andrie

## Determine CRAN package clusters (communities)


library(miniCRAN)
library(igraph)
library(magrittr)


# Download matrix of available packages at specific date ------------------

timelyportfolio

# devtools::install_github("jeroenooms/curl")
# devtools::install_github("jeroenooms/V8")

library(V8)

ct = new_context()

# get source and remove anonymous function wrapper
t = readLines(
  "https://raw.githubusercontent.com/twitter/twitter-text/master/js/twitter-text.js"

hrbrmstr

library(httr)
library(magrittr)
library(dplyr)
library(ggplot2)

# data retrieval ----------------------------------------------------------

pg <- GET("http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt",
          user_agent("Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/7046A194A"))

timelyportfolio

library(DiagrammeR)


mat <-  structure(
  c(0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 
    0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 
    0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 
    1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
    0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 

timelyportfolio

Pan and Zoom Financial Charts in R

The svgPanZoom htmlwidget featured in WEEK 02 | PAN & ZOOM R PLOTS claims that it easily adds pan and zoom interactivity to nearly all R graphs. Here we try out svgPanZoom on a couple of finance charts.

Live Examples

1. Efficient Frontier from fPortfolio
2. Snail Trail from PerformanceAnalytics

Code to reproduce code.r

jeroen

library(V8)
stopifnot(packageVersion("V8") >= "0.5")

# Create V8 context and load viz.js
ct <- new_context("window")
invisible(ct$source('http://mdaines.github.io/viz.js/viz.js'))

# This runs: Viz("digraph { a -> b; }", "svg")
svg <- ct$call("Viz", "digraph { a -> b; }", "svg")
cat(svg)

rich-iannone

library(V8)

# Create function to generate palette of visually different colors
hex_palette <- function(no_colors,
                        hue_range = NULL,
                        chroma_range = NULL,
                        lightness_range = NULL){
  
  pal <- function(col, border = "light gray", ...){
    n <- length(col)

sckott

geojsonio has two ways to validate geojson (specifically geojson, not json structure itself). Via a web API from geojsonlint, or locally using the Mapbox's javascript geojsonhint library (included in the package).

Both can act directly on many different inputs, including character strings, lists, data.frame's, all the sp class spatial objects.

Install and load

hrbrmstr

library(dplyr)
library(streamgraph)
library(pbapply)

# Grab some employment data from BLS --------------------------------------

url <- "http://www.bls.gov/lau/ststdsadata.txt"
dat <- readLines(url)

# Small function to grab data for a particular state ----------------------

timelyportfolio

# Obtain 'dogs.csv'
# Created by Alex Bresler, available at:
# https://github.com/abresler/abresler.github.io/blob/master/blog/2015/february/exploring_agility_show/data/dogs.csv
dat_url <- "http://asbcllc.com/blog/2015/february/exploring_agility_show/data/dogs.csv"
dogs <- read.csv(dat_url, header = TRUE, stringsAsFactors = FALSE)


library(shiny)
# Install the latest DiagrammeR package from GitHub
#  devtools::install_github("rich-iannone/DiagrammeR")