My EmacsConf presentation will stream in a few hours, and someone's probably going to ask where I find the time for all of this. =) So I dug through my time logs (a custom system I wrote in 2012 with Ruby on Rails; has an Emacs interface, of course) and came up with this table of hours I logged in my Discretionary - Productive - Emacs category over the past 11 years.

Notes:

• Data collection in this system started in 2012.
• Some months look like they have under-collected data, that's fine.
• Big spike in 2022 was when we decided to go with two tracks for EmacsConf, so I needed to figure out all the infrastructure.
• I started doing Emacs News in 2016, which is maybe 0.5 - 1.5 hours a week.
• EmacsConf restarted in 2019 with an online conference. I've been handling speaker/volunteer coordination, captioning, publishing, and automation/infrastructure.

I can visualize this as a heatmap with:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(time_data[1:], columns=time_data[0]).drop('Total', axis=1).set_index('Year')
df = df.apply(pd.to_numeric)
plt.title("sachac's Emacs hours by month and year")
plot = sns.heatmap(df, annot=True, cmap='crest', fmt='.0f')
fig = plot.get_figure()
fig.savefig('emacs-time.png')

Yes, I do actually sleep

Overall balance of activities

Activity	Jan 2023	Feb 2023	Mar 2023	Apr 2023	May 2023	Jun 2023	Jul 2023	Aug 2023	Sep 2023	Oct 2023	Nov 2023	Total
A-	39.0%	47.5%	44.4%	44.2%	44.1%	44.4%	41.8%	40.5%	33.2%	33.3%	34.3%	40.6%
Business	1.3%	0.5%	3.5%	1.9%	3.8%	2.8%	3.5%	7.0%	8.8%	1.9%	1.7%	3.3%
Discretionary >	10.8%	3.9%	4.5%	6.0%	6.6%	8.3%	6.9%	6.2%	8.8%	14.5%	16.1%	8.4%
Personal >	10.1%	10.2%	11.7%	10.4%	9.3%	8.8%	9.9%	8.4%	9.4%	10.6%	11.9%	10.0%
Sleep	34.6%	35.4%	32.9%	34.9%	33.4%	32.1%	34.1%	34.7%	33.9%	35.7%	33.4%	34.1%
Unpaid work >	4.3%	2.5%	3.0%	2.8%	2.9%	3.6%	3.9%	3.3%	5.9%	4.0%	2.6%	3.5%

Notes:

• `A-` is my top-level category for child-care

Sleep over the years:

Sleep percentage

Year	Jan	Feb	March	April	May	June	July	Aug	Sept	Oct	Nov	Dec
2013	36.0%	34.9%	37.0%	36.5%	32.5%	35.8%	37.4%	43.1%	37.9%	36.9%	37.8%	34.5%
2014	36.4%	38.2%	37.0%	36.7%	35.0%	37.6%	37.8%	34.7%	36.1%	34.3%	41.3%	37.4%
2015	35.5%	35.3%	38.5%	35.0%	35.1%	39.7%	36.9%	40.1%	40.3%	37.4%	37.8%	41.4%
2016	35.5%	35.3%	38.5%	35.0%	35.1%	39.7%	36.9%	40.1%	40.3%	37.4%	37.8%	41.4%
2017	38.2%	35.9%	34.3%	35.1%	33.0%	30.8%	32.6%	27.2%	26.5%	32.8%	34.0%	29.6%
2018	29.7%	33.4%	32.8%	32.5%	31.0%	29.3%	35.1%	33.0%	37.7%	36.9%	29.3%	34.0%
2019	33.2%	34.7%	35.3%	35.6%	33.9%	30.4%	32.0%	32.9%	35.7%	34.6%	33.1%	34.4%
2020	32.8%	34.7%	37.9%	32.2%	32.8%	30.3%	31.5%	30.1%	32.4%	31.8%	28.9%	33.1%
2021	28.6%	30.1%	34.6%	33.3%	31.6%	31.8%	36.1%	33.6%	35.1%	34.7%	32.5%	32.4%
2022	35.5%	35.6%	38.5%	37.2%	38.1%	38.4%	36.5%	38.1%	35.3%	32.0%	30.1%	27.8%
2023	34.6%	35.4%	32.9%	34.9%	33.4%	32.1%	34.1%	34.7%	33.9%	35.7%	33.4%	35.2%

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(data[1:], columns=data[0]).set_index('Year')
df = df.applymap(lambda x: float(x.strip('%'))*24/100.0)
plt.title("sachac's sleep hours per day by month and year")
plot = sns.heatmap(df, annot=True, cmap='crest', fmt='.1f')
fig = plot.get_figure()
fig.savefig('sleep-time.png')

The kiddo was born in 2016. You can see how much less sleep I've been getting since then. =)