The goal of rwalkr is to provide APIs to the pedestrian data from the City of Melbourne in tidy data form.


You could install the stable version from CRAN:

You could install the development version from Github using:



There are two APIs available to access Melbourne pedestrian data: compedapi and Socrata. The former drives the melb_walk() function, where counts are uploaded on a daily basis; the latter powers the melb_walk_fast() function, where counts are uploaded on a monthly basis. Given the function names, the function melb_walk_fast() pulls the data at a much faster speed than melb_walk().

The function melb_walk() specifies the starting and ending dates to be pulled, whereas melb_walk_fast() requires years to define the time frame. If a selection of sensors are of interest, melb_walk_fast() provides the flexibility for sensor choices.

There are missing values (i.e. NA) in the dataset. By setting na.rm = TRUE in both functions, missing values will be removed.

Here’s an example to use ggplot2 for visualisation:

ggplot(data = subset(ped_walk, Sensor == "Melbourne Central")) +
  geom_line(aes(x = Date_Time, y = Count))

The dictionary for checking sensor names between two functions is available through lookup_sensor().

It’s recommended to include an application token in melb_walk_fast(app_token = "YOUR-APP-TOKEN"), which you can sign up here.

Shiny app

The function melb_shine() launches a shiny app to give a glimpse of the data. It provides two basic plots: one is an overlaying time series plot, and the other is a dot plot indicating missing values. Below is a screen-shot of the shiny app.