From 0cd0fe53e82a646b6d998673425d454443fb35a0 Mon Sep 17 00:00:00 2001
From: chenc29 <chenc29@rpi.edu>
Date: Tue, 30 Nov 2021 09:40:53 -0500
Subject: [PATCH] add floor data and adjusted bar plot

---
 app.R          |  3 ++-
 read_wapData.R | 25 ++++++++++++++++++-------
 2 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/app.R b/app.R
index bea0d359..ae74faa7 100644
--- a/app.R
+++ b/app.R
@@ -260,7 +260,8 @@ server <- function(input, output, session) {
   # "google-maps" style plot 
   output$hourly_crowd <- renderPlot({
     # get data to feed into make_plot 
-    data <- user_predictions %>% filter(weekday == application_state$day & Building == application_state$building) 
+    data <- user_predictions_fl %>% filter(weekday == application_state$day & Building == application_state$building) 
+    # browser()
     make_plot(data, application_state$time, application_state$building, hits_per_wap_semester_by_building_max)
   }, bg="transparent")
   
diff --git a/read_wapData.R b/read_wapData.R
index 7cd0451b..8e9d64e2 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/median_last3wks_with_floors.rds")
+user_predictions_fl <- readRDS("../COVID_RPI_WiFi_Data/median_last3wks_with_floors.rds")
 
 ################################################################################################################
 ###CLEANING DATA
@@ -81,11 +81,12 @@ 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 %>% 
+user_predictions <- user_predictions_fl %>%
   dplyr::group_by(Building, Weekday, Hour, lat, lng, BuildingType) %>%
   dplyr::summarize(totalusers = sum(users)) %>%
   ungroup()
 
+names(user_predictions_fl)[names(user_predictions_fl) == 'Weekday'] <- 'weekday'
 names(user_predictions)[names(user_predictions) == 'Weekday'] <- 'weekday'
 names(user_predictions)[names(user_predictions) == 'totalusers'] <- 'users'
 
@@ -323,18 +324,27 @@ capacity_intercept <- function(capacity){
   cut <- c(c1, c2, c3, capacity)
 }
 
+cbPalette <- c("#8dd3c7", "#ffffb3", "#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5", "#d9d9d9", "#bc80bd")
+
 # Function to make bar plot 
 make_plot <- function(dat, time_now, building_select, hits_per_wap_semester_by_building_max){
   building_select_cap <- subset(hits_per_wap_semester_by_building_max, hits_per_wap_semester_by_building_max$Building == building_select)
   cap_line <- capacity_intercept(building_select_cap$capacity)
-  ggplot(dat, aes(x=factor(Hour), y=users, fill=(Hour==time_now))) +
-    geom_bar(stat="identity") +
-    scale_fill_manual(values=c("skyblue3","mediumvioletred")) +
-    geom_vline(xintercept = as.integer(time_now)+1, linetype = "dotted") +
+  dat$Floor <- factor(dat$Floor)
+  ggplot(dat, aes(x=factor(Hour), y=users, fill=forcats::fct_rev(Floor))) +
+  # ggplot(dat, aes(x=factor(Hour), y=users, fill=(Hour==time_now))) +
+    geom_rect(alpha.f=0.1, xmin=as.integer(time_now)+.5, xmax=as.integer(time_now)+1.5, ymin=-Inf, ymax=Inf, fill="#c8f7c8") +
+    geom_bar(stat="identity", color = "black") +
+    # scale_fill_manual(values=cbPalette) +
+    scale_fill_manual(values = c("0"="#8dd3c7", "1"="#ffffb3", "2"="#bebada", "3"="#fb8072", "4"="#80b1d3", "5"="fdb462", 
+                                  "6"="#b3de69", "7"="fccde5", "8"="#d9d9d9", "9"="#bc80bd")) +
+    # scale_fill_manual(values=c("skyblue3","mediumvioletred")) +
+    # geom_vline(xintercept = as.integer(time_now)+1, linetype = "dotted") +
     geom_hline(yintercept = cap_line, linetype = "dotted") +
     scale_x_discrete(labels= Time_AMPM) +
     scale_y_continuous(breaks = cap_line, labels = c("25%", "50%", "75%", "100%")) +
     ylab("Building Capacity") +
+    labs(fill = "Floor") +
     theme(panel.grid.major = element_blank(), 
           panel.grid.minor = element_blank(),
           panel.background = element_blank(), 
@@ -342,7 +352,8 @@ make_plot <- function(dat, time_now, building_select, hits_per_wap_semester_by_b
           axis.ticks.y = element_blank(),
           axis.ticks.x = element_blank(),
           axis.title.x=element_blank(),
-          legend.position="none")
+          legend.position="bottom",
+          legend.background = element_blank())
 }
 
 icon <- awesomeIcons(icon = 'ios-close', iconColor="black", library='ion', markerColor="green")
\ No newline at end of file