Monthly Archives: April 2019

Archer and Tidy Data Principles (Part 3)

April 4, 2019
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Archer and Tidy Data Principles (Part 3)

Motivation The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog. In the GitHub repo of this project you shall find not just Rick and...

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Gravity Falls and Tidy Data Principles (Part 3)

April 4, 2019
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Gravity Falls and Tidy Data Principles (Part 3)

Motivation The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog. In the GitHub repo of this project you shall find not just Rick and...

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How to Animate a Control Chart

April 4, 2019
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How to Animate a Control Chart

Recently, I wrote a post about creating control charts in R, and now I want to experiment with animating one of those charts. Lets start with the tidyverse, gganimate, and ggQC. library(tidyverse) library(gganimate) library(ggQC) Now, lets rebuild the last control chart from my previous post. # Generate sample data set.seed(20190117) example_df % add_row(values = rnorm(n=2*5, mean = 25 + .006, sd = .005), ...

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Gravity Falls and Tidy Data Principles (Part 3)

April 4, 2019
By
Gravity Falls and Tidy Data Principles (Part 3)

Motivation The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog. In the GitHub repo of this project you shall find not just Rick and...

Read more »

Archer and Tidy Data Principles (Part 3)

April 4, 2019
By
Archer and Tidy Data Principles (Part 3)

Motivation The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog. In the GitHub repo of this project you shall find not just Rick and...

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Winners of the 1st Shiny Contest

April 4, 2019
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Winners of the 1st Shiny Contest

Back in January we announced the first Shiny contest. The time has come to share the results with you! First and foremost, we were overwhelmed (in the best way possible!) by the 136 submissions! Reviewing all these submissions was incredib...

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What are the Popular R Packages?

April 4, 2019
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What are the Popular R Packages?

“R is its packages”, so to know R we should know its popular packages (CRAN). Or put it another way: as R is a typical “the reference implementation is the specification” programming environment there is no true “de jure” R, only a de facto R. To look at popular R packages I defined “popular” as … Continue reading What...

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How to Create a Bar Chart Race in R – Mapping United States City Population 1790-2010

April 3, 2019
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How to Create a Bar Chart Race in R – Mapping United States City Population 1790-2010

In my corner of the internet, there's been an explosion over the last several months of a style of graph called a bar chart race. Essentially, a bar chart race shows how a ranked list of something--largest cities, most valuable companies, most-followed Youtube channels--evolves over time. Maybe you've been following this trend with the same curiosity that I have,...

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How to share R visualizations in Microsoft PowerPoint

April 3, 2019
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How to share R visualizations in Microsoft PowerPoint

Hadrien Dykiel is an RStudio Customer Success Engineer Microsoft PowerPoint is often the de facto choice for creating presentation slides, especially at larger companies. In many organizations, it comes pre-installed on workstations and pretty much everybody knows how to use it. This can make it an effective medium for sharing information, since most folks are comfortable with it. Unfortunately, valuable...

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(Trying) to get to the top of R-bloggers emails

(Trying) to get to the top of R-bloggers emails

(TL;DR: Author analyses R-Bloggers emails using Gmail API. Decides on when to post to get to the top of the email list. This could either work well or fail spectacularly if he’s missed something. Either way, he learnt a lot. Let’s take a chance!

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