Datasets in R: Loading & Saving Data

R Programming 4.0+    |    Beginner
  • 14 videos | 1h 44m 17s
  • Includes Assessment
  • Earns a Badge
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Transforming and manipulating massive amounts of data is crucial for all organizations. The R programming language offers a plethora of packages to load, explore, manipulate, and transform data. R is ideal for data analysis, mutation, and cleaning, making it a choice language for statisticians and data scientists. In this course, you'll learn how to write R script files using the RStudio environment. You'll use different panes to debug and evaluate your R program, import data in various file formats, and access files embedded in an R package and stored on your machine. Additionally, you'll learn how to export data to different file formats. Once you've completed this course, you'll know how to work R using RStudio, import and export data in R, and perform simple data transformation and exploration operations.


  • discover the key concepts covered in this course
    install and set up the R programming language on macOS
    download and set up the RStudio IDE on macOS
    install and set up the R programming language on Windows
    download and set up the RStudio IDE on Windows
    run commands on the RStudio console
    recall the use of the different panes available in RStudio
  • create a new project and a new R script file and execute code
    demonstrate and visualize built-in R datasets
    use vignettes for help on packages
    read in csv files from the file system and built-in packages
    import data from XML, Excel, and JSON files
    export data to various text, CSV, JSON, and Excel files
    summarize the key concepts covered in this course


  • 1m 51s
    In this video, you’ll learn more about this course and the instructor. In this course, you’ll learn to write R script files using the RStudio environment. You’ll also learn to use different panes within RStudio to help you debug and understand the details of your R program. You'll then learn to import data into R, data stored in different file formats on your machine. You’ll also learn to access files embedded in an R package. FREE ACCESS
  • 9m 2s
    In this video, you’ll watch a demo. You’ll download and install RStudio, an integrated development environment for writing R code. You’ll learn there are several different options available for writing R scripts. You could write your R code using Jupyter notebooks, which run within a browser window. For every line of R code you write, you can execute that line as you write it, and see the results displayed onscreen. FREE ACCESS
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    3.  Installing RStudio on macOS
    5m 7s
    In this video, you’ll watch a demo. You’ll head back to the tab where the download RStudio link is open. You’ve already installed R, the latest version is 4.0.3. Now, you’ll download and install RStudio Desktop. You’ll click on the blue button you see onscreen. This will bring up a Finder window that will download a dmg file to your Downloads folder. This is what you'll use to install RStudio. FREE ACCESS
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    4.  Installing R on Windows
    8m 47s
    In this video, you’ll watch a demo. You’ll download and install the R programming environment to execute R code and the RStudio integrated development environment on your local machine. There are many ways you can run R on your local machine. The easiest environment to work with is Jupyter notebooks. Jupyter notebooks offer a browser-based REPL, that’s a read, evaluate, print, loop environment which allows you to write R code and see your results instantly. FREE ACCESS
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    5.  Installing RStudio on Windows
    5m 2s
    In this video, you’ll watch a demo. Now that you have the R programming environment installed you’re ready to move forward. You’ll first head to your browser window. You’ll now download and install RStudio Desktop. Click on the blue button that you see onscreen. You’ll download the exe file onto your local machine. Then click on the arrow next to that downloaded file and Show in Folder as you see onscreen. FREE ACCESS
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    6.  Running Commands Using the RStudio Console
    9m 20s
    In this video, you’ll watch a demo. First, you’ll run the pwd command to see what your current working directory is. Then, you’ll hit Enter and see your current working directory. You’ll now set up your environment. All of your R demos will be placed within the subfolder you’ll create here. You’ll use mkdir -p to create a Skillsoft subfolder, and within that subfolder an R subfolder, and then hit Enter. FREE ACCESS
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    7.  Working with Panes in RStudio
    6m 33s
    In this video, you’ll watch a demo. This demo will pick up where you left off in the last demo. So far, you’ve defined two variables, numbers.1 and numbers.2. You’ve also defined a function called compute_cube. You’ve also seen how to view this compute cube function to see what the code of that function does. On the top right of the screen, you see the Environment pane, which is currently on the global environment. FREE ACCESS
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    8.  Creating a New Project and Examining Datasets
    10m 58s
    In this video, you’ll watch a demo. You’ll see the R console is great for prototyping your R code. However, if you're writing more complex programs, you’ll develop these programs in the form of R scripts. Then you’ll save these scripts within projects. In this demo, you’ll create a new project. At the top right corner of your RStudio you’ll click on the Project tab and select New Project. FREE ACCESS
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    9.  Demonstrating and Visualizing Built-in Datasets
    8m 48s
    In this video, you’ll watch a demo. You’ll learn how to install and access a dataset available in a particular package. The first step is to install the R package to access a dataset. You’ll see that any package you install in R can be done within your R code using the install.packages function. The install.packages function will automatically download and install the package you’ve specified from the webserver supported by CRAN. FREE ACCESS
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    10.  Browsing Package Vignettes
    6m 53s
    In this video, you’ll watch a demo. As you’ve learned, when you're working in R, you can use the question mark symbol to get help documentation for a particular function. If you don't know the exact function, it's often useless to just use the question mark. That's where vignettes are useful. Every R package is associated with a vignette, which is basically a long-form guide to that package in R. FREE ACCESS
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    11.  Reading from CSV Files
    11m 10s
    In this video, you’ll watch a demo. In this demo, you’ll learn to load data into our R program from external sources. Here, you’ll read in data from R data and CSV files. First, you’ll click on the maximize icon for the bottom right pane. This will bring up the pane that contains the tabs, files, plots, packages, and help. You’ll have a file explorer available within your RStudio IDE. FREE ACCESS
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    12.  Reading from Text, XML, Excel, and JSON Files
    10m 48s
    In this video, you’ll watch a demo. In this demo, you’ll see how to can read in data from different file formats. First, you’ll click on mtcars.txt. This contains your data in a tab-separated format. This is a file that’s available within the datasets folder. The row on line 1 of this file is the header row. You’ll also learn to use the read.delim function to read in a text file. FREE ACCESS
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    13.  Writing Data Out to Different File Formats
    8m 4s
    In this video, you’ll watch a demo. In this demo, you’ll assume you have datasets loaded into R. You’ll write this data out to different file formats, to the Excel format, to the CSV format, and so on. You’ll first work with a built-in dataset. The mtcars dataset is a part of the datasets package in R, and it's directly available within your R program in the mtcars variable. FREE ACCESS
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    14.  Course Summary
    1m 55s
    In this video, you’ll summarize what you’ve learned in the course. In this course, you’ve learned to install the R programming language on your local machine and set up RStudio. You learned about to CRAN and you explored the RStudio environment and the various panes it offers to help develop R programs. You also worked with data in a number of different file formats such as CSV, Excel, JSON, and XML files. FREE ACCESS


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