Lately, I have been spending a lot of time with DataCamp expanding my abilities in R. I have been learning R off and on for the past few years, but I have a hard time with the basic stuff. At this point, it's still a lot easier to do things in Excel and in SPSS, but I want to be able to learn how to do them in R as well! Instead, I know how to do things like structural equation modeling, confirmatory factor analysis, and multilevel modeling because the first two can't be done in SPSS and the third is just a lot simpler in R than SPSS.I have finished a number of the courses on DataCamp so far. The latest one I was working on was Joining Data in R with dplyr. However, I got to the end of the tutorial, where it goes into a case study, and felt lost with everything I had learned. And this stuff was important to me! I routinely have to merge datasets together so I really need to know how to do this. So I decided to put everything I learned into a resource for myself that is hopefully also useful to others. I also used this opportunity as a sort of "case study" with fun data that I found on Kaggle (it's powerlifting data!).You can view the resource here: Merging Datasets with dplyrAt the same, I have been wanting to learn how to post on GitHub, so I used this opportunity to finally create a GitHub repository on there (thank you to Daniel Lakens for posting a tutorial on computational reproducibility, which I used to get on GitHub and create my first repository!). I still feel very novice in this, but if you have any suggestions to improve the resource, please use GitHub to make those requests if you have the capability of doing so. The best way to learn is to go head-first into the deep end of the water!