R Tutorial: Writing Efficient R Code | Intro

Key Takeaways

hi I'm Colin Gillespie and I'm associate professor at Newcastle University and also a consultant at jumpin rivers welcome to the fishing our programming course ah his unfortunate reputation for being slow pilot is certainly true than a competition of raw speed with the language like C I would come in a distant second this is an unfair comparison to make instead the total programming time is made up of three components thinking coding and running and many statistical analyses you want to try multiple algorithms therefore you want the thinking encoding parts to be optimized and this is our strength by the time you've ordered your data into our created a scatter plot and fitted a regression line a friendly C programmer would have just launched their code editor and we'll be checking stack over for for how to read in a CSV file we use our because it's good with statistics however with the advent of big data and complex statistical algorithms you may have to optimize your code before you jump straight in at the deep end you should remember what Donald can have said premature optimization is the root of all evil that is only optimized when necessary before we finish this introduction there is one simple optimization you should ensure you use keep our up-to-date new versions of our rarely break caught in fact I have our code from the ton of the century that still works new versions of our often provide speed boosts such as improved handling of data frames see a quarter chest goes a little bit faster version 2.13 introduced baked compiling for speeding up functions and version three supported large vectors just by upgrading your code runs faster mean our releases happen every April with smaller incremental updates occurring throughout the year and the following exercise you'll see how to check which version of are you run