To transform the currently opened Graphviz source file (in DOT Language) into a PNG:
{
"cmd": [ "dot", "-Tpng", "-o", "$file_base_name.png", "$file"],
"selector": "source.dot"
}| ############################################################################## | |
| # Calendar Heatmap # | |
| # by # | |
| # Paul Bleicher # | |
| # an R version of a graphic from: # | |
| # http://stat-computing.org/dataexpo/2009/posters/wicklin-allison.pdf # | |
| # requires lattice, chron, grid packages # | |
| ############################################################################## | |
| ## calendarHeat: An R function to display time-series data as a calendar heatmap |
To transform the currently opened Graphviz source file (in DOT Language) into a PNG:
{
"cmd": [ "dot", "-Tpng", "-o", "$file_base_name.png", "$file"],
"selector": "source.dot"
}This snippet uses:
First, download the CSV (included below in this gist as sunlight-legislators.csv:
| library(rgeos) | |
| library(rgdal) # needs gdal > 1.11.0 | |
| library(ggplot2) | |
| # map theme | |
| devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df") | |
| map = readOGR("readme-swiss.json", "cantons") | |
| map_df <- fortify(map) |
| import numpy as np | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.linear_model import Lasso | |
| from sklearn.cross_validation import train_test_split | |
| from sklearn.metrics import r2_score | |
| import networkx as nx | |
| import pandas as pd | |
| from scipy.optimize import minimize | |
| import time |
Type Title Speaker Link Accelerate AI Keynote Blockchain and AI: future data systems must be built differently Prof. Alex Pentland, MIT https://youtu.be/KSErDMLaGqQ
Accelerate AI Keynote Gary Marcus https://youtu.be/FEILBLTpA9U?
Accelerate AI Keynote Current and Future Trends in AI, Machine Learning, and Data Science Kirk Borne https://youtu.be/ljFmXMS0c78
ODSC Award ODSC Award https://youtu.be/SRNpuW0xQ8g