter is a package that is using a JavaScript library. And we're gonna go to package, we're going to click on Install. And then high charter. So hi charter has been installed. We are going to load it now to be able to use the different functions. Okay, so now high charter at the package is loaded and we can use the functions to create different types of charts. So we're going to use the function h chart here to create a new interactive graph in one line of code. So here's the line of code here. The function is H chart and the first argument is empty cars. Empty cars is the data set that we'll want to use. Then the second argument is the type of plot that we want. Again, we can go on the Internet and typing R package high charter. There's gonna be a website that is dedicated to the heart charter package where you can see the different options and different arguments of the different functions that are available. The second argument here is that the type of plots that you want here we want a scatter plots. And this line here is saying on the x-axis, I want the weight and on the y-axis I want the miles per gallon. And on the z-axis, I want the GRAT variable and all the colors are going to be depending on the variable HP. And we have an interactive graph here that is displayed with the high charter package. So again, the height shoulder package is using a JavaScript library and we are able to create beautiful, I think, really beautiful plots with this package. And I like it a lot, even if sometimes it can be difficult to understand how it works, but the rendition is amazing. So that is our second package we can use to create interactive plots. Now we're going to use another package to create an interactive map. The third package we're going to use it is called leaflet. So let's install it. Leaf. Let. Leaflet is used to create beautiful maps, especially if you are creating a R Shiny application. So you click on leaflet and then click on Install. So it's installing the leaflet packet right now. And then I went on this website, lat long dotnet, and I typed in my CD, Huntsville, Alabama. I wanted to know what's the longitude, the latitude of the city, because that's what we're going to use to create our map here. So now the lifted package is installed. We are going to load it. Light. Here. We're going to click on this checkbox. Solely flight is installed. We're going to make some room here. And here we're going to use the function leaflet. And then we're going to say, Hey, I want to use some tiles. And then we're going to set the view to here. We put longitude and latitude and Zoom at 12%. So again, to do that, we just go to Google and you type R package leaflet. It will bring you to the website where you can see all the possible options that you can use with Leaflet package. So if I press Enter, I have a map of my town here in Alabama. And as you can see here, it's interactive. I can zoom in, zoom out drug and drug, my-map, et cetera. So this was three little lines of code that you can use to create an interactive table, an interactive map, and an interactive graph. Again, if you want to know all the different options and how you can change the different options. You can just go to Google and type in R package and the name of the package. And it will bring you to the website with all the options that are available. So that is it for this video. The next video is going to be an activity that you're gonna do on your RStudio Cloud instance. 8. Practice Activity: So this is a practice activity for the horse to do course, getting started with RStudio. In this practice activity, you're going to perform the following three tasks. First, on RStudio Desktop or RStudio Cloud, if you created an account, go change the theme to modern tomorrow night '80s with a courier font with 12 sides. Second task, install and load the package tidyverse through the RStudio interface. Third task, install, load and use the package d t to show an interactive table of the data set called iris that is already pre-installed with R. So you don't have to install or get this dataset iris. It's already pre-installed when RStudio starts. So again, just like with the quiz, pause the video now performed the three tasks, and then you can come back here and I will give you the answers. So first, change the theme to modern tomorrow 1980s with the Courier font with size 12th. Once you are in RStudio Desktop or Cloud, you go to Tools and you click on Global Options. Then in the tab, appearance under osteo theme, choose modern, under font, choose Courier, font size, choose 12, and under editor theme, choose tomorrow night AB's second task, install and load the package tidyverse through the RStudio interface. When you are in the RStudio IDE, you have four panes. And usually at the top right corner of the screen you have this tab called packages. Click on package. And then there's gonna be a button called Install. You click on install windows going to pop up to install. And under Packages, you type in tidyverse. Then you click on Install. Once it's installed, you need to load it. And to load it, you just need to find the package tidyverse and you click on the check mark, then you can use all the functions available in the tidy verse. Third task is to install, load and use the package d t to show you the interactive table of the data set called iris, that it's already pre-installed with R. So again, just like the previous task, you go to packages and install, and then you type in d t and you click on install. Then of course, after the install you need to load the package. So find the package and you click on the check mark to load the package. Finally, under the console, usually it's on the top right of your screen. You have the console and you can use the function data table from the package d t so that our table iris. You press Enter and you have an interactive table under the viewer of the dataset iris. 9. Closing Remarks and Next Steps: So this is the end of part one in this series of getting started with RStudio. And this first part we learned about RStudio Cloud and how you can use the different options to configure your account. And part two is about how to import data into RStudio to work with programming language R. And then on the third part, you're going to learn how to use R packages to transform data into R, to get from messy data into a clean data. And you can find all these links below in the description of the course. Thank you very much. This is the end of part two in this series on getting started with RStudio. You just learned how to import data into R. With RStudio, especially the RStudio Cloud accounts that you created in part one. And if you haven't followed the part one of our studio course, I encourage you to go and look at the videos and practice. And the third part here is going to be about transforming data with RStudio. And again, you can find all these links to these videos within Skill Share under the, under the description for this course. Thank you very much. Welcome to part three of this series on getting started with RStudio. So the first part of the series was about RStudio Cloud and how you can use different options to configure your Cloud account. The second part was all about in polling data. And this one is about how to clean and transform data into RStudio. So as you can see here, there are eight lessons. The first lesson, first video, is about how to select groups of observations. So we are going to look at several functions and we're going to learn different functions from especially the deep layer package or the tidyverse package. Then video 2.3 or two parts, really two videos on how to transform a messy data to clean data. First of all, I'm going to define what constitutes a messy dataset and how to clean it. So two videos, and of course, to clean a dataset, you are going to have missing values or null values. So it's important to know how to handle missing values in R. That is the object of this video. The next video is how to split and combine different cells. So it's using some functions to split and combines string data. The video here is how to combine or join or gather different tables. So it's the equivalent of the inner join, the left or the right or the full outer join in. And finally, you're going to have to practice video to build your confidence in cleaning and transforming data into RStudio. Of course, at the end, you're going to have a project and the description of the project is under this video here, under the project section. So I propose that we just dive right in and learn how to clean and transform data within RStudio. This is the end of part three of this series, Getting Started with RStudio, this particular video course was about cleaning and transforming data into RStudio. If you mess to the two previous video courses, the first one is on RStudio Cloud. I had to set it up and use all the options to configure your Cloud account. And part two was how to import all sorts of data into RStudio. You can look under this video and you can find links to this previous video courses on getting started with RStudio. And I hope you enjoy the course and the series on getting started with our studio. Thank you very much.