Jackson Two

Category: Quantified Self

  • Dear Data 2019 – Week 8, Phone Addiction

    Dear Data 2019 – Week 8, Phone Addiction

    After the terrible flurry of complaints, Sarah and I continued on with week 8 of the dear data postcard project we’re conducting. Week 8 was a welcome change, tracking how often we use our phones.

    I was excited to jump on this topic to know more insight into what I’m doing on my phone (although there is also screen time now). To track the data for the week, I created IFTTT buttons that identified the first reason that I picked up my phone. This allowed me to keep track of the time, category, and also add on a more detailed reason.

    Because I chose to capture only the first thing I did on my phone, I feel that the data well represents that, but may miss out on additional tasks or items I was doing after I unlocked my phone. It may be better to say that these items were what caused my attention to be diverted to my phone OR were a necessary task to be done (mapping/music) via my phone.

    Dear Data 2019 – Ann’s week 8, phone addiction
    Dear Data 2019 – Ann’s week 8, phone addiction (legend)

    This postcard has the most detail from me to-date. After tracking the data, I was really only able to whittle it down to 15 different distinct categories. I felt that any further combining would ruin the detail of the data (and I didn’t like that I had to put flashlight and calculator together).

    Each segment of the line represents one usage and the time. It is almost like a running total chart with the lines connected at the points in time for the day. It was the best way I could think to figure out how often I use my phone for something and when (ex: is it all day or only in the morning). You’ll notice that this is the second week where I’ve started to use a ruler and pencil to draw out my visualizations in advance – I’m getting much more precise with what I want to convey.

    As no surprise and apparent by the back side, texting, email, and social media tend to make up the majority of my phone time. I was surprised by a lot of the smaller things that I don’t think about, but only do on my phone – in particular shopping, which includes both grocery shopping at the store and online shopping.

    I forgot to check my screen time at the end of the week, so here’s the most recent 7 days (does not align with my postcard, but should be good for additional context).

    Twitter dominates Wednesday & Thursday due to Workout Wednesday

    Here’s Sarah’s postcard for the week:

    Dear Data 2019 – Sarah’s Week 8, phone addiction
    Dear Data 2019 – Sarah’s week 8, phone addiction (legend)

    My first reaction when I saw this postcard was just amazement that Sarah was able to create such a beautiful picture with her data. The choice of colors, dots, and final shapes are so pretty. Then of course I’m immediately drawn to noticing that her social media habit picks up dramatically on the weekend (no surprise there), as does her usage of entertainment apps.

    In short, Sarah managed to take a topic that we both probably don’t feel the best about and portray it in a beautiful way!

    And that’s a wrap on this week. I really enjoyed this one, both from the two visualizations we made, to tracking and recognizing what I use my phone for. It’s not all evil (social media), there are lots of little things I depend on it for – including mapping, music, calculator, a time – the list goes on. So while it may be most known for communicating with others, it really does serve it’s purpose to help me in all facets of my life.

    At the post office again!

    Don’t forget to check out Sarah’s take on the week!

  • Dear Data – Week 7, Complaints

    Dear Data – Week 7, Complaints

    Week 7 postcards have long been delivered and this blog post is overdue. As if the subject for the cards had some influence, the theme of complaints seemed to have an extremely negative impact on having the desire to write the companion blog post.

    During this week I tried to track all of my verbal complaints or times when I felt actively frustrated or annoyed. I genuinely try not to complain very often, so most of my tracked complaints represent high amounts of escalated annoyance or dissatisfaction.

    For data collecting I documented all of these moments on my phone, writing a small sentence that expressed the complaint to document the subject and frustration level. In retrospect I think capturing this data wasn’t very accurate and it seemed to me that the more complaints I tracked, the more grumpy I was about it.

    Here’s my postcard, which really clearly sums up how I felt in general about the topic:

    Dear Data 2019 – Ann’s week 7, complaints
    Dear Data 2019 – Ann’s week 7, complaints (legend)

    Each column represents a day of data (Monday to Friday) which are chunked into different sections based on the complaint. You can see that Tuesday was not a great day for me, I had 10 different things that I complained about. In contrast, Friday had no data which is more due to me being distracted by other things and less aware of my complaints.

    Each complaint is categorized into a major topic: traffic, the temperature around me, technology, people, and myself. The most vivid complaints for me this week were around the cold. During this week it was extremely cold (comparatively for Phoenix, AZ) and I was in a very drafty building. There’s nothing worse than being cold and trying to work and that was very apparent throughout Monday, Tuesday, and Wednesday.

    Here’s Sarah’s postcard for the week:

    Dear Data 2019 – Sarah’s week 7, complaints
    Dear Data 2019 – Sarah’s week 7, complaints (legend)

    Once again Sarah has done a better job at capturing data detail throughout the week making her postcard more rich with information than mine! I like that she ended up separating out the different buckets into 2 large themes: personal vs. external. I think it probably helps retrospectively to know if the complaints were valid or within her control to change. And I also like the traditional use of a bar chart on the right side to offset the more abstract complaint loops on the left.

    I’m glad to see there are some common themes among our complaints: people, technology, and transportation. We chatted about how cold I was that week afterward and Sarah reminded me kindly that 40 degrees F is not very cold.

    And the best part of the week – mailing off the complaints and being done with data collection on the topic!

    I don’t like how rusty this blue box is.

    Don’t forget to check out Sarah’s take on the week!

  • Dear Data 2019 – Week 6, Physical Contact

    Dear Data 2019 – Week 6, Physical Contact

    Week 6 postcards of the data project Sarah Bartlett and I are working on are here and I couldn’t be more excited. The theme of week 6 was physical contact.

    During the original project Giorgia and Stefanie tracked people they touched and who touched them, but I decided to switch things up and include my cats. Since I primarily work from home I figured it was a great opportunity to add in additional data elements. I was also genuinely curious to see at the end of the week who gets the most physical affection from me in my house.

    I used IFTTT again to track touches and set up buttons on my phone to represent the 5 major buckets I was likely to encounter: my husband, my cats, family/friends, and strangers. I chose to only represent intentional touches and those that I gave – making data collection a bit less awkward.

    Here’s my postcard:

    Dear Data 2019 – Ann’s week 6, physical contact
    Dear Data 2019 – Ann’s week 6, physical contact (legend)

    Cutting to the chase, the bar chart on the back of the postcard clearly indicates that I give out most of my affection to Starly.

    Starly interfering with work

    Focusing back on the design of the postcard, this week I wanted to continue to push the boundaries and go further into an abstract representation of data. The postcard started out as a 10 x 10 grid that was going to have clearly defined borders for each individual square. The design continued to morph as I started using metallic pen markers that made the edges softer and revealed something I hadn’t considered, the connected blocks (Tetris pieces) of touches. Through the imprecision of my drawing what was originally a very strict grid turned into a more quilt-like representation of my week.

    As two last design elements, I chose to outline the entire pattern with pink – no data elements represented there, but I felt that it completed the transformation that the data took on. And then one cognition piece was adding on dots to help read the grid appropriately, left to right and top down.

    Although the data revealed what I had suspected, visually seeing how connected and integrated my pets are into my life and well-being has been extremely impactful. It’s a reminder of the companionship they offer and of our shared affection.

    Here’s Sarah’s postcard:

    Dear Data 2019 – Sarah’s week 6, physical contact
    Data 2019 – Sarah’s week 6, physical contact (legend)

    I love this design by Sarah. She managed to take a data subject and turn it into a complete picture. I especially like how she chose 3 specific types of touches, hugs, kisses, and handshakes – and then how they correspond to different social circles. It’s amazing when counting the petals how sacred physical contact is to those closest to us vs. colleagues and other outer circle individuals.

    And that’s a wrap on the week – save one last off topic thought I had. After crafting my postcard I couldn’t help but think that it looked similar to some other artwork I’ve seen before.

    Patchwork vs. Postcard

    Although I’m probably biased on creating the connection, I enjoyed the idea that the game had somehow influenced the final drawing.

    A new blue box!

    Don’t forget to check out Sarah’s take on the week!

  • Dear Data 2019 – Week 4, Mirrors

    Dear Data 2019 – Week 4, Mirrors

    Week 4 of the data postcard project Sarah Bartlett and I are working on this year is here. We still have yet to reach consistent timing for postcard arrival. Sarah usually receives mine 2 days or more before I receive hers, but this week we were only one day apart.

    Week 4’s topic was all about mirrors and reflections of ourselves. I was intrigued by this one, I had no sense as to how often I look at myself properly in a mirror. Also, I decorate my house with a lot of mirrors (which you’ll see) – not because I am vain, but because they are great at reflecting natural light and making spaces appear larger.

    I ended up re-purposing my IFTTT buttons for this week, but found the data collection process much less labor intensive. In the original collections from Giorgia and Stefanie, they had both captured accidental glances, however I chose not to go down this path since I would likely spend way too much mental energy determining if it was accidental or on purpose (or turned to having a purpose).

    Dear Data 2019 – Ann’s week 4, mirrors
    Dear Data 2019 – Ann’s week 4, mirrors (legend)

    For the final visualization, I also decided NOT to use time as a dimension. Time has shown up in several of our previous postcards, so it was time to do something different. Instead I chose to represent the 5 different types of mirrors/reflective surfaces that I am around. I also captured some meta data related to the mirrors themselves, with each sketch being a rough estimate of the shape and proportion of each mirror.

    As with previous weeks, I chose to collect from Monday through Friday – and there’s some good insight with that knowledge. Looking at my bathroom mirror, there are 16 glances, 10 of which are me brushing my teeth. After seeing the results, I think what surprised me most was the kitchen mirror. My kitchen is in the center of a very open floor plan, but I didn’t realize how often I used it to check my appearance. In converse, the green mirror (my bedroom) is where I apply makeup or do my hair.

    I’m not impressed with my postcard this week, while I think it is an effective unit chart, I’m struck by the imprecision of the dimensions and some of the sloppy sketching. And the hashing of the corners to denote whether it was at home or not didn’t add much to the overall look.

    And here’s Sarah’s week 4 postcard:

    Dear Data 2019 – Sarah’s week 4, mirrors
    Dear Data 2019 – Sarah’s week 4, mirrors (legend)

    I really like Sarah’s this week. She managed to pull off a lot of depth by using different textures and writing instruments (there’s pencil vs. marker). If my assumptions are correct, then she and I start our mirror glancing the same way – in the bathroom. I also appreciate that she spent more time being specific about what was happening when she was looking at the mirror, and conscious of using mirrors for makeup.

    Mailed Sunday night from my favorite blue box!

    And that’s it for week 4 with mirrors. Don’t forget to check out Sarah’s blog post and get her take on the week.

  • Dear Data 2019 – Week 3, Thank Yous

    Dear Data 2019 – Week 3, Thank Yous

    Week 3 postcards for the data project Sarah Bartlett and I are working on this year have finally reached their destinations. I think we both felt that the mail was slower than normal, perhaps due to the abnormally cold weather here in the US.

    Week 3’s topic was tracking how often we say “thank you.” I knew going into this week that it wasn’t going to be an easy task. I say “thank you” a lot, so I decided to set up IFTTT buttons on my phone. They also show up on my Apple Watch to make it much easier to record the data as soon as it happens.

    IFTTT button set to record values when button pushed

    The recipe for each Applet is very simple. Once a button is pushed it will write a row to a spreadsheet called DD3 with the following columns of information. I customized the last 2 fields based on my desire to capture the medium (in person/virtual) and who the person was. Here are the final buttons, they reside in the widgets area of my phone. I put IFTTT at the very top to make sure they’d be easy to access.

    So many buttons!

    Sarah and I also talked about how we were each going to track data this week and she also ended up using IFTTT. And after using the buttons over the course of the week, I will definitely be reusing this technique for future weeks.

    Now that the data collection backstory is out of the way, here’s my poscard:

    Dear Data 2019 – Ann’s week 3, thank yous
    Dear Data 2019 – Ann’s week 3, thank yous (legend)

    In this visualization each vertical line represents a day of the week (Monday to Friday). Each shape coming off the line is a thank you. Those on the left side are for people outside of my inner circle (business contacts, strangers, people on social media). Conversely those on the right represent my close friends, family, and my husband (Josh). I chose to use shapes to represent whether the thank you was for Josh or not, as seen by the triangles vs. circles.

    The shapes are also plotted in sequential order throughout the day, with the top being the first thank you and the bottom being the last thank you. And the final two pieces are: pink or green to represent in person vs. digital and an additional < next to business related thank yous.

    I also cheated a bit this week and mocked up the postcard in Tableau. I wanted to make sure the faint idea I had in my head would look okay on paper. You’ll notice very quickly that quite a bit of the detail was reduced for the postcard.

    Initial visualization in Tableau

    I really enjoyed the pattern that my data revealed this week. On most days I start my morning sending thank you emails and then as it builds, I end up leaving my house or talking to other people. The thank yous I dish out to Josh seem to be very dependent on what the focus of the day is.

    In contrast, here’s Sarah’s week 3 postcard:

    Dear Data 2019 – Sarah’s week 3, thank yous
    Dear Data 2019 – Sarah’s week 3, thank yous (legend)

    I say “in contrast” jokingly here, because I think we took a VERY similar approach. Not only superficially in the usage of lines for passage of time and the choice of triangles, but also in the choice of detail we decided to track. Her breakout of people is similar to mine, leaving a special place for her husband, and carving out social layers from friend/family, to work, social media, and finally strangers. One thing she did that I really like was include the vertical line for AM/PM. I think that adds a little more context to the flow of each day.

    Same blue box as last week!

    That’s a wrap for this week! Don’t forget to check out Sarah’s blog post and get her take on the week.

  • Statistical Process Control Charts

    I’ve had this idea for a while now – create a blog post and video tutorial discussing what Statistical Process Control is and how to use different Control Chart “tests” in Tableau.

    I’ve spent a significant portion of my professional career in business process improvement and always like it when I can integrate techniques learned from a discipline derived from industrial engineering and apply it in a broader sense.

    It also gives me a great chance to brush up on my knowledge and learn how to order my thoughts for presenting to a wide audience.  And let’s not forget: an opportunity to showcase data visualization and Tableau as the delivery mechanism of these insights to my end users.

    So why Statistical Process Control?  Well it’s a great way to use the data you have and apply different tests to start early detection.  Several of the rules out there are aimed at finding “out-of-control,” non-normal, or repetitive parts within a stream of data.  Different rules have been developed based on how we might be able to detect them.

    The video tutorial above goes through the first 3 Western Electric rules.  Full details on Western Electric via Wikipedia: here.

    Rule 1: Very basic, uses the principle of a bell curve to put a spotlight on points that are above or below the Upper Control Limit (UCL) or Lower Control Limit (LCL) also known as +/- 3 standard deviations from the mean.  These are essentially outlier data points that don’t fall within our typical span of 99.7%.

    Rule 2: Takes into consideration surrounding observations.  Looking at 3 consecutive observations are 2 out of 3 above or below the 2 SD mark from the average.  In this rule the observations must be on the same side of the average line when beyond 2 SD.  Since we’re at 95% at 2 SD, having 2 out of 3 in a set in that range could signal an issue.

    Rule 3: Starts to consider even more data points within a collection of observations.  In this scenario we’re now looking for 4 out of 5 observations +/- 1 SD from the average.  Again, we’re retaining the positioning above/below the average line throughout the 5 points.  This one really shows the emergence of a trend.

    I applied the first 3 rules to my own calorie data to see detect any potential issues.  It’s very interesting to see the results.  For my own particular data set, Rule 3 was of significant value.  Having it in line as the new daily data funnels in could prevent me from going on a “streak” of either over or under consuming.

     

    Interact with the full version on my Tableau Public profile here.

  • Quick and easy – parameters to aggregate dates


    (Now with video – video uses different data as an example)

    One of my favorite uses of parameters is to dynamically change date aggregations without the pesky drill symbol.  Sometimes I want to just see week or month, quarters tend to be pretty worthless.  Especially if I’m doing something discrete, I really don’t like the way Tableau breaks apart the data view.

    The creation of the parameter leaves me with a very clean user experience and ensures users don’t negatively interact with the viz.

    Here’s the setup – first create a parameter of data type string and type in your allowed values.  Mine is going to have week and month:

    parameter1

    (note I changed the casing on the words later on for look/feel)

    Next create custom dates based on your primary date field.  Mine is called “Start”:

    dates

    Right click on the field, go to Create -> Custom date…

    For the sake of this viz, I made both a Months and Weeks (both are date value).  At one point I had days (shown as “Exact”, but I abandoned it)

    Last step is create the calculated field.  Right click on the parameter at the bottom, create calculated field:

    dates2

    Place it on your designated shelf.  You may have to do some wrangling on the field to get it to display right.  For the viz I ended up with, the field is set to “Exact Date” and “Discrete.”

    Check out the final experience below: