Difference between revisions of "Finding relations between variables in time series"
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| − | + | A frequent need when engaging in personal science is finding relationships between different variables across a time series, an example could be the question "does eating chocolate improve focus?". | |
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| − | == | + | == How does one find relations between variables == |
| + | To do this you need to have your data parsed, cleaned, all in one place, and ideally even visualized. The next step is to find relations between variables. Some people do this by themselves, by using programming languages such as R and Python in notebooks or apps. Coding platforms such as the notebooks on [[Open Humans]], Kaggle or GitHub can help, but it frequently | ||
| + | requires technical skills. | ||
| − | + | There are also a number of tools or apps that can semi-automatically perform these correlations and help in doing the analyses. | |
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| − | ==== | + | == List of less technical tools == |
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| − | ==== [ | + | ==== [[Open Humans]] and their Personal Analysis notebooks ==== |
| − | + | Open Humans provides a library of notebooks that can be used to visualize data across data sources and find relations between different variables. It also supports the upload of generic data files through the [https://blog.openhumans.org/2021/05/17/file-upload-data-types/ File Uploader]. | |
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| − | [https:// | + | ==== [[Zenobase]] ==== |
| + | [https://blog.zenobase.com/post/81497604762 Zenobase] can test correlations based on user-specified questions. User must configure lag, regression method and aggregation method using a UI. Powerful filtering tools too. | ||
| − | + | ==== [[Exist.io]] ==== | |
| + | From the [https://exist.io/ Exist.io] main site :"Which habits go together? Correlations are the most powerful part of Exist. By combining your data, we can answer questions like: “What makes me happiest?”, “What can I do to be more active?”, “When am I most productive?”" | ||
| − | [https:// | + | ==== DailyViz notebook ==== |
| + | The [https://dailyvis.com/posts/self-analysis-with-my-quantified-self-data/ DailyViz blog] gives an example of finding all relevant correlations in a large, personal self-tracking data set. The blog post show the interactive analysis of their data with some surprising results. The notebook [https://dailyvis.com/vis/compare/demo/ is available]. | ||
| − | + | ==== [[Habitdash]] ==== | |
| − | searches for hidden patterns. | + | [https://habitdash.com/ Habitdash]'s ''Automatic data analysis searches for hidden patterns'' to find relationships between activity, sleep, weight and other habits. |
| − | + | ==== [[Optimized app]] ==== | |
| − | + | [http://optimized-app.com/ Optimized] claims to do ''"automatic correlation mining"'' | |
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| − | http://optimized-app.com/ | ||
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| − | "automatic correlation mining" | ||
== List of very technical tools == | == List of very technical tools == | ||
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Revision as of 08:54, 2 December 2021
A frequent need when engaging in personal science is finding relationships between different variables across a time series, an example could be the question "does eating chocolate improve focus?".
How does one find relations between variables
To do this you need to have your data parsed, cleaned, all in one place, and ideally even visualized. The next step is to find relations between variables. Some people do this by themselves, by using programming languages such as R and Python in notebooks or apps. Coding platforms such as the notebooks on Open Humans, Kaggle or GitHub can help, but it frequently requires technical skills.
There are also a number of tools or apps that can semi-automatically perform these correlations and help in doing the analyses.
List of less technical tools
Open Humans and their Personal Analysis notebooks
Open Humans provides a library of notebooks that can be used to visualize data across data sources and find relations between different variables. It also supports the upload of generic data files through the File Uploader.
Zenobase
Zenobase can test correlations based on user-specified questions. User must configure lag, regression method and aggregation method using a UI. Powerful filtering tools too.
Exist.io
From the Exist.io main site :"Which habits go together? Correlations are the most powerful part of Exist. By combining your data, we can answer questions like: “What makes me happiest?”, “What can I do to be more active?”, “When am I most productive?”"
DailyViz notebook
The DailyViz blog gives an example of finding all relevant correlations in a large, personal self-tracking data set. The blog post show the interactive analysis of their data with some surprising results. The notebook is available.
Habitdash
Habitdash's Automatic data analysis searches for hidden patterns to find relationships between activity, sleep, weight and other habits.
Optimized app
Optimized claims to do "automatic correlation mining"
List of very technical tools
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