The city of Melbourne

• Sourced from Melbourne open data portal
• Access the pedestrian data through the rwalkr package
• Up-to-date data till 2017-12-09

#> # A tibble: 378,024 x 6
#>                            Sensor  Date_Time       Date
#>                             <chr>     <dttm>     <date>
#>  1                   Alfred Place 2017-01-01 2017-01-01
#>  2           Australia on Collins 2017-01-01 2017-01-01
#>  3                 Birrarung Marr 2017-01-01 2017-01-01
#>  4    Bourke St-Russell St (West) 2017-01-01 2017-01-01
#>  5     Bourke Street Mall (North) 2017-01-01 2017-01-01
#>  6     Bourke Street Mall (South) 2017-01-01 2017-01-01
#>  7 Chinatown-Lt Bourke St (South) 2017-01-01 2017-01-01
#>  8  Chinatown-Swanston St (North) 2017-01-01 2017-01-01
#>  9                    City Square 2017-01-01 2017-01-01
#> 10          Collins Place (North) 2017-01-01 2017-01-01
#> # ... with 378,014 more rows, and 3 more variables:
#> #   Time <int>, Count <int>, Holiday <lgl>

• Southbank
• Southern Cross Station
• Victoria Market

The grid position for any day in the month is given by

$$\begin{array}{cc}i& =\lceil \left(g\mathrm{mod}35\right)/7\rceil \\ j& =g\mathrm{mod}7.\end{array}$$

Let $h$ and $c$ be the scaled hour and count, respectively, then the final coordinates are given by:

$$\begin{array}{cc}x& =j+h\\ y& =i-c.\end{array}$$

The frame_calendar() function

frame_calendar(
  data, x, y, date, calendar = "monthly", dir = "h",
  sunday = FALSE, nrow = NULL, ncol = NULL, polar = FALSE,
  scale = "fixed", width = 0.95, height = 0.95
)

• x, y: a unquoted (or bare) variable mapping to x and y axis.
• date: a Date variable mapping to dates in the calendar.
• calendar: type of calendar. "monthly", "weekly", "daily".
• sunday: FALSE indicating to starting with Monday in a week, or TRUE for Sunday.
• nrow, ncol: number of rows and columns defined for "monthly" calendar layout.
• scale: "fixed", "free", "free_wday", and "free_mday".

Rearrange the data

sx_cal <- pedestrian %>% 
  filter(Sensor == "Southern Cross Station") %>% 
  frame_calendar(x = Time, y = Count, date = Date)

#> # A tibble: 8,232 x 5
#>     Time Count       Date     .Time    .Count
#>  * <int> <int>     <date>     <dbl>     <dbl>
#>  1     0  1335 2017-01-01 0.2583333 0.9860202
#>  2     1   463 2017-01-01 0.2598631 0.9721692
#>  3     2   219 2017-01-01 0.2613929 0.9682934
#>  4     3   122 2017-01-01 0.2629227 0.9667527
#>  5     4    21 2017-01-01 0.2644525 0.9651484
#>  6     5    28 2017-01-01 0.2659823 0.9652596
#>  7     6    26 2017-01-01 0.2675121 0.9652278
#>  8     7    35 2017-01-01 0.2690419 0.9653708
#>  9     8    55 2017-01-01 0.2705717 0.9656884
#> 10     9    82 2017-01-01 0.2721014 0.9661173
#> # ... with 8,222 more rows

Initialise the plot with ggplot2

p1_sx <- sx_cal %>% 
  ggplot(aes(x = .Time, y = .Count, group = Date)) +
  geom_line()

Add reference lines and labels

prettify(p1_sx)

Colour mapping as usual

p2_sx <- sx_cal %>%
  ggplot(aes(x = .Time, y = .Count, colour = Holiday, group = Date)) +
  geom_line()
prettify(p2_sx)

Zoom in to March

Naturally work with the group_by()

facet_cal <- subdat %>% 
  group_by(Sensor) %>% 
  frame_calendar(
    x = Time, y = Count, date = Date, nrow = 2
  )
p_facet <- facet_cal %>% 
  ggplot(aes(x = .Time, y = .Count, group = Date)) +
  geom_line(aes(colour = Sensor)) +
  facet_grid(Sensor ~ .) +
  scale_colour_brewer(palette = "Dark2")
prettify(p_facet, label = NULL)

Faceted calendar plots