HIIIIIIIIIIIIII PROFESSSOR

MiniProject.R

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+### ~ Libraries ~ ###
+library(ggplot2)
+library(dplyr)
+library(lubridate)
+
+### ~ Extraction ~ ###
+
+#Trains!
+boston_rail <- read.csv('/Users/benjaminfox/Desktop/Data400_Spring26/data/Public Transit Fer Mini Project/Boston_Rail.csv')
+ny_rail <- read.csv('/Users/benjaminfox/Desktop/Data400_Spring26/data/Public Transit Fer Mini Project/New_York_Rail.csv')
+chicago_rail <- read.csv('/Users/benjaminfox/Desktop/Data400_Spring26/data/Public Transit Fer Mini Project/Chicago_Rail.csv')
+
+#Tour dates for reference - csvs I made from existing data
+newYorkDates <- read.csv('/Users/benjaminfox/Downloads/Artists Playing Dates - In New York.csv')
+bostonDates <- read.csv('/Users/benjaminfox/Downloads/Artists Playing Dates - In Boston.csv')
+chicagoDates <- read.csv('/Users/benjaminfox/Downloads/Artists Playing Dates - In Chicago.csv')
+
+### ~ Refactoring ~ ###
+
+#Boston Rail
+boston_rail_by_day <- summarise(group_by(boston_rail, servicedate), sum_boardings = sum(estimated_boardings))
+
+boston_rail_by_day <- boston_rail_by_day %>% mutate(generous_revenue = sum_boardings * 2.4,
+                                                    kinda_generous = sum_boardings * 1.1)
+
+#Chicago Rail
+which(grepl('202',chicago_rail$service_date)==TRUE)
+trimmed_chicago <- chicago_rail[which(grepl('202',chicago_rail$service_date)==TRUE),]
+
+chicago_dates_for_r <- mdy(trimmed_chicago$service_date)
+
+doubleTrain <- cbind(chicago_dates_for_r,weekdays(chicago_dates_for_r),
+                     trimmed_chicago[2:5])
+colnames(doubleTrain) <- c("Service Date", "Day of Week", "Day Type", "Bus", "Rail", "Total")
+
+#New York Rail
+newYorkNoPercent <- ny_rail |> select(Date, Subways..Total.Estimated.Ridership,
+                                      Buses..Total.Estimated.Ridership, 
+                                      LIRR..Total.Estimated.Ridership,
+                                      Metro.North..Total.Estimated.Ridership,
+                                      Access.A.Ride..Total.Scheduled.Trips,
+                                      Bridges.and.Tunnels..Total.Traffic,
+                                      Staten.Island.Railway..Total.Estimated.Ridership)
+
+colnames(newYorkNoPercent) <- c("Date","Subway Total", "Bus Total", 
+                                "LIRR Total","Metro North Total", 
+                                "Access A Ride Total","Bridge & Tunnel Total", 
+                                "Staten Island Total")
+#FYI, LIRR = Long Island Rail Road
+#Access A Ride is those with disabilities to get around the city
+
+#I ran a bunch of commands to clean the numbers into being processed as numbers
+newYorkNoPercent <- newYorkNoPercent %>% mutate(TransitTotal = `Subway Total` + `Bus Total` +
+                                                  `LIRR Total` + `Metro North Total` + 
+                                                  `Access A Ride Total` + `Bridge & Tunnel Total` +
+                                                  `Staten Island Total`)
+
+CleanedNYTransit <- newYorkNoPercent %>% select(Date, TransitTotal,`Subway Total`, `Bus Total`,
+                                                 `LIRR Total`, `Metro North Total`, `Access A Ride Total`,
+                                                 `Bridge & Tunnel Total`, `Staten Island Total`)
+
+#Where do I actually need to analyze tho? Like which trains for which shows?
+#I will make vectors of concert dates for each artist
+
+CleanedNYTransit$Date <- mdy(CleanedNYTransit$Date)
+
+#Focusing on looking at the mean and median travel by year in each city
+newYorkByYear <- summarise(group_by(CleanedNYTransit, substr(Date, 1,4)), 
+                           meanBoardings = mean(TransitTotal),
+                           medianBoardings = median(TransitTotal))
+
+bostonByYear <- summarise(group_by(boston_rail_by_day, substr(servicedate, 1,4)),
+                          meanBoardings = mean(sum_boardings),
+                          medianBoardings = median(sum_boardings))
+
+chicagoByYear <- summarise(group_by(doubleTrain, substr(`Service Date`, 1, 4)),
+                           meanBoardings = mean(Total),
+                           medianBoardings = median(Total))
+
+colnames(chicagoByYear) <- c("Year", "Mean Transit", "Median Transit")
+
+###Making final date charts before doing a wee bit of analysis
+blankVector <- c(1:nrow(chicagoDates))
+
+#Ensuring dates are built well
+chicagoDates$Date <- ymd(chicagoDates$Date)
+chicagoDates <- arrange(chicagoDates, Date)
+
+#which(boston_rail_by_day$servicedate %in% bostonDates$Date[6])
+
+#Building Basic Columns
+for (i in blankVector){
+  replaceRow <- which(doubleTrain$`Service Date` %in% chicagoDates$Date[i])
+
+  blankVector[i] <- doubleTrain$Total[replaceRow]
+}
+
+for (i in blankVector){
+  yur <- substr(chicagoDates$Date[i], 1, 4)
+  blankVector[i] <- yur
+}
+
+chicagoDates <- cbind(chicagoDates,blankVector)
+
+#Switch case loop
+for (i in blankVector){
+  the <- switch(chicagoDates$Year[i],
+                "2020" = chicagoByYear$`Mean Transit`[1],
+                "2021" = chicagoByYear$`Mean Transit`[2],
+                "2022" = chicagoByYear$`Mean Transit`[3],
+                "2023" = chicagoByYear$`Mean Transit`[4],
+                "2024" = chicagoByYear$`Mean Transit`[5],
+                "2025" = chicagoByYear$`Mean Transit`[6])
+
+  blankVector[i] <- the
+}
+
+for (i in blankVector){
+  and <- switch(chicagoDates$Year[i],
+                "2020" = chicagoByYear$`Median Transit`[1],
+                "2021" = chicagoByYear$`Median Transit`[2],
+                "2022" = chicagoByYear$`Median Transit`[3],
+                "2023" = chicagoByYear$`Median Transit`[4],
+                "2024" = chicagoByYear$`Median Transit`[5],
+                "2025" = chicagoByYear$`Median Transit`[6])
+
+  blankVector[i] <- and
+}
+
+#Mutate the differentials
+chicagoDates <- chicagoDates %>% 
+  mutate(meanDifferential = `Transit Total` - `Yearly Average`,
+         medianDifferential = `Transit Total` - `Yearly Median`)
+
+colnames(chicagoDates) <- c("Date", "Artist", "Venue", "Transit Total", "Year", 
+                           "Yearly Average", "Yearly Median",
+                           "Mean Differential", "Median Differential")
+
+chicagoDates <- cbind(chicagoDates[1:4],chicagoDates[6:9])
+
+CutYorkDates <- newYorkDates[1:38,]
+
+meanDifferentials <- data.frame("Cities" = c("New York", "New York","Boston", "Boston","Chicago","Chicago","Total","Total"),
+                                "Mean Differentials" = c(
+                                  mean(CutYorkDates$`Mean Differential`),
+                                  mean(CutYorkDates$`Median Differential`),
+                                  mean(bostonDates$`Mean Differential`),
+                                  mean(bostonDates$`Median Differential`),
+                                  mean(chicagoDates$`Mean Differential`),
+                                  mean(chicagoDates$`Median Differential`),
+                                  mean(allDates$`Mean Differential`),
+                                  mean(allDates$`Median Differential`)
+                                ), "Mode" = c("Mean","Median","Mean","Median","Mean","Median","Mean","Median"))
+
+allDates <- rbind(CutYorkDates, bostonDates, chicagoDates)
+allDates <- arrange(allDates, Date)
+
+write.csv(meanDifferentials, file = '/Users/benjaminfox/Desktop/Data400_Spring26/data/Final Charts For Mini Project Presentation/meanDifferentials.csv')