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R package to provide API to Melbourne pedestrian data
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earowang/rwalkr
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The goal ofrwalkr is to provide APIs to the pedestrian andmicroclimate data from the City of Melbourne in tidy data form.
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")
There are two APIs available to access hourly Melbourne pedestrian data:compedapi andSocrata. The former drives themelb_walk() function,where counts are uploaded on a daily basis; the latter powers themelb_walk_fast() function, where counts are uploaded on a monthlybasis. Given the function names, the functionmelb_walk_fast() pullsthe data at a much faster speed thanmelb_walk().
The functionmelb_walk() specifies the starting and ending dates to bepulled, whereasmelb_walk_fast() requires years to define the timeframe. 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 rowsped_run<- melb_walk_fast(year=2016:2017,sensor=NULL)# NULL means all sensorsped_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 settingna.rm = TRUE in both functions, missing values will be removed.
Here’s an example to useggplot2 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 lasthour, please check out themelb_walk_directional().
It’s recommended to include an application token inmelb_walk_fast(app_token = "YOUR-APP-TOKEN"), which you can sign uphere.
The functionmelb_shine() launches a shiny app to give a glimpse ofthe data. It provides two basic plots: one is an overlaying time seriesplot, and the other is a dot plot indicating missing values. Below is ascreen-shot of the shiny app.
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R package to provide API to Melbourne pedestrian data