12 thoughts on “Monthly Challenge – Sofia Air – Solution – Lone Fighter”
1
votes
Giving code is a good initial step, but in order to receive constructive feedback you should write expllanations and put some visualisations. We need argumentation.
Hi Akila, i agree with junior, please try to upload your script as a media file (using Jupyter notebook) so that we can actually understand your code concepts .
Its quite difficult to be constructive based only on a bare R code without output! I can recommend that you use the “Compile Report” function in RStudio (shortcut: Ctrl + Shift + K) and post the results again.
Hi team,
When I selected article, I got a docx document for download. Went through your document file. I will caveat my feedback saying that I don’t R at all. That said, I liked your approach to breaking down the problem and filtering. You did great to filter out anomalous stations from citizen data and filtering data to Sofia only. Have you considered normalizing the data to the Sofia ranges for temperature, humidity and pressure. If I am looking at your older page, let me know, will be glad to come back and look at your latest.
12 thoughts on “Monthly Challenge – Sofia Air – Solution – Lone Fighter”
Giving code is a good initial step, but in order to receive constructive feedback you should write expllanations and put some visualisations. We need argumentation.
Ok, thank you. I will write a report for this.
Your assignments to peer review (and give feedback below the coresponding articles) for week 1 of the Monthly challenge are the following teams:
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-jacob-avila/
https://www.datasciencesociety.net/air-quality-week-1/
https://www.datasciencesociety.net/data-exploration-observations-planning/
Hi Akila, i agree with junior, please try to upload your script as a media file (using Jupyter notebook) so that we can actually understand your code concepts .
Sure, I will provide as soon as possible.
Its quite difficult to be constructive based only on a bare R code without output! I can recommend that you use the “Compile Report” function in RStudio (shortcut: Ctrl + Shift + K) and post the results again.
Sure, I will provide as soon as possible.
Your assignments to peer review (and give feedback below the coresponding articles) for week 2 of the Monthly challenge are the following teams:
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-iseveryonehigh/
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-kiwi-team/
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-dirty-minds/
Please publish your article in a text form, not as attatchment
Your assignments to peer review (and give feedback below the coresponding articles) for week 3 of the Monthly challenge are the following teams:
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-jeremy-desir-weber/
https://www.datasciencesociety.net/air-sofia-pollution-case/
https://www.datasciencesociety.net/the-pumpkins/
Your assignments to peer review (and give feedback below the coresponding articles) for week 4 of the Monthly challenge are the following teams:
https://www.datasciencesociety.net/sofia-air-week-1/
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-newbees/
https://www.datasciencesociety.net/monthly-challenge-sofia-air-solution-kung-fu-panda/
Hi team,
When I selected article, I got a docx document for download. Went through your document file. I will caveat my feedback saying that I don’t R at all. That said, I liked your approach to breaking down the problem and filtering. You did great to filter out anomalous stations from citizen data and filtering data to Sofia only. Have you considered normalizing the data to the Sofia ranges for temperature, humidity and pressure. If I am looking at your older page, let me know, will be glad to come back and look at your latest.
@Kams