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@jamesthomson
jamesthomson / HClust_Dendogram.R
Last active Jun 10, 2016
R HClust to d3.js Dendogram
View HClust_Dendogram.R
library(rjson)
#convert output from hclust into a nested JSON file
HCtoJSON<-function(hc){
labels<-hc$labels
merge<-data.frame(hc$merge)
for (i in (1:nrow(merge))) {
@jamesthomson
jamesthomson / install from github.R
Created Jul 30, 2014
install R package from github
View install from github.R
install.packages("devtools")
library("devtools")
install_github("jamesthomson/R2D3")
View gist:d68002a7d80de7a2095c
library(SpotifyAPI)
out<-visDiscography(artist="Shuggie Otis", output_file="Shuggie Disco.html")
View r2d3 demo.R
data(counties)
JSON<-jsonNestedData(structure=counties[,1:3], values=counties[,4], top_label="UK")
D3Tree(JSON, file_out="Tree.html")
hc.ave <- hclust(dist(USArrests), "ave")
hc.single <- hclust(dist(USArrests), "single")
hc.ward <- hclust(dist(USArrests), "ward.D")
cut.ave<-cutree(hc.ave, k=6)
View bpi_plotly.R
bpi_genre_sales_perc <- read.csv("http://myinspirationinformation.com/wp-content/uploads/2014/08/bpi_genre_sales_perc.csv")
bpi_sales_revenue <- read.csv("http://myinspirationinformation.com/wp-content/uploads/2014/08/bpi_sales_revenue.csv")
library(ggplot2)
library(reshape2)
library(ggthemes)
library(plotly)
sales_perc_melt<-melt(bpi_genre_sales_perc)
sales_perc_albums<-sales_perc_melt[sales_perc_melt$Type=="Albums",]
View Timing a Neural Net Scoring Process.R
library(nnet)
library(ggplot2)
neuralNetScoreTime<-function(var, score){
modelset<-NULL
for (i in 1:var) {
eval(parse(text=paste0("temp.", i, "<-rnorm(150, mean=1, sd=1)")))
eval(parse(text=paste0("modelset<-cbind(modelset, temp.", i, ")")))
View lastfmApp_ui.R
# ui.R
library(shiny)
shinyUI(fluidPage(
titlePanel("Lastfm Dashboard"),
sidebarLayout(position ="left",
sidebarPanel(
img(src = "logo.png", height = 80, width = 80),
View lastfmApp_server.R
#server.r
library(shiny)
shinyServer(function(input, output) {
library(rjson)
library(ggplot2)
library(grid)
View msd_files_list.R
library(XML)
#read url
search<-readLines('http://tbmmsd.s3.amazonaws.com/')
#convert to data.frame
df<-xmlToDataFrame(search)
#pull out files list
Files<-df$Key
#clean up NAs
Files2<-Files[!is.na(Files)]
View msd_song_length_analysis.sql
DROP TABLE IF EXISTS msd_data;
CREATE EXTERNAL TABLE msd_data
(
ref string,
analysis_sample_rate float ,
artist_7digitalid int ,
artist_familiarity float ,
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