Difference between revisions of "IS428-AY2019-20T1 Climate Vizards: Proposal - Others"
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− | [[IS428-AY2019-20T1_Climate_Vizards: | + | [[IS428-AY2019-20T1_Climate_Vizards:_Proposal_-_Others| <font color="#FFFFFF">Proposal</font>]] |
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! Challenges !! Proposed solution | ! Challenges !! Proposed solution | ||
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+ | | Understanding deeper levels of data visualisations to provide users with better insights on climate changes|| | ||
+ | Research more on different types of R packages | ||
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+ | | Creating interactive storyboards that can provide a holistic overview of the cause-and-effect of climate conditions. For example, being able to filter and zoom on-demand. || Continuous prototyping of the ideal dashboard | ||
+ | |- | ||
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+ | | Using R and Rshiny to design the desired plots as close as we can to the ideal storyboard || Diligently complete the lessons in datacamp to understand how different attributes in different R packages function | ||
+ | |- | ||
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| Building complex visualisation such as spatial interpolation || Read up early and start trying at an earlier stage of the project as it will require time | | Building complex visualisation such as spatial interpolation || Read up early and start trying at an earlier stage of the project as it will require time | ||
|- | |- | ||
− | | Getting | + | |
+ | | Getting Singapore's temperature and rainfall data|| Diligently download the data by month and location | ||
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Latest revision as of 20:44, 13 October 2019
Climate Vizards
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Timeline
Challenges
Challenges | Proposed solution |
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Understanding deeper levels of data visualisations to provide users with better insights on climate changes |
Research more on different types of R packages |
Creating interactive storyboards that can provide a holistic overview of the cause-and-effect of climate conditions. For example, being able to filter and zoom on-demand. | Continuous prototyping of the ideal dashboard |
Using R and Rshiny to design the desired plots as close as we can to the ideal storyboard | Diligently complete the lessons in datacamp to understand how different attributes in different R packages function |
Building complex visualisation such as spatial interpolation | Read up early and start trying at an earlier stage of the project as it will require time |
Getting Singapore's temperature and rainfall data | Diligently download the data by month and location |