This presentation will discuss the switch from SPSS to R in an advanced graduate-level applied statistics course in an educational psychology program.
I am currently taking a psychometrics courses, and in this psychometrics course we have just finished reviewing exploratory factor analysis (EFA), where we mostly used the psych package. I was surprised that the fa function did not produce a clean table to display EFA results. So, with a little exploration and help on stack exchange, two great ways to create tables for eigenvalue tables (include percentage of variance explained) and factor loadings with item suppression at whatever value we tell it to suppress.
I am currently taking a multilevel modeling class in my PhD program. It is week 3-ish and I am learning a lot. The course is software-agnostic, meaning we can use any software we want (SPSS, SAS, Stata, R, HLM). The first assignment comes as a data set and Word document. I am working completely in R / R Markdown to generate both the data and model analyses and to complete the Word-based homework.
R has always been a very powerful statistical analysis tool. With the development of ggplot, it has also become an extremely powerful data visualization tool. However, in my experience, R has lacked the ability to easily create nice tables, especially tables suitable for APA publication.
To be sure, there are a number of excellent table packages, all with their benefits and drawbacks. The ones I have personal experience with include:
**Note: this is now a [package](https://github.com/acircleda/footprint)!**
I am hoping to soon be working with a data set of travel data for which I will need to calculate carbon emissions of flights. There are a number of online calculators, but none that I know of that could be pulled into R to call as a function and or be used to process data.
Initial analysis of one year's worth of TidyTuesday tweets.
I recently started using TAGS to start archiving Twitter posts with key search phrases for later exploration and possible research. One of my search phrases was the hashtag #flyingless. #flyingless typically is appended to posts related to reducing the carbon footprint associated with flying, often flying to and from conferences, but also flying in general.
By just scraping the past few days worth of data, I found a few interesting takeaways.
Google collects a lot of data on us. If you have Google Maps, chances are your location is being tracked, too. Unless, of course, you have it disabled. But, if you don’t, you’d be surprised by the amount of location data (and its accuracy) contained in your Google Timeline. Many worry about Google’s tracking, but for those of us who don’t, there is some potential fun we can have with our own data.
Read this aloud: “We need more data.”
How did you pronounce the word “data”? Was the “da” in data pronounced like day or was the “a” in “da” pronounced with the same “a” as in apple? In other words, do you say day-tuh (IPA: /deɪtə/) or dah-tuh (IPA: /dætə/; like the “a” in “apple”)?
Two things got me thinking about this. First, there was a brief article and video that popped up on Twitter arguing that the creation of the character “Data” from Star Trek shifted the pronunciation of the word from /dætə/ to /deɪtə/.