Deploying Your Very Own Shiny Server

Nicole has been having a lot of fun the last few days creating her own Shiny apps. We work in the same space, and let’s just say her enthusiasm is very contagious. While she focused on deploying R-based web apps on ShinyApps.io, I’m more of a web development geek, so I put my energy towards setting up a server where she could host her apps. This should come in handy, since she blew through all of her free server time on ShinyApps after just a couple of days!

Before you begin, you can see a working example of this at https://shinyisat.net/sample-apps/sampdistclt/.

In this tutorial, I’m going to walk you through the process of:

  1. Setting up an Ubuntu 14.04 + NGINX server at DigitalOcean
  2. Installing and configuring R
  3. Installing and configuring Shiny and the open-source edition of Shiny Server
  4. Installing a free SSL certificate from Let’s Encrypt
  5. Securing the Shiny Server using the SSL cert and reverse proxy through NGINX
  6. Setting appropriate permissions on the files to be served
  7. Creating and launching the app Nicole created in her recent post

Setting Up an Ubuntu 14.04 Server at DigitalOcean

DigitalOcean is my new favorite web host. (Click this link to get a $10 credit when you sign up!) They specialize in high-performance, low-cost, VPS (virtual private servers) targeted at developers. If you want full control over your server, you can’t beat their $5/month offering. They also provide excellent documentation. In order to set up your server, you should start by following these tutorials:

  1. How to Create Your First DigitalOcean Droplet Virtual Server
  2. How to Connect to Your Droplet with SSH
  3. Initial Server Setup with Ubuntu 14.04
  4. Additional Recommended Steps for New Ubuntu 14.04 Servers
  5. How To Protect SSH with Fail2Ban on Ubuntu 14.04

I followed these pretty much exactly without any difficulties. I did make a few changes to their procedure, which I’ll describe next.

Allowing HTTPS with UFW

I found that the instructions for setting up ufw needed a tweak. Since HTTPS traffic uses port 443 on the server, I thought that sudo ufw allow 443/tcp should take care of letting HTTPS traffic through the firewall. Unfortunately, it doesn’t. In addition you should run the following:


$ sudo ufw allow https

$ sudo ufw enable

Your web server may not accept incoming HTTPS traffic if you do not do this. Note: you may not have noticed, but you also installed NGINX as part of the UFW tutorial.

Setting up Automatic Updates on Your Server

The default install of Ubuntu at DigitalOcean comes with the automatic updates package already installed. This means your server will get security packages and upgrades without you having to do it manually. However, this package needs to be configured. First, edit /etc/apt/apt.conf.d/50unattended-upgrades to look like this:

Unattended-Upgrade::Allowed-Origins {
   "${distro_id}:${distro_codename}-security";
   "${distro_id}:${distro_codename}-updates";
};
Unattended-Upgrade::Mail "admin@mydomain.com";
Unattended-Upgrade::Remove-Unused-Dependencies "true";
Unattended-Upgrade::Automatic-Reboot "true";
Unattended-Upgrade::Automatic-Reboot-Time "02:00";

Note, that this configuration will install upgrades and security updates, and will automatically reboot your server, if necessary, at 2:00AM, and it will purge unused packages from your system completely. Some people don’t like to have that much stuff happen automatically without supervision. Also, my /etc/apt/apt.conf.d/10periodic file looks like:

APT::Periodic::Update-Package-Lists "1";
APT::Periodic::Download-Upgradeable-Packages "1";
APT::Periodic::AutocleanInterval "7";
APT::Periodic::Unattended-Upgrade "1";

Which sets upgrades to happen daily, and purges to happen once a week.

Installing and Configuring R

Okay, now that your server is set up (you should be able to view the default NGINX page at http://your-domain-name.com), it’s time to install R.

Set the CRAN Repository in Ubuntu’s sources.list

The first step is to add your favorite CRAN repository to Ubuntu’s sources list. This will ensure that you get the latest version of R when you install it. To open and edit the sources list, type the following:


$ sudo nano /etc/apt/sources.list

Move the cursor down to the bottom of this file using the arrow keys, and add the following line at the bottom:


deb https://cran.cnr.berkeley.edu/bin/linux/ubuntu trusty/

Of course, you can substitute your favorite CRAN repo here. I like Berkeley. Don’t miss that there is a space between “ubuntu” and “trusty”. Hit CTRL+x to exit from this file. Say “yes” when they ask if you want to save your changes. The official docs on installing R packages on Ubuntu also recommend that you activate the backports repositories as well, but I found that this was already done on my DigitalOcean install.

Add the Public Key for the Ubuntu R Package

In order for Ubuntu to be able to recognize, and therefore trust, download, and install the R packages from the CRAN repo, we need to install the public key. This can be done with the following command:


$ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 51716619E084DAB9

Install R

Run the following:


$ sudo apt-get update

$ sudo apt-get install r-base

When this is finished, you should be able to type R –version and get back the following message:


$ R --version

R version 3.2.2 (2015-08-14) -- "Fire Safety"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under the terms of the
GNU General Public License versions 2 or 3.
For more information about these matters see
http://www.gnu.org/licenses/.

If you get this, you’ll know that R was successfully installed on your server. If not, you’ll need to do some troubleshooting.

Configure R to Use curl and Your CRAN Repository of Choice

Type the following to open up the Rprofile.site file:


$ sudo pico /etc/R/Rprofile.site

You may delete all of the content and add the following:


options(download.file.method="libcurl")

local({
    r <- getOption("repos")
    r["CRAN"] <- "https://cran.rstudio.com/"
    options(repos=r)
})

This will allow us to run install.packages('packagename') without specifying the repository later.

Install Dependencies and Packages Needed by Shiny Server

We’re going to need the devtools package, which means we need to install the libraries upon which it depends first (libcurl and libxml2):


$ sudo apt-get -y build-dep libcurl4-gnutls-dev

$ sudo apt-get -y install libcurl4-gnutls-dev

$ sudo apt-get -y build-dep libxml2-dev

$ sudo apt-get -y install libxml2-dev

Now we can install devtools, rsconnect, and rmarkdown:


$ sudo su - -c "R -e \"install.packages('devtools')\""

$ sudo su - -c "R -e \"devtools::install_github('rstudio/rsconnect')\""

$ sudo su - -c "R -e \"install.packages('rmarkdown')\""

$ sudo su - -c "R -e \"install.packages('shiny')\""

Install Shiny Server

Okay! Now we’re finally ready to install Shiny Server. Run the following:


$ cd ~ 
$ sudo apt-get install gdebi-core
$ wget https://download3.rstudio.org/ubuntu-12.04/x86_64/shiny-server-1.4.1.759-amd64.deb
$ sudo gdebi shiny-server-1.4.1.759-amd64.deb

At this point, your Shiny Server should be up and running, but we can’t visit it on the web yet because by default, it runs on port 3838, which is blocked by the firewall we set up earlier. We’re now going to secure it, and use a reverse proxy to run it through NGINX.

Install an SSL Certificate with Let’s Encrypt

Let’s Encrypt is a new, free service that will allow you to install a trusted SSL certificate on your server. Since Google and Mozilla are working hard to phase out all non-HTTPS traffic on the web, it’s a good idea to get into the habit of installing SSL certs whenever you set up a new website. First install git, then use it to download letsencrypt:


$ sudo apt-get install git
$ git clone https://github.com/letsencrypt/letsencrypt
$ cd letsencrypt

Now before we install the certificate, we have to stop our web server (NGINX). In the code below, replace yourdomain.com with your actual domain name that you registered for this site.


$ sudo service nginx stop
$ sudo ./letsencrypt-auto certonly --standalone -d yourdomain.com -d www.yourdomain.com

If all goes well, it should have installed your new certificates in the /etc/letsencrypt/live/yourdomain.com folder.

Configure the Reverse Proxy on NGINX

Open up the following file for editing:


$ sudo nano /etc/nginx/nginx.conf

And add the following lines near the bottom of the main http block, just before the section labeled “Virtual Host Configs”. In my file, this started around line 62:


...

##
# Map proxy settings for RStudio
##
map $http_upgrade $connection_upgrade {
    default upgrade;
    '' close;
}

##
# Virtual Host Configs
##
...

And then open up the default site config file:


$ sudo nano /etc/nginx/sites-available/default

And replace its contents with the following. Note you should replace yourdomain.com with your actual domain name, and 123.123.123.123 with the actual IP address of your server.


server {
   listen 80 default_server;
   listen [::]:80 default_server ipv6only=on;
   server_name yourdomain.com www.yourdomain.com;
   return 301 https://$server_name$request_uri;
}
server {
   listen 443 ssl;
   server_name yourdomain.com www.yourdomain.com;
   ssl_certificate /etc/letsencrypt/live/yourdomain.com/fullchain.pem;
   ssl_certificate_key /etc/letsencrypt/live/yourdomain.com/privkey.pem;
   ssl_protocols TLSv1 TLSv1.1 TLSv1.2;
   ssl_prefer_server_ciphers on;
   ssl_ciphers AES256+EECDH:AES256+EDH:!aNULL;

   location / {
       proxy_pass http://123.123.123.123:3838;
       proxy_redirect http://123.123.123.123:3838/ https://$host/;
       proxy_http_version 1.1;
       proxy_set_header Upgrade $http_upgrade;
       proxy_set_header Connection $connection_upgrade;
       proxy_read_timeout 20d;
   }
}

Now start NGINX up again:


$ sudo service nginx start

And if all went well, your new Shiny Server should be up and running at https://yourdomain.com!

Note that even if you try to go to the insecure URL, traffic will be automatically redirected through HTTPS.

Setting Appropriate Permissions

Sometimes, your Shiny apps will need access to the filesystem to read or write files. Since the Shiny server runs as the user shiny, and since all the files that are being served are owned by root, then your apps will crash when they try to access files. I like Dean Attali’s solution. Run the following commands, substituting yourusername with the username you are using to access the server:


$ sudo groupadd shiny-apps
$ sudo usermod -aG shiny-apps yourusername
$ sudo usermod -aG shiny-apps shiny
$ cd /srv/shiny-server
$ sudo chown -R yourusername:shiny-apps .
$ sudo chmod g+w .
$ sudo chmod g+s .

In the future, any time you add files under /srv/shiny-server, you may need to change the permissions so the Shiny server can read them. We’ll do that in a moment.

Installing a New App

Finally, I’m going to show you how to put a new app on the server. We’re going to use the app that Nicole created and add it into the “sample apps” folder. Run the following:


$ cd /srv/shiny-server/sample-apps
$ mkdir sampdistclt
$ cd sampdistclt
$ nano server.R

This will create a new file called server.R and open it for editing. Copy and paste the second half of the code from Nicole’s post (the part that starts with ## server) into this file. Save and exit. Now create a second file in this directory called ui.R and paste the code from the first half of Nicole’s post (the part that starts with ## ui up to but not including the part that starts ## server). Save and exit.

Now you need to make sure that the permissions are set correctly:


$ chown -R :shiny-apps .

You may also need to restart the Shiny and/or NGINX servers. The commands to do that are:


$ sudo service nginx restart
$ sudo service shiny-server restart

If all has gone well, you can now view the app up and running at https://yourdomain.com/sample-apps/sampdistclt!

Conclusion

I haven’t had a lot of time to use this configuration, so please let me know if you find any bugs, or things that need to be tweaked. On the plus side, this configuration may be cheaper than using ShinyApps.io, but it also doesn’t have all the cool bells and whistles that you get there, either, like their user interface and monitoring traffic. At the very least, it should be a way to experiment, and put things out in the public for others to play with. Enjoy!

A Discrete Time Markov Chain (DTMC) SIR Model in R

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

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

There are many different techniques that be used to model physical, social, economic, and conceptual systems. The purpose of this post is to show how the Kermack-McKendrick (1927) formulation of the SIR Model for studying disease epidemics (where S stands for Susceptible, I stands for Infected, and R for Recovered) can be easily implemented in R as a discrete time Markov Chain using the markovchain package.

A Discrete Time Markov Chain (DTMC) is a model for a random process where one or more entities can change state between distinct timesteps. For example, in SIR, people can be labeled as Susceptible (haven’t gotten a disease yet, but aren’t immune), Infected (they’ve got the disease right now), or Recovered (they’ve had the disease, but no longer have it, and can’t get it because they have become immune). If they get the disease, they change states from Susceptible to Infected. If they get well, they change states from Infected to Recovered. It’s impossible to change states between Susceptible and Recovered, without first going through the Infected state. It’s totally possible to stay in the Susceptible state between successive checks on the population, because there’s not a 100% chance you’ll actually be infected between any two timesteps. You might have a particularly good immune system, or maybe you’ve been hanging out by yourself for several days programming.

Discrete time means you’re not continuously monitoring the state of the people in the system. It would get really overwhelming if you had to ask them every minute “Are you sick yet? Did you get better yet?” It makes more sense to monitor individuals’ states on a discrete basis rather than continuously, for example, like maybe once a day. (Ozgun & Barlas (2009) provide a more extensive illustration of the difference between discrete and continuous modeling, using a simple queuing system.)

To create a Markov Chain in R, all you need to know are the 1) transition probabilities, or the chance that an entity will move from one state to another between successive timesteps, 2) the initial state (that is, how many entities are in each of the states at time t=0), and 3) the markovchain package in R. Be sure to install markovchain before moving forward.

Imagine that there’s a 10% infection rate, and a 20% recovery rate. That implies that 90% of Susceptible people will remain in the Susceptible state, and 80% of those who are Infected will move to the Recovered Category, between successive timesteps. 100% of those Recovered will stay recovered. None of the people who are Recovered will become Susceptible.

Say that you start with a population of 100 people, and only 1 is infected. That means your “initial state” is that 99 are Susceptible, 1 is Infected, and 0 are Recovered. Here’s how you set up your Markov Chain:

library(markovchain)
mcSIR <- new("markovchain", states=c("S","I","R"),
    transitionMatrix=matrix(data=c(0.9,0.1,0,0,0.8,0.2,0,0,1),
    byrow=TRUE, nrow=3), name="SIR")
initialState <- c(99,1,0)

At this point, you can ask R to see your transition matrix, which shows the probability of moving FROM each of the three states (that form the rows) TO each of the three states (that form the columns).

> show(mcSIR)
SIR
 A  3 - dimensional discrete Markov Chain with following states
 S I R 
 The transition matrix   (by rows)  is defined as follows
    S   I   R
S 0.9 0.1 0.0
I 0.0 0.8 0.2
R 0.0 0.0 1.0

You can also plot your transition probabilities:

plot(mcSIR,package="diagram")

dtmc-sir-transitionnetwork

But all we’ve done so far is to create our model. We haven’t yet done a simulation, which would show us how many people are in each of the three states as you move from one discrete timestep to many others. We can set up a data frame to contain labels for each timestep, and a count of how many people are in each state at each timestep. Then, we fill that data frame with the results after each timestep i, calculated by initialState*mcSIR^i:

timesteps <- 100
sir.df <- data.frame( "timestep" = numeric(),
 "S" = numeric(), "I" = numeric(),
 "R" = numeric(), stringsAsFactors=FALSE)
 for (i in 0:timesteps) {
newrow <- as.list(c(i,round(as.numeric(initialState * mcSIR ^ i),0)))
 sir.df[nrow(sir.df) + 1, ] <- newrow
 }

Now that we have a data frame containing our SIR results (sir.df), we can display them to see what the values look like:

> head(sir.df)
  timestep  S  I  R
1        0 99  1  0
2        1 89 11  0
3        2 80 17  2
4        3 72 22  6
5        4 65 25 10
6        5 58 26 15

And then plot them to view our simulation results using this DTMC SIR Model:

plot(sir.df$timestep,sir.df$S)
points(sir.df$timestep,sir.df$I, col="red")
points(sir.df$timestep,sir.df$R, col="green")

dtmc-sir-simulation

It’s also possible to use the markovchain package to identify elements of your system as it evolves over time:

> absorbingStates(mcSIR)
[1] "R"
> transientStates(mcSIR)
[1] "S" "I"
> steadyStates(mcSIR)
     S I R
[1,] 0 0 1

And you can calculate the first timestep that your Markov Chain reaches its steady state (the “time to absorption”), which your plot should corroborate:

> ab.state <- absorbingStates(mcSIR)
> occurs.at <- min(which(sir.df[,ab.state]==max(sir.df[,ab.state])))
> (sir.df[row,]$timestep)+1
[1] 58

You can use this code to change the various transition probabilities to see what the effects are on the outputs yourself (sensitivity analysis). Also, there are methods you can use to perform uncertainty analysis, e.g. putting confidence intervals around your transition probabilities. We won’t do either of these here, nor will we create a Shiny app to run this simulation, despite the significant temptation.

My Second (R) Shiny App: Sampling Distributions & CLT

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

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

I was so excited about my initial foray into Shiny development using jennybc‘s amazing googlesheets package that I stayed up half the night last night (again) working on my second Shiny app: a Shiny-fied version of the function I shared in March to do simulations illustrating sampling distributions and the Central Limit Theorem using many different source distributions. (Note that Cauchy doesn’t play by the rules!) Hope this info is useful to all new Shiny developers.

If the app doesn’t work for you, it’s possible that I’ve exhausted my purchased hours at http://shinyapps.io — no idea how much traffic this post might generate. So if that happens to you, please try getting Shiny to work locally, cutting and pasting the code below into server.R and ui.R files, and then launching the simulation from your R console.

Here are some important lessons I learned on my 2nd attempt at Shiny development:

  • Creating a container (rv) for the server-side values that would change as a result of inputs from the UI was important. That container was then available to the portions of my Shiny code that prepared data for the UI, e.g. output$plotSample.
  • Because switch only takes arguments that are 1 character long, using radio buttons in the Shiny UI was really useful: I can map the label on each radio button to one character that will get passed into the data processing on the server side.
  • I was able to modify the CSS for the page by adding a couple lines to mainPanel() in my UI.
  • Although it was not mentally easy (for me) to convert from an R function to a Shiny app when initially presented with the problem, in retrospect, it was indeed straightforward. All I had to do was take the original function, split out the data processing from the presentation (par & hist commands), put the data processing code on the server side and the presentation code on the UI side, change the variable names on the server side so that they had the input$ prefix, and make sure the variable names were consistent between server and UI.
  • I originally tried writing one app.R file, but http://shinyapps.io did not seem to like that, so I put all the code that was not UI into the server side and tried deploying with server.R and ui.R, which worked. I don’t know what I did wrong.
  • If you want to publish to http://shinyapps.io, the directory name that hosts your files must be at least 4 characters long or you will get a “validation error” when you attempt to deployApp().
## Nicole's Second Shiny Demo App
## N. Radziwill, 12/6/2015, http://qualityandinnovation.com
## Used code from http://github.com/homerhanumat as a base
###########################################################
## ui
###########################################################

ui <- fluidPage(
titlePanel('Sampling Distributions and the Central Limit Theorem'),
sidebarPanel(
helpText('Choose your source distribution and number of items, n, in each
sample. 10000 replications will be run when you click "Sample Now".'),
h6(a("Read an article about this simulation at http://www.r-bloggers.com",
href="http://www.r-bloggers.com/sampling-distributions-and-central-limit-theorem-in-r/", target="_blank")),
sliderInput(inputId="n","Sample Size n",value=30,min=5,max=100,step=2),
radioButtons("src.dist", "Distribution type:",
c("Exponential: Param1 = mean, Param2 = not used" = "E",
"Normal: Param1 = mean, Param2 = sd" = "N",
"Uniform: Param1 = min, Param2 = max" = "U",
"Poisson: Param1 = lambda, Param2 = not used" = "P",
"Cauchy: Param1 = location, Param2 = scale" = "C",
"Binomial: Param1 = size, Param2 = success prob" = "B",
"Gamma: Param1 = shape, Param2 = scale" = "G",
"Chi Square: Param1 = df, Param2 = ncp" = "X",
"Student t: Param1 = df, Param2 = not used" = "T")),
numericInput("param1","Parameter 1:",10),
numericInput("param2","Parameter 2:",2),
actionButton("takeSample","Sample Now")
), # end sidebarPanel
mainPanel(
# Use CSS to control the background color of the entire page
tags$head(
tags$style("body {background-color: #9999aa; }")
),
plotOutput("plotSample")
) # end mainPanel
) # end UI

##############################################################
## server
##############################################################

library(shiny)
r <- 10000 # Number of replications... must be ->inf for sampling distribution!

palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))

server <- function(input, output) {
set.seed(as.numeric(Sys.time()))

# Create a reactive container for the data structures that the simulation
# will produce. The rv$variables will be available to the sections of your
# server code that prepare output for the UI, e.g. output$plotSample
rv <- reactiveValues(sample = NULL,
all.sums = NULL,
all.means = NULL,
all.vars = NULL)

# Note: We are giving observeEvent all the output connected to the UI actionButton.
# We can refer to input variables from our UI as input$variablename
observeEvent(input$takeSample,
{
my.samples <- switch(input$src.dist,
"E" = matrix(rexp(input$n*r,input$param1),r),
"N" = matrix(rnorm(input$n*r,input$param1,input$param2),r),
"U" = matrix(runif(input$n*r,input$param1,input$param2),r),
"P" = matrix(rpois(input$n*r,input$param1),r),
"C" = matrix(rcauchy(input$n*r,input$param1,input$param2),r),
"B" = matrix(rbinom(input$n*r,input$param1,input$param2),r),
"G" = matrix(rgamma(input$n*r,input$param1,input$param2),r),
"X" = matrix(rchisq(input$n*r,input$param1),r),
"T" = matrix(rt(input$n*r,input$param1),r))

# It was very important to make sure that rv contained numeric values for plotting:
rv$sample <- as.numeric(my.samples[1,])
rv$all.sums <- as.numeric(apply(my.samples,1,sum))
rv$all.means <- as.numeric(apply(my.samples,1,mean))
rv$all.vars <- as.numeric(apply(my.samples,1,var))
}
)

output$plotSample <- renderPlot({
# Plot only when user input is submitted by clicking "Sample Now"
if (input$takeSample) {
# Create a 2x2 plot area & leave a big space (5) at the top for title
par(mfrow=c(2,2), oma=c(0,0,5,0))
hist(rv$sample, main="Distribution of One Sample",
ylab="Frequency",col=1)
hist(rv$all.sums, main="Sampling Distribution of the Sum",
ylab="Frequency",col=2)
hist(rv$all.means, main="Sampling Distribution of the Mean",
ylab="Frequency",col=3)
hist(rv$all.vars, main="Sampling Distribution of the Variance",
ylab="Frequency",col=4)
mtext("Simulation Results", outer=TRUE, cex=3)
}
}, height=660, width=900) # end plotSample

} # end server

My First (R) Shiny App: An Annotated Tutorial

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

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
(This page has SPECIAL SECRET INFO CUSTOMIZED JUST FOR YOU ON IT!!) I had lots 
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 
https://www.shinyapps.io/admin/#/dashboard window I had opened, and then clicked 
"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:
devtools::install_github('jennybc/googlesheets')
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
from https://github.com/jennybc/googlesheets/tree/master/inst/shiny-examples/01_read-public-sheet 
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(googlesheets)
library(DT)

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

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)
library(googlesheets)

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 
from a Google Spreadsheet, and displays it here. I      
also used lots of examples from"),
     h6(a("http://shiny.rstudio.com/gallery/", 
href="http://shiny.rstudio.com/gallery/", target="_blank")),
     br(),
     h6(a("Click Here for a Tutorial on How It Was Made", 
href="https://qualityandinnovation.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:

devtools::install_github('hadley/dplyr')
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()

CHECK OUT MY SHINY APP!!

If Japan Can, Why Can’t We? A Retrospective

if-japan-canJune 24, 1980 is kind of like July 4, 1776 for quality management… that’s the pivotal day that NBC News aired its one hour and 16 minute documentary called “If Japan Can, Why Can’t We?” introducing W. Edwards Deming and his methods to the American public. The video has been unavailable for years, but as of just last week, it’s been posted on YouTube. So my sophomore undergrads in Production & Operations Management took a step back in time to get a taste of the environment in the manufacturing industry in the late 1970’s, and watched it during class this week.

The last time I watched it was in 1997, in a graduate industrial engineering class. It didn’t feel quite as dated as it does now, nor did I have the extensive experience in industry as a lens to view the interviews through. But what did surprise me is that the core of the challenges they were facing aren’t that much different than the ones we face today — and the groundbreaking good advice from Deming is still good advice today.

  • Before 1980, it was common practice to produce a whole bunch of stuff and then check and see which ones were bad, and throw them out. The video provides a clear and consistent story around the need to design quality in to products and processes, which then reduces (or eliminates) the need to inspect bad quality out.
  • It was also common to tamper with a process that was just exhibiting random variation. As one of the line workers in the documentary said, “We didn’t know. If we felt like there might be a problem with the process, we would just go fix it.” Deming’s applications of Shewhart’s methods made it clear that there is no need to tamper with a process that’s exhibiting only random variation.
  • Both workers and managers seemed frustrated with the sheer volume of regulations they had to address, and noted that it served to increase costs, decrease the rate of innovation, and disproportionately hurt small businesses. They noted that there was a great need for government and industry to partner to resolve these issues, and that Japan was a model for making these interactions successful.
  • Narrator Lloyd Dobyns remarked that “the Japanese operate by consensus… we, by competition.” He made the point that one reason Japanese industrial reforms were so powerful and positive was that their culture naturally supported working together towards shared goals. He cautioned managers that they couldn’t just drop in statistical quality control and expect a rosy outcome: improving quality is a cultural commitment, and the methods are not as useful in the absence of buy-in and engagement.

The video also sheds light on ASQ’s November question to the Influential Voices, which is: “What’s the key to talking quality with the C-Suite?” Typical responses include: think at the strategic level; create compelling arguments using the language of money; learn the art of storytelling and connect your case with what it important to the executives.

But I think the answer is much more subtle. In the 1980 video, workers comment on how amazed their managers were when Deming proclaimed that management was responsible for improving productivity. How could that be??!? Many managers at that time were convinced that if a productivity problem existed, it was because the workers didn’t work fast enough, or with enough skill — or maybe they had attitude problems! Certainly not because the managers were not managing well. Implementing simple techniques like improving training programs and establishing quality circles (which demonstrated values like increased transparency, considering all ideas, putting executives on the factory floor so they could learn and appreciate the work being done, increasing worker participation and engagement, encouraging work/life balance, and treating workers with respect and integrity) were already demonstrating benefits in some U.S. companies. But surprisingly, these simple techniques were not widespread, and not common sense.

Just like Deming advocated, quality belongs to everyone. You can’t go to a CEO and suggest that there are quality issues that he or she does not care about. More likely, the CEO believes that he or she is paying a lot of attention to quality. They won’t like it if you accuse them of not caring, or not having the technical background to improve quality. The C-Suite is in a powerful position where they can, through policies and governance, influence not only the actions and operating procedures of the system, but also its values and core competencies — through business model selection and implementation. 

What you can do, as a quality professional, is acknowledge and affirm their commitment to quality. Communicate quickly, clearly, and concisely when you do. Executives have to find the quickest ways to decompose and understand complex problems in rapidly changing external environments, and then make decisions that affect thousands (and sometimes, millions!) of people. Find examples and stories from other organizations who have created huge ripples of impact using quality tools and technologies, and relate them concretely to your company.

Let the C-Suite know that you can help them leverage their organization’s talent to achieve their goals, then continually build their trust.

The key to talking quality with the C-suite is empathy.

 

You may also be interested in “Are Deming’s 14 Points Still Valid?” from Nov 19, 2012.

Control Charts in R: A Guide to X-Bar/R Charts in the qcc Package

xbar-chartStatistical process control provides a mechanism for measuring, managing, and controlling processes. There are many different flavors of control charts, but if data are readily available, the X-Bar/R approach is often used. The following PDF describes X-Bar/R charts and shows you how to create them in R and interpret the results, and uses the fantastic qcc package that was developed by Luca Scrucca. Please let me know if you find it helpful!

Creating and Interpreting X-Bar/R Charts in R

What if Your Job Was Focused on Play?

james-siegal

James Siegal (picture from his Twitter profile, @jsiegal at http://twitter.com/jsiegal)

Last weekend, I had the opportunity to talk to James Siegal, the President of KaBOOM! – a non-profit whose mission is lighthearted, but certainly not frivolous: to bring balanced and active play into the daily lives of all kids! James is another new Business Innovation Factory (BIF) storyteller for 2015… and I wanted to find out how I could learn from his experiences to bring a sense of play into the work environment. (For me, that’s at a university, interacting with students on a daily basis.)

Over the past 20 years, KaBOOM! has built thousands of playgrounds, focusing on children growing up in poverty. By enlisting the help of over a million volunteers, James and his organization have mobilized communities using a model that starts with kids designing their dream playgrounds. It’s a form of crowdsourced placemaking.

Now, KaBOOM! is thinking about a vision that’s a little broader: driving social change at the city level. Doing this, they’ve found, requires answering one key question: How can you integrate play into the daily routine for kids and families? If play is a destination, there are “hassle factors” that must be overcome: safety, travel time, good lighting, and restroom facilities, for starters. So, in addition to building playgrounds, KaBOOM! is challenging cities to think about integrating play everywhere — on the sidewalk, at the bus stop, and beyond.

How can this same logic apply to organizations integrating play into their cultures? Although KaBOOM! focuses on kids, he had some more generalizable advice:

  • The desire for play has to be authentic, not forced. “We truly value kids, and we truly value families. Our policies and our culture strive to reflect that.” What does your organization value at its core? Seek to amplify the enjoyment of that.
  • We take our work really seriously,” he said. “We don’t take ourselves too seriously. You have to leave your ego at the door.” Can your organization engage in more playful collaboration?
  • We drive creativity out of kids as they grow older, he noted. “Kids expect to play everywhere,” and so even ordinary elements like sidewalks can turn into experiences. (This reminded me of how people decorate the Porta-Potties at Burning Man with lights and music… although I wouldn’t necessarily do the same thing to the restrooms at my university, it did make me think about how we might make ordinary places or situations more fun for our students.)

KaBOOM! is such a unique organization that I had to ask James: what’s the most amazing thing you’ve ever observed in your role as President? He says it’s something that hasn’t just happened once… but happens every time KaBOOM! organizes a new playground build. When people from diverse backgrounds come together with a strong shared mission, vision, and purpose, you foster intense community engagement that yields powerful, tangible results — and this is something that so many organizations strive to achieve.

If you haven’t made plans already to hear James 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

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