Adding Initial Files

read_me_wap_data.R

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+#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'

read_wapData.R

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+#Reading in the data and necessary libraries and data 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(plyr)
+library(scales)
+library(zoo)
+library(ggalt)
+library(leaflet)
+library(plotly)
+library(wesanderson)
+library(reactable)
+
+################################################################################################################
+###READING IN NECESSARY FILES
+################################################################################################################
+#rpi_wap_raw: Min_30, devname, maccount, usercount, datetime, date_time, Date, Building, Floor, Room, latitude, longitude, buldingType, abbrev, time
+rpi_wap_raw <- readRDS("../../COVID_RPI_WiFi_Data/rpi_wap_raw.rds")
+
+#combined_wap_data: building, time, Date, latitude, longitude, buildingType, abbrev, users, macs, 
+combined_wap_data <- readRDS("../../COVID_RPI_WiFi_Data/combined_wap_data.rds")
+
+#rpi_wap_stats: devname, Day, maccount_mean, maccount_med, maccount_max, usrecount_mean, usercount_med, usercount_max
+rpi_wap_stats <- readRDS("../../COVID_RPI_WiFi_Data/rpi_wifi_semester_day_summary.rds")
+
+#rpi_wap_week: devname, maccount, usercount, datetime, date_time, Date, Day
+rpi_wap_week <- readRDS("../../COVID_RPI_WiFi_Data/rpi_wifi_semester_extended.rds")
+
+#buildinginfo: Building, latitude, longitude, buildingType, abbrev
+#buildinginfo <- readRDS("../../COVID_RPI_WiFi_Data/buildinginfo.rds")
+
+################################################################################################################
+###CLEANING DATA
+################################################################################################################
+#rpi_wap_raw: devname, users, Date, Building, Hour, lat, lng, BuildingType
+rpi_wap_raw <- rpi_wap_raw %>% mutate(Hour = hour(as.POSIXct(time))) %>% select(devname, usercount, Date, Building, Hour, latitude, longitude, buildingType)
+colnames(rpi_wap_raw) <- c('devname', 'users', 'Date', 'Building', 'Hour', 'lat', 'lng', 'BuildingType')
+
+#combined_wap_data: Building,  Hour, lat, lng, BuildingType
+rpi_wap_last7 <- combined_wap_data %>% mutate(Hour = hour(as.POSIXct(time))) %>% select(Building, users, Date, Building, Hour, latitude, longitude, buildingType)
+rpi_wap_last7 <- rpi_wap_last7 %>% group_by(Building, Date, Hour, latitude, longitude, buildingType) %>% summarise_all(funs(max)) %>% ungroup() 
+colnames(rpi_wap_last7) <- c('Building', 'Date', 'Hour','lat','lng', 'BuildingType', 'users' )
+
+#Building Tagging, Use Later for whatever categories we have
+#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, ]$BuildingType <- 'OtherOffCampus'
+
+#bldgs(getting buildings to append to devnames): devname, Building
+bldgs <- rpi_wap_raw %>% filter(Date == min(rpi_wap_last7$Date)+1) %>% filter(Hour==12) %>% select(devname, Building)
+
+# hits_per_wap_semester_by_building_max(getting maximum number of users for each building): Building, capacity
+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')
+
+################################################################################################################
+## DEFININING LISTS AND DATA FRAMES FOR CONVENIENCE
+################################################################################################################
+
+#Possible tagging for later
+#placesOfInterest <- c('CII', 'DCC', 'Folsom Library', 'Rensselaer Union', 'Voorhees Computing Center')
+#placesOfWellness <- c('ASRC', 'ECAV arena', 'ECAV stadium', 'Houston Field House', 'Fitness Center', '87 Gym', 'Robison Pool')
+#placesOfDeliciousness <- c('Moes, College Ave', 'Commons Dining Hall', 'Russell Sage Dining Hall')
+#BuildingTypes <- c('Academic','Housing','placesOfInterest', 'placesOfWellness', 'placesOfDeliciousness',  'Greek',  'OtherOnCampus', 'OtherOffCampus')
+
+#creating a time list for the graphs so they aren't in military time
+time <- list('12:00am'= 0,'1:00am'=1, '2:00am'=2, '3:00am'=3, '4:00am'=4, '5:00am'=5, '6:00am'=6, '7:00am'=7, '8:00am'=8, '9:00am'=9, '10:00am'=10, "11:00am"=11, "12:00pm"=12, "1:00pm"=13, "2:00pm"=14, "3:00pm"=15, "4:00pm"=16, "5:00pm"=17, "6:00pm"=18, "7:00pm"=19, "8:00pm"=20, "9:00pm"=21,"10:00pm"=22, "11:00pm"=23)
+Time_num <- c(0:23)
+Time_AMPM <- c('12am', '1am', '2am', '3am', '4am', '5am', '6am', '7am', '8am', '9am', '10am', '11am', '12pm', '1pm', '2pm','3pm', '4pm', '5pm', '6pm', '7pm', '8pm', '9pm', '10pm', '11pm' )
+Time_noLabel <- c(12,1,2,3,4,5,6,7,8,9,10,11,12,1,2,3,4,5,6,7,8,9,10,11)
+time.data <- data.frame(Time_num, Time_AMPM, Time_noLabel, stringsAsFactors=FALSE)
+
+#defining the max date and min date info
+min_date <- min(rpi_wap_last7$Date)
+max_date <- max(rpi_wap_last7$Date)
+max_time_of_max_date <- max(rpi_wap_last7[rpi_wap_last7$Date == max(rpi_wap_last7$Date),]$Hour)+1
+min_time_of_min_date <- min(rpi_wap_last7[rpi_wap_last7$Date == min(rpi_wap_last7$Date),]$Hour)+1
+
+#weekday information
+#getting selected weekday
+day <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" )
+dayVal<- c(1,2,3,4,5,6,7)
+weekpair <- data.frame(day,dayVal, stringsAsFactors=FALSE)
+
+#weekly3_stats(Getting average devname users of the last three weeks by day): devname, Day, Hour, users 
+weekly3_stats <- rpi_wap_week %>% filter(Date >= min(rpi_wap_last7$Date)-21) %>% mutate(Hour = hour(as.POSIXct(date_time))) 
+weekly3_stats <- weekly3_stats %>% select(devname, usercount, Day, Hour) %>% group_by(devname, Day, Hour) %>% summarise_all(funs(max)) %>% ungroup()
+colnames(weekly3_stats) <- c('devname', 'Day', 'Hour', 'users')
+