You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


# tibbletime 0.1.4

* Minor release to stay compatible with tibble 3.0.0 and the upcoming release
  of dplyr 1.0.0.

# tibbletime 0.1.3

* Features

    * Methods for `tidyr::nest()` and `tidyr::unnest()` have been updated to be
    compliant with tidyr 1.0.0. An error will be triggered if you have a version 
    of tidyr installed that is < 1.0.0 and try to use one of these functions 
    on a `tbl_time` object.

* Bug fixes

    * `collapse_by()` no longer errors when there is a column 
    named `start_date` in the `tbl_time` object (#81).

## tibbletime 0.1.2

* Features

    * Export a constructor for `tbl_time` object through `new_tbl_time()`. Only
    to be used by package developers extending `tibbletime`.

* Bug fixes

    * For `tibble` 2.0.1, an internal bug fix was made to pass along an `nrow`
    argument.
    
    * For `dplyr` 0.8.0, an internal fix was made in one of the tests.
    
    * A new line was added to `inst/include/is_ordered.h` to appease CRAN.

    * The tbl_time time zone for `POSIXct` indices is now set as the first
    non NA value of: "tzone" attribute -> `Sys.timezone()` -> "UTC". Previously
    there was no `Sys.timezone()` step as there were problems with local time zones
    such as `America/New_York` interacting with collapsing by day. Those problems have 
    been fixed by using `DSTday` with all `POSIXct` indices even if `day` is
    specified.

## tibbletime 0.1.1

* General

    * `collapse_index()` and `collapse_by()` support a `clean` argument. This
    will round your index up/down to the next period boundary, allowing for
    prettier dates that can be used in summaries.

    * A new helper function, `collapse_by()`, wraps the common idiom of 
    `.tbl_time %>% mutate(date = collapse_index(date, "yearly"))` and is the
    easiest way to use `tibbletime` with the rest of the tidyverse.

    * You can now pass an index vector (easily created with `create_series()`) to
    the `period` argument of functions like `as_period()` or `collapse_by()`
    to specify custom periods to collapse at.

    * Added support for `millisecond` and `microsecond` grouping. 
    See `?create_series` for examples and `?filter_time` Details 
    for more information.
    
    * `partition_index()` (and therefore higher level functions like
    `collapse_by()`) now round using the entire `period` argument to figure
    out the default start date. Meaning if `2 years` is passed, it will round
    down the start of the series to the lower `2 year` boundary, rather than
    just `year`. This is a small change, but is technically breaking.
    
    * More efficient parsing of periods. Only noticably faster
    with a large number of groups.
    
* Bug Fixes

    * `tidyr::gather()` and `tidyr::spread()` now work appropriately.

## tibbletime 0.1.0

This is a major update. It introduces a huge number of breaking changes as
we heavily reworked the internals of the package. This should ensure the
longevity of the package and provide maximum flexibility for its use with `dplyr`.
As this was still early in package development with minimal usage, 
and because we had issued a Warning in 
the README of the last update that we may change things, we have not made any
attempt to support backwards compatability. From this point forward, however,
we will support backwards compatability as we feel that we have reached a 
more stable implementation.

With that out of the way, here is a complete list of changes.

* General
    
    * The `period` argument no longer supports the 'period formula'
    (e.g. `1~year`). It added unnecessary complication with little benefit.
    Rather, a character should be used like `'1 year'`. See the documentation
    of `partition_index()` for full details.
    
    * `time_formula` arguments still support the `from ~ to` style syntax,
    but the left and right hand sides must now be characters, rather than
    bare date specifications. In English, rather than `2013 ~ 2014`,
    you must use `'2013' ~ '2014'`. This is easier to program with and 
    also allows you to pass in variables to the time formula, which
    previously did not work well.
    
    * `time_filter()` has become `filter_time()`. This naming is easier to 
    remember now that a suite of `time_*()` functions is not being developed
    and is easier to find with autocompletion.
    
    * `time_group()` and `time_collapse()` have become `partition_index()`
    and `collapse_index()`. Both functions accept `index` vectors and
    are commonly used inside `dplyr::mutate()`.
    
    * `partition_index()` splits an index by period and returns an
    integer vector corresponding to the groups.
    
    * `collapse_index()` collapses an index by period so that all
    observations falling in that interval share the same date. This
    is most useful when used to then group on the index column.
    
    * There is full support for `Date` and `POSIXct` classes as the 
    index, and there is experimental support for
    `yearmon`, `yearqtr`, and `hms` classes.
    
    * `ceiling_index()` and `floor_index()` are thin wrappers 
    around `lubridate` functions of similar names, but they also
    work for `yearmon`, `yearqtr` and `hms`.
    
    * `create_series()` now has an explicit `class` argument.
    
    * `as_period()` gains an `include_endpoints` argument
    for including the last data point if `side = "start"` is
    specified or the first data point if `side = "end"` is used.
    
    * There are a number of new "getter" functions for accessing the
    index and time zone of `tbl_time` objects. These are useful for 
    package development.
    
    * `filter_time()`, `as_period()` and other "getter" functions 
    now use `.tbl_time` as a consistent first argument rather than
    `x`. `collapse_index()` and `partition_index()` use `index` as
    their first arguments.
    
    * Exported `parse_period()` for general use in other related packages.
    
    * Warnings are now generated if the user is not using a sorted index.

* Bug Fixes

    * All `dplyr` functions should now retain the `tbl_time` class and 
    relevant attributes.

    * Ensure that `tidyr::spread()` passes the `fill` argument through.
    
    * Default time zone is now `UTC` rather than `Sys.timezone()` to handle
    a daylight savings issue.

## tibbletime 0.0.2

* New functionality
  
    * `time_floor()` and `time_ceiling()` are convenient wrappers to 
    `lubridate` functions for altering dates to period boundaries.
  
    * `time_unnest()` is used to specifically unnest a `tibble` object
    with a list-column of `tbl_time` objects.
  
    * `create_series()` allows the user to create a `tbl_time` object with
    a regularly spaced sequence of dates.
    
    * `time_group()` has become the workhorse function for creating time based
    groups used in changing periodicity and other grouped 
    time based calculations.
    
    * `time_summarise()` and `tmap()` now also accept a formula-based `period`.
    
    * `as_period()` now accepts a formula-based `period` that provides an 
    incredible amount of flexibility in creating groups. (#9, #14, #15)

    * `rollify()` creates a rolling version of any function for 
    use in `dplyr::mutate()`. (#7)

* General

    * You now have to explicitely load `dplyr` or `tidyr` to use any functions 
    from those packages. Previously they were reexported, but this seems 
    unnecessary.

    * Added vignettes on intro, filtering, and `as_period()`.
    
    * Added more extensive `dplyr` support.
    
    * Speed increases for `as_period()` and `create_series()`.

    * Internal global utilities moved to `utils.R`.

    * Added test coverage. (#2)

    * Added package documentation page. (#3)

    * Added versions to all imported packages.

* Bug Fixes

    * Fixed an issue with `[` in combination with `tibble::add_column()`. Use 
    `tibble (>= 1.3.4.9001)` for correct behavior.

    * Fixed a bug where using `tidyr::nest()` would cause the nested tibbles
    to lose their time attributes.

    * Fix a bug where filter_time(data, ~yyyy-mm-dd) would be parsed as
    `yyyy-mm-dd 00:00:00 ~ yyyy-mm-dd 00:00:00` instead of 
    `yyyy-mm-dd 00:00:00 ~ yyyy-mm-dd 23:59:59`.

    * Fix a bug with as.Date / as.POSIXct operator collision in `filter_time()`.

## tibbletime 0.0.1 

* Initial release of `tibbletime`, a package for time aware tibbles.