Here I discuss statistical and logistical details involved in my attempts at modeling kicker talent. This includes model selection as well as a brief history of research. I use some quantitative assessments to compare my approach to previous attempts and it does quite well.
Observers note that in the 2020 Democratic primary, candidate support in polls is closely correlated with the amount of attention candidates get in the news. This begs the question of whether and how these two things might be causally related, particularly whether news coverage is helpful to candidates. Here I show some evidence that news coverage does indeed help.
It has been just a little more than a day since I announced my new R package, `panelr`, to the wider world. There's at least one comparable package for R, called `plm`, which is very good and should be particularly appealing for economists. This leads to the understandable question as to how `panelr` differs from `plm`.
It has been a long time coming, but my R package panelr is now on CRAN. Since I started work on it well over a year ago, it has become essential to my own workflow and I hope it can be useful for others.
panel_data object class One key contribution, that I hope can help other developers, is the creation of a panel_data object class. It is a modified tibble, which is itself a modified data.
The survey package is one of R’s best tools for those working in the social sciences. For many, it saves you from needing to use commercial software for research that uses survey data. However, it lacks one function that many academic researchers often need to report in publications: correlations. The svycor function in jtools ( more info) helps to fill that gap.
There are a lot of reasons to use R instead of my field’s standby software, SPSS. With that said, I won’t get into them here. Instead, I just want to talk about a few things in R that might help a beginner get the hang of it.