Why Clean and Analyze Data in R?
You don't need to be a coder to write code
Learning R is like jumping into a green swamp filled with fanged, half-starved creatures.
I swear they don’t eat humans. But how do we know what’s prowling in that water? It’s too murky to see through, and I haven’t dipped my net in…yet.
R contains thousands of packages and functions lurking behind the scenes, waiting for you to try them so they can fail to execute and throw an unintelligible, alien-speak error in your face. Who wouldn’t want to dive in and test what happens? And then spend hours trying to fix it and searching for the solution, only to not find one and still be frustrated days (or weeks or months!) later.
I studied statistics and math because the classes were required for my wildlife BS degree. I wanted to observe animals in the jungle, and I didn’t understand how learning math and data analysis would help me in my future career. I struggled to do the bare minimum to pass, and I even failed a calculus class. Years later, I wished I’d paid more attention in those classes.
Some day you might analyze data for a science fair project, work through data for undergraduate courses or a capstone project, or end up in a career that depends on using data to answer questions. Or you might love data and play around with your own data for fun. The sooner you learn how to process, analyze, and visualize data, the sooner you can leverage those skills to build your future and accomplish your dreams (or just have fun! That’s important too.).
Yeah, I know, learning R sucks. I’ve been learning it for over a decade. I’m working on free resources, Tiktoks, and posts to help you learn enough to do something with your data and to see the value in using R rather than Excel (or SAS or other statistical software). There are lots of resources online, some may help you and some may expect you to know too many other bits of info before they’re helpful.
I hope my resources help in your data analysis journey. I’m here for you! Need help understanding something? Please let me know.
Here’s why it’s worth your time to start learning R for data cleaning, visualization, and analysis:
1. R and R Studio are free
You don’t have to be a student at a school with a paid subscription or pay for it yourself to learn how to use it.
Not every company, government agency, or non-profit organization can afford the licenses for other statistical software. You can bring value to an employer if you know how to work with data in R.
2. High school students can quickly enhance science reports and add skills to college applications
3. College students (and graduates) can help their resumes stand out from the crowd
Skills for working with data are useful in many jobs, even if you don’t know it yet.
4. Quickly clean up messy data
Ever tried to fix data in Excel? It can be done, and I’m sure there are tricks to doing so, but why make your life miserable? It’s much faster in R, and easy too. Once you have code for cleaning data, you can apply similar code or the functions you used to other data.
5. Create impressive visualizations to tell your story
I won’t lie. Many visualizations in R take a long time and many lines of code to make beautiful. BUT you can use existing code from examples on the web to make great graphs that you only need to tweak according to your data. Plus, it’s easy to make more graphs with new data once you have code for the types of visualizations you use frequently.
R code offers more control and options over Excel visualizations. And making another graph takes less time with code than building a graph in Excel.
6. Leverage expanding analytical methods
R is regularly used by scientists and researchers at academic institutions, government agencies, and non-profit organizations. Many researchers maintain existing R packages and build new ones with functions that improve processes or develop new analytical methods. Sometimes SAS or other software doesn’t support the analytical method you need.
Before I learned R, I spent many years as a wildlife biologist struggling to clean and summarize data in Excel and Access databases. Those programs are great—I still use them for certain purposes—but you can do more with your data faster in R.
You don’t need to have studied statistics or be a math whiz or a programmer to start using R today.
Lacy