https://github.com/earowang/rwalkr

R package to provide API to Melbourne pedestrian data
https://github.com/earowang/rwalkr

r web-scraper

Last synced: 3 months ago
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R package to provide API to Melbourne pedestrian data

• Host: GitHub
• URL: https://github.com/earowang/rwalkr
• Owner: earowang
• License: other
• Created: 2017-07-31T10:53:07.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-11-13T01:41:32.000Z (about 2 years ago)
• Last Synced: 2023-08-06T04:44:46.489Z (over 1 year ago)
• Topics: r, web-scraper
• Language: R
• Homepage: https://pkg.earo.me/rwalkr
• Size: 7.51 MB
• Stars: 12
• Watchers: 1
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.Rmd
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
---

output:

  github_document:

    html_preview: false

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE, message = FALSE,

  comment = "#>",

  fig.path = "man/figures/"

)

options(tibble.print_min = 5)

```

[![Travis-CI Build Status](https://travis-ci.org/earowang/rwalkr.svg?branch=master)](https://travis-ci.org/earowang/rwalkr)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/rwalkr)](https://cran.r-project.org/package=rwalkr)

[![Downloads](http://cranlogs.r-pkg.org/badges/rwalkr?color=brightgreen)](https://cran.r-project.org/package=rwalkr)

# rwalkr

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

## Installation

You could install the stable version from CRAN:

```{r, eval = FALSE}

install.packages("rwalkr")

```

You could install the development version from Github using:

```{r, eval = FALSE}

# install.packages("devtools")

devtools::install_github("earowang/rwalkr")

```

## Usage

### APIs

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()`.

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.

```{r data}

library(rwalkr)

start_date <- as.Date("2017-07-01")

ped_walk <- melb_walk(from = start_date, to = start_date + 6L)

ped_walk

ped_run <- melb_walk_fast(year = 2016:2017, sensor = NULL) # NULL means all sensors

ped_run

```

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:

```{r plot, fig.height = 4}

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 `melb_walk_directional()`.

It's recommended to include an application token in `melb_walk_fast(app_token = "YOUR-APP-TOKEN")`, which you can sign up [here](https://data.melbourne.vic.gov.au/profile/app_tokens).

### 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.

![](man/figures/shiny.png)