Chapter 1 Preface

This is a friendly book on how to work with data from start to finish. It is intended to be easy reading for a beginner who is not aware of any R vocabulary, or Information Technology. I’m a Data Analyst. I got my job mostly because of a Master’s in Math and good references (people to speak about his qualities). In the first year of working, I made many mistakes and had many headaches. I constantly Google’d solutions for the problems I faced while working. This allowed me to get my work done but it did not teach me the best way to do my work to avoid future headaches. Mistakes and headaches can be great learning challenges, but in the case of data analysis (and especially for those who use R), it can be demotivating. Mistakes in code cause errors, mistakes in approach cause messy files, and messy files require messy code with more mistakes.

Please remember that the R code taught in Statistics courses is almost always not focused on teaching us how to generally handle data; it is only focused on teaching us Statistics. Statistics has a reputation for being hard, especially on those who are learning the subject only because it is required in a not-so-mathematical university program. R has two completely different reputations. Among the students in Statistics courses, it is usually hated. For those who use the tool for work, it is usually loved. Of course, this may due to the fact that students pay to learn R, and workers are paid to learn R. But this is not the only reason for the two different reputations. The workers realize, once they use R day-to-day, that there is an amazing community of other workers who help each other. And not only do they help each other do the work that is currently needed to be done, but they help each other do the work in a smart and beautiful way that is nothing like the work needed in university Statistics courses. Note that the professor teaching university Statistics probably uses R for Statistics. The person learning R outside of university uses R for Data. Data is not Statistics! Statistics is specific to the mathematics used on the data; it is not about work-flow, project-management, and coding to get data and data reports ready. All the things that Statistics is not about, R does beautifully and more. Hence people who pick up R to work and who are not university professors, end up passionate about R and teaching others about R.