R Tutorial: Flipping coins in R

Want to learn more? Take the full course at https://learn.datacamp.com/courses/foundations-of-probability-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Many DataCamp courses teach the process of statistical inference. That's the process where you have some observed data, and you use it to build an underlying model. This is essential in data analysis, but it's only part of a useful statistical understanding. Probability is the study of how data can be generated from a model. This serves as the foundation for inference, and understanding both of these directions will make you a better statistician. I'm Dave Robinson, and I'll be your instructor for this course, where we'll be talking about one of the simplest models for generating random data: a coin flip. By exploring coin flipping with the R programming language, you'll learn the basic laws and methods of probability. Each time I flip a fair coin, it has a 50% chance of being heads and a 50% chance of being tails. Before I look at this coin, it is a random variable. We'll call one case of simulating from this random variable, a "draw". If you don't have a coin handy, you can use R to simulate this random event. Specifically, you can use the rbinom() function. This is named because it's a random draw from a binomial distribution, which you'll learn about in a moment. rbinom() takes three arguments: first is the number of random draws we're doing: a draw is a single outcome from a random variable. Second is the number of coins we're flipping on each draw, which is also just one. Third is the probability of a "heads", which for a fair coin is 50%. There are two possible outcomes of this function: 0 and 1. In this case we got a result of "1"- throughout this course, we're going to interpret a 1 as "heads". (Recall that the starting bracket one bracket simply indicates that this is a vector in R: we can completely ignore it). This is a random