This week, Ellis Hughes and I work on code for producing bootstrap resamples and simulated data. There is a distinction between sampling and simulation and we show this using coded examples, which also serve to set up the next series of screen casts on Bayesian analysis.
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I begin a new series titled, Sampling, Simulations, and Intro to Bayes. To get us started we begin this first episode by discussing how to use the various distribution functions in R for doing things like simulating a random draw, cumulative density function, probability mass/probability density functions, and quantiles. We then walk through an example of how to use these functions to create random samples of intercepts and slopes from a regression model and plot our uncertainty.
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.
Wrapping up our RMarkdown series, Ellis Hughes and I discuss how to report RMarkdown chunk results asis. What asis does is allows you to type comments directly into the chunk, via the cat() function, and have those comments reported in your RMarkdown report as normal text, meaning it it wont look like a code chuck (with a grey background and border around it). This can be incredibly useful, as you will see with our NBA example, if you are parsing long strings of text that you then want to automatically turn into readable text in your final report.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
In additional to being our 95th episode it also marks 2 full years of TidyX! What an incredible journey and thanks to all of those who continue to watch and support our work.
This week, Ellis Hughes and I discuss how you can make your RMarkdown reports interactive using html widgets. More specifically, we go through how to build data tables from the {DT} package and visualizations from the {plotly} package to make your reports come to life!
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I discuss how you can add controls to your RMarkdown by setting the parameters arguments within the YAML.
If you have a custom report that you need to reproduce frequently, changing different groups or pieces of information, parameterized reports are a great way to save time and ensure reproducibility.
For example, say you work for an NBA team and the head coach wants to see a team report on the Miami Heat, Dallas Mavericks, and Phoenix Suns. Rather than changing the contents within the RMarkdown itself (copying and pasting the new team name, seasons, weeks of year, etc.), which opens you up to making errors, you can set specific parameters that you want to exert control over within the YAML. Once you Knit the document with those parameters you can make the changes you need (IE, select the team, season, and weeks of the year) and the report will be produced with the desired info.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.