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# Section 1. Multiple Journey Days

# this section generates a report of the to,from and days that have more 
# journeys than days travelled (i.e. there was on average more than 1 trip per day)

# select the days/to/from that have more journeys than journey days
out.m <- which(journey.count.x>pred.journey.x, arr.in=TRUE) 

# select the journey count for the multi-trip dates
journey.count <- journey.count.x[journey.count.x>pred.journey.x]

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# Section 1. Load the data from journey file

# this section takes in the dataframes (reads them in first from CSV files)

l.journeys <- list.files(pattern = "*_journey.csv")

# select a particlar car and day to examine
x.journey.number <- 4

df.x <- read.csv(l.journeys[x.journey.number]

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# select a particlar car and day to examine
x.journey.number <- 4
x.day <- "Monday"

# Big look to process all the records
#for (x.journey.number in 1:length(l.journeys)) {
#} # end big loop


df.x <- read.csv(paste(x.results.dir,"pattern_",l.journeys[x.journey.number]