Data Science: Everything You Need to Know About Using R for the Very First Time

So, what are you looking at?

In blue is your console — you can run code here that won’t be saved as part of your script (you may want to do use this option to install a package, or to quickly view a table), and this is where you’ll see information about code that you’ve run and any errors that you may receive. The other tabs allow you to access your terminal and jobs, but you will use these far less frequently as you get familiar with R.

In green is the environment. Any named entity you create (e.g. a dataframe, a vector, a function) will show up here. Often times you can click on what is in the environment to view it — this is very useful when you load in external data and are cleaning it. The little broom icon can be used to empty this environment. Once we set up git and a connection to GitHub, a “Git” tab will be visible here as well.

In red is a viewer pane. Currently it is on the “Packages” tab, which allows you to see what packages are installed locally. Those with a checkmark are currently loaded. Depending on your RStudio settings, output from your code may show up in the “Plots” and “Viewer” tabs. You can search package names in the “Help” tab for useful information.

3. File Types

The workflow that I will be walking through utilizes R Markdown files whenever possible, but I want to quickly cover R Scripts and Shiny Web Apps.

Scripts:

Scripts are the most basic file type for writing code in R, and are often used to store functions or other code that will eventually be deployed. Even if you are able to opt for exclusive R Markdown usage while getting started with R, you will often receive code from others in scripts and it is important to be familiar with this format. As you can see, enclosed in the blue box is a new panel that is available now that you are in a script. This is where you can enter your code.

I’ve created three named values: “x”, “y”, and “sum”, which is the sum of x and y. Since all three are named, they can be found in the environment panel with their associated numeric values.

Take a look at the console.

The first three blue lines showed up when I ran the three lines of code — they simply indicate that the code has run.

In the fourth line, I typed “x + y” directly into the console and pressed “enter.” The black text below provides the answer to this equation. Since this was only entered in the console, this output won’t be saved anywhere or impact the script.

Try to replicate the very simple code that I have here. You can run code by clicking the “Run” button, or by pressing the command + return keys at the same time on a Mac (for Windows, press control + enter).

Shiny:

I’m not going to explain how to create Shiny apps as this is something to tackle once you’re fairly comfortable coding in R, but it’s good to know that they exist. Shiny allows you to create interactive dashboards and is a great tool for data visualization. You can host dashboards publicly through RStudio’s servers, embed them in websites, and more.

Here is some further information on Shiny if you’re interested: https://shiny.rstudio.com/

R Markdown files:

Use of Markdown files is an essential tool for creating reproducible, understandable code. In addition to the actual Markdown file that you write code in, you can “knit” it (I’ll cover what this means) to an output file which is easy to read and can be opened by people without R installed. You can select what kind of output you want from the bubbles below (HTML, PDF, and Word). I like to stick with HTML because it formats well and allows for online sharing, but I often create PDFs for final reports that I share with coworkers outside of the data analysis team.

Go ahead and click “OK”! This is what you should see next:

So, what are you looking at?

In blue is your YAML header with the information you just entered. Want to change your title, the date, or output type? You can easily do that here by editing the text between the quotation marks associated with title, author, and date and by swapping out “html_document” for “pdf_document” or “word_document.” If you aren’t interested in having a title, author, or date listed in your output file, you can simply delete the associated rows.

In green is a code chunk. All of your code will be contained within code chunks. Code chunks all follow the following format:

You can insert a code chunk directly on Macs through the option + command + i shortcut (on Windows, control + Alt + i). You can run a single code chunk by pressing the green arrow in the top right corner of the chunk.

It is good practice to name your code chunks. This is useful for anyone else who might use your code, as well as for troubleshooting efficiently when your code produces errors. You want to use the same guidelines for code chunk naming as you use for variable naming: unique, descriptive, and without spaces. For example, you might have code chunks named “data_import,” “data_cleaning,” “analysis,” and “plots”.

There are several specifications that you can use to change how the code in any given code chunk appears in your output file, and the most common ones are provided below:

List by Jeff Goldsmith: https://p8105.com/writing_with_data.html

In red is a header. You can determine the size of the header in your output file using pound signs, with each additional “#” making the header smaller:

In grey is regular text, which will appear as such in your output file. Good Markdown files usually have a lot of text to help guide others through your code and make it more understandable. Well-documented code will also make it easier to understand your own code when you go back to it later!

It’s useful to know that you can bold and italicize text, as well as add in-line code:

A useful cheat sheet for using and formatting R Markdown files can be found here: https://rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf

In orange is the “Knit” button — this is how you create your output file! The file will only knit if there are no errors in the code, so knitting is also a useful way to check your work. Try clicking the Knit button now to see how the sample code looks in an output file.