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:
# 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:
To access minute by minute directional pedestrian counts for the last hour, please check out the
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.