From f0611ce0432fc8d33938442fa7ef54f12f2b142e Mon Sep 17 00:00:00 2001 From: chenc29 Date: Thu, 16 Sep 2021 07:50:10 -0400 Subject: [PATCH 1/2] update file to current version --- read_wapData.R | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/read_wapData.R b/read_wapData.R index 976d4546..693efa28 100644 --- a/read_wapData.R +++ b/read_wapData.R @@ -6,7 +6,7 @@ ################################################################################################################ #load the packages if they are not already loaded packages <- c("shiny", "shinydashboard", "shinyjs", "ggplot2", "shinyWidgets", "tidyverse", "tidyr", - "lubridate", "plyr", "scales", "zoo", "ggalt", "leaflet", "plotly", "wesanderson", "reactable") + "lubridate", "plyr", "scales", "zoo", "ggalt", "leaflet", "plotly", "wesanderson", "reactable") new.packages <- packages[!(packages %in% installed.packages()[,"Package"])] if (length(new.packages) > 0) { install.packages(new.packages) @@ -49,6 +49,9 @@ rpi_wap_stats <- readRDS("../COVID_RPI_WiFi_Data/rpi_wifi_semester_day_summary.r #buildinginfo: Building, latitude, longitude, buildingType, abbrev #buildinginfo <- readRDS("../COVID_RPI_WiFi_Data/buildinginfo.rds") +#user_prediction: Building, weekday, Hour, users, Mean_Usercount, latitude, longitude, buildingType +user_predictions <- readRDS("../COVID_RPI_WiFi_Data/rpi_user_predictions.rds") + ################################################################################################################ ###CLEANING DATA ################################################################################################################ @@ -78,6 +81,13 @@ hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max % 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') + +#user_predictions <- user_predictions %>% mutate(Hour = hour(as.POSIXct(time))) %>% select(Building, Median_Usercount, Mean_Usercount, Hour, latitude, longitude, buildingType, weekday) +user_predictions <- user_predictions %>% group_by(Building, Hour, latitude, longitude, buildingType, weekday) %>% ungroup() +colnames(user_predictions) <- c('Building', 'weekday', 'Hour','users', 'Mean_Usercount' ,'lat','lng', 'BuildingType') +user_predictions <- user_predictions %>% filter(BuildingType != "housing" & BuildingType != "greek") %>% + filter(!(Building %in% remove_list)) + ################################################################################################################ ## DEFININING LISTS AND DATA FRAMES FOR CONVENIENCE ################################################################################################################ @@ -198,10 +208,10 @@ Other <- c("City Station South", "Old Bumstead Garage, behind Colonie Apts", # "SAE, 12 Myrtle Ave off Pawling Ave", "Rensselaer at Hartford") - # "Peoples Ave #1002", - # "Peoples Ave #1516", - # "Peoples Ave #901", - # "Peoples Ave #907") +# "Peoples Ave #1002", +# "Peoples Ave #1516", +# "Peoples Ave #901", +# "Peoples Ave #907") #Sleep: Housing # Sleep <- unique((rpi_wap_last7 %>% filter(BuildingType %in% c('housing')) %>% select(Building))$Building) @@ -216,7 +226,7 @@ byCat_single <- list( "Other Off Campus" = as.vector(unique((rpi_wap_last7 %>% filter(BuildingType=='otherOffCampus') %>% select(Building))$Building)) # "Greek" = as.vector(unique((rpi_wap_last7 %>% filter(BuildingType=='greek') %>% select(Building))$Building)), # "Housing" = as.vector(unique((rpi_wap_last7 %>% filter(BuildingType=='housing') %>% select(Building))$Building)) - ) +) byCat_multi <- list( "Nothing Selected" = as.vector('None'), @@ -225,7 +235,7 @@ byCat_multi <- list( "Other Off Campus" = as.vector(unique(rpi_wap_last7 %>% filter(BuildingType=='otherOffCampus') %>% select(Building))$Building) # "Greek" = as.vector(unique(rpi_wap_last7 %>% filter(BuildingType=='greek') %>% select(Building))$Building), # "Housing" = as.vector(unique(rpi_wap_last7 %>% filter(BuildingType=='housing') %>% select(Building))$Building) - ) +) byAct_single <- list( "Common Favorites" = as.vector(Favorites), @@ -334,5 +344,4 @@ make_plot <- function(dat, time_now, building_select, hits_per_wap_semester_by_b legend.position="none") } -icon <- awesomeIcons(icon = 'ios-close', iconColor="black", library='ion', markerColor="green") - +icon <- awesomeIcons(icon = 'ios-close', iconColor="black", library='ion', markerColor="green") \ No newline at end of file From 0a97d46cd2500262354205451d271a121cc6464f Mon Sep 17 00:00:00 2001 From: chenc29 Date: Thu, 28 Oct 2021 15:22:42 -0400 Subject: [PATCH 2/2] update prediction data --- read_wapData.R | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/read_wapData.R b/read_wapData.R index 693efa28..7cd0451b 100644 --- a/read_wapData.R +++ b/read_wapData.R @@ -50,7 +50,7 @@ rpi_wap_stats <- readRDS("../COVID_RPI_WiFi_Data/rpi_wifi_semester_day_summary.r #buildinginfo <- readRDS("../COVID_RPI_WiFi_Data/buildinginfo.rds") #user_prediction: Building, weekday, Hour, users, Mean_Usercount, latitude, longitude, buildingType -user_predictions <- readRDS("../COVID_RPI_WiFi_Data/rpi_user_predictions.rds") +user_predictions <- readRDS("../COVID_RPI_WiFi_Data/median_last3wks_with_floors.rds") ################################################################################################################ ###CLEANING DATA @@ -81,12 +81,13 @@ hits_per_wap_semester_by_building_max <- hits_per_wap_semester_by_building_max % 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') +user_predictions <- user_predictions %>% + dplyr::group_by(Building, Weekday, Hour, lat, lng, BuildingType) %>% + dplyr::summarize(totalusers = sum(users)) %>% + ungroup() -#user_predictions <- user_predictions %>% mutate(Hour = hour(as.POSIXct(time))) %>% select(Building, Median_Usercount, Mean_Usercount, Hour, latitude, longitude, buildingType, weekday) -user_predictions <- user_predictions %>% group_by(Building, Hour, latitude, longitude, buildingType, weekday) %>% ungroup() -colnames(user_predictions) <- c('Building', 'weekday', 'Hour','users', 'Mean_Usercount' ,'lat','lng', 'BuildingType') -user_predictions <- user_predictions %>% filter(BuildingType != "housing" & BuildingType != "greek") %>% - filter(!(Building %in% remove_list)) +names(user_predictions)[names(user_predictions) == 'Weekday'] <- 'weekday' +names(user_predictions)[names(user_predictions) == 'totalusers'] <- 'users' ################################################################################################################ ## DEFININING LISTS AND DATA FRAMES FOR CONVENIENCE