jennybc

scale_x_discrete(limits = rev(levels(the_factor)))

muschellij2

rm(list=ls())
library(git2r)
library(plyr)
library(daff)

differ = function(file1, file2){
  path <- tempfile(pattern="git2r-")
  dir.create(path)
  repo <- init(path)
  

hrbrmstr

shagunsodhani

Visualizing Data using t-SNE

Introduction

• Method to visualize high-dimensional data points in 2/3 dimensional space.
• Data visualization techniques like Chernoff faces and graph approaches just provide a representation and not an interpretation.
• Dimensionality reduction techniques fail to retain both local and global structure of the data simultaneously. For example, PCA and MDS are linear techniques and fail on data lying on a non-linear manifold.
• t-SNE approach converts data into a matrix of pairwise similarities and visualizes this matrix.
• Based on SNE (Stochastic Neighbor Embedding)
• Link to paper

hrbrmstr

.DS_Store

romainfrancois

library(dplyr)
library(purrr)
library(rlang)

derange <- function(data, ..., by_group = FALSE){
  arrange( data, !!!map( quos(...), ~ quo(desc(!!.)) ), by_group = by_group )
}

d <- data.frame( x = 1:10, y = letters[1:10])
derange(d, x, y)

Qfl3x

NOTE: commands and UI are deprecated

Content:

• Negative Engineering
• What is workflow orchestration?
• Introduction to Prefect 2.0
• First Prefect flow and Basics

Workflow Orchestration