tidyverse   284

« earlier    

home page for tidyverse.org
9 days ago by garrettmd
jennybc/analyze-github-stuff-with-r: Marshal data from the GitHub API with R
analyze-github-stuff-with-r - Marshal data from the GitHub API with R
R  github  api  tidyverse  purrr  tutorial 
28 days ago by eric-ruser
tidygraph 1.1 – A tidy hope
I am very pleased to tell you that the next version of tidygraph (1.1) is now available on CRAN. This is not a bug-fix release, nor a change-it-all release, but rather a more-of-it-all release, and in this post I’m going to tell you all about it.

The idea of tidygraph
Before we enter the goldmine of new features that makes this release I’m going to talk a bit about my reasons for making tidygraph and what I want it to become. These ideas have been rummaging in my head for a while and has taken more form as I prepared for my RStudio::conf 2018 talk. They will probably be fleshed out even more in a (series of) blog post(s), or — dare I say — a book, but you’ll get the earliest version of it here…

Network analysis is daunting… Sure, have you spend the better part of your life working with it (I haven’t) it might seem common nature, but for most people it will be an area they enter late, unprepared, and already well-versed in the manners of rectangular data analysis. For many, the instinct will be to quickly produce a plot, which will often end up creating very little insight due to the curse of the hairball, and they will leave the world of network analysis with a sense of broken promises. While all of this sounds overtly melodramatic, I honestly feel that the tools we use to do network analysis can do better in guiding the user towards a meaningful network analysis workflow and I hope that tidygraph (and ggraph) will prove to be a decent attempt at that.

With tidygraph I set out to make it easier to get your data into a graph and perform common transformations on it, but the aim has expanded since its inception. The goal of tidygraph is to empower the user to formulate complex questions regarding relational data as simple steps, thus enabling them to retrieve insights directly from the data itself. The central idea this all boils down to is this: you don’t have to plot a network to understand it. While I absolutely love the field of network visualisation, it is in many ways overused in data science — especially when it comes to extracting knowledge from a network. Just as you don’t need a plot to tell you which car in a dataset is the fastest, you don’t need a plot to tell you which pair of friends are the closest. What you do need, instead of a plot, is a tool that allow you to formulate your question into a logic sequence of operations. For many people in the world of rectangular data, this tool is increasingly dplyr (and friends), and I do hope that tidygraph can take on the same role in the world of relational data.

This is not just about preparing your data for a plot — this is about answering questions.
statistics:networks  R_packages  R  tidyverse  visualization  data_management 
4 weeks ago by hallucigenia
mjskay/tidybayes: Bayesian analysis + tidy data + geoms (R package)
tidybayes is an R package that aims to make it easy to integrate popular Bayesian modelling methods into a tidy data + ggplot workflow.

Tidy data frames (one observation per row) are particularly convenient for use in a variety of R data manipulation and visualization packages. However, when using MCMC / Bayesian samplers like JAGS or Stan in R, we often have to translate this data into a form the sampler understands, and then after running the model, translate the resulting sample ...
statistics:bayesian  tidyverse  R  r_packages  visualization  to_try 
5 weeks ago by hallucigenia
Data Wrangling Part 2: Transforming your columns into the right shape
This is a second post in a series of dplyr functions. The first one covered basic and advanced ways to select, rename and reorder columns,and can be found here: Data Wrangling Part 1 This second one covers tools to manipulate your columns to get them the way you want them. Mutating columns: the basics Mutating several columns at once Mutate_all Mutate_if Mutate_at Working with discrete columns Recoding discrete columns Creating new discrete column (two levels) Creating new discrete column (multiple levels) Splitting and merging columns Bringing in columns from other data tables Spreading and gathering data Turning data into NA
r  rstudio  dplyr  tidyverse 
6 weeks ago by fiamh
(429) https://twitter.com/i/web/status/959958292633083905
RT : An audience member asks if there will ever be a manifesto of principles. It’s a draft but there totally…
tidyverse  from twitter_favs
6 weeks ago by rukku

« earlier    

related tags

api  base-r  base  bayesian-inference  bayesian  beer  book  brms  cds_101  cheat-sheet  cheatsheet  code  computers  data-analysis  data-science  data  data_management  data_science  databases  datascience  dplyr  excel  ggplot2  github  howto  introduction  json  lubridate  microsoft  mnist  network  numby  package  packages  pandas  path-analysis  path-modeling  pedogogy  pocket  powerpoint  presentations  programming  purrr  python  r-example  r-project  r  r4ds  r_packages  reporting  research  rstan  rstats  rstudio  rstudioconf  sem  shiny  sql  stackoverflow  stan  statistical-rethinking  statistics  statistics:bayesian  statistics:networks  stm  strings  style_guide  teaching  text-analysis  text-mining  text  tidy  tidyeval  tidytext  to  to_try  tutorial  twitter  visualization  wizardry  xml 

Copy this bookmark: