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Colin Fay 🤘 @_ColinFay
, 4 tweets, 2 min read Read on Twitter
Soon in all your Shiny Apps:
#RStats #RShiny
Ever wanted an 8-bit dataTable?
Remember the Old Faithful Geyser Data Shiny App?
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More from @_ColinFay see all

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#NowReading — Writing #RStats Extensions.
colinfay.me/writing-r-exte…
[Notes on terminology]
"A package is not a library."
Library refers to:
- "A directory into which packages are installed"
- "That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’."
"Installation takes a source package and installs it in a library using R CMD INSTALL or install.packages."
"Source packages can be built. This involves taking a source directory and creating a tarball ready for distribution"
Read 24 tweets
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#RStats — What's up CRAN?

In the past 7 months:

🆕 1178 new 📦

🆓 18 unarchived 📦

⏹ 493 archived 📦
At the end of June, there were around 12500 📦 on the CRAN
Changes on CRAN check ✅

See stats.ox.ac.uk/pub/bdr/Rblas/… for more info
Read 9 tweets
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What an opening line 😝
"You should absolutely not dismiss simplicity for something easy. It takes design and good taste to be simple"
"The beauty of programming — It's an exercise in creativity"
Read 3 tweets

Related threads

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For R users wanting to understand how to write their own R functions:

A function is just like a receipe for baking a pie.

Read the thread below for more mouth watering details. #rstats
Always start writing a function by writing this:

myfun <- function(){

}

This is the function 'skeleton'.

The function name, myfun, is R's name for the receipe.

The receipe ingredients will go inside function().

The receipe steps will go inside {}, along with end product.
Let's say we'll bake a pie which requires a single ingredient. The receipe is:

myfun <- function(x){

res <- x*2

return(res)

}

When we call the function:

mypie <- myfun(5)

R puts the pie in the oven and returns the end result (x*2 = 5*2=10) .
Read 10 tweets
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Hey all! I'm excited to reveal my new R package. It fills a gap I've been sorely missing since I've been moving from Stata to R: a quick and easy VARIABLE BROWSER! One you can use Find-in-Page on, and variable labels! Introducing the package vtable()! (thread)
vtable() takes your data and spits out a formatted table of information about your variables. It then opens up that table in the Viewer pane (in RStudio) or in a web browser (elsewhere) so you can continue to refer to it while working on your data. Can give value details too.
It works with variable labels. Provide your own (in one of three flexible formats), or if you've embedded some with Hmisc/haven/sjlabelled, it will pick them up. It detects sjlabelled value labels too. All where Ctrl-F can repeatedly and reliably help you sort through vague names
Read 11 tweets
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over in Slack-land a colleague asked for resources on learning #rstats, with a particular emphasis on resources aimed at beginners.

🌟SO🌟

here is a thread of my personal favorites:
Modern Dive is 🙌🙌🙌 and probably one of the best "I NEED TO LEARN DATA SCIENCE IN R RIGHT MEOW" resources out there because it gets you up, running, and through the whole DS cycle pretty quickly.

moderndive.com
R for Data Science is perfect for leveling up your R skills in an applied context, and gives you plenty of practice exploring the tidyverse 🌌

r4ds.had.co.nz
Read 7 tweets
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What does one get when mindlessly applying logistic regression to a too small dataset? Well…

[thread]
Just to be clear, I have zero interest in shaming these authors. Small sample logistic regression analyses are very common! But for those of you interested here is the link: journalacs.org/article/S1072-…
“The aim of this study was to evaluate the impact of medical student placement of Foley catheters on rates of postoperative catheter-associated urinary tract infection (CAUTI)”.
Read 24 tweets
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Getting ready to teach dplyr joins to new #rstats users tomorrow, so naturally I productively procrastinated by getting to know the new gganimate. It is the coolest!
The readme is a great intro -- github.com/thomasp85/ggan…. I basically just replaced `+facet_wrap(~ key)` with `+transition_states(key)`.
I really like how easily the gganimate calls fit in with the ggplot pipeline. It's very clear @thomasp85 put a lot of thought and care into the API design.
Read 8 tweets
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I’ve peer reviewed A LOT of meta-analyses over the past few years. While I’ve noticed that the overall quality of meta-analyses are improving, many that I review suffer from the same issues. Here’s a thread listing the most common problems and how to avoid them
#1 Not using PRISMA reporting guidelines (or stating that PRISMA guidelines were followed, but not *actually* following them).

prisma-statement.org/Default.aspx
These guidelines provide a framework for the transparent reporting of meta-analysis, which can help the reader assess the strengths/weaknesses of a meta-analysis.
Read 32 tweets

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