MarkEdmondson1234

## Finding number of components
pc <- princomp(model_data)
plot(pc, type="l")

# look for dimension that is ~ 85% variance
summary(pc)
loadings(pc)

# run more convenient pca needed for k-means
pc <- prcomp(k_data)

MarkEdmondson1234

# Determine number of clusters
## run kmeans for varying number of clusters 1 to 15
wss <- (nrow(comp)-1)*sum(apply(comp,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(comp,
                                     centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares")


# From scree plot elbow occurs at k = 4-6

MarkEdmondson1234

kResults <- data.frame(k_data, cluster = k$cluster)

## Transform data for columns of cluster, rows of Sku with value of mean total for each
rl <- as.data.frame(lapply(1:4, function(x){ r3 <- kResults[kResults$cluster == x, 
                                                            setdiff(names(kResults), 'cluster')] 
                               r4 <- colSums(r3) / nrow(r3)
                               r4
                               }))
names(rl) <- paste("cluster",1:4)

MarkEdmondson1234

library(ggraph)
library(gganimate)
library(igraph)
# Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious
infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE)
infect$V3 <- NULL
names(infect) <- c('from', 'to', 'time')
infect$timebins <- as.numeric(cut(infect$time, breaks = 100))

# We want that nice fading effect so we need to add extra data for the trailing

MarkEdmondson1234

function uploadData() {
  var accountId = "xxxxxxxx";
  var webPropertyId = "UA-xxxxxxxx-x";
  var customDataSourceId = "xxxxxxxx";
  var ss = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
  var maxRows = ss.getLastRow();
  var maxColumns = ss.getLastColumn();
  var data = [];
  for (var i = 1; i < maxRows;i++) {
    data.push(ss.getRange([i], 1,1, maxColumns).getValues());

MarkEdmondson1234

library(idbr) # devtools::install_github('walkerke/idbr')
library(ggplot2)
library(animation)
library(dplyr)
library(ggthemes)

idb_api_key("Your Census API key goes here")

male <- idb1('JA', 2010:2050, sex = 'male') %>%
  mutate(POP = POP * -1,

MarkEdmondson1234

# devtools::install_github("hrbrmstr/vegalite")
library(vegalite)
library(htmltools)

dat <- jsonlite::fromJSON('[
      {"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
      {"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
      {"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
    ]')

MarkEdmondson1234

Rscript -e "setwd('/srv/shiny-server/xxxxx/'); zz<-file('rscript.log', open='wt');sink(zz, type = 'm'); rmarkdown::render('getData.Rmd')" 

MarkEdmondson1234

library(shiny)

#' Safe subset
#'
#' @param df Dataframe
#' @param column One name of column to subset within
#' @param subset Vector of entries in column to subset to
#'
#' If column not in df, returns back the df
safeSubset <- function(df, column, subset){

MarkEdmondson1234

library(shiny)

dynamicSelectInput <- function(id, label, multiple = FALSE){
  
  ns <- shiny::NS(id)
  
  shiny::selectInput(ns("dynamic_select"), label,
                     choices = NULL, multiple = multiple, width = "100%")
  
}