The rnorm command takes three arguments: how many numbers you want, and the mean and standard deviation of the distribution you’re drawing from. The standard deviation is the “spread” of the data. The mean is the “average” value, which you can see as the peak of the distributions above. The two most important parameters characterizing a normal distribution are the mean and standard deviation. I probably could have accomplished the same point with the graphs above by just using 1,000 or 10,000 values… but too late now! You can see these values by typing data and hitting enter, but R will show you all the values… so I wouldn’t do that unless you want to feel like you’re in the Matrix. Yup, I generated 10 million random values just like that. These distributions reflect data that were generated with one line of code: R Histograms divide the data into bins and show how many points are in each bin. Density plots are particularly useful if you have a lot of data and are estimating the underlying distribution generating that data. The above plots are a density plot and a histogram, which are pretty similar ways of showing the data. Let’s start with the normal - also called Gaussian distribution. Randomness might seem like a weird place to start, but it’s actually very useful for learning about distributions and plotting. (Also, here’s a great link comparing the raw R exports and then the final publication graphics for the book London: The Information Capital.) Random data: histograms Introduction Ramnath Vaidyanathan: baseball strikeouts using the R package Shiny Miguel Rios: every geotagged tweet 2009-2013 For my courteous Facebook friends supporting my blog but not terribly interested in coding: enjoy some pretty pictures! If you’re determined to start learning at this post instead of the previous one, you can download R here, and you can download RStudio here.įinally, to give you a sample of the graphical possibilities in R, below are a few beautiful graphs I found online. # "Concatenate." This is how you create vectors in R c () # Assign to x a two-element vector of the numbers 5 and 10 x 7 ] The most important code from that post is listed here: R It covers importing data, introduction to viewing data, assigning variables, and the help function. The first post in the series is an introduction to R. This is the second post in a series on R. With constant feedback from the plots in R, we’ll start learning R syntax along the way. In this post, we’ll use R’s excellent plotting capabilities to learn about normal distributions, linear regression, and a bit more. Producing effective and interesting graphs can not only explain the data better - it can draw viewers in who wouldn’t otherwise be interested. A key step early in this journey to unlock insights is data visualization. With the widespread availability of cheap computational power and online resources for learning how to code, it is easier than ever to pick up a language and start learning from data.
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