[!WARNING]
This package is highly experimental and is still a WIP. Expect uncomplete features, frequent breaks, uncomplete documentation and changes in the API.
The goal of OSMdashboard is to create interactive dashboards that visualise OSM-data locally and just by filling a csv file.

Installation
You can install the development version of OSMdashboard from GitHub with:
# install.packages("devtools")
devtools::install_github("WarwickCIM/OSMdashboard")
Example
You can easily create a dashboard displaying group contributions by:
-
Create a template running the code below: ``` r # Create a template OSMdashboard::create_dashboard(“my_folder”)
```
Edit
data/group_info.csv
and replace the default values, keeping the column names.Edit
data/group_users.csv
and replace<demo_user>
with an actual OSM username. Add as many rows as needed, but keep the column name. New columns will be ignored.Run
data_retrieval.R
to retrieve all the data needed for the dashboard.Render
dashboard.qmd
to generate the dashboard. To do so, you will need quarto installed (see instructions) and then either:Run the following command in the terminal from the folder:
bash quarto render dashboard.qmd
From RStudio click on render
Contributors
This project welcomes any type of contributions, not just coding. It follows the all-contributors specification as a way to recognise that, while addressing Katherine d’Ignazio and Lauren F Klein’s Principle #7 of Data Feminism is to Make Labor Visible:
Make labor visible: “Starting with questions of data provenance helps to credit the bodies that make visualization possible – the bodies that collect the data, that digitize them, that clean them, and that maintain them. However, most data provenance research focuses on technical rather than human points of origination and integration [66]. With its emphasis on under-valued forms of labor, a feminist approach to visualization can help to render visible the bodies that shape and care for data at every stage of the process. This relates to the concept of provenance rhetoric [44] in which authors of narrative visualizations cite data sources and methods which may help build credibility with the audience.” (Ignazio and Klein, 2016, p. 3)
![]() ESRC Digital Good Network 💵 |
Carlos Cámara 💻 🤔 🎨 🚇 🔬 |
timothymonteath 🤔 🔬 |
Selene Yang 🤔 |
silvira 🤔 |
malecanclini 🤔 |
Geochicas 📣 |