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rwalkr

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

Installation

You could install the stable version from CRAN:

install.packages("rwalkr")

You could install the development version from Github using:

# install.packages("devtools")
devtools::install_github("earowang/rwalkr")

Usage

APIs

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

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

library(rwalkr)
start_date <- as.Date("2017-07-01")
# tweak = TRUE gives the consistent sensors to the ones from run_melb().
# By default it's FALSE for back compatibility.
ped_walk <- walk_melb(from = start_date, to = start_date + 6L, tweak = TRUE)
ped_walk
#> # A tibble: 7,224 x 5
#>                  Sensor  Date_Time       Date  Time Count
#>                   <chr>     <dttm>     <date> <int> <int>
#> 1         State Library 2017-07-01 2017-07-01     0   334
#> 2 Collins Place (South) 2017-07-01 2017-07-01     0    82
#> 3 Collins Place (North) 2017-07-01 2017-07-01     0    51
#> 4     Flagstaff Station 2017-07-01 2017-07-01     0     0
#> 5     Melbourne Central 2017-07-01 2017-07-01     0   826
#> # ... with 7,219 more rows
ped_run <- run_melb(year = 2016:2017, sensor = NULL) # NULL means all sensors
ped_run
#> # A tibble: 705,502 x 5
#>                        Sensor  Date_Time       Date  Time Count
#>                         <chr>     <dttm>     <date> <int> <int>
#> 1                Alfred Place 2016-01-01 2016-01-01     0    NA
#> 2        Australia on Collins 2016-01-01 2016-01-01     0  1081
#> 3              Birrarung Marr 2016-01-01 2016-01-01     0  1405
#> 4 Bourke St-Russell St (West) 2016-01-01 2016-01-01     0  1900
#> 5  Bourke Street Mall (North) 2016-01-01 2016-01-01     0   461
#> # ... with 7.055e+05 more rows

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:

library(ggplot2)
ggplot(data = subset(ped_walk, Sensor == "Melbourne Central")) +
  geom_line(aes(x = Date_Time, y = Count))

It’s worth noting that some sensor names are coded differently by these two APIs. The argument tweak = TRUE ensures the sensor names returned by walk_melb() consistent to the ones in run_melb() and pull_sensor(), both of which are supported by Socrata. The dictionary for checking sensor names between two functions is available through lookup_sensor().

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

Shiny app

The function shine_melb() 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.