Would you like to know how your mood impact your sleep and how your parents influence your happiness levels?

您想知道您的心情如何影响您的睡眠以及您的父母如何影响您的幸福感吗？

Become a data nerd, and track it!

成为数据书呆子，并对其进行跟踪！

on这到底是什么？ (又名数据源) (🤔 What on earth is all this? (aka data sources))

In October 2018 I started tracking several metrics about myself.

在2018年10月，我开始跟踪有关自己的几个指标。

Every day I score my mood, sleep, vitamins I’m taking, and many other variables. Later, I also implemented a weekly review practice to log average times spent on different activities (e.g. work time), gratefulness, favourite quotes etc.

每天我都会为自己的心情，睡眠，所服用的维生素以及许多其他变量评分。 后来，我还实施了每周的检查练习，以记录在不同活动上花费的平均时间(例如工作时间)，感激之情，喜欢的报价等。

Data Collection

数据采集

The daily and weekly reviews were logged in using online forms—daily one filled in the evening or morning the next day, weekly at the end of every Sunday. Starting from Google Forms in 2018, I then moved the forms to Coda and, in early 2020, to Airtable.

每天和每周的评论都使用在线表格登录-每天在第二天的晚上或第二天的早晨填写评论，每周一次在每个星期日结束。 从2018年的Google Forms开始，我将表单移至Coda，并于2020年初移至Airtable。

Weekly activity data (e.g. time spent on paid work, or self-improvement) were tracked daily using Toggl. The sums from the week were logged in at the end of the week.

每天使用Toggl跟踪每周的活动数据(例如花在有偿工作或自我完善上的时间)。 该周的总和在该周结束时登录。

Sleep data–duration of deep sleep and the total sleep time—were tracked using an Oura ring, with a weekly average logged at the end of the week.

睡眠数据–深度睡眠的持续时间和总睡眠时间–使用Oura环进行跟踪，并在一周结束时记录每周平均值。

In mid-2020 I accumulated enough data, or rather enough data analytical skills to investigate the results. Are there any patterns? Correlations? What’s the coolest looking chart I can plot?

在2020年中期，我积累了足够的数据，或者说是足够的数据分析技能来调查结果。 有没有模式？ 相关性？ 我能绘制出的最酷的图表是什么？

This post below is a human-friendly summary of the process, stand-out results, and the nicest charts. 👌

下面的帖子是对流程 ，出色的结果和最好的图表的人性化总结 。 👌

The full Jupyter notebook includes a list of many ideas for further analysis.

完整的Jupyter笔记本包含许多想法的列表，需要进一步分析。

让我们得到数字：数据访问 (Let’s get the numbers: data access)

You can access the freshest version of the data using the Airtable API, and this awesome Python wrapper.

您可以使用Airtable API和该功能强大的Python包装器访问数据的最新版本。

We do the same with the data frame with weekly stats and, voila, we have two pandas data frames to work with.

我们对具有每周统计信息的数据框进行同样的操作， 瞧，我们有两个可处理的熊猫数据框。

Let’s see what they can tell us.

让我们看看他们能告诉我们什么。

🆙跌宕起伏和皮尔逊 (🆙 Ups, downs, and Pearson)

With all the mood data we can plot some delightful charts.

利用所有的情绪数据，我们可以绘制一些令人愉快的图表。

For instance, here is a function that plots two chosen moods over a selected number of days.

例如，这是一个在选定的天数内绘制两个选定的情绪的函数。

Output:

输出：

This is nice to visualise a period, but to see wider trends we can turn to statistics. In comes our best friend Pearson 🙌. I could just summarise the top values, but a correlation matrix is just tooooo pretty not to show.

可以很形象地看到一个时期，但是要查看更广泛的趋势，我们可以转向统计。 我们最好的朋友皮尔森(Pearson) 我只可以总结出最高值，但是相关矩阵只是不显示而已 。

Output:

输出：

Following the standard benchmark values (above 0.5, and below -0.5— medium positive/negative correlation, above 0.7 or below -0.7—strong positive/negative correlation) there are no strong correlations.

遵循标准基准值(高于0.5且低于-0.5-中等正/负相关，高于0.7或低于-0.7-强烈正/负相关)，则没有强相关性。

Here is a function to pick and display correlations above 0.5 and below -0.5, I’m running it here with the number of days equal to the length of the data frame to get values form the whole dataset.

这是一个选择并显示高于0.5且低于-0.5的相关性的函数，我在这里运行它的天数等于数据帧的长度，以获取整个数据集的值。

Output:

输出：

What does it tell us? Nothing out of ordinary, really.

它告诉我们什么？ 真的没有什么不寻常的。

• As self-confidence goes down, my depression levels increase, and the reverse: as depression does down self-confidence increases.
  随着自信心的下降，我的抑郁水平增加，反之：随着抑郁的下降，自信心也增加。
• The same relationship exists between motivation and depression.
  动机与抑郁之间存在相同的关系。

Additionally, self-confidence and motivation are positively correlated with each other—as ones does up so does the other. Nothing that surprising.

此外， 自信和动力之间正相关，彼此之间正相关 。 没什么奇怪的。

I was however expecting sleep quality to be more strongly correlated with mood, which doesn’t seem to be the case.

但是，我期望睡眠质量与情绪更加紧密相关，但事实并非如此。

my我最幸福的城市是哪里？ (🌇 What’s my happiest city?)

I’ve travelled a lot in the last 648 days, and was curious to see whether and how the location impacted my mood.

在过去的648天里，我旅行了很多次，很想知道这个地点是否以及如何影响了我的心情。

All these results need to be taken with a bowl of salt — the number of observations from each location is not the same.

所有这些结果都需要用一碗盐来获取-每个位置的观测次数都不相同。

There were several cities in which I spent only ~1 week. This typically meant I was there for holiday or a special event, so the daily setup and routine were not comparable with long term stay. I eliminated these rows from the analysis.

我在几个城市只花了大约1周的时间。 这通常意味着我要去度假或参加特殊活动，因此日常设置和日常活动无法与长期住宿相提并论。 我从分析中删除了这些行。

To get the top average mood values by city, we just use group by and extract the min and max values.

要获得城市最高的平均情绪值，我们只使用group by并提取最小值和最大值。

Output:

输出：

Looks like I should sleep in Warsaw, and for productivity go to Chiang Mai — that’d be a bit of a long commute. 🤷‍♀️

看来我应该在华沙睡觉，要提高生产力，就要去清迈-这将是一个漫长的通勤时间。 ♀️

Output:

输出：

Most of the mood data matches with the reports of my remembering self, except for productivity values being low in Paris. I don’t remember it being this way.

大多数情绪数据与我记忆中的自我报告相符，但巴黎的生产力值较低。 我不记得是这样的。

What’s interesting to notice that minimum productivity, minimum depression as well as maximum creativity were observed in the same city, Paris. And this is not how I remember this stay — I wouldn’t say it was below average productive, or in any way more creative.

有趣的是，在同一城市巴黎观察到了最低的生产率，最低的压抑以及最大的创造力 。 这不是我记得的时光-我不会说这低于平均水平的生产力，或者说是更有创造力的。

📍最繁忙的城市是… (📍 And the most workaholic city is…)

Work and feeling productive aren’t the same thing. But, guess what? Someone was scrupulous enough to track the time spent on different activities during the week.

工作和生产力并不相同。 但猜猜怎么了？ 有人认真地跟踪一周中花费在不同活动上的时间。

Working with datetime is a mess—you can see all my attempts in the notebook—for this post I’ll stick to float values. Let’s see highest and lowest values for the main activities I track daily—paid work, self-improvement, and life organising.

使用日期时间是一团糟-您可以在笔记本上看到我的所有尝试-对于这篇文章，我将坚持浮动值。 让我们看看我每天跟踪的主要活动的最高和最低值，即有酬工作，自我完善和生活组织。

Output:

输出：

Looks like the most “productive” city, Chiang Mai, was not the one where I spent most time on work. Likewise, spending many hours on work in Lisbon didn’t make me feel very productive.

看起来像是“生产力最高”的城市清迈，并不是我花大量时间在工作上的城市。 同样，在里斯本花很多时间工作并没有使我感到非常有生产力。

How about a chart to visualise these duration values and show how much of an outlier Lisbon was.

图表如何可视化这些持续时间值并显示里斯本有多少离群值 。

Output:

输出：

For context, I spent the time in Lisbon without my partner, and was very isolated. My interpretation:

就上下文而言，我在没有伴侣的情况下在里斯本度过了时光，并且非常孤立。 我的解释：

A lack of social interaction leads to overwork, without even a positive subjective feeling of productivity.

缺乏社交互动会导致工作过度，甚至没有积极的主观生产力感觉。

What a waste, don’t do it at home. ☝️

真是浪费，不要在家中做。 ☝️

I我在哪里睡得最长？ (💤 Where did I sleep the longest?)

The self-reported sleep quality was highest in Warsaw and smallest in Penang. How does it square with the sleep length? As a reminder: total sleep and deep sleep values were tracked using an Oura ring.

自我报告的睡眠质量在华沙最高，在槟城最低。 它与睡眠时间如何平方？ 提醒一下： 总睡眠和深度睡眠值 使用Oura戒指追踪

Here is a plot of sleep duration values per city.

这是每个城市的睡眠持续时间值图。

Output:

输出：

It’s not very clear which are the top and bottom values. I managed to convert the sleep values from floats to hours.

不清楚哪个是最高值和最低值。 我设法将睡眠值从浮点数转换为小时数。

Output:

输出：

• The longest average total & deep sleep duration per week I had in Warsaw — 8:16hrs and 2:34hrs respectively.
  我在华沙每周最长的平均总睡眠时间和深度睡眠时间分别为8：16hrs和2：34hrs。
• The shortest average deep sleep duration per week in Kuala Lumpur — 1:03hrs, and
  吉隆坡每周平均平均深度睡眠时间最短-1：03hrs，以及
• Shortest total average sleep per week, in Penang — 6:57hrs.
  槟城每周平均平均睡眠时间最短— 6:57小时。

Kuala Lumpur was very close to Penang both in the deep sleep and total sleep hours as well subjectivity ranking. (KL: 1:16hrs, 7:03hrs, 6.97, Penang: 1:22hrs, 6:57hrs, 6.57).

吉隆坡在深度睡眠和总睡眠时间以及主观性方面都非常接近槟城。 (KL：1：16hrs，7：03hrs，6.97，Penang：1：22hrs，6：57hrs，6.57)。

🏙城市冠军！ (🏙 City winner!)

Of course, any conclusions about the cities are not linked to the cities per se, but rather to the specific life setup I had there—including the apartment, work and sleep stations, social life, and weather.

当然，关于城市的任何结论都与城市本身无关，而是与我在那里的特定生活设置有关，包括公寓，工作和睡眠站，社交生活和天气。

It would be interesting to identify the conditions that created in the most promising locations. E.g. What did I have in Chiang Mai that was not the case elsewhere, which made my (impression of) productivity so much higher?

确定在最有希望的地区创造的条件将是很有趣的。 例如，我在清迈拥有什么，而其他地方却没有，这使我的(印象)生产力大大提高了？

Is the availability of Mo Bikes and easy access to mango sticky rice the key to productivity?!

Mo Bikes的可用性和容易获得芒果糯米饭是生产力的关键吗？

This is where I decided on the title of this post.

这是我决定该帖子标题的地方。

🧀加入奶酪！ 我要感谢什么？ (🧀 In for the cheese! What am I grateful for?)

Gratitude practice is a part of my weekly review*, and a great source of text data to analyse!

感谢练习是我每周评论的一部分*，也是分析文本数据的重要来源！

Why not a word cloud to display the most frequent words? 🤩 Who doesn’t like a good word cloud.

为什么词云无法显示最频繁的单词？ 🤩 谁不喜欢一个好词云。

Here is how you can do it. Skipping the part where I cleaned the column, nobody likes that bit.

这是您的操作方法。 跳过我清洗色谱柱的部分，没有人喜欢。

Results:

结果：

Clearly, and perhaps surprisingly for an introverted recluse like me, people-related terms take a prominent place on this cloud. Special mentions to mum, and Tom (my partner), and a shoutout to the EA community.

显然，也许令人惊讶的是，对于像我这样内向的人而言 ， 与人相关的术语在这朵云上占有重要地位 。 特别向妈妈，汤姆(我的搭档)提及，并向EA社区大喊大叫。

with我到底要做什么？ (⏰ What on earth do I do with my time?)

They say days are long, but months are short.

他们说日子很长，但是几个月很短。

When you don’t pay attention days turn into a blur, especially in the 2020 lockdown times when every day is a Tuesday (or a Wednesday, as Tim Urban would have it).

当您不注意时，日子就会变得模糊，尤其是在2020年的锁定时间，每天都是星期二(或Tim Urban会认为是星期三)。

Writing down stand-out events helps develop a habit of paying closer attention. It also helps appreciate each day more.

写下杰出的事件有助于养成更密切注意的习惯。 它还可以帮助您每天更多地欣赏。

Let’s see what have been the most frequent stand out events for me.

让我们看看对我来说最频繁的脱颖而出的事件。

I do take into account that it’s a subjective report, comprised of what I managed to notice, remember at the end of the day, and decided to put down. Probably not fool-proof.

我确实考虑到这是一个主观的报告，其中包含我设法注意到的内容，在一天结束时记得并决定放下。 可能不是万无一失。

The code is the same as for the gratefulness cloud, just using a different data frame and column as a source of the text.

该代码与感恩云相同，只是使用不同的数据框和列作为文本源。

Unsurprisingly Tom again takes a prominent place. That’s I suppose what happens when you live with your partner — they are bloody everywhere, init! 😛

毫不奇怪，汤姆再次占据重要位置。 那就是我想，当您与伴侣一起生活时，会发生什么事- 他们到处都是血腥的，一开始！ 😛

Talking (call, coaching, conversation) and walks (went, walk) are some of my most popular activities. Several specific people are also “standout events”, so to speak (Jan, Oliver, and Mum).

聊天(打电话，教练，谈话)和散步(散步，散步)是我最受欢迎的活动。 可以这么说，几个特定的​​人也是“杰出事件”(Jan，Oliver和Mum)。

结论 (Conclusions)

What did I learn? Nothing groundbreaking. At least, there is no clear wow moment and an immediate actionable CTA. As many investigations, this one too ends with: more research needed!

我学到了什么？ 没什么突破性的。 至少，没有明确的哇声和立即可采取的CTA。 正如许多调查一样，这一结果也以结尾： 需要更多的研究！

On the philosophical front, working the data in detail encouraged a deeper reflection about, for example:

在哲学方面，详细处理数据鼓励对以下方面进行更深入的思考：

• the difference between a feeling of productivity vs. length of time spent working, and
  生产力感觉与工作时间之间的差异，以及
• prompts me to investigate more closely what exact life and sleep set up in specific locations (e.g. Warsaw) could have contributed to better sleep.
  促使我更仔细地研究在特定位置(例如华沙)建立的确切生活和睡眠对改善睡眠的影响。

As next steps, I’ll probably run more consciously designed experiments. For example, eating mangos for 4 weeks to see if it impact my (perceived levels of) productivity. 😉

接下来，我可能会进行更有意识的设计实验。 例如，吃芒果4周，看看它是否影响我的(感知水平)生产力。 😉

*I know what you want to say: I should note my gratitude daily. I’ll submit this motion to my IFS board.

*我知道你想说什么：我应该每天感谢我。 我将此议案提交至IFS董事会。

Follow me for more adventures in data!

跟随我，获取更多数据冒险！