## Agile vs. Lean: Explained by Cats

Over the past few years, Agile has gained popularity. This methodology emerged as a solution to manage projects with a number of unknown elements and to counter the typical waterfall method. Quality practitioners have observed the numerous similarities between this new framework and Lean. Some have speculated that Agile is simply the next generation’s version of Lean. These observations have posed the question: Is Agile the new Lean?

ASQ Influential Voices Roundtable for December 2019

The short answer to this question is: NO.

The longer answer is one I’m going to have to hold back some emotions to answer. Why? I have two reasons.

## Reason #1: There is No Magic Bullet

First, many managers are on a quest for the silver bullet — a methodology or a tool that they can implement on Monday, and reap benefits no later than Friday. Neither lean nor agile can make this happen. But it’s not uncommon to see organizations try this approach. A workgroup will set up a Kanban board or start doing daily stand-up meetings, and then talk about how they’re “doing agile.” Now that agile is in place, these teams have no reason to go any further.

## Reason #2: There is Nothing New Under the Sun

Neither approach is “new” and neither is going away. Lean principles have been around since Toyota pioneered its production system in the 1960s and 1970s. The methods prioritized value and flow, with attention to reducing all types of waste everywhere in the organization. Agile emerged in the 1990s for software development, as a response to waterfall methods that couldn’t respond effectively to changes in customer requirements.

Agile modeling uses some lean principles: for example, why spend hours documenting flow charts in Visio, when you can just write one on a whiteboard, take a photo, and paste it into your documentation? Agile doesn’t have to be perfectly lean, though. It’s acceptable to introduce elements that might seem like waste into processes, as long as you maintain your ability to quickly respond to new information and changes required by customers. (For example, maybe you need to touch base with your customers several times a week. This extra time and effort is OK in agile if it helps you achieve your customer-facing goals.)

Both lean and agile are practices. They require discipline, time, and monitoring. Teams must continually hone their practice, and learn about each other as they learn together. There are no magic bullets.

Information plays a key role. Effective flow of information from strategy to action is important for lean because confusion (or incomplete communication) are forms of waste. Agile also emphasizes high-value information flows, but for slightly different purposes — that include promoting:

• Rapid understanding
• Rapid response
• Rapid, targeted, and effective action

The difference is easier to understand if you watch a couple cat videos.

## This Cat is A G I L E

This cat is continuously scanning for information about its environment. It’s young and in shape, and it navigates its environment like a pro, whizzing from floor to ceiling. If it’s about to fall off something? No problem! This cat is A G I L E and can quickly adjust. It can easily achieve its goal of scaling any of the cat towers in this video. Agile is also about trying new things to quickly assess whether they will work. You’ll see this cat attempt to climb the wall with an open mind, and upon learning the ineffectiveness of the approach, abandoning that experiment.

## This Cat is L E A N

This cat is using as LITTLE energy as possible to achieve its goal of hydration. Although this cat might be considered lazy, it is actually very intelligent, dynamically figuring out how to remove non-value-adding activity from its process at every moment. This cat is working smarter, not harder. This cat is L E A N.

Hope this has been helpful. Business posts definitely need more cat videos.

## An Easy Way to Make Minimum Viable Product (MVP) Totally Not Viable

The Minimum Viable Product (MVP) concept has taken off over the past few years. Indeed, its heart is in the right place. MVP encourages product managers to scope features and functionality carefully so that customer needs are satisfied at every stage of development — not just in a sweeping finale at the end of development.

It’s a great way to shorten time-to-value and test new market concepts before committing. Zappos, for example, started by posting pictures of shoes on the internet without having an inventory. They wanted to quickly test to see whether people would even consider buying shoes without trying them on.

Unfortunately, adhering to MVP won’t guarantee success thanks to one critical caveat. And that is: if your product already exists, you have to consider your product’s base state. What can your customers do right now with your product? Failure to take this into consideration can be disastrous.

### An Example: Your Web Site

Here’s what I mean: let’s say the product is your company’s web site. If you’re starting from scratch, a perfectly suitable MVP would be a splash page with one or two sentences about what you do. Maybe you’d add some contact information. Customers will be able to find you and communicate with you, and you’ll be providing greater value than without a web presence.

But if you already have a 5000-page site online, that solution is not going to fly. Customers and prospects returning to your site will wonder why it vaporized. If they’re relying on the content or functionality you previously provided, chances are they will not be happy. Confused, they may choose to go elsewhere.

The moral of the story is: in defining the scope of your MVP, take into consideration what your customers can already do, and don’t dare give them less in your next release.

## Designing Experiences for Authentic Engagement: The Design for STEAM Canvas

As Industry 4.0 and Digital Transformation efforts bear their first fruits, capabilities, business models, and the organizations that embody them are transforming. A century ago, we thought of organizations as machines to be rigidly designed and controlled. In the latter part of the 20th century, organizations were thought of as knowledge to be cultivated, shared, and expanded. But “as intelligent systems gain traction, we are once again at a crossroads – where organizations must create complete and meaningful experiences” for their customers, stakeholders, and employees.

How do you design those complete, meaningful, and radically engaging experiences? To provide a starting point, check out “Design for Steam: Creating Participatory Art with Purpose” by my former student Nick Kamienski and me. It was just published today by the STEAM Journal.

“Participatory Art” doesn’t just mean creating things that are pretty to look at in your office lobby or tradeshow booth. It means finding ways to connect with your audience in ways that help them find meaning, purpose, and self-awareness – the ultimate ingredient for authentic engagement.

Designing experiences to make this happen is challenging, but totally within reach. Learn more in today’s new article!

## Reinforcement Learning: Q-Learning with the Hopping Robot

Overview: Reinforcement learning uses “reward” signals to determine how to navigate through a system in the most valuable way. (I’m particularly interested in the variant of reinforcement learning called “Q-Learning” because the goal is to create a “Quality Matrix” that can help you make the best sequence of decisions!) I found a toy robot navigation problem on the web that was solved using custom R code for reinforcement learning, and I wanted to reproduce the solution in different ways than the original author did. This post describes different ways that I solved the problem described at http://bayesianthink.blogspot.com/2014/05/hopping-robots-and-reinforcement.html

The Problem: Our agent, the robot, is placed at random on a board of wood. There’s a hole at s1, a sticky patch at s4, and the robot is trying to make appropriate decisions to navigate to s7 (the target). The image comes from the blog post linked above.

To solve a problem like this, you can use MODEL-BASED approaches if you know how likely it is that the robot will move from one state to another (that is, the transition probabilities for each action) or MODEL-FREE approaches (you don’t know how likely it is that the robot will move from state to state, but you can figure out a reward structure).

• Markov Decision Process (MDP) – If you know the states, actions, rewards, and transition probabilities (which are probably different for each action), you can determine the optimal policy or “path” through the system, given different starting states. (If transition probabilities have nothing to do with decisions that an agent makes, your MDP reduces to a Markov Chain.)
• Reinforcement Learning (RL) – If you know the states, actions, and rewards (but not the transition probabilities), you can still take an unsupervised approach. Just randomly create lots of hops through your system, and use them to update a matrix that describes the average value of each hop within the context of the system.

Solving a RL problem involves finding the optimal value functions (e.g. the Q matrix in Attempt 1) or the optimal policy (the State-Action matrix in Attempt 2). Although there are many techniques for reinforcement learning, we will use Q-learning because we don’t know the transition probabilities for each action. (If we did, we’d model it as a Markov Decision Process and use the MDPtoolbox package instead.) Q-Learning relies on traversing the system in many ways to update a matrix of average expected rewards from each state transition. This equation that it uses is from https://www.is.uni-freiburg.de/ressourcen/business-analytics/13_reinforcementlearning.pdf:

For this to work, all states have to be visited a sufficient number of times, and all state-action pairs have to be included in your experience sample. So keep this in mind when you’re trying to figure out how many iterations you need.

## Attempt 1: Quick Q-Learning with qlearn.R

• Input: A rewards matrix R. (That’s all you need! Your states are encoded in the matrix.)
• Output: A Q matrix from which you can extract optimal policies (or paths) to help you navigate the environment.
• Pros: Quick and very easy. Cons: Does not let you set epsilon (% of random actions), so all episodes are determined randomly and it may take longer to find a solution. Can take a long time to converge.

Set up the rewards matrix so it is a square matrix with all the states down the rows, starting with the first and all the states along the columns, starting with the first:

hopper.rewards <- c(-10, 0.01, 0.01, -1, -1, -1, -1,
-10, -1, 0.1, -3, -1, -1, -1,
-1, 0.01, -1, -3, 0.01, -1, -1,
-1, -1, 0.01, -1, 0.01, 0.01, -1,
-1, -1, -1, -3, -1, 0.01, 100,
-1, -1, -1, -1, 0.01, -1, 100,
-1, -1, -1, -1, -1, 0.01, 100)

HOP <- matrix(hopper.rewards, nrow=7, ncol=7, byrow=TRUE)
> HOP
[,1]  [,2]  [,3] [,4]  [,5]  [,6] [,7]
[1,]  -10  0.01  0.01   -1 -1.00 -1.00   -1
[2,]  -10 -1.00  0.10   -3 -1.00 -1.00   -1
[3,]   -1  0.01 -1.00   -3  0.01 -1.00   -1
[4,]   -1 -1.00  0.01   -1  0.01  0.01   -1
[5,]   -1 -1.00 -1.00   -3 -1.00  0.01  100
[6,]   -1 -1.00 -1.00   -1  0.01 -1.00  100
[7,]   -1 -1.00 -1.00   -1 -1.00  0.01  100


Here’s how you read this: the rows represent where you’ve come FROM, and the columns represent where you’re going TO. Each element 1 through 7 corresponds directly to S1 through S7 in the cartoon above. Each cell contains a reward (or penalty, if the value is negative) if we arrive in that state.

The S1 state is bad for the robot… there’s a hole in that piece of wood, so we’d really like to keep it away from that state. Location [1,1] on the matrix tells us what reward (or penalty) we’ll receive if we start at S1 and stay at S1: -10 (that’s bad). Similarly, location [2,1] on the matrix tells us that if we start at S2 and move left to S1, that’s also bad and we should receive a penalty of -10. The S4 state is also undesirable – there’s a sticky patch there, so we’d like to keep the robot away from it. Location [3,4] on the matrix represents the action of going from S3 to S4 by moving right, which will put us on the sticky patch

qlearn <- function(R, N, alpha, gamma, tgt.state) {
Q <- matrix(rep(0,length(R)), nrow=nrow(R))
for (i in 1:N) {
cs <- sample(1:nrow(R), 1)
while (1) {
next.states <- which(R[cs,] > -1)  # Get feasible actions for cur state
if (length(next.states)==1)        # There may only be one possibility
ns <- next.states
else
ns <- sample(next.states,1) # Or you may have to pick from a few
if (ns > nrow(R)) { ns <- cs }
# NOW UPDATE THE Q-MATRIX
Q[cs,ns] <- Q[cs,ns] + alpha*(R[cs,ns] + gamma*max(Q[ns, which(R[ns,] > -1)]) - Q[cs,ns])
if (ns == tgt.state) break
cs <- ns
}
}
return(round(100*Q/max(Q)))
}


Run qlearn with the HOP rewards matrix, a learning rate of 0.1, a discount rate of 0.8, and a target state of S7 (the location to the far right of the wooden board). I did 10,000 episodes (where in each one, the robot dropped randomly onto the wooden board and has to get to S7):

r.hop <- qlearn(HOP,10000,alpha=0.1,gamma=0.8,tgt.state=7)
> r.hop
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,]    0   51   64    0    0    0    0
[2,]    0    0   64    0    0    0    0
[3,]    0   51    0    0   80    0    0
[4,]    0    0   64    0   80   80    0
[5,]    0    0    0    0    0   80  100
[6,]    0    0    0    0   80    0  100
[7,]    0    0    0    0    0   80  100


The Q-Matrix that is presented encodes the best-value solutions from each state (the “policy”). Here’s how you read it:

• If you’re at s1 (first row), hop to s3 (biggest value in first row), then hop to s5 (go to row 3 and find biggest value), then hop to s7 (go to row 5 and find biggest value)
• If you’re at s2, go right to s3, then hop to s5, then hop to s7
• If you’re at s3, hop to s5, then hop to s7
• If you’re at s4, go right to s5 OR hop to s6, then go right to s7
• If you’re at s5, hop to s7
• If you’re at s6, go right to s7
• If you’re at s7, stay there (when you’re in the target state, the value function will not be able to pick out a “best action” because the best action is to do nothing)

Alternatively, the policy can be expressed as the best action from each of the 7 states: HOP, RIGHT, HOP, RIGHT, HOP, RIGHT, (STAY PUT)

## Attempt 2: Use ReinforcementLearning Package

I also used the ReinforcementLearning package by Nicholas Proellochs (6/19/2017) described in https://cran.r-project.org/web/packages/ReinforcementLearning/ReinforcementLearning.pdf.

• Input: 1) a definition of the environment, 2) a list of states, 3) a list of actions, and 4) control parameters alpha (the learning rate; usually 0.1), gamma (the discount rate which describes how important future rewards are; often 0.9 indicating that 90% of the next reward will be taken into account), and epsilon (the probability that you’ll try a random action; often 0.1)
• Output: A State-Action Value matrix, which attaches a number to how good it is to be in a particular state and take an action. You can use it to determine the highest value action from each state. (It contains the same information as the Q-matrix from Attempt 1, but you don’t have to infer the action from the destination it brings you to.)
• Pros: Relatively straightforward. Allows you to specify epsilon, which controls the proportion of random actions you’ll explore as you create episodes and explore your environment. Cons: Requires manual setup of all state transitions and associated rewards.

First, I created an “environment” that describes 1) how the states will change when actions are taken, and 2) what rewards will be accrued when that happens. I assigned a reward of -1 to all actions that are not special, e.g. landing on S1, landing on S4, or landing on S7. To be perfectly consistent with Attempt 1, I could have used 0.01 instead of -1, but the results will be similar. The values you choose for rewards are sort of arbitrary, but you do need to make sure there’s a comparatively large positive reward at your target state and “negative rewards” for states you want to avoid or are physically impossible.

my.env <- function(state,action) {
next_state <- state
if (state == state("s1") && action == "right")  { next_state <- state("s2") }
if (state == state("s1") && action == "hop")    { next_state <- state("s3") }

if (state == state("s2") && action == "left")  {
next_state <- state("s1"); reward <- -10 }
if (state == state("s2") && action == "right") { next_state <- state("s3") }
if (state == state("s2") && action == "hop")   {
next_state <- state("s4"); reward <- -3 }

if (state == state("s3") && action == "left")  { next_state <- state("s2") }
if (state == state("s3") && action == "right") {
next_state <- state("s4"); reward <- -3 }
if (state == state("s3") && action == "hop")   { next_state <- state("s5") }

if (state == state("s4") && action == "left")  { next_state <- state("s3") }
if (state == state("s4") && action == "right") { next_state <- state("s5") }
if (state == state("s4") && action == "hop")   { next_state <- state("s6") }

if (state == state("s5") && action == "left")  {
next_state <- state("s4"); reward <- -3 }
if (state == state("s5") && action == "right") { next_state <- state("s6") }
if (state == state("s5") && action == "hop")   {
next_state <- state("s7"); reward <- 10 }

if (state == state("s6") && action == "left")  { next_state <- state("s5") }
if (state == state("s6") && action == "right") {
next_state <- state("s7"); reward <- 10 }

if (next_state == state("s7") && state != state("s7")) {
reward <- 10
} else {
reward <- -1
}
out <- list(NextState = next_state, Reward = reward)
return(out)
}


Next, I installed and loaded up the ReinforcementLearning package and ran the RL simulation:

install.packages("ReinforcementLearning")
library(ReinforcementLearning)
states <- c("s1", "s2", "s3", "s4", "s5", "s6", "s7")
actions <- c("left","right","hop")
data <- sampleExperience(N=3000,env=my.env,states=states,actions=actions)
control <- list(alpha = 0.1, gamma = 0.8, epsilon = 0.1)
model <- ReinforcementLearning(data, s = "State", a = "Action", r = "Reward",
s_new = "NextState", control = control)


Now we can see the results:

> print(model)
State-Action function Q
hop     right      left
s1  2.456741  1.022440  1.035193
s2  2.441032  2.452331  1.054154
s3  4.233166  2.469494  1.048073
s4  4.179853  4.221801  2.422842
s5  6.397159  4.175642  2.456108
s6  4.217752  6.410110  4.223972
s7 -4.602003 -4.593739 -4.591626

Policy
s1      s2      s3      s4      s5      s6      s7
"hop" "right"   "hop" "right"   "hop" "right"  "left"

Reward (last iteration)
[1] 223


The recommended policy is: HOP, RIGHT, HOP, RIGHT, HOP, RIGHT, (STAY PUT)

If you tried this example and it didn’t produce the same response, don’t worry! Model-free reinforcement learning is done by simulation, and when you used the sampleExperience function, you generated a different set of state transitions to learn from. You may need more samples, or to tweak your rewards structure, or both.)

## Analytic Hierarchy Process (AHP) with the ahp Package

On my December to-do list, I had “write an R package to make analytic hierarchy process (AHP) easier” — but fortunately gluc beat me to it, and saved me tons of time that I spent using AHP to do an actual research problem. First of all, thank you for writing the new ahp package! Next, I’d like to show everyone just how easy this package makes performing AHP and displaying the results. We will use the Tom, Dick, and Harry example that is described on Wikipedia. – the goal is to choose a new employee, and you can pick either Tom, Dick, or Harry. Read the problem statement on Wikipedia before proceeding.

AHP is a method for multi-criteria decision making that breaks the problem down based on decision criteria, subcriteria, and alternatives that could satisfy a particular goal. The criteria are compared to one another, the alternatives are compared to one another based on how well they comparatively satisfy the subcriteria, and then the subcriteria are examined in terms of how well they satisfy the higher-level criteria. The Tom-Dick-Harry problem is a simple hierarchy: only one level of criteria separates the goal (“Choose the Most Suitable Leader”) from the alternatives (Tom, Dick, or Harry):

To use the ahp package, the most challenging part involves setting up the YAML file with your hierarchy and your rankings. THE MOST IMPORTANT THING TO REMEMBER IS THAT THE FIRST COLUMN IN WHICH A WORD APPEARS IS IMPORTANT. This feels like FORTRAN. YAML experts may be appalled that I just didn’t know this, but I didn’t. So most of the first 20 hours I spent stumbling through the ahp package involved coming to this very critical conclusion. The YAML AHP input file requires you to specify 1) the alternatives (along with some variables that describe the alternatives; I didn’t use them in this example, but I’ll post a second example that does use them) and 2) the goal hierarchy, which includes 2A) comparisons of all the criteria against one another FIRST, and then 2B) comparisons of the criteria against the alternatives. I saved my YAML file as tomdickharry.txt and put it in my C:/AHP/artifacts directory:

#########################
# Alternatives Section
# THIS IS FOR The Tom, Dick, & Harry problem at
#
Alternatives: &alternatives
# 1= not well; 10 = best possible
# Your assessment based on the paragraph descriptions may be different.
Tom:
age: 50
experience: 7
education: 4
Dick:
age: 60
experience: 10
education: 6
Harry:
age: 30
experience: 5
education: 8
#
# End of Alternatives Section
#####################################
# Goal Section
#
Goal:
# A Goal HAS preferences (within-level comparison) and HAS Children (items in level)
name: Choose the Most Suitable Leader
preferences:
# preferences are defined pairwise
# 1 means: A is equal to B
# 9 means: A is highly preferable to B
# 1/9 means: B is highly preferable to A
- [Experience, Education, 4]
- [Experience, Charisma, 3]
- [Experience, Age, 7]
- [Education, Charisma, 1/3]
- [Education, Age, 3]
- [Age, Charisma, 1/5]
children:
Experience:
preferences:
- [Tom, Dick, 1/4]
- [Tom, Harry, 4]
- [Dick, Harry, 9]
children: *alternatives
Education:
preferences:
- [Tom, Dick, 3]
- [Tom, Harry, 1/5]
- [Dick, Harry, 1/7]
children: *alternatives
Charisma:
preferences:
- [Tom, Dick, 5]
- [Tom, Harry, 9]
- [Dick, Harry, 4]
children: *alternatives
Age:
preferences:
- [Tom, Dick, 1/3]
- [Tom, Harry, 5]
- [Dick, Harry, 9]
children: *alternatives
#
# End of Goal Section
#####################################


Next, I installed gluc’s ahp package and a helper package, data.tree, then loaded them into R:

devtools::install_github("gluc/ahp", build_vignettes = TRUE)
install.packages("data.tree")

library(ahp)
library(data.tree)


Running the calculations was ridiculously easy:

setwd("C:/AHP/artifacts")
Calculate(myAhp)


And then generating the output was also ridiculously easy:

> GetDataFrame(myAhp)
Weight  Dick   Tom Harry Consistency
1 Choose the Most Suitable Leader 100.0% 49.3% 35.8% 14.9%        4.4%
2  ¦--Experience                   54.8% 39.3% 11.9%  3.6%        3.2%
3  ¦--Education                    12.7%  1.0%  2.4%  9.2%        5.6%
4  ¦--Charisma                     27.0%  5.2% 20.1%  1.7%        6.1%
5  °--Age                           5.6%  3.8%  1.5%  0.4%        2.5%
>
> print(myAhp, "weight", filterFun = isNotLeaf)
levelName     weight
1 Choose the Most Suitable Leader 1.00000000
2  ¦--Experience                  0.54756924
3  ¦--Education                   0.12655528
4  ¦--Charisma                    0.26994992
5  °--Age                         0.05592555
> print(myAhp, "weight")
levelName     weight
1  Choose the Most Suitable Leader 1.00000000
2   ¦--Experience                  0.54756924
3   ¦   ¦--Tom                     0.21716561
4   ¦   ¦--Dick                    0.71706504
5   ¦   °--Harry                   0.06576935
6   ¦--Education                   0.12655528
7   ¦   ¦--Tom                     0.18839410
8   ¦   ¦--Dick                    0.08096123
9   ¦   °--Harry                   0.73064467
10  ¦--Charisma                    0.26994992
11  ¦   ¦--Tom                     0.74286662
12  ¦   ¦--Dick                    0.19388163
13  ¦   °--Harry                   0.06325174
14  °--Age                         0.05592555
15      ¦--Tom                     0.26543334
16      ¦--Dick                    0.67162545
17      °--Harry                   0.06294121


You can also generate very beautiful output with the command below (but you’ll have to run the example yourself if you want to see how fantastically it turns out — maybe that will provide some motivation!)

ShowTable(myAhp)


I’ll post soon with an example of how to use AHP preference functions in the Tom, Dick, & Harry problem.

## My First (R) Shiny App: An Annotated Tutorial

Image Credit: Doug Buckley of http://hyperactive.to

I’ve been meaning to learn Shiny for 2 years now… and thanks to a fortuitous email from @ImADataGuy this morning and a burst of wild coding energy about 5 hours ago, I am happy to report that I have completely fallen in love again. The purpose of this post is to share how I got my first Shiny app up and running tonight on localhost, how I deployed it to the http://shinyapps.io service, and how you can create a “Hello World” style program of your own that actually works on data that’s meaningful to you.

If you want to create a “Hello World!” app with Shiny (and your own data!) just follow these steps:

0. Install R 3.2.0+ first! This will save you time.
1. I signed up for an account at http://shinyapps.io.
2. Then I clicked the link in the email they sent me.
3. That allowed me to set up my https://radziwill.shinyapps.io location.
4. Then I followed the instructions at https://www.shinyapps.io/admin/#/dashboard
of problems with devtools::install_github('rstudio/shinyapps') - Had to go
into my R directory, manually delete RCurl and digest, then
reinstall both RCurl and digest... then installing shinyapps worked.
Note: this last command they tell you to do WILL NOT WORK because you do not have an app yet!
If you try it, this is what you'll see:
> shinyapps::deployApp('path/to/your/app')
Error in shinyapps::deployApp("path/to/your/app") :
C:\Users\Nicole\Documents\path\to\your\app does not exist
5. Then I went to http://shiny.rstudio.com/articles/shinyapps.html and installed rsconnect.
6. I clicked on my name and gravatar in the upper right hand corner of the
"tokens". I realized I'd already done this part, so I skipped down to read
"A Demo App" on http://shiny.rstudio.com/articles/shinyapps.html
7. Then, I re-installed ggplot2 and shiny using this command:
install.packages(c('ggplot2', 'shiny'))
8. I created a new directory (C:/Users/Nicole/Documents/shinyapps) and used
setwd to get to it.
9. I pasted the code at http://shiny.rstudio.com/articles/shinyapps.html to create two files,
server.R and ui.R, which I put into my new shinyapps directory
under a subdirectory called demo. The subdirectory name IS your app name.
10. I typed runApp("demo") into my R console, and voila! The GUI appeared in
my browser window on my localhost.
-- Don't just try to close the browser window to get the Shiny app
to stop. R will hang. To get out of this, I had to use Task Manager and kill R.
--- Use the main menu, and do Misc -> Stop Current Computation
11. I did the same with the "Hello Shiny" code at http://shiny.rstudio.com/articles/shinyapps.html.
But what I REALLY want is to deploy a hello world app with MY OWN data. You know, something that's
meaningful to me. You probably want to do a test app with data that is meaningful to you... here's
how you can do that.
12. A quick search shows that I need jennybc's (Github) googlesheets package to get
data from Google Drive viewable in my new Shiny app.
13. So I tried to get the googlesheets package with this command:
but then found out it requires R version 3.2.0. I you already have 3.2.0 you can skip
to step 16 now.
14. So I reinstalled R using the installr package (highly advised if you want to
overcome the agony of upgrading on windows).
See http://www.r-statistics.com/2013/03/updating-r-from-r-on-windows-using-the-installr-package/
for info -- all it requires is that you type installR() -- really!
15. After installing R I restarted my machine. This is probably the first time in a month that
I've shut all my browser windows, documents, spreadsheets, PDFs, and R sessions. I got the feeling
that this made my computer happy.
16. Then, I created a Google Sheet with my data. While viewing that document, I went to
File -> "Publish to the Web". I also discovered that my DOCUMENT KEY is that
looooong string in the middle of the address, so I copied it for later:
1Bs0OH6F-Pdw5BG8yVo2t_VS9Wq1F7vb_VovOmnDSNf4
17. Then I created a new directory in C:/Users/Nicole/Documents/shinyapps to test out
jennybc's googlesheets package, and called it jennybc
18. I copied and pasted the code in her server.R file and ui.R file
into files with the same names in my jennybc directory
19. I went into my R console, used getwd() to make sure I was in the
C:/Users/Nicole/Documents/shinyapps directory, and then typed
runApp("jennybc")
20. A browser window popped up on localhost with her test Shiny app! I played with it, and then
closed that browser tab.
21. When I went back into the R console, it was still hanging, so I went to the menu bar
to Misc -> Stop Current Computation. This brought my R prompt back.
22. Now it was time to write my own app. I went to http://shiny.rstudio.com/gallery/ and
found a layout I liked (http://shiny.rstudio.com/gallery/tabsets.html), then copied the
server.R and ui.R code into C:/Users/Nicole/Documents/shinyapps/my-hello --
and finally, tweaked the code and engaged in about 100 iterations of: 1) edit the two files,
2) type runApp("my-hello") in the R console, 3) test my Shiny app in the
browser window, 4) kill browser window, 5) do Misc -> Stop Current Computation
in R. ALL of the computation happens in server.R, and all the display happens in ui.R:

server.R:

library(shiny)
library(DT)

my_key <- "1Bs0OH6F-Pdw5BG8yVo2t_VS9Wq1F7vb_VovOmnDSNf4"
my_ss <- gs_key(my_key)

shinyServer(function(input, output, session) {
output$plot <- renderPlot({ my_data$type <- ordered(my_data$type,levels=c("PRE","POST")) boxplot(my_data$score~my_data$type,ylim=c(0,100),boxwex=0.6) }) output$summary <- renderPrint({
aggregate(score~type,data=my_data, summary)
})
output\$the_data <- renderDataTable({
datatable(my_data)
})

})

ui.R:

library(shiny)
library(shinythemes)

shinyUI(fluidPage(

# Application title
titlePanel("Nicole's First Shiny App"),

# Sidebar with controls to select the random distribution type
# and number of observations to generate. Note the use of the
# br() element to introduce extra vertical spacing
sidebarLayout(
sidebarPanel(
helpText("This is my first Shiny app!! It grabs some of my data
also used lots of examples from"),
h6(a("http://shiny.rstudio.com/gallery/",
href="http://shiny.rstudio.com/gallery/", target="_blank")),
br(),
href="http://atomic-temporary-5081318.wpcomstaging.com/2015/12/08/my-first-shin
y-app-an-annotated-tutorial/",
target="_blank")),
br()
),

# Show a tabset that includes a plot, summary, and table view
# of the generated distribution
mainPanel(
tabsetPanel(type = "tabs",
tabPanel("Plot", plotOutput("plot")),
tabPanel("Summary", verbatimTextOutput("summary")),
tabPanel("Table", DT::dataTableOutput("the_data"))
)
)
)
))


23. Once I decided my app was good enough for my practice round, it was time to
deploy it to the cloud.
24. This part of the process requires the shinyapps and dplyr
packages, so be sure to install them:

library(dplyr)
devtools::install_github('rstudio/shinyapps')
library(shinyapps)
25. To deploy, all I did was this: setwd("C:/Users/Nicole/Documents/shinyapps/my-hello/")
deployApp()


## A Chat with Jaime Casap, Google’s Chief Education Evangelist

“The classroom of the future does not exist!”

That’s the word from Jaime Casap (@jcasap), Google’s Chief Education Evangelist — and a highly anticipated new Business Innovation Factory (BIF) storyteller for 2015.  In advance of the summit which takes place on September 16 and 17, Morgan and I had the opportunity to chat with Jaime about a form of business model innovation that’s close to our hearts – improving education. He’s a native New Yorker, so he’s naturally outspoken and direct. But his caring and considerate tone makes it clear he’s got everyone’s best interests at heart.

At Google, he’s the connector and boundary spanner… the guy the organization trusts to “predict the future” where education is concerned. He makes sure that the channels of communication are open between everyone working on education-related projects. Outside of Google, he advocates smart and innovative applications of technology in education that will open up educational opportunities for everyone.  Most recently, he visited the White House on this mission.

The current system educational system is not broken, he says. It’s doing exactly what it was designed to do: prepare workers for a hierarchical, industrialized production economy. The problem is that the system cannot be high-performing because it’s not doing what we need it to for the upcoming decades, which requires leveraging the skills and capabilities of everyone.

He points out that low-income minorities now have a 9% chance of graduating from college… whereas a couple decades ago, they had a 6% chance. This startling statistic reflects an underlying deficiency in how education is designed and delivered in this country today.

So how do we fix it?

“Technology gives us the ability to question everything,” he says.  As we shift to performance-based assessments, we can create educational experiences that are practical, iterative, and focused on continuous improvement — where we measure iteration, innovation, and sustained incremental progress.

Measuring these, he says, will be a lot more interesting than what we tend to measure now: whether a learner gets something right the first time — or how long it took for a competency to emerge. From this new perspective, we’ll finally be able to answer questions like: What is an excellent school? What does a high-performing educational system look (and feel) like?

Jaime’s opportunity-driven vision for inclusiveness  is an integral part of Google’s future. And you can hear more about his personal story and how it shaped this vision next month at BIF.

If you haven’t made plans already to hear Jaime and the other storytellers at BIF, there may be a few tickets left — but this event always sells out! Check the BIF registration page and share a memorable experience with the BIF community this year: http://www.businessinnovationfactory.com/summit/register