Assign 3

This Assignment will be about assessing cyclists behavior on Melbourne Bicycle tracks / routes in and around the Melbourne CBD which may include neighboring suburbs

All data in the form of zipped csv or excel is accessed from the stated site below

https://www.data.vic.gov.au/data/dataset/bicycle-volume-and-speed

Discriptions to the above tables / data can be accessed from the site stated below

http://data.vicroads.vic.gov.au/Metadata/VicRoads%20Bicycle%20Volume%20and%20Speed%20Data.html

In Particular the Data relates to :

VicRoads Bicycle Volume and Speed Data Bicycle Volume and speed Cycling flows in both directions recorded at 42 off-road counter sites and 4 on-road counter sites .

Last Updated 15/05/2017

Related Articles

https://www.racv.com.au/membership/member-benefits/royalauto/motoring/cycling/melbourne-bicycle-revolution-is-a-tale-of-two-cyclists.html

URL to live interactive

https://danno.shinyapps.io/assign3f/

URL to RPubs

http://rpubs.com/fast_Eddie/432401

IND_D208_X6411_16 <- read.csv("C:/Users/dan/Desktop/a Vis 3/site x6411 2106 -17 -18/IND_D208_X6411_16.csv", stringsAsFactors = FALSE)
#View(IND_D208_X6411_16)
IND_D208_X6411_17 <- read_csv("C:/Users/dan/Desktop/a Vis 3/site x6411 2106 -17 -18/IND_D208_X6411_17.csv")

## Parsed with column specification:
## cols(
##   DATA_TYPE = col_character(),
##   TIS_DATA_REQUEST = col_integer(),
##   SITE_XN_ROUTE = col_integer(),
##   LOC_LEG = col_integer(),
##   DATE = col_character(),
##   TIME = col_time(format = ""),
##   CLASS = col_integer(),
##   LANE = col_integer(),
##   SPEED = col_double(),
##   WHEELBASE = col_double(),
##   HEADWAY = col_double(),
##   GAP = col_double(),
##   AXLE = col_integer(),
##   AXLE_GROUPING = col_integer(),
##   RHO = col_double(),
##   VEHICLE = col_character(),
##   DIRECTION = col_character()
## )

#View(IND_D208_X6411_17)
IND_D208_X6411_18 <- read_csv("C:/Users/dan/Desktop/a Vis 3/site x6411 2106 -17 -18/IND_D208_X6411_18.csv")

## Parsed with column specification:
## cols(
##   DATA_TYPE = col_character(),
##   TIS_DATA_REQUEST = col_integer(),
##   SITE_XN_ROUTE = col_integer(),
##   LOC_LEG = col_integer(),
##   DATE = col_character(),
##   TIME = col_time(format = ""),
##   CLASS = col_integer(),
##   LANE = col_integer(),
##   SPEED = col_double(),
##   WHEELBASE = col_double(),
##   HEADWAY = col_double(),
##   GAP = col_double(),
##   AXLE = col_integer(),
##   AXLE_GROUPING = col_integer(),
##   RHO = col_double(),
##   VEHICLE = col_character(),
##   DIRECTION = col_character()
## )

#View(IND_D208_X6411_18)

I’m going to choose Date, Time, speed, Gap, and Direction as the Variables I will be looking at

The Question is how many people , in what direction and at what time for site 6411

#   Pre Process and Extract Section / columns / 

library(hms)

###############################     2016

bike16 <- IND_D208_X6411_16[ c(6, 9, 12,17 )]  #c(5, 6, 9, 12, 17)
bike16$GAP <- round((bike16$GAP / 60), 0)
bike16$SPEED <- round(bike16$SPEED, 0)

bike16$TIME <-  as.hms(bike16$TIME)  # Change to same str as yrs 17, 18 with package hms


###############################     2017

bike17 <- IND_D208_X6411_17[ c(6, 9, 12, 17 )] 
bike17$GAP <- round((bike17$GAP / 60), 0)
bike17$SPEED <- round(bike17$SPEED, 0)


###############################    2018


bike18 <- IND_D208_X6411_18[ c(6, 9, 12, 17 )] 
bike18$GAP <- round((bike18$GAP / 60), 0)
bike18$SPEED <- round(bike18$SPEED, 0)

#########################

## Separate into directions


bike18 <- bike18 %>% mutate( ifelse(bike18$DIRECTION == 'S', "1", "0 "))
bike18 <- bike18 %>% mutate( ifelse(bike18$DIRECTION == 'N', "1", "0 "))  
#View(bike18) 

##  Change col Names

#colnames(bike18)[4] <- "Combined"
colnames(bike18)[5] <- "South"
colnames(bike18)[6] <- "North"
#colnames(bike18)


############################
# n = sum(is.na(bike16))
# n
# n1 = sum(is.na(bike17))
# n1
# n2 = sum(is.na(bike18))
# n2
#View(bike18)
# str(bike16)
# str(bike17)
#str(bike18)

## Single file Shiny app.R ------------------ working 

# Load packages and prepare data

#library(ggplot2)
#library(shiny)
#library(dplyr)
#library(lubridate)
#library(htmlwidgets)

# Assign server function
#library(plotly)  
    
server <- function(input, output) {

  output$Plot3 <- renderPlot({
   ggplot(data = bike16, aes(x = TIME, y = get(input$var1))) +  
      geom_count( colour="#999999") + labs(y = input$var) +
      labs( 
       x="Time = 24 hrs" 
          )
    
  })
  
  output$Plot2 <- renderPlot({
   ggplot(data = bike17, aes(x = TIME, y = get(input$var2))) +    #"#56B4E9"
      geom_count( colour="#FF0099") + labs(y = input$var) +
      labs( 
       x="Time = 24 hrs" 
          )   
    
  })  
  
   output$Plot1 <- renderPlot({
   ggplot(data = bike18, aes(x = TIME, y = get(input$var3))) +
      geom_count( colour="#0072B2") + labs(y = input$var) +
      labs( 
       x="Time = 24 hrs" 
          )  
  })
  
}

# Create ui

ui <- fluidPage(
  titlePanel("Bike Data for Melbourne : Site 6411"),
  sidebarLayout(
    sidebarPanel(
      selectInput("var1", "Bike Path Traffic :: 28/12/2015 - 3/1/2016:: Top", colnames(bike18[2:4]), 
                  selected = colnames(bike18[3])),
     
      selectInput("var2", "Bike Path Traffic :: 18/9/2017 - 25/9/2017:: Middle", colnames(bike17[2:4]), 
                  selected = colnames(bike17[3])),
      
      selectInput("var3", "Bike Path Traffic :: 15/1/2018 - 21/1/2018:: Bottom ", colnames(bike16[2:4]), 
                  selected = colnames(bike16[3])), 
                  #checkboxInput("var1n", "North", FALSE),
                    # actionButton("go", "North"),
                    # #numericInput("n", "n", 50),
                    # plotOutput("output$Plot1n"),
      
      HTML(
     paste('<br/>',   #'<br/>',
      h1("Bike Path Usuage. \n"),'<br/>',                   ##  br/ is html \n equivalent
      h3("Speed is in km/hr. \n") ,                   #'<br/>',
      h3("Gap is the time between each cyclist \n"),  #'<br/>',
      h3("measured in minutes. \n"),                  #'<br/>',
      h3("Directions are North or South. \n"),'<br/>' ,  ##  h1-6 is text height
      h4("Time and n are accumulated values per graph. \n"),'<br/>' ,
      h4("Time = 24hrs  \n"),
      h4("n    = number of people (averaged)  \n")
      ))
      ),
               
    

    
    
    mainPanel(plotOutput("Plot3", height=260),
              plotOutput("Plot2", height=260),
              plotOutput("Plot1", height=260)
               )))
              

## Deploy app

#shinyApp(ui = ui, server = server )  ## this below code corrected from half page to full page

shinyApp(ui = ui, server = server, options = list(height = 1080)) ## needed this for the deploy

Shiny applications not supported in static R Markdown documents

Conclusion

This was an interesting and in the end somewhat satisfying project to complete. The data itself shows an increasing trend towards cycle usage which can only be a good thing, it also infers that as the inner city population grows cycle paths will need to grow also. It is my belief these paths should be kept away from interactions with road traffic or at least minimized where ever possible. A cleaner greener more vibrant population would be an achievement certainly worth aiming for. What was a surprise although not really evident in large numbers was a slight increase in bike usage which included volume and speed, it seems as though bikes are getting faster or perhaps the cycle population is getting fitter ? Unfortunately the visual once deployed to shiny isn’t as clear as it is when being run on my pc using rmd, it does though I believe show an increasing usage of these paths.

The only downside to this last Assign for me was not being able to integrate a plotly hover thing over the data, I’m sure its do-able however in all the attempts I made to find a way, I just couldn’t get there.

References / Citations

As mentioned at the start of this Assignment, all data was accessed from the above mentioned sites.