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]


Q:

Why do my geom_lines fail to break into the correct colours?

I have run into a little issue concerning the use of the geom_line() function.
My data consists of a frame-by-frame manual video assessment of certain behaviour by trained observers, this leads to several thousand data points per observer. This is basically a vector consisting of 0s and 1s per observer, in which the 1 represents wanted behaviour and the 0 unwanted behaviour.
Playing around, I came up with the following:
# a dataset from a manual videoanalysis with frame by frame behaviour assessment in binary. 0 = no, 1 = yes.
data1<-read.csv("ObserversBehaviour.csv", ",", header=T)

# my solution of giving each observer his own line, without having to transform the entire set
Obsy0 <- rep(0,4528)
Obsy1 <- rep(1,4528)
Obsy2 <- rep(2,4528)
Obsy3 <- rep(3,4528)
Obsy4 <- rep(4,4528)
Obsy5 <- rep(5,4528)
Obsy6 <- rep(6,4528)
Obsy7 <- rep(7,4528)
Obsy8 <- rep(8,4528)
Obsy9 <- rep(9,4528)
Obsy10 <- rep(10,4528)

ObsData <- data.frame(data1,Obsy0,Obsy1,Obsy2,Obsy3,Obsy4,Obsy5,Obsy6,Obsy7,Obsy8,Obsy9,Obsy10)

#vector giving each observer a number
Obsall <- c(0:10)

#The list of individual frames of video M01 (4528 in total)
Framerange <- ObsData[["Frames.M01"]]

ylabels <- c("Observer0","Observer1","Observer2","Observer3","Observer4","Observer5","Observer6","Observer7","Observer8","Observer9","Observer10")

#Ob<n>value is the 1 or 0 assessment
#had to use as.factor() because for some reason my 0s and 1s are seen as continuous
GraphObserve <-ggplot(ObsData,ylim=range(Obsall),xlim=max(Framerange),aes(x=Framerange))
geom_point(aes(x=Frames.M01, y = Obsy0, colour = as.factor(Ob0value), size=as.factor(Ob0value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy1, colour = as.factor(Ob1value), size=as.factor(Ob1value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy2, colour = as.factor(Ob2value), size=as.factor(Ob2value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy3, colour = as.factor(Ob3value), size=as.factor(Ob3value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy4, colour = as.factor(Ob4freeze.0.no.1.yes), size=as.factor(Ob4freeze.0.no.1.yes)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy5, colour = as.factor(Ob5value), size=as.factor(Ob5value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy6, colour = as.factor(Ob6value), size=as.factor(Ob6value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy7, colour = as.factor(Ob7value), size=as.factor(Ob7value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy8, colour = as.factor(Ob8value), size=as.factor(Ob8value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy9, colour = as.factor(Ob9value), size=as.factor(Ob9value)), shape=15) +
geom_point(aes(x=Frames.M01, y = Obsy10, colour = as.factor(Ob10value), size=as.factor(Ob10value)), shape=15) +

scale_colour_manual(breaks = c(0, 1),
  labels = c("No","Yes"),
  values = c("green4","red"),
  name="Assessment")+
#needed to let the wanted behaviour stand out, so I changed pointsize
scale_size_manual(breaks = c(0, 1), values=c(1,2), guide="none")+
scale_y_discrete(limit=Obsall, labels=ylabels, expand=c(0,0))+
scale_x_continuous(expand=c(0,0),breaks = round(seq(min(0), max(Framerange), by = 200),5000))+
expand_limits(y=c(1,-.5))

update_labels(GraphObserve,list(x="Frames (M01)",y ="Observers"))

This led me to a fair graph consisting of nicely coloured dots for every datapoint, but since the points were overlapping and still quite small, this was not my way to go. Instead of using geom_point() I went with geom_line(). The graph does represent every colour break as I want it.
So next I changed every geom_point() line to geom_line(), while keeping the rest the same. (The scale_size_manual() became quite redundant)
geom_line(aes(x=Framerange, y=Obsy0, colour=as.factor(Ob0value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy1, colour=as.factor(Ob1value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy2, colour=as.factor(Ob2value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy3, colour=as.factor(Ob3value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy4, colour=as.factor(Ob4value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy5, colour=as.factor(Ob5value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy6, colour=as.factor(Ob6value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy7, colour=as.factor(Ob7value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy8, colour=as.factor(Ob8value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy9, colour=as.factor(Ob9value)),size=14) +
geom_line(aes(x=Framerange, y=Obsy10, colour=as.factor(Ob10value)),size=14) +

I thought this would work out just fine, but it didn't. 
Instead of switching colours for every 0 and 1 in the file, it seems as though the color switches at the first and last 1 occuring in the data set.
The graphs from the above scripts: http://imgur.com/2baseCa,bJa2Ab7#0
I can not seem to find an error in my code, nor do I seem to find a solution on the web. Is there anyone here who can help me solve this?
Update
For a clearer overview I placed links to the resulting graphs from my previous scrips underneath them.
After the suggestion of putting my data in a "long" format, I used the following script:
data1<-read.csv("ObserversBehaviour.csv", ",", header=T)

Frames<-data1[["Frames.M01"]]
Obs<-paste0("Observer",0:10)
Obsy <- sort(rep(0:10,4528),decreasing=F)
Obsvalue <- stack(data1[,c(Obs)])
ObsData2 <- expand.grid(Frames=data1[["Frames.M01"]],Obs=paste0("Observer",0:10))  
ObsData2$Observer = Obsy
ObsData2$Assessment = Obsvalue$values

ggplot(ObsData2, aes(Frames, Observer, colour=Assessment)) +
  geom_line(show_guide=T) +
  scale_y_discrete(limit=0:10, labels=Obs, expand=c(0,0))+
  scale_x_continuous(expand=c(0,0),breaks = round(seq(min(0), max(Frames), by = 200),5000))+
  expand_limits(y=c(1,.5)) +
  #The manual colorcoding actually failed, since it keeps returning this error "Continuous value supplied to discrete scale".
  scale_color_manual(breaks = c(0,1),
                 labels = c("No","Yes"),
                 values = c("green4","red"),
                 name="Assessment")

Although the colours now actually changed based on the value of the behaviour assessment, new problems arose.
The values of Observer5-10 have all been replaced by the values of Observer10.
By changing several parameters, I found that by changing the line size, values returned to normal. However, values for Observer10 disappeared completely.
The graphs from the new scripts:
http://imgur.com/AiKeXLc,kPgIKKZ#1 (second image is the first graph)
Combining these issues with the fact that I cannot change the colours manually (even though I tried using as.factor() and as.discrete() on my values) I don't know what I can try now.
I am probably missing something obvious here, as a beginner with R.
Update
Output of dput(head(ObsData2))
## structure(list(Frames = 1:6, Obs = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Observer0", "Observer1", "Observer2", "Observer3", 
## "Observer4", "Observer5", "Observer6", "Observer7", "Observer8", 
## "Observer9", "Observer10"), class = "factor"), Observer = c(0L, 
## 0L, 0L, 0L, 0L, 0L), Assessment = c(0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("Frames", 
## "Obs", "Observer", "Assessment"), out.attrs = structure(list(
##     dim = structure(c(4528, 11), .Names = c("Frames", "Obs")), 
##     dimnames = structure(list(Frames = c("Frames=   1", "Frames=   2", 
##     "Frames=   3", "Frames=   4", "Frames=   5", "Frames=   6", 
##     "Frames=   7", "Frames=   8", "Frames=   9", "Frames=  10", 
##     "Frames=  11", "Frames=  12", "Frames=  13", "Frames=  14", 
##     "Frames=  15", "Frames=  16", "Frames=  17", "Frames=  18", 
##     "Frames=  19", "Frames=  20", "Frames=  21", "Frames=  22", 
##     "Frames=  23", "Frames=  24", "Frames=  25", "Frames=  26", 
##     "Frames=  27", "Frames=  28", "Frames=  29", "Frames=  30", 
##     "Frames=  31", "Frames=  32", "Frames=  33", "Frames=  34", 
##     "Frames=  35", "Frames=  36", "Frames=  37", "Frames=  38", 
##     "Frames=  39", "Frames=  40", "Frames=  41", "Frames=  42", 
##     "Frames=  43", "Frames=  44", "Frames=  45", "Frames=  46", 
##     "Frames=  47", "Frames=  48", "Frames=  49", "Frames=  50", 
##     "Frames=  51", "Frames=  52", "Frames=  53", "Frames=  54", 
##     "Frames=  55", "Frames=  56", "Frames=  57", "Frames=  58", 
##     "Frames=  59", "Frames=  60", "Frames=  61", "Frames=  62", 
##     "Frames=  63", "Frames=  64", "Frames=  65", "Frames=  66", 
##     "Frames=  67", "Frames=  68", "Frames=  69", "Frames=  70", 
##     "Frames=  71", "Frames=  72", "Frames=  73", "Frames=  74", 
# Long patch of "Frames= <75-4502>"  omitted due to space saving 
##     "Frames=4503", "Frames=4504", "Frames=4505", "Frames=4506", 
##     "Frames=4507", "Frames=4508", "Frames=4509", "Frames=4510", 
##     "Frames=4511", "Frames=4512", "Frames=4513", "Frames=4514", 
##     "Frames=4515", "Frames=4516", "Frames=4517", "Frames=4518", 
##     "Frames=4519", "Frames=4520", "Frames=4521", "Frames=4522", 
##     "Frames=4523", "Frames=4524", "Frames=4525", "Frames=4526", 
##     "Frames=4527", "Frames=4528"), Obs = c("Obs=Observer0", "Obs=Observer1", 
##     "Obs=Observer2", "Obs=Observer3", "Obs=Observer4", "Obs=Observer5", 
##     "Obs=Observer6", "Obs=Observer7", "Obs=Observer8", "Obs=Observer9", 
##     "Obs=Observer10")), .Names = c("Frames", "Obs"))), .Names = c("dim", 
## "dimnames")), row.names = c(NA, 6L), class = "data.frame")

A:

This will be much easier if you put your data in "long" format. Here's an example with fake data:
## Create fake data in long format
ObsData = expand.grid(Frames=1:4258, Obs=paste0("Observer",0:10))

# Add y values
set.seed(10)
ObsData$y = cumsum(rnorm(4258*11))

In a long-form data frame, all the observers are "stacked" into a single factor variable (Obs) with 11 categories--one for each observer. Now you can use this as the grouping variable for a colour aesthetic in ggplot.
## Plot with a different color for each observer
ggplot(ObsData, aes(Frames, y, colour=Obs)) +
         geom_line()

Here's what the graph looks like with the default colors, but you can change that by adding scale_colour_manual() to your plot and setting whatever colors you prefer.

A:

Problem circumvented
With the help of my colleague and the use of geom_tile() instead of geom_line() the graph is now exactly as I want it to.
require("ggplot2")

data1<-read.csv("ObserversBehaviour.csv", ",", header=T)

Frames<-data1[["Frames.M01"]]
Obs.lab<-paste0("Observer",0:10)
Obsy <- sort(rep(1:11,4528),decreasing=F)
Obsvalue <- stack(data1[,c(Obs.lab)])

ObsData2 <- expand.grid(Frames=data1[["Frames.M01"]],Obs.lab=paste0("Observer",0:10))  
ObsData2$Observer = Obsy
ObsData2$Assessment = Obsvalue$values

GraphObserve <- ggplot(ObsData2, aes(Frames, Observer, height=.9)) +
  geom_tile(aes(fill = factor(Assessment)))+
  scale_fill_manual(values=c("0"="green4", "1"="red"), labels= c("No", "Yes"))+
  scale_y_discrete(expand=c(0,0), limit=1:11, labels=Obs.lab)+
  scale_x_continuous(expand=c(0,0), breaks = round(seq(min(0), max(Frames), by = 200),5000))
  update_labels(GraphObserve,list(x="Frames (M01)",y ="Observers"))

The colour breaks occur exactly where they need to, there is no overlap and all observers are listed in the plot.
Although this does not actually solves the problems occurring in my former script, it does provide a better result.
Final graph:
http://i.imgur.com/pW8Qh0I.png
Thank you eipi10, for showing me how to compact my script.