6 Tools
To work comfortably with data, we take care of the working environment in the first step. This includes R & Python, Quarto, RStudio, and Git.
Last updated
Was this helpful?
To work comfortably with data, we take care of the working environment in the first step. This includes R & Python, Quarto, RStudio, and Git.
Last updated
Was this helpful?
Follow these steps to set up your working environment.
To get you started, you can download the code examples repository from GitHub, which contains an RStudio project that you can open in your new environment.
You can download the code examples as a ZIP-file. Make sure you unpack it in a folder you find convenient:
R and Python are powerful programming languages that offer several advantages over graphical tools like Excel and Tableau for data analysis.
R and Python offer greater flexibility and customization options. They allow users to create complex data transformations, build statistical models, and craft beautiful, insightful visualizations tailored to specific needs. If a feature isn't available, it can be added through coding.
There's often no need to code missing functionality yourself. Both R and Python boast extensive libraries and packages that enable advanced data manipulation, machine learning, and automation of repetitive tasks. Their open-source nature has fostered large, active communities. This results in numerous user-contributed packages and libraries, as well as abundant help from the community, Stack Overflow, books, and many other resources.
R and Python as programming languages promote reproducibility in research and analysis, as the entire workflow can be documented and shared through code. Ask an Excel user how they arrived at their final results, and they frequently can't tell you precisely. This isn't accidental—in Excel, many actions are performed by clicking through menus and selecting options from a UI. None of this is saved for future reproduction. Moreover, Excel is challenging to debug, as formulas are hidden behind cells and many options aren't visible to the user. R and Python are fundamentally different: we write programs (scripts) that perform the analysis step by step, making the final result easily reproducible.
Another significant advantage of R and Python is their open-source nature. This means the software and its source code are freely available for anyone to use, modify, and distribute. Open-source software promotes transparency, collaboration, and continuous improvement within the developer community. It also allows users to inspect the underlying algorithms and methodologies, which is crucial for ensuring the reliability and reproducibility of scientific research and data analysis.
While the number of companies seeking employees with R and Python skills is rapidly increasing, many invest in commercial analytics software like Tableau, Power BI, and QlikView. These tools primarily rely on graphical user interfaces rather than scripting (though they do allow for it). I've outlined the benefits of scripting over clicking above, but there's one more crucial advantage: learning to script your analyses provides a much deeper understanding of the underlying concepts of data and data analytics compared to learning through a clickable UI. Once you've mastered scripting in R or Python, picking up Tableau or any other tool becomes a breeze—often a matter of hours. This is because the fundamental concepts are universal across platforms.
R is an open-source software that is available for all popular operating systems. First, download the latest version of R for your operating system, and then proceed to install it.
Along with the language and interpreter for R, the installation also provides a rudimentary graphical user interface called RGui (GUI = Graphical User Interface). This consists of a simple console through which you can enter and execute R commands. The RGui is limited in its functionality and usability, and we'll be using RStudio Desktop instead.
To download and install RStudio Desktop for your operating system, click the following link:
For those who want to take their first steps with R and don't want to go through the hassle of installing it, the cloud solution is ideal. RStudio offers free accounts that are limited to a certain usage time per month (25 hours) and computer resources (up to 1 GB RAM, 1 CPU). The advantage is that you can get started right away. The link below leads directly to the free registration:
Because of the limitations, I strongly recommend you use RStudio Desktop for this course.
Quarto is software for creating various output documents from a single source. It allows you to combine your analysis code in R or Python with the written text of your paper, report, or presentation. Using the Quarto command line tool, you can render the source document into multiple formats, including HTML, PDF, Microsoft Word, and even presentations. If you've installed RStudio, you already have Quarto on your computer. However, you can also use Quarto independently from RStudio. In this case, you can download and install it from here.
Python is a widely adopted programming language and the de facto standard for machine learning applications. To run Python programs on our computer, you must install it first. You can download the latest version of Python from the following website:
On Windows, Python comes as an executable file with the extension .exe. Save the file anywhere on your hard drive and double-click on it when the download has finished. When you do, the first window appears that looks similar to the one in the screenshot below:
Make sure you check the boxes "Use admin privileges when installing py.exe" and "Add python.exe to PATH". The former ensures that Python can write anywhere on your computer, which is important when you install packages later. The latter enables you to use Python from anywhere on your computer. We'll use Python mostly from the command line (or terminal), and with this setting, we can type python regardless of which folder we are in.
Next, click "Install Now", no need to customize the installation. When the installation has finished, you see the following screen:
Click "Close" and you're done with this step.
Data Science projects often involve collaboration within a team, which requires the sharing of code among team members. This is analogous to working together on a text document or a spreadsheet with classmates. A helpful tool for this purpose is Git, which you will primarily use in this course to obtain the example code for the corresponding module from the designated GitHub repository. It is necessary to download and install Git on your computer to proceed with the course.
Today, RStudio is the standard tool for working with R and provides many useful features. RStudio is an Integrated Development Environment (IDE) for R and even for Python. You can find a quick overview of the graphical user interface in the .
