This week, Ellis Hughes and I discuss two different approaches to dealing with players who have the same name in {shiny} apps. This is a common issue when working with sports data (and lots of other data where you have a large number of people). If you don’t have a way of correcting for it, your user will select an individual’s name and get returned data for all people with that same name (not ideal).
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
Two-weeks ago I wrote an article about creating interactive reports using Rmarkdown without the need for a shiny server. This is a very real need for some people who might be working in settings where security prevents them from having a web-based server to host we apps, settings where there aren’t financial resources to purchase server space, or in settings where there isn’t sufficient technical support to run and manage a web server.
This week on TidyX, Ellis Hughes and I talk through my blog article and go over the nuts and bolts of the Rmarkdown code and share ways for you to build these interactive reports using {tidyverse}, {plotly} and {crosstalk}.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I go over Advent of Code Day 1. For those that don’t know, Advent of Code is a fun thing to do around the holidays. The producer creates a new computer science type problem to solve each day and you can solve it in whatever computer language you would like.
We decided to tackle the first day of Advent of Code, where you were provided a bunch of data that required you to create summary statistics. The catch is that the data didn’t have any indexes to it. It was simply a text file with spaces that indicated where one group of observations end and another group of observations begin. To handle this, Ellis and I show two different options for how to identify indexes for the groups of unique observations and build a usable data frame for answering the problem statement.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I tackle a question from one of our patreon viewers. The individual was curious about name matching when dealing with a large amount of public sports data sets. For anyone working in analysis and dealing with public data sets, name matching can be the bane of your existence. Each website seems to have its own unique way of spelling the names for people. To work this out, we create a small sample of data and talk through two different way to to handle the issue.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
Working more on data engineering/data cleaning steps, Ellis and I talk about the pivot_wider() function within {tidyverse}. One of the issues is that when pivoting a data set to a wide format, if you have duplicate rows in the id columns pivot_wider() will collapse them into a list. This may be problematic if you are needing to retain all rows in the original data set. Thus, Ellis and I discuss a method to pivot_wider() while retaining all rows of data, even when there are duplicate values in the id_cols.
To view the screen cast, CLICK HERE.
To access our code, CLICK HERE.