R is a programming language that is specialized for working with datasets, especially tabular datasets. It is like SQL in that it lets you manipulate datasets across multiple sources and tables, but it is much more powerful. The connection to statistics is just because it is setup to make it very easy to do them - there’s a single command to run linear regression, simple tools to plot things, and very human-readable code. One key feature of R is that a lot of functions are “vectorized”, which lets you do the same thing to a bunch of numbers all at once without having to write your own loop (like you would in a lower language like C). So, if the variable a has a single number stored in it, I can write sqrt(a) and get the square root. But if the variable a has a vector of 1000 numbers stored in it, I can still just write sqrt(a) and it will return a vector with all 1000 square roots. Regarding “tidyverse”: like many programming languages, R has some free “add-ons” called packages or libraries. These are just collections of new functions, tools, methods, and data objects that someone else has written and made available. For example, the “parallel” package (which comes default) gives you tools for parallel computing (using multiple cores at once). Well, there is a guy named Hadley Wickham that is one of the most well known R gurus, and he and his team have written a whole “ecosystem” of R packages that help manipulate and visualize data. These packages include a very customizable plotting package called ggplot2, and two data manipulating packages called tidyr and dplyr. Along with some other packages (e.g. stringr for pattern matching/regular expressions), these are all bundled up into the “tidyverse” collection of packages. The reason tidyverse is mentioned so much and so explicitly is that those packages introduce a lot of their own syntax and structure that’s shared between them, such that it’s almost like a mini-language within the larger R language. You can almost always accomplish the same things with both tidyverse and non-tidyverse methods, but they will look extremely different, sometimes involve two different ways of thinking about the problem, and definitely use different syntax. Describing an entire programming language is a bit difficult, but I’m happy to answer any follow up questions you might have. More on reddit.com

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Ok let's say I have a list of all the people who live in Chicago. We'll name that variable popChi. This variable is a text vector, so imagine popChi = c("name1", "name2", ...) So if I were to call popChi, the program would return something like "Bob Jones", "Susan Smith", ... Just for this example, assume nobody shares a name. If I give you a name, for example, testname = "Bobby Brown", and I asked you if he lives in Chicago, you could run the function: testname %in% popChi and it would return TRUE or FALSE depending on if any of the observations in popChi match testname. %in% is just a boolean operator. Otherwise we'd have to write a list of OR operators like so: testname == popChi[1] | testname == popChi[2] | testname == popChi[3] | ... You can see how that could get tedious if you have a list of 10 million names. Another way to understand it, it simplifies this procedure: matchvar = which(popChi == testname) !is.na(matchvar) More on reddit.com

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