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require(caret)

#load some data
data(USArrests)

### Prepare Data (postive observations)
# add a column to be the strata.  In this case it is states, it can be sites, or other locations
# the original data has 50 rows, so this adds a state label to 10 consecutive observations
USArrests$state <- c(rep(c("PA","MD","DE","NY","NJ"), each = 5))
# this replaces the existing rownames (states) with a simple numerical index

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##### Your code leading up to this point ######

# initialize active cases
SWDetail$active_cases <- 0
SWDetail[1,"active_cases"] <- 1
# initialize most recent cases
SWDetail$last_case_given <- 0
SWDetail[1,"last_case_given"] <- 1
# resorting this by date
cps_test_assignment <- cps_test_assignment[order(cps_test_assignment$assignment_date),]

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library('foreach')
library('ggplot2')
library('gganimate')
# library('animation') # require for original code
library('reshape2')
library('doParallel') # for multicore support

## Guts and working concept from post below.  
## http://johnramey.net/blog/2011/06/05/conways-game-of-life-in-r-with-ggplot2-and-animation/#comments
## I updated ggplot, melt, varnames, gganimate, and faded cells

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library("data.table")
library("rowr") 
library("inTrees")
library("dplyr")
library("randomForest")
library("xtable")
library("caret")
library("gbm")
library("rpart")
library("reshape2")

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#Package installs -------------------------------------------------------------
load.fun <- function(x) { 
  x <- as.character(x) 
  if(isTRUE(x %in% .packages(all.available=TRUE))) { 
    eval(parse(text=paste("require(", x, ")", sep=""))) 
    print(paste(c(x, " : already installed; requiring"), collapse=''))
  } else { 
    #update.packages()
    print(paste(c(x, " : not installed; installing"), collapse=''))
    eval(parse(text=paste("install.packages('", x, "')", sep=""))) 

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library(corrplot)
library(viridis)
library(stargazer)
library(tidyverse)
library(dplyr)
library(sf)
library(tigris)
library(ggplot2)
library(rgdal)
library(maptools)

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library(tidyverse)
g <- glimpse

g(iris)

dat <- iris %>% 
  nest(data = c(-Species))

dat$data[[1]]

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### Example of ggplot code for multiclass confusion matrix with caret::confusionMatrix and ggplot
### `Example_plot1` is the result of applying `caret::confusionMatrix()` to the outcome ...
### of a model that included a reference class and a predicted class; both as factors
### calling `as.data.frame(Example_plot1$table)` casts the predicted class frequency table from the ...
### `caret::confusionMatrix()` object into a nice long format table of columns `Reference`, `Prediction`, and `Freq`.
### Do this for a bunch of models, and then use `cowplot::plot_grid()` to arrange them.

library(tidyverse)
library(cowplot)
library(caret)

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########################################################################
### Bespoke Neural Network R code attributed to: David Selby
### From blog post: http://selbydavid.com/2018/01/09/neural-network/
### Adapted here for making animated GIF of node density
### output gifs compiled at gifmaker.me for final output
### output tweeted here:
### https://twitter.com/Md_Harris/status/951257342418608128
########################################################################

two_spirals <- function(N = 200,

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### ------- Load Packages ---------- ###
library("purrr")
library("future")
library("dplyr")
library("randomForest")
library("rsample")
library("ggplot2")
library("viridis")
### ------- Helper Functions for map() ---------- ###
# breaks CV splits into train (analysis) and test (assessmnet) sets