Month: February 2020

iPad Life: Additonal Thoughts on the iPad Pro

I am going to largely ignore the hubbub and criticism that surrounded the iPad 10th Anniversary. For the most part my comments in the Almost Four Years retrospective still stand: I remain divided about the iPad and its place in my life. It is both one of my favorite Apple devices, and one of the most frustrating.

I will say I find iPadOS to be a frustrating release. I frequently have to jiggle my smart-connected keyboard to get it to work, often needing to force quit Messages for the keyboard to work again.

The battery life on my first gen 12.9 is horrible. Two visits to the Apple Store in the last 6 months yield a battery that is close to eligibility for the battery replacement. In September I was between 85-87% battery health; on February 10th it was at 82%. The battery drain is worse than 17% loss, and feels more like 50%. In a meeting it dropped from 72 to 52% in about 20 minutes. The trip to Apple prompted a review of background apps. Nothing really stood out, but there were a few interesting data points. Things 3 had 4.5 hours of background refresh over the last 10 days, and Home and Lock Screen was also at 4 hours background over the same period. When I got home I took the drastic step of wiping my iPad, setting it up as new, and turning off almost all background app refresh. The results are slightly better, but it’s too early to say. The battery life in iOS 13 continues to be a hot topic on the MacRumors forums.

However, over the last week or so I’ve started brining my iPad instead of my MacBook Pro when I leave the house. The draw of the iPad is a light I can’t veer away from. In the parlance of Patrick Rhone, it is often enough. While I enjoy using apps like Tableau, which isn’t available on the iPad, a decent amount of my Tableau time is just refreshing data and viewing the results. Publishing the workbooks on Tableau Public with an auto-refresh of Google Sheets data fills that need.

My yearly theme for 2020 is Creativity. I constantly ding myself for not drawing, and this year drawing is one of my creative goals. The iPad is the perfect tool for that. Even AutoCAD, which kinda sucks on the iPad, is enough for me to do some hard-line drawings on the iPad.

The mantra I frequently use is the iPad is the ideal mobile creation tool, and I want to get back into having that focus in my life.

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Year of Evaluation in Review

To be honest, I had forgotten my yearly theme was to evaluate my current tech footprint. I was reviewing an old article and found a reference to it:

My theme for 2019 Evaluation. For the record, I’m not looking at making major life changes. I am, however, evaluating the devices, apps, and services I use. For now, it’s a lot of data collection. What do I use my Mac and iPad for? I say I want to use x app more, but over the year I use y app instead. I promised myself it was unlikely I was going to upgrade any of my devices. Some of this is price. The increased price of the new iPad Pro may not have completely turned me away, but also needing to buy a new Smart Keyboard and Pencil (also at a roughly 20% price premium) surely did. The same with my iPhone. I used my 6 for three years, and I expect to get 4-5 out of my 8 Plus. New iPhones are more expensive and my existing one works just fine. For me to upgrade I need to see real-world improvement; not just benchmarked improvement.

The Hardware Situation

After a trip into Boston where I felt I needed a Sherpa, I made a conscious effort to return to a belief I had strayed away from: iPhone +11. That means on most trips out of the house, I will bring my iPhone and one other device. Too often this year I felt I was bringing a MacBook (Pro or Air), iPad, and iPhone. It was too much, and I wasn’t using the devices to justify the load. It is not a hard and fast rule, but for me to bring both I really need to feel that the two devices are needed.

Once I readopted this philosophy, I also found myself leaving the iPad at home. Some of this is the Logitech Slim Combo case bulks the iPad up to where my 11” MacBook Air is lighter, and iPadOS still presenting a few barriers. All that said, most of the time when I leave the house it is to go to work, where I can’t use personal devices as my primary work computer. I do use my own stuff for supplemental tasks like taking notes. All my work notes end up in OneNote, but I am thinking of going back to just using my iPad and Pencil to take handwritten notes.

At the end of the yearly theme, the winners are: MacBook Pro first; iPad second; and MacBook Air is the third choice. The few times I feel I need both, the Air and iPad will be the first choice.

Writing Apps

The main evaluation I wanted to complete in 2019 was a decision on writing apps. All my other apps are situational, and the task at hand will drive the proper app. Writing, however, is pretty much just pushing the cursor to the right.

The winner at the end of the year is Ulysses and IA Writer. Ulysses iI use for about 95% of my writing, and the odd use case it can’t handle, I will use IA Writer. A blog post I am working on uses tables. Ulysses doesn’t support them, so I will use IA writer for that.

The main requirement I walked away from at the year was my writing tools needed to be flexible between macOS and iOS. This is why Scrivener loses out. The iOS version is not as feature-complete as the macOS version and uses a modal Dropbox sync. Scrivener may be the final step for a long-form publication, but I will not be using it as the main writing apps.

Cloud Storage

This was a challenging evaluation. I waffled between all the major players: OneDrive, Dropbox, Google Drive, and iCloud. My goal was to get to one for the main file storage. I would count iCloud+1 if I only used iCloud for apps that wrote to their own folders, but the bulk of my files were in say, Dropbox. At the end of the year, I ended up just using iCloud. I decided that paying for Dropbox was basically leasing a hard drive, so I moved all of my files to iCloud and uninstalled all of the other cloud providers. I am hoping that later versions of iOS will enable the promised iCloud Drive improvements. This does make syncing files between my MacBooks something I need to make sure happens. I keep the Air closed and plugged in, so sync doesn’t happen in the background. Every now and then I make sure everything syncs up ok.

2020’s Yearly Theme

The Year of Evaluation was a bridge year to my planned 2020 theme: Creativity. My desire was by evaluating tools and processes in 2019, I’d just get it out of my system. So far, it is working well. Creativity is a vague theme that is basically fuck off less. Working on my trains, Tableau, photo editing, etc. all count as creativity. The year is just getting going, but I am feeling great about my progress so far.

  1. And, if I am honest, just the iPhone is enough most times. ↩

A Very Geeky Analysis of 8 Months of Data Collection on Running Trains

I rejoined my model railroad club in 2016. Over those 3 years, I had a feeling I ran certain trains and locomotives more than others. Since I work as an analyst, I decided to do some data collection on this. This post is a geeky analysis of how I collected the data, the results, and the plan for this year.

The Collection Goals

My goals for this exercise were simple: how often did I run specific locomotives and specific trains1. For example, how often did I run my Erie Lackawanna SD45s, and how often did I run my coal train?

Collecting the Data:

In the beginning, I just kept a Numbers file with the following information:

Date Reporting Mark Engines Train Name
4/13/19 EL SD45s Freight
4/13/19 UP WideCabs Freight

It was basic information. The date the train ran, the road name of the engines pulling the train, the specific engines that I ran, and a generic name of the train. The challenge of modeling two eras is I do need to be specific on the locomotives and trains. I can’t run my Erie Lackawanna power on a modern train since the railroad ceased to exist in 1976.

By the end of the year, I realized the information was too basic. In the case of the Union Pacific train, generalizing the locomotives made sense since I only had two UP locomotives. By the end of the year, I had four engines that were era-appropriate. I had to get more specific, but not screw up my count. If I ran the coal train with 4 engines, I wanted to count the train running once, and each of the engines running once, but I didn’t want to count the coal train running four times. So, I couldn’t list each of the locomotives as running a coal train since that would inflate the count.

I also didn’t want to track how many laps around the layout each train ran. I thought about it, but decided there were too many variables. A good example is a train I am making adjustments too at the club. I may make 5 laps with it, but I am adjusting problematic cars. Meanwhile, the train that runs fine may only make one lap. I decided I didn’t care about that data. I also didn’t care often a specific locomotive ran a specific train.

In the end, I ended up with a Numbers spreadsheet with the following data:

Date Reporting Mark Engines Train Name Train Class
12/7/2019 UP SD70ACE UPFRT1 Manifest
12/7/2019 UP ES44AC DPU No count
12/7/2019 CSX SD40-2 DPU No count

This is a single entry for one train running: UPFTR1. I got away from generic names, and now most trains have detailed names. What this entry notes is on 12/7/2019 I ran my UP Freight. Pulling the train were 3 engines (two UP and one CSX). DPU is a railroading term for Distributed Power Unit — typically power that runs mid- or end-of-train. I adopted it to mean any additional power that runs on a train. This means that I can get a single count for running the SD70ACE, the ES44AC, and the SD40, but keeping the count for UPFRT1 as one. Train class is a higher-level designation to see how often I ran a manifest, passenger, etc., train2. A No Count entry just means don’t count the two DPU units as Manifest train. It’s just a value to filter out in the report. As with the train name, I only care about one train class per train name. Now, if I run two different manifest trains with the same power on each, then I want two counts. There is also a count for running locomotives during the club’s operating session, but I didn’t need much specificity on the usage.

There is also a worksheet that lists all of my locomotives and rolling stock. The rolling stock also shows what cars the train is currently assigned to.

Analyzing the Data

I was just using a variety of CountIF statements in Numbers. When the data I collected was basic this worked ok. As I wanted to do a deeper analysis on this the limits of CountIFs became a burden. Adjusting each of the CountIFs as I changed the naming was a pain. Numbers doesn’t really support pivot tables, and even then, I wanted a little more flexibility and ease of use.

Enter Tableau.

I use Tableau at work infrequently as a reporting package. I was rusty and wanted to try and keep sharp on using the tool. Tableau doesn’t support Numbers as a data source, so I moved it into Google Sheets.

Tableau made short work of filtering out the data. I could exclude the No Counts, filter out a few other things. Counting operating sessions became more of an aside.

While I cared more about locomotives and trains, the rolling stock sheet also lets me easily print out a grouped list for when I go to the shows. This way I can reduce the likelihood I buy a duplicate car.

Results of the data

Train Class Run Count
Passenger 25
Manifest 20
Unit 12
Operations 7
Local 5
Steam 2
Grand Total 71

I am not going to get into the specific trains run. Instead, I will just talk about the high-level view: the train class.

That passenger trains were around 30% of the total train types run does not surprise me. There are two reasons for this: they are my favorite type of train to run, and they are the easiest to set up and run. Each of my passenger trains can be stored and transported in one storage bin, locomotives included. If I am only going to be at the club for a short time, or just want something easy to set up, the passenger train is my usual choice.

There are only three trains that I classify as Unit trains (Coal, Grain, and Intermodal), so I was a little surprised to see them count as high as they did. Numbers-wise, it’s a wash on them. I ran the Intermodal 5 times, the coal train 4, and the grain 3. The grain train was the last train I put together, so it being low is unsurprising. After thinking about it, I like the look of passenger trains, and unit trains — where all the cars are the same type — have the same look.

Operations is just a catch-all that I ran units 6 times at the club operating sessions. From a data collection standpoint, it is hard to define. For instance, if I loan out a locomotive, how do I count it? For now, I am just noting a train class of Operations for the entire day, and counting which units I ran at each session.

Collecting Data in 2020.

The main item I am looking forward to in 2020 is getting an entire year’s data. I decided to gather the data in July 2019, and only had verifiable data from February onwards3.

I also decided I am going to collect data on the laps run. I don’t know what it will tell me due to a lot of variables that affect running trains: how long am I at the club; how the layout behaving; and how well my train is running4. I decided at least for 2020 I would collect the data to see if there are trains that make more laps than others. It’s easier to collect that from the start of the year.

Another goal that I am carrying over from 2019 is to not cook the books and run a train just to inflate a run count. I do track the last time I ran a train, so I will use that to run a train I haven’t run in a while. There is one train I haven’t run since July, so that is a candidate for running soon.

I am also tracking car usage. Mostly the date the car ran, the train it ran on, and its current assignment. Since the Google Sheets roster is kept up-to-date, it’s a quick cut and paste when I run a train. I can also capture if a car ran on multiple trains on a single visit.

Admittedly, this was a solution in need of a problem. It started with just ticking off a virtual sheet running a train and a locomotive and ended with a 5-tab Google Sheet fed into a Business Intelligence tool. Two goals were met: I have some data on running trains, and I got better at data collection and analysis.

If you are curious about the data, I posted the Viz here. There is a lot in the Viz I didn’t cover in the post, and the average laps data is obviously only going to be accurate for 2020.

  1. I didn’t really care about how often specific locomotives ran specific trains ↩︎
  2. I made a late-year change to make trains that ran cars of the same type (Coal, Grain, etc.) Unit Trains. ↩︎
  3. I’m not sure I am missing much. Grad School meant I didn’t get to the club much. ↩︎
  4. A poorly running train could require a lot of fixing, and thus more laps for testing. ↩︎