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library(brms)
library(tidyverse)
library(caret)

rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores ()) # Run on multiple cores
set.seed(3875)

ir <- data.frame (scale (iris[, -5]), Species=iris[, 5])
system.time (b2 <- brm (Species ~ Petal.Length + Petal.Width + Sepal.Length + Sepal.Width,

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library("data.table")
library("rowr") 
library("dplyr")
library("ggplot2")
library("Information")
library("knitr")
library("ggrepel")
library("ggthemes")
library("ggalt")
library("xtable")

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## DR Set up
# predict on all 65M rows of DRPrimaryv4.csv
# using deployment [BOOKED predcitons] (634728e0ba6623c209179662)
# using model [eXtreme Gradient Boosted Trees Classifier with Early Stopping (learning rate =0.01) (Fast Feature Binning) M60 BP52] (63457d2fcc011ba8bee7ec78)
# from project Test1004 created by Adam

## Post-processing (R & SQL)
# load predictions (result-6356a08dabe595d288af9a3e.csv) into SQLite DB

sqlite_file <- "AMN.sqlite"

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library(readr)
library(tidyverse)
library(glue)
library(DataRobotColors)

pred <- read.csv("DATA/Predictions/[MDH]_DRPrimaryV3_10pcnt_SF_20221003_eXtreme_Gradient_Boosted_Trees_Classifier_with_Ear_(57)_79.93_IF_-_MDH_Reduced_v2_holdout.csv")

pred_rank <- pred %>% 
  group_by(TRAVELERID) %>% 
  arrange(TRAVELERID, desc(Cross.Validation.Prediction)) %>% 

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# google distance example
library(tidyverse)
library(googleway)

api_key <- "GET A GOOGLE API KEY"

## set up a data.frame of locations
## can also use 'lat/lon' coordinates as the origin/destination
df_locations <- data.frame(
  origin = c("Melbourne, Australia", "Sydney, Australia","Canberra, Australia")

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library(gmailr)
library(tidyverse)
library(glue)
library(kable)
library(knitr)
library(googledrive)

# need to get these values for your google drive account
gm_auth_configure(key = "LOTS_OF_NUMBERS_AND_LETTERS.apps.googleusercontent.com",secret = "SECRET_KEY")
gm_oauth_app()

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dat <- data.frame(Response = c("Yes","No"), Value = c(0.32,0.68))

ggplot(dat, aes(x = Response, y = Value)) +
  geom_bar(stat = "identity", width = 0.5) +
  xlab("") +
  ylab("") +
  theme_bw() +
  scale_y_continuous(limits = c(0,1)) +
  geom_text(aes(label = paste0(Value*100,"%")), vjust = -1, 
            hjust = 0.5, face = "bold", size = 6) +

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library('rnoaa')
library("tidyverse")
library("lubridate")
library("ggrepel")

token = 'GET YOUR API KEY at: http://www.ncdc.noaa.gov/cdo-web/token'
locs <- ncdc_locs(locationcategoryid='CITY', sortfield='name', sortorder='desc', token = token, limit = 800)
loc_data <- locs$data
dplyr::filter(loc_data, grepl(", PA",loc_data$name))

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library(shiny)
library(shinydashboard)
# devtools::install_github("nik01010/dashboardthemes")
library(dashboardthemes)
library(tidyverse)
library(sf)
library(leaflet)
library(usmap) # us counties and states as table
library(leaflet)

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lancCounty <- st_read("C:/Users/matthew.d.harris/Documents/GitHub/Public-Policy-Analytics-Landing/DATA/Chapter2/LancasterCountyBoundary.geojson") %>%
  st_transform('ESRI:102728')

uga <- st_read("C:/Users/matthew.d.harris/Documents/GitHub/Public-Policy-Analytics-Landing/DATA/Chapter2/Urban_Growth_Boundary.geojson") %>% 
  st_transform('ESRI:102728')     

studyAreaTowns <- st_read("C:/Users/matthew.d.harris/Documents/GitHub/Public-Policy-Analytics-Landing/DATA/Chapter2/StudyAreaTowns.geojson") %>%
  st_transform('ESRI:102728')    

buildings <- st_read("C:/Users/matthew.d.harris/Documents/GitHub/Public-Policy-Analytics-Landing/DATA/Chapter2/LancasterCountyBuildings.geojson") %>% st_transform('ESRI:102728')