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Going to be posting another #blogdown post soon on Laplace approximation for Bayesian linear regression, using simple examples comparing the relationships between #NFL teams EPA/att differentials and score differential for both passing and rushing #nflscrapR #THROWTHEFOOTBALL https://t.co/oRnHVBhNrK
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"There are only two hard things in Computer Science: cache invalidation and naming thing"... But what is cache invalidation? A perfect explanation by @xieyihui https://t.co/QumhDmMmUu
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@DenubisX @benmarwick I think I'd prefer working with markdown or R markdown and use #blogdown or #bookdown (which gives you the power of latex if you need it - but I don't usually need it). Also, I prefer html over PDF. https://t.co/OMcyrAlTdP
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Rnw/#knitr support landed in the new #LaTeX editor. https://t.co/AaQCf95F8B
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I think the true greatness of #xaringan #rstats package is not well known. If you are working on your slides, use xaringan::inf_mr() and then the file will be automatically knit after every save and goes to the slide your working on, NOT the 1st slide. Works with #rmarkdown too! https://t.co/ybSDgyiuUJ
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I serendipitously discovered @robjhyndman's and George Athanasopoulos's "Forecasting: Principles and Practice" yesterday. This is the 4th bookdown project that easily replaces the vast majority of my resources for a given topic. #rstats https://t.co/KXLN148BZl https://t.co/3XuDlTtI9f
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Developing a free statistics for undergrads in psychology textbook this summer. https://t.co/uQppEHK6eh Will update this tweet as we progress. Textbook written in #Rmarkdown, using #bookdown. Will publish as a web-book, with integrated #shiny apps. #ATextbookIsEatingMySummerBreak
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Our book with @wolfgangkhuber was generated from a giant Rnw/Rmd file. We generated the tex -->pdf and html all programmatically using #knitr, #rstats and even used the #tufte style from @Rstudio. Online version is and will stay free: https://t.co/O2N5qoLIzB https://t.co/muLBcYqOJD
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Finally managed to migrate my #rstats blog to #blogdown. Here is why and how you can use #travis to deploy with #netlify without committing html or md derivatives from your R Markdown. Merge previews included. Thanks @xieyihui for #blogdown. https://t.co/rdeL4lBd8Y
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Have you tried using your own data to walk through the chaper Exploratory Data Analysis in R for Data Science (https://t.co/DRfZ2veYWg)? I've just done that with open data from @ForestGEO: https://t.co/AtCqJkaKqp #rstats #tidyverse #fgeo