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Can you share more about your process? I'm very interested in learning more. How do you track your daily data points?

by sh_123 6y ago

tuatoru 6y ago
There are a lot of these comments on HN, that baldly ask for more information without contributing anything or providing any context. So many that I suspect a bot.
jvanderbot 6y ago
I have a text file in a known location. It's backed up on git. I can open and edit that file quickly using bash. From within VIM I can quickly commit to reduce the # keystrokes. More details: https://github.com/jodavaho/bashlog#git-integration https://github.com/jodavaho/bashlog#git-integration (Really though, you can just save as an environment variable your file location) The text file is structured with space-delimited text, like this: https://github.com/dkogan/vnlog https://github.com/dkogan/vnlog My current structure for tracking a contribution to an OKR item (e.g. "meditate") by date (e.g., 2020-01-15) and amount (e.g., "1") is simply: "# date item amount" Easy to open, easy to plop down today's date and the item / amount. Save multiple date/item entries and add them inscripts ... Easy to save. Done. The work came in building stuff to plot it: I have other files that link "items" to goals or other metadata like categories, etc. Like a relational database, but easily edited in text. For example, to make a link from daily items to Key Results "# item okr" The OKR categories are usually Fighting (I box), Tech, Mental health, and Social, but YMMV Another example is "workout-mapping.vnl" with structure: "# item muscle-group factor" etc etc. vnlog has nice vnl-join commands that quickly build the table and all linked metadata and output formatted text (one command). Then reading that in R is easy ... ``` data <- read.table('../2020-Q4-OKR.vnl',skip=2,stringsAsFactors = FALSE) ``` Aggregating by date is easy: ``` data.agg<- aggregate(x=data$Amount,by=list(data$Date),FUN=sum) ``` Then plot using ggplot2. Examples from 2020 Q4 (just ran the numbers yesterday!) https://josh.vanderhook.info/media/okr.png https://josh.vanderhook.info/media/okr.png https://josh.vanderhook.info/media/workouts.png https://josh.vanderhook.info/media/workouts.png https://josh.vanderhook.info/media/mood.png https://josh.vanderhook.info/media/mood.png
- thelittleone 6y ago
  Thanks for sharing. Would you consider writing a blog post on your process?

Can you share more about your process? I'm very interested in learning more. How do you track your daily data points?

by sh_123 6y ago

Can you share more about your process? I'm very interested in learning more. How do you track your daily data points?

There are a lot of these comments on HN, that baldly ask for more information without contributing anything or providing any context. So many that I suspect a bot.

jvanderbot 6y ago

I have a text file in a known location. It's backed up on git. I can open and edit that file quickly using bash. From within VIM I can quickly commit to reduce the # keystrokes. More details: https://github.com/jodavaho/bashlog#git-integration https://github.com/jodavaho/bashlog#git-integration (Really though, you can just save as an environment variable your file location) The text file is structured with space-delimited text, like this: https://github.com/dkogan/vnlog https://github.com/dkogan/vnlog My current structure for tracking a contribution to an OKR item (e.g. "meditate") by date (e.g., 2020-01-15) and amount (e.g., "1") is simply: "# date item amount" Easy to open, easy to plop down today's date and the item / amount. Save multiple date/item entries and add them inscripts ... Easy to save. Done. The work came in building stuff to plot it: I have other files that link "items" to goals or other metadata like categories, etc. Like a relational database, but easily edited in text. For example, to make a link from daily items to Key Results "# item okr" The OKR categories are usually Fighting (I box), Tech, Mental health, and Social, but YMMV Another example is "workout-mapping.vnl" with structure: "# item muscle-group factor" etc etc. vnlog has nice vnl-join commands that quickly build the table and all linked metadata and output formatted text (one command). Then reading that in R is easy ... ``` data <- read.table('../2020-Q4-OKR.vnl',skip=2,stringsAsFactors = FALSE) ``` Aggregating by date is easy: ``` data.agg<- aggregate(x=data$Amount,by=list(data$Date),FUN=sum) ``` Then plot using ggplot2. Examples from 2020 Q4 (just ran the numbers yesterday!) https://josh.vanderhook.info/media/okr.png https://josh.vanderhook.info/media/okr.png https://josh.vanderhook.info/media/workouts.png https://josh.vanderhook.info/media/workouts.png https://josh.vanderhook.info/media/mood.png https://josh.vanderhook.info/media/mood.png

thelittleone 6y ago
Thanks for sharing. Would you consider writing a blog post on your process?