read_me_wap_data.R

#Reading in the data and necessary libraries for RPI StudySafe App
#Created by Kara Kniss

#Reading in the necessary libraries
library(shiny)
library(shinydashboard)
library(shinyjs)
library(ggplot2)
library(shinyWidgets)
library(tidyverse)
library(tidyr)
library(lubridate)
library(xlsx)
library(plyr)
library(scales)
library(zoo)
library(ggalt)
library(leaflet)
library(RColorBrewer)
library(viridis)
library(reactable)

###READING IN NECESSARY FILES

#reading in the WAP last seven days data

rpi_wap_raw <- readRDS("../../COVID_RPI_WiFi_Data/rpi_wap_raw.rds")
combined_wap_data <- readRDS("../../COVID_RPI_WiFi_Data/combined_wap_data.rds")

#Reading in WAP semester with devname summary statistics (usercount mean, median, max)
rpi_wap_stats <- readRDS("../../COVID_RPI_WiFi_Data/rpi_wifi_semester_day_summary.rds")


#Reading in semester WAP maximums per building data
# hits_per_wap_semester_by_building_max <- readRDS("wap_data/rpi_wap_semester_max.Rds")

###CLEANING DATA FOR USAGE IN APP

#rpi_wap_raw: getting devname, users, Date, Building, Hour
rpi_wap_raw <- rpi_wap_raw %>% mutate(Hour = hour(as.POSIXct(time))) %>% select(devname, usercount, Date, Building, Hour)
colnames(rpi_wap_raw) <- c('devname', 'users', 'Date', 'Building', 'Hour')

#getting buildings to append to devnames
bldgs <- rpi_wap_raw %>% filter(Date == min(rpi_wap_last7$Date)+1) %>% filter(Hour==12) %>% select(devname, Building)

# Get maximum number of users for each building
hits_per_wap_semester_by_building_max <- merge(rpi_wap_stats, bldgs, by.x= "devname", by.y="devname")
hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max %>% group_by(devname, Building, Day) %>% summarise_all(funs(max)) %>% ungroup() %>% select(devname, Day, usercount_max, Building)
hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max[,2:ncol(hits_per_wap_semester_by_building_max)]
hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max %>% group_by(Building, Day) %>% summarise_all(funs(sum)) %>% ungroup()
hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max %>% group_by(Building) %>% summarise_all(funs(max)) %>% ungroup() %>% select(Building, usercount_max)
colnames(hits_per_wap_semester_by_building_max) <- c('Building', 'capacity')


#combined_wap_data: Building, lat, lng
combined_wap_data <- combined_wap_data %>% group_by(Building, latitude, longitude, time, Date) %>% summarise_all(funs(max)) %>% ungroup() 
combined_wap_data <- combined_wap_data %>% mutate(Hour = hour(as.POSIXct(time))) %>% filter(Date== min(combined_wap_data$Date)+1) %>% filter(Hour == 12) %>% select(Building, latitude, longitude)
colnames(combined_wap_data) <- c('Building','lat','lng' )

combined_wap_data$BuildingType <- ''
combined_wap_data[combined_wap_data$Building %in% Academic == TRUE, ]$BuildingType <- 'Academic'
combined_wap_data[combined_wap_data$Building %in% Greek == TRUE, ]$BuildingType <- 'Greek'
combined_wap_data[combined_wap_data$Building %in% Housing == TRUE, ]$BuildingType <- 'Housing'
combined_wap_data[combined_wap_data$Building %in% OtherOnCampus == TRUE, ]$BuildingType <- 'OtherOnCampus'
combined_wap_data[combined_wap_data$Building %in% OtherOffCampus == TRUE, ]$BuildingType <- 'OtherOffCampus'