Using R to visualize Arduino-iot weather sensor data
Last updated: 2022-09-30
Checks: 7 0
Knit directory: Rduinoiot-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 98f9bfc | FlavioLeccese92 | 2022-09-30 | environment-setup |
| html | 3e4ed7a | FlavioLeccese92 | 2022-09-30 | Build site. |
| Rmd | a3cf2de | FlavioLeccese92 | 2022-09-30 | wflow_publish(c(“analysis/index.Rmd”, “dashboard/weather-report.Rmd”)) |
| html | e397546 | FlavioLeccese92 | 2022-09-30 | Build site. |
| Rmd | e86011b | FlavioLeccese92 | 2022-09-30 | index schema |
| html | e86011b | FlavioLeccese92 | 2022-09-30 | index schema |
| html | f8456f2 | FlavioLeccese92 | 2022-09-27 | push |
| html | a729819 | FlavioLeccese92 | 2022-09-25 | rearrange site |
| html | 2f3dcbb | FlavioLeccese92 | 2022-09-25 | test |
| html | 50dcd8c | FlavioLeccese92 | 2022-09-24 | index.html update |
| html | 71b5efd | FlavioLeccese92 | 2022-09-24 | Build site. |
| Rmd | e44daf0 | FlavioLeccese92 | 2022-09-24 | Publish the initial files for myproject |
| Rmd | e1dee4f | FlavioLeccese92 | 2022-09-24 | commit new index |
| html | e1dee4f | FlavioLeccese92 | 2022-09-24 | commit new index |
| Rmd | 9a79767 | FlavioLeccese92 | 2022-09-24 | merge |
| Rmd | 555d36a | FlavioLeccese92 | 2022-09-24 | the first commit |
| html | 555d36a | FlavioLeccese92 | 2022-09-24 | the first commit |
Motivation
I have always been fascinated by the potential of open-source tools interaction, among which R (long live R!) and lately Arduino.
For those who don’t know, Arduino is an open-source hardware and software company which designs and produces microcontroller kits for the deployment of digital services, both at a professional, hobby and educational level. Furthermore, the community is very active and smart.
Here you can find many projects, including Home Automation, Robotics and even more.
To me as a data scientist with a statistical background, electronics is a black box, for this reason I chose to a 99% plug-and-play solution: Arduino Oplà IoT Kit.
If you want to know more, here’s a video introducing you the kit:
The kit comes with 4 integrated sensor measuring Humidity, Pressure, Temperature and Light.
Starting to collect data from these sensor is very simple: you just need to deploy an appropriate sketch to the mother board, which can be done through the dedicated IDE or a guided procedure.
Once everything is set up, you will have data flowing from sensors to the cloud and visibile via a dashboard hosted on Arduino Cloud, free for 12 months with the kit purchase. Here you can access mine to have an idea.
Data are stored into a cloud database and retrival of data is possible throught an API which can be queried, guess what…
R!
Goal
The goal of the project is to deploy a dashboard on github which shows Arduino sensor data and it is updated every 15 minutes. Documentation (and this very document you are reading) must automatically versionised at every commit and every version easily accessible. Every software used must be for free.
Project setup
The R ecosystem is constantly growing, adding new amazing productive tools such as Workflowr and Flexdashboard, which perfectly exploit Github actions in order to automatize their scope.
Furthermore, in order to make it easier to access Arduino Iot Cloud API , I developed an R package through pkgdown. The package is called Rduinoiot and can be found on CRAN.
Workflowr
Workflowr is an R package that supports you in creating an organized, reproducible and shareable project.
Practically speaking, when opening RStudio to start a new project, if you have Worflowr installed, you will see a new option. By chosing it, you are creating automatically a git-versioned project with organized subdirectories.
Then you can create rmarkdown analyses and make them accessible by a customizable website hosted for free on Github or Gitlab.
The two type of files relevant for a workflowr site are *.Rmds and _site.yml.
Analysis files *.Rmd
Any type of rmarkdown file can be added to your site. Only restriction, of course, is that your files cannot be shiny markdowns since they need a server to process live user interactions and in our setup we do not have it, so avoid runtime: shiny. Rmarkdown files are static html and usually not optimal for reporting analysis of data which require frequent updates. Additionally, for my porpuse an ordinary markdown would not have satisfied my graphical obsession.
For these two reasons, I decided to go with flexdashboard (to obtain a catchy but static report) + github actions (to update the static HTML with new data every 15 minutes). But we will talk about it later in this document.
The most important part of the rmarkdown is the header, which will be automatically generated by workflowr and is customizable:
---
title: "Using R to visualize Arduino-iot weather sensor data"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: true
toc_float: yes
theme: flatly
highlight: textmate
css: style.css
editor_options:
chunk_output_type: console
---Analysis files *.Rmd need to be stored under analysis/ folder.
Once you are done with your analysis, you can build your site locally:
wflow_build()If not specified, workflowr will add to the local site each of your analysis in the analysis/ folder.
Then, if you are ready to put it online, simply choose which analysis you want to publish and run the following:
wflow_publish(c("analysis/index.Rmd", "analysis/license.Rmd"))Structure site file _site.yml
Flexdashboard
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 compiler_4.1.0 pillar_1.8.1 bslib_0.4.0
[5] later_1.3.0 git2r_0.30.1 jquerylib_0.1.4 tools_4.1.0
[9] getPass_0.2-2 digest_0.6.29 jsonlite_1.8.0 evaluate_0.15
[13] tibble_3.1.8 lifecycle_1.0.2 pkgconfig_2.0.3 rlang_1.0.5
[17] cli_3.4.0 rstudioapi_0.13 yaml_2.3.5 xfun_0.30
[21] fastmap_1.1.0 httr_1.4.4 stringr_1.4.0 knitr_1.37
[25] fs_1.5.2 vctrs_0.4.1 sass_0.4.2 rprojroot_2.0.2
[29] glue_1.6.2 R6_2.5.1 processx_3.5.2 fansi_1.0.3
[33] rmarkdown_2.13 callr_3.7.0 magrittr_2.0.3 whisker_0.4
[37] ps_1.6.0 promises_1.2.0.1 htmltools_0.5.3 httpuv_1.6.6
[41] utf8_1.2.2 stringi_1.7.6 cachem_1.0.6