LIBD rstats club remote useR!2018 notes

For our July 13th 2018 LIBD rstats club meeting we decided to check as much as we could the useR!2018 conference. Here’s what we were able to figure out about it in about an hour. Hopefully our quick notes will help other rstats enthusiasts, users and developers get a glimpse of the conference. Although there’s bound to me more videos and material about the conference coming out in the following days.


From checking Twitter, we can say that there lots of great talks and tutorials. Here are some of the main ones we found in this hour.

Roger Peng talking about Teaching R to New Users got lots of attention. Here are some tweets about it:

Jenny Bryan talked about Code Smells and Feels which was one of the major highlights. We wish we could have been there. Here are some tweets about it:

Check out her presentation materials on github

The talk was centered around the idea of writing good code. Using senses such as smell and feel as an extended metaphor, Bryan explains that coding is a sense that is developed through experience. Taking a very supportive tone, she provides pro-tips to writing efficient and effective code, such as writing simple conditions and functions instead of relying on complex function and “Tip #1: Do not comment and uncomment sections of code to alter behavior.”

Thomas Lin Pedersen talked about the gganimate package which seems to have included gifs in the talk.

Steph de Silva started the useR!2018 keynotes with her Beyond syntax, towards culture talk which covered different R communities and how we all interact.

The slides and video for the workflowr talk by John Blischak are already online too which got the big thumbs up by Peter Hickey!

If you are starting out with the tidyverse, this tutorial about Wrangling with the Tidyverse by Simon Jackson seems interesting!

Did anyone else think about the Diablo game with the deckard package? This new package by Verge Labs could be very useful when working with large datasets.

Jim Hester’s talk about the glue package was highly recommended by Jenny Bryan. And more likely than not, you are using R packages that Jim has helped in some way or the other.

Thomas Lumley talked about fasteR: ways to speed up R code; check the video of his talk at YouTube.

Major takehomes:

  • If you repeat a task frequently, it is worth taking the time to optimize it for speed. (See xkcd cartoon!)
  • Packages are available to measure how “efficient” your code is, in time and/or memory. Options: microbenchmark(), Rprof(), system.time()
  • Reasons your code may be slower than necessary:
    • Dataframes are slower than matrices, data.tables, tbls, and lists
    • Vectorize your code whenever possible
    • Preallocate for the size of your objects, rather than “growing” your objects.
    • Linear algebra / matrix algebra functions can be much faster than alternatives because they are coded in C. E.g. for a large matrix, crossprod(scale(x)) if you know there is no missing data or NAs is many times faster than cor(x). If you know the linear algebra, use matrix operations when possible.
    • Packages exist for modeling large data. Example: biglm for linear models.
    • Thomas Lumley is a Rosalind Franklin fan :)


They made an awesome hex wall with the hex stickers from packages represented at useR!2018. It’s awesome!

It’s awesome to see the RLadies community thriving! A few of us didn’t know about RLadies Remote which everyone can join.

These are some awesome socks!

And who doesn’t love this image of Hadley Wickham being mobbed by deers? He even meme-fied it himself on this tweet.

useR!2019 is already lined up, check it out. It’ll be in Toulouse, France! Follow them on Twitter at


We are greatful to everyone that tweeted about the conference and shared their materials online!

This blog post was made possible thanks to:


[1] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.8. 2017. URL:

[2] H. Wickham, J. Hester and W. Chang. devtools: Tools to Make Developing R Packages Easier. R package version 1.13.6. 2018. URL:

[3] Y. Xie, A. P. Hill and A. Thomas. blogdown: Creating Websites with R Markdown. ISBN 978-0815363729. Boca Raton, Florida: Chapman and Hall/CRC, 2017. URL:


## Session info ----------------------------------------------------------------------------------------------------------
##  setting  value                       
##  version  R version 3.5.1 (2018-07-02)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/New_York            
##  date     2018-07-13
## Packages --------------------------------------------------------------------------------------------------------------
##  package       * version date       source                            
##  backports       1.1.2   2017-12-13 cran (@1.1.2)                     
##  base          * 3.5.1   2018-07-05 local                             
##  bibtex          0.4.2   2017-06-30 CRAN (R 3.5.0)                    
##  BiocStyle     * 2.8.2   2018-05-30 Bioconductor                      
##  blogdown        0.7     2018-07-07 CRAN (R 3.5.0)                    
##  bookdown        0.7     2018-02-18 CRAN (R 3.5.0)                    
##  colorout      * 1.2-0   2018-05-03 Github (jalvesaq/colorout@c42088d)
##  compiler        3.5.1   2018-07-05 local                             
##  datasets      * 3.5.1   2018-07-05 local                             
##  devtools      * 1.13.6  2018-06-27 cran (@1.13.6)                    
##  digest          0.6.15  2018-01-28 CRAN (R 3.5.0)                    
##  evaluate        0.10.1  2017-06-24 cran (@0.10.1)                    
##  graphics      * 3.5.1   2018-07-05 local                             
##  grDevices     * 3.5.1   2018-07-05 local                             
##  htmltools       0.3.6   2017-04-28 cran (@0.3.6)                     
##  httr            1.3.1   2017-08-20 CRAN (R 3.5.0)                    
##  jsonlite        1.5     2017-06-01 CRAN (R 3.5.0)                    
##  knitcitations * 1.0.8   2017-07-04 CRAN (R 3.5.0)                    
##  knitr           1.20    2018-02-20 cran (@1.20)                      
##  lubridate       1.7.4   2018-04-11 CRAN (R 3.5.0)                    
##  magrittr        1.5     2014-11-22 cran (@1.5)                       
##  memoise         1.1.0   2017-04-21 CRAN (R 3.5.0)                    
##  methods       * 3.5.1   2018-07-05 local                             
##  plyr            1.8.4   2016-06-08 cran (@1.8.4)                     
##  R6              2.2.2   2017-06-17 CRAN (R 3.5.0)                    
##  Rcpp            0.12.17 2018-05-18 cran (@0.12.17)                   
##  RefManageR      1.2.0   2018-04-25 CRAN (R 3.5.0)                    
##  rmarkdown       1.10    2018-06-11 CRAN (R 3.5.0)                    
##  rprojroot       1.3-2   2018-01-03 cran (@1.3-2)                     
##  stats         * 3.5.1   2018-07-05 local                             
##  stringi         1.2.3   2018-06-12 CRAN (R 3.5.0)                    
##  stringr         1.3.1   2018-05-10 CRAN (R 3.5.0)                    
##  tools           3.5.1   2018-07-05 local                             
##  utils         * 3.5.1   2018-07-05 local                             
##  withr           2.1.2   2018-03-15 CRAN (R 3.5.0)                    
##  xfun            0.3     2018-07-06 CRAN (R 3.5.0)                    
##  xml2            1.2.0   2018-01-24 CRAN (R 3.5.0)                    
##  yaml            2.1.19  2018-05-01 CRAN (R 3.5.0)
Continuous rstats learning

We are researchers at the @LieberInstitute, blogging about R packages, how-to guides & occasionally our own open-source software (opinions r our own) #rstats

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