Late last year, Donald Trump officially announced his candidacy for the 2024 presidential election. To secure the Republican party’s nomination, he’ll have to best Florida’s governor, Ron DeSantis, who, despite not having yet announced his intention to run, is currently viewed as the only serious challenger to Trump for the nomination.
Over the past few years, the hospital system I work for has transitioned from the old metric for measuring patient satisfaction, Likelihood to Recommend (LTR), to a newer metric, Net Promoter Score (NPS).
Umm… twitter is a weird place right now. Since Elon Musk’s $44 billion deal to take over twitter closed in late October, there have been mass layoffs, a floodgate of advertisers leaving the platform, and a near-daily deluge of disasters (for in depth coverage, I recommend visiting Platformer).
Hierarchical Hospitals If the past year of working at a large hospital system has taught me one thing, it’s that hospitals are a Russian nesting doll of structure. Within the hospital system, there are several campuses.
For those who aren’t glued to electoral politics, Labor Day typically marks the beginning of election season. Primaries are settled, pollsters stop sampling all adults to prioritize likely voters, and campaigns kick into high-gear.
Last November, I (finally) popped the big question and proposed! Since then, my fiance and I have been diligently planning our wedding. While we have most of the big-ticket items checked off (venue, catering, photography, etc.
Data manipulation and transformation is a fundamental part of any analysis. There are excellent tools in the R ecosystem for manipulating data frames (dplyr, data.table, and arrow, to name a few).
Generating prediction intervals with workboots hinges on a few core concepts: bootstrap resampling, estimating prediction error for each resample, and aggregating the resampled prediction errors for each observation. The bootstraps() documentation from {rsample} gives a concise definition of bootstrap resampling:
I recently picked up David Robinson’s book, Introduction to Empirical Bayes. It’s available online for a price of your own choosing (operating under a “pay-what-you-want” model), so you can technically pick it up for free, but it’s well worth the suggested price of $9.
I am an avid R user and will always advocate that others use R (or another programming language) for generating reproducible visualizations. In just about every organization, however, Excel plays an important role in an analyst’s toolkit.