Test Post

 Posted on February 26, 2020  |  21 minutes  |  4414 words  |  Justin Millar

“The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.” The core eight packages of the tidyverse are: Package Purpose Notably replaces ggplot2 Graphics Core graphics dplyr Data manipulation aggregate, common row and column operations tidyr Data ‘tidying’ melt, dcast readr Text file input read.table, etc purrr Functional programming tools apply family tibble Tables data. [Read More]

Introduction to Tidyverse

 Posted on February 14, 2020  |  21 minutes  |  4414 words  |  Matthew Hall

“The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.” The core eight packages of the tidyverse are: Package Purpose Notably replaces ggplot2 Graphics Core graphics dplyr Data manipulation aggregate, common row and column operations tidyr Data ‘tidying’ melt, dcast readr Text file input read.table, etc purrr Functional programming tools apply family tibble Tables data. [Read More]
rstudio  tidyverse  dplyr  ggplot 

Version Control with Git, GitHub, and RStudio

Science is collaborative!

Version Control with Git, GitHub, and RStudio

 Posted on November 10, 2019  |  12 minutes  |  2556 words  |  Justin Millar

Some Background Science is collaborative. Even a “solo” projects should be reproducible, meaning we should be thinking about how other researchers can benefit from our work. And we will always have at least one collaborator - our future selves. Each of us has at some point struggled to pick up a project after weeks or months (or even years) of inactivity. Science is collaborative, and collaboration is hard. Often we aren’t trained to collaborate effectively and may be unaware of tools and practices to improve our collaborations. [Read More]
git  github  rstudio  reproducibility 

Utilizing the maximum computation power of your computer on R

 Posted on October 1, 2019  |  4 minutes  |  714 words  |  Sanjay Rathee

Note Please open your R-Studio as an administrator. Overview In this tutorial, we introduce a package to use multiple cores of your system. After this tutorial you will be able to: Train a neural network model without using multicores Train a neural network model without using multicores Compare results Using R Markdown The tutorial that follows was created using R Markdown. As an exercise in using R Markdown, here, we ask you to save your work in an R Markdown file. [Read More]
caret  parallel