View scales.R
library(ggplot2)
library(tidyr)
dat <- data.frame(x = rnorm(n = 1000, mean = 2.8, sd = 0.05),
y1 = sample(64503:73034, size = 1000, replace = TRUE),
y2 = sample(18738:19602, size = 1000, replace = TRUE))
dat %>%
ggplot() +
geom_point(aes(x,y1)) +
View outbreak_animation_list.R
# script to demonstrate outbreak animation with a subset of mers_korea_2015 data
# must install github release of threejs package
# devtools::install_github("bwlewis/rthreejs")
library(threejs)
library(outbreaks)
library(dplyr)
# use dplyr to subset results to only include hospital visit exposure
View scratch.R
# install.packages("babynames")
library(babynames)
# install.packages("tidyverse")
library(tidyverse)
# install.packages("ggplot2")
# install.packages("dplyr")
# let's take a look at the data
babynames %>%
View()
View outbreak_animation.R
# script to demonstrate outbreak animation with a subset of mers_korea_2015 data
# must install github release of threejs package
# devtools::install_github("bwlewis/rthreejs")
library(threejs)
library(outbreaks)
library(dplyr)
# use dplyr to subset results to only include hospital visit exposure
View app.R
library(shiny)
options(shiny.reactlog=TRUE)
# define home dir for shiny app
homed <- getwd()
# set up choices to be retrievable in server.R
progchoices <- c("This is a TEST APP" = "testapp/",
"Yet Another Program" = "anotherprog/")
ui <- fluidPage(
View listmanipulation.R
# list manipulation workshop
# scratch script
# november 9 2016
#############################################
slamwins <- list(17,14,14,12,11)
#############################################
View panderlatexpost.Rmd
title author date output
RMarkdown(Pandoc(LaTeX))
VP Nagraj
March 3, 2016
html_document
keep_md
true
View moma.Rmd
title author date output runtime
MOMA
VP Nagraj
February 29, 2016
html_document
shiny

The Museum of Modern Art (MOMA) collection database is publicly available via Github:

View outlier_rm.R
# install.packages("shiny")
# install.packages("miniUI")
# install.packages("ggplot2")
library(shiny)
library(miniUI)
library(ggplot2)
outlier_rm <- function(data, xvar, yvar) {
ui <- miniPage(
View app.R
library(shiny)
library(dplyr)
server <- function(input, output) {
output$downloadData <- downloadHandler(
filename = function() {
paste('data_', Sys.Date(), '.csv', sep='')
},