The goal of rwalkr is to provide APIs to the pedestrian and microclimate 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:
# install.packages("devtools") devtools::install_github("earowang/rwalkr")
There are two APIs available to access hourly 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() 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.
library(rwalkr) start_date <- as.Date("2017-07-01") ped_walk <- melb_walk(from = start_date, to = start_date + 6L) ped_walk #> # A tibble: 10,584 x 5 #> Sensor Date_Time Date Time Count #> <chr> <dttm> <date> <int> <int> #> 1 Bourke Street Mall (North) 2017-07-01 00:00:00 2017-07-01 0 280 #> 2 Bourke Street Mall (South) 2017-07-01 00:00:00 2017-07-01 0 177 #> 3 Melbourne Central 2017-07-01 00:00:00 2017-07-01 0 826 #> 4 Town Hall (West) 2017-07-01 00:00:00 2017-07-01 0 682 #> 5 Princes Bridge 2017-07-01 00:00:00 2017-07-01 0 0 #> # … with 10,579 more rows ped_run <- melb_walk_fast(year = 2016:2017, sensor = NULL) # NULL means all sensors ped_run #> # A tibble: 912,288 x 5 #> Sensor Date_Time Date Time Count #> <chr> <dttm> <date> <int> <int> #> 1 Alfred Place 2016-01-01 00:00:00 2016-01-01 0 NA #> 2 Birrarung Marr 2016-01-01 00:00:00 2016-01-01 0 1405 #> 3 Bourke St-Russell St (West) 2016-01-01 00:00:00 2016-01-01 0 1900 #> 4 Bourke Street Mall (North) 2016-01-01 00:00:00 2016-01-01 0 461 #> 5 Bourke Street Mall (South) 2016-01-01 00:00:00 2016-01-01 0 883 #> # … with 912,283 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))
To access minute by minute directional pedestrian counts for the last hour, please check out the
It’s recommended to include an application token in
melb_walk_fast(app_token = "YOUR-APP-TOKEN"), which you can sign up here.
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.