Posted by Kelsie Ferin
First of all, I was very impressed by Wickham’s work that he done. I find it awesome that he is so young, went to Iowa State, AND is the Chief Scientist at RStudio. He did a very good job at describing why it is extremely important to be “tidy” and how R can benefit your research to make things easier on the human end of R. He went through the six key tools and how you need to be able to do all of these things in order to use R. These tools were to import your data, tidy your data, transform your data into something you can use, visualize you data and where you see it going, create a model of your data and then communicate this model with R and others. By following these six key tools, you can make sure your data analysis “flows” and you can catch any mistakes you might make along the way. He went through the two important concepts that make up the tidyverse. These were (1) uniform data structures (tidy data) and (2) uniform APIs (tidy APIs). For tidy data, the most important take away I got from his speech was that R is very conservative. Basically, if you write code in base R or an older version, since things don’t change much, you can use the same code later on and it will still work! Also, even though tibbles are the lazy way of doing a data frame, these are a blessing in disguise because this allows you to confront any issues early on since it is so basic. For tidy APIs, he did a very good job at explaining using pipes and by doing many examples during his explanations, it gave me a greater understanding on how to use them. The more I learned, the more I liked the thought of using them. Also, I liked how he created formulas to use them in creating functions, the tidy way.
I was unaware of all of the amazing things that R has to offer in terms of data analysis. I can’t believe how user friendly their functions and tools are and how much they have expanded the data analysis/programming world. I believe that the future of data analysis is going to expand into something so large that literally everyone doing anything with a data set will have to a data analysis program like R. It would be stupid not to. If you have large amounts of data, and the data analysis world (especially R) is only expanding and increasing their abilities, it would be the most efficient way to properly analyze your data.