Pinned Repositories
bookdown
Authoring Books and Technical Documents with R Markdown
cheatsheets
RStudio Cheat Sheets
data-science-in-tidyverse
Data Science in the tidyverse, a two-day workshop @ rstudio:conf(2018)
datasciencecoursera
The Data Scientist’s Toolbox: Project
genentech-build-tidy-tools
Materials for the Building Tidy Tools workshop at Genentech 2020
PharmaSUG-R-Workshop
Intro to Shiny, R Markdown, & HTML Widgets - Application in Drug Development - 2017 PharmaSUG Baltimore
RStudioConf2018Slides
Links to slides for talks at the 2018 rstudio::conf
shiny
Easy interactive web applications with R
TensorFlow-v1
tidyverse
Easily install and load packages from the tidyverse
Alyselin's Repositories
Alyselin/data-science-in-tidyverse
Data Science in the tidyverse, a two-day workshop @ rstudio:conf(2018)
Alyselin/genentech-build-tidy-tools
Materials for the Building Tidy Tools workshop at Genentech 2020
Alyselin/PharmaSUG-R-Workshop
Intro to Shiny, R Markdown, & HTML Widgets - Application in Drug Development - 2017 PharmaSUG Baltimore
Alyselin/RStudioConf2018Slides
Links to slides for talks at the 2018 rstudio::conf
Alyselin/shiny
Easy interactive web applications with R
Alyselin/bookdown
Authoring Books and Technical Documents with R Markdown
Alyselin/cheatsheets
RStudio Cheat Sheets
Alyselin/datasciencecoursera
The Data Scientist’s Toolbox: Project
Alyselin/TensorFlow-v1
Alyselin/tidyverse
Easily install and load packages from the tidyverse
Alyselin/datasharing
The Leek group guide to data sharing
Alyselin/Getting-and-Cleaning-Data-Course-Project
Getting and Cleaning Data Course Project submitted by Edgar Manukyan
Alyselin/Getting-and-Cleaning-Data-Peer-Assesment
Getting and Cleaning Data Peer Assesment
Alyselin/Getting-and-Cleaning-Data-Project
The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected.
Alyselin/interAdapt
Shiny app for looking at the pros and cons of adaptive trial designs
Alyselin/LearnAnalytics-mr4ds
R and Microsoft R Workflows for Data Science
Alyselin/malmo-challenge
Task and example code for the Malmo Collaborative AI Challenge
Alyselin/mixADA
GUI for mixture model fit
Alyselin/mixADAexamples
Simulated example data sets for trying mixADA
Alyselin/mrgsolve
Simulate from ODE-based population PK/PD and QSP models in R
Alyselin/packrat
Packrat is a dependency management system for R
Alyselin/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
Alyselin/rocker
R configurations for Docker
Alyselin/RStudioConf2019-ePoster
"Modernizing the Clinical Trial Analysis Pipeline with R and JavaScript" ePoster presented at RStudio::Conf(2019)
Alyselin/ShinyWorkshopDeps
Alyselin/summarytools
R Package for quickly and neatly summarizing vectors and dataframes
Alyselin/test-repo
testing