Why do we need to use statistics in biology? If the hypothesis is clear, the experiment is designed correctly, and the data are carefully collected, anyone should be able to just look at the data and clearly see whether or not the hypothesis is supported. Statistical procedures are simply safety nets for sloppy science. Here is a brief list of reasons why statistics, mathematics, and appropriate visualizations are critical for understanding biological systems:.

## A Primer in Biological Data Analysis and Visualization Using R | Columbia University Press

Statistical procedures help us determine whether data are consistent with hypotheses. Data from modern biological experiments are unable to speak for themselves. Data, instead, require rigorous evaluation, which is appropriate because they are often hard to collect. Based on our results from data analyses we often develop formal mathematical models that help us to understand and explain how systems work.

We do this by developing quantitative predictions that we assess with data. Biologists often work to understand how multiple factors work together, often in complex, non-linear ways, to affect biological systems. To determine the individual effects and the combined interactive effects we need to develop and conduct complex experiments to illuminate biological patterns and mechanisms that cause these patterns.

Biology is one of the more complex sciences.

I will admit that, at times, some questions can be pretty simple. Imagine, for instance, that we have randomly selected pea pods and expect a phenotypic ratio of yellow to green peas. We should expect to see a ratio of 75 to 25 yellow to green peas. We, however, are unlikely to see exactly this ratio. If, instead, we find a ratio of we can see immediately without statistics! Are you prepared, based on this finding, to conclude that this system does not follow the well established rules of segregation?

### Genomic Data Visualization and Interpretation

Scientists are predisposed by their profession to be skeptical and, therefore, will not accept a statement like Trust me that our finding of a ratio demonstrates that Mendel was wrong! Our goal is to understand biological systems.

Unfortunately, anything interesting nowadays is complex even determining if our data adhere to a simple ratio! With quantitative tools we can better understand how natural systems work. Only then might we be able to make accurate and useful predictions. Science relies on a strong foundation of statistics, mathematics, and the visualization of results, all of which are available to you through the R statistical and programming environment.

There are far too many great sources of information on data analysis, statistics, visualizing information, and programming to list them all here. This book is a very basic introduction to all of these topics. I hope you seek more information in all of these areas. If you do, here are a few recommendations that go more deeply into different subsets of the topics covered in this book:.

Introductory statistics: a conceptual approach using R. Ware et al. Foundations and applications of statistics: an introduction using R. Pruim In science we are interested in understanding systems that are complicated. Our use of quantitative approaches gives us the ability to not only understand these systems but also to predict how a system might behave in the future or maybe even how it behaved in the past. As we work to understand and predict complex biological systems we need computational help.

You probably have written lab reports using only a calculator. This should be avoided for a variety of important reasons:.

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## PDF A Primer in Biological Data Analysis and Visualization Using R Read Online

Difficulty in verifying that you entered the data correctly. I think the numbers are right. Difficulty in repeating the analysis. Inability to share your analytical approaches and results. Sorry, I hit the all-clear button!

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You have to trust me. Inflexibility in how the data are analyzed. You wanted me to do what? Inability to make and share appropriate graphs. Can I take a picture of the graph on my calculator with my phone and incorporate that in my lab report?

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You may be somewhat familiar with Excel but probably have little or no experience with R. Therefore, I welcome you to the world of R!

## A Primer in Biological Data Analysis and Visualization Using R

I know this might be a scary place for you at first. Fortunately, this introduction is intended for newcomers. But as you proceed you will learn how to do some really amazing things with R. R is like playing an instrument, a sport, or learning a foreign language—they all require practice. I have confidence that you are capable of using R to solve interesting problems. And the more time you spend at it the better you will get.

For many analytical problems we will be able to use just R. However, in biology, we often test our ideas, or hypotheses, with large amounts of data.

We, therefore, will try to use Excel for what it does well allows us to enter and organize our data. Instead, these core scientific skills are best done with R. It is important to recognize that doing things well is rarely easy.

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Writing a good poem, playing tennis well, or doing ballet well are all hard. And conducting hypothesis tests correctly and making professional-quality graphs are not simple, one-click operations. At first you will likely think that making graphs and performing statistical tests in R are absolute nightmares. You can do it in R! Therefore, in this introduction we will discuss Excel but focus mainly on R. It is the combination of using Excel to organize our data and R for analyses and visualizations that will allow you to ask and answer questions in biology.

Here is a sampling of reasons why R is clearly better than Excel for problem solving in biology. With R you can:. These tests might rely on their own data, data read from a file, or data acquired directly from a website;. This environment allows one to compile together in one document words, mathematical equations, computer code, statistical tests and output, and professional-quality graphs, all within the free, open-source LATEX typesetting environment;. If you keep this skill set you will be highly marketable. R helps you speak the language of science, which is written in mathematics, statistics, and data evaluation and visualization.

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