jkaupp

# The following code was written by Jake Kaupp at Queen's University, Kingston, Canada
# for looking at LOPUS observation data
# Date: February 2017

library(jsonlite)
library(plyr)
library(tidyverse)
library(lubridate)

# Data file is located in the same direoctory as this script

jkaupp

require(digest)

require(shiny)


ui <- basicPage(
  uiOutput("register"),
  actionButton("doRegister",label="Register"),
  verbatimTextOutput("resultRegister"),
  uiOutput("login"),

jkaupp

library(proto)

stat_smooth_func <- function (mapping = NULL, data = NULL, geom = "smooth", position = "identity",
                         method = "auto", formula = y ~ x, se = TRUE, n = 80, fullrange = FALSE,
                         level = 0.95, na.rm = FALSE, ...) {
  StatSmoothFunc$new(mapping = mapping, data = data, geom = geom, position = position,
                 method = method, formula = formula, se = se, n = n, fullrange = fullrange,
                 level = level, na.rm = na.rm, ...)
}

jkaupp

Mode <- function(x) {
     ux <- unique(x)
     ux[which.max(tabulate(match(x, ux)))]
 }

jkaupp

Utilize d3js (Data Driven Documents JavaScript Library) and r to produce a collapsible dendrogram visualization of the outcomes structure of first-year engineering from the basemap.

jkaupp

stock <- "MSFT"
start.date <- "2012-01-01"
end.date <- Sys.Date()
quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=",
                stock,
                "&a=", substr(start.date,6,7),
                "&b=", substr(start.date, 9, 10),
                "&c=", substr(start.date, 1,4), 
                "&d=", substr(end.date,6,7),
                "&e=", substr(end.date, 9, 10),

jkaupp

# See also the entry on stackoverflow: http://goo.gl/KUvmW2
library(lattice)
library(chron)
source("http://blog.revolutionanalytics.com/downloads/calendarHeat.R")
# Plot as calendar heatmap
calendarHeat(stock.data$Date, stock.data$Adj.Close, 
             varname="MSFT Adjusted Close")

jkaupp

library(plyr)
library(gdata)
library(ggmap)
library(ggplot2)

setwd("/Users/Jake/Documents/MOOCs/IVMOOC")

data <- read.csv("NSF Master LandL.csv")
data.state <-ddply(data, .(state, Latitude, Longitude), summarize, expected_total_amount=sum(expected_total_amount))