Shiny: data presentation with an extra

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Shiny: data presentation with an extra

(This article was first published on R-posts.com, and kindly contributed to R-bloggers)

A Shiny app with three tabs presenting different sections of the same data.

Shiny is an application based on R/RStudio which enables an interactive exploration of data through a dashboard with drop-down lists and checkboxes—programming-free. The apps can be useful for both the data analyst and the public.

Shiny apps are based on the Internet: This allows for private consultation of the data on one’s own browser as well as for online publication. Free apps can handle quite a lot of data, which can be increased with a subscription.

The target user of Shiny is extremely broad. Let’s take science—open science. At a time when openly archiving all data is becoming standard practice (e.g., OSF.io, Figshare.com, Github.com), Shiny can be used to walk the extra mile by letting people tour the data at once without programming. It’s the right tool for acknowledging all aspects of the data. Needless to say, these apps do not replace raw data archiving.

The apps simply add. For instance, the data in the lower app is a little noisy, right? Indeed it shows none of the succession of waves that characterizes word reading. The app helped me in identifying this issue. Instead of running along a host of participants and electrodes through a heavy code score, I found that the drop-down lists of the app let me seamlessly surf the data. By Participant 7, though, my wave dropped me…
 

Those data were very poor—systematically poorer than those of any other participants. I then checked the EEG preprocessing logs, and confirmed that those data had to be discarded. So much for the analysis utility of such an app. On the open science utility, what I did on discovering the fault was maintain the discarded data in the app, with a note, so that any colleagues and reviewers could consider it too. Now, although this example of use concerns a rather salient trait in the data, some other Shiny app might help to spot patterns such as individual differences, third variables.

Building a Shiny app is not difficult. Apps basically draw on some data presentation code (tables, plots) that you already have. Then just add a couple of scripts into the folder: one for the user interface (named iu.R), one for the process (named server.R), and perhaps another one compiling the commands for deploying the app and checking any errors.

The steps to start a Shiny app from scratch are:

1: Tutorials. Being open-source software, the best manuals are available through a Google search.

2: User token. Signing up and reading in your private key—just once.

3: GO. Plunge into the ui and server scripts, and deployApp().

4: Bugs and logs. They are not bugs in fact—rather fancies. For instance, some special characters have to get even more special (technically, UTF-8 encoding). For a character such as μ, Shiny prefers Âμ. Just cling to error logs by calling:

showLogs(appPath = getwd(), appFile = NULL, appName = NULL, account = NULL, entries = 50, streaming = FALSE)

The log output will mention any typos and unaccepted characters, pointing to specific lines in your code.

It may take a couple of intense days to get a first app running. As usual with programming, it’s easy to run into the traps which are there to spice up the way. The app’s been around for years, so tips and tricks abound on the Internet. For greater companionship, there are dedicated Google groups, and then there’s StackExchange etc., where you can post any needs/despair. Post your code, log, and explanation, and you’ll be rescued out in a couple of days. Long live those contributors.

It will often be enough to upload a bare app, but you might then think it can look better.

5 (optional): Pro up. Use tabs to combine multiple apps in one, use different widgets, etc. Tutorials like this one on Youtube can take you there, especially those that provide the code, as in the description of that video. Use those scripts as templates. For example, see this script in which the function conditionalPanel() is used to modify the app’s sidebar based on which tab is selected. The utility of tabs is illustrated in the upper cover of this article and in the app shown in the text: When having multiple data sections, the tabs allow you to have all in one (cover screenshot), instead of linking to other apps in each (screenshot in text).

Time for logistics. You can include any text in your app’s webpage, such as explanations of any length, web links, and author information. Oh, also importantly: the Shiny application allows for the presentation of data in any of the forms available in R—notably, plots and tables. Costs: Shiny is free to use, in principle. You may only have to pay if you use your app(s) a lot—first month, commonly—, in which case you may pay 9 euros a month. There are different subscription plans. The free plan allows 25 hours of use per month, distributed among up to five apps.

There do exist alternatives to Shiny. One is fairly similar: It’s called Tableau. A nice comparison of the two apps is available here. Then, there’s a more advanced application called D3.js, which allows for lush graphics but proves more restrictive to newbies.

In sum, if you already program in R or even in Python, but have not tried online data presentation yet, consider it.

Feel free to share your ideas, experiences, or doubts in a comment on the original post.

To leave a comment for the author, please follow the link and comment on their blog: R-posts.com.

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New online datacamp course: Forecasting in R

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New online datacamp course: Forecasting in R

(This article was first published on R-posts.com, and kindly contributed to R-bloggers)

Forecasting in R is taught by Rob J. Hyndman, author of the forecast package

Forecasting involves making predictions about the future. It is required in many situations such as deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

What You’ll Learn
Chapter 1: Exploring and Visualizing Time Series in R
The first thing to do in any data analysis task is to plot the data.
Chapter 2: Benchmark Methods and Forecast Accuracy
In this chapter, you will learn general tools that are useful for many different forecasting situations.
Chapter 3: Exponential Smoothing
is framework generates reliable forecasts quickly and for a wide range of time series.
Chapter 4: Forecasting with ARIMA Models
ARIMA models provide another approach to time series forecasting.
Chapter 5: Advanced Methods
In this chapter, you will look at some methods that handle more complicated seasonality.

You can start the free chapter for free of Forecasting in R.

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simmer 3.6.2

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simmer 3.6.2

(This article was first published on R – Enchufa2, and kindly contributed to R-bloggers)

The second update of the 3.6.x release of simmer, the Discrete-Event Simulator for R, is on CRAN, thus inaugurating a bi-monthly release cycle. I must thank Duncan Garmonsway (@nacnudus) for creating and now maintaining “The Bank Tutorial: Part I” vignette, Franz Fuchs for finding an important and weird memory bug (here) that prevented simmer from freeing the allocated memory (all 3.x.x versions are affected up to this release), and the Rcpp people for enduring me while I was helplessly searching for a solution to this.

My special thanks to Kevin Ushey (@kevinushey), who finally found the bug. As it happens, the bug was not in simmer or Rcpp but in magrittr, and the problem is that the pipe operator, in its inscrutable magic, creates a new environment for unnamed functions (instead of the current one, as it should be), and there it stores a reference to the first object in the pipe. More or less. Further details here.

Anyway, if somebody faces the same problem, know that there is a workaround: you just need to delete that hidden reference, as simmer does in this release to get rid of the memory issues. Happy simmering!

Minor changes and fixes: Update “The Bank Tutorial: Part I” vignette (@nacnudus in #90). Fix trap()’s handler cloning and associated test (#91). Apply select()’s policy also when resources is a function (#92). Accept dynamic timeouts in batches (#93). Change rollback()’s default behaviour to times=Inf, i.e., infinite loop (#95). Stop and throw an error when timeout() returns a missing value (#96 and #97). Fix memory management: resetting the environment was clearing but not deallocating memory (#98, fixed in #99). Fix object destruction: workaround for tidyverse/magrittr#146 (#98, fixed in effcb6b).

Article originally published in Enchufa2.es: simmer 3.6.2.

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Data Science Podcasts

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Data Science Podcasts

(This article was first published on Jon Calder's R Blog, and kindly contributed to R-bloggers)

Make the most of your commute! –

Podcasts are awesome. Especially when you’re stuck in traffic on the way to work.

Below are some podcasts I listen to that relate to data science and statistics. Each of them has something slightly different to offer, so if this is an area of interest to you then I recommend you give these a try!

Not So Standard Deviations

Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

Data Skeptic

Data Skeptic is your source for a perspective of scientific skepticism on topics in statistics, machine learning, big data, artificial intelligence, and data science.

More or Less: Behind the Stats

Tim Harford and the More or Less team from BBC Radio 4 try to make sense of the statistics that surround us.

The R-Podcast

Giving practical advice on how to use R for powerful and innovative data analyses. The host of the R-Podcast is Eric Nantz, a statistician working in the life sciences industry who has been using R since 2004.

Partially Derivative

Hosted by Jonathon, Vidya, and Chris, Partially Derivative is a podcast about data science in the world around us. Episodes are a mix of explorations into the techniques used in data science and discussions with the field’s leading experts.

Linear Digressions

Hosts Katie Malone and Ben Jaffe explore machine learning and data science through interesting (and often very unusual) applications.

Are there other data science podcasts missing from this list that you can recommend? Feel free to comment below and let me know!

To leave a comment for the author, please follow the link and comment on their blog: Jon Calder's R Blog.

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Reflections on ROpenSci Unconference 2017

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Reflections on ROpenSci Unconference 2017

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Last week I attended the ROpenSci Unconference in Los Angeles, and it was fantastic. Now in its fourth year, the ROpenSci team brought together a talented and diverse group of about 70 R developers from around the world to work on R-related projects in an intense 2-day hackathon. Not only did everyone have a lot of fun, make new connections and learn from others, but the event also advanced the ROpenSci mission of creating packages, tools and resources to support scientific endeavours using R.

During the two-way workshop, the attendees self-organized into teams of 4-8 to work on projects. There were 20 projects started at the ROpenSci conference, and all of them produced a working package. You can details on all the projects on Github, but here are a few examples to give you a sense of what the

skimr, a tidyverse-compatible way of summarizing data sets A census of R packages containing interesting data sets A set of best practices for security in R packages and scripts, and a package for signing and verifying R packages Test infrastructure for code in Rmarkdown documents An R interface to Minecraft, to encourage young developers to learn R

@LucyStats @rdpeng Including an R-generated maze with an R-generated bot that can find his way out! @Inchio @revodavid @kwbroman @daroczig @alikzaidi pic.twitter.com/92SQg4xmcu

— Brooke Anderson (@gbwanderson) May 30, 2017

I was very fortunate to be part of that last team: we had a blast connecting R to Minecraft, like creating a procedurally-generated maze and an AI bot to navigate it. (I plan to write a full blog post about the project in due course.) But for more on #runconf17, take a look at Karthik Ram's storify which will give you a good sense of the conference as it unfolded in tweets. I also highly recommend checking out these post-conference wrap-ups by Jasmine Dumas, Karl Broman and Bob Rudis.

Thanks to the organizers for such a fantastic event, and also to the sponsors (including my own employer, Microsoft) for making it all possible. I'm looking forward to next year already! 

ROpenSci: Unconference 2017

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

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Dynasty Dugout: Starting Pitcher Risers and Fallers

Fantrax

Dynasty Dugout: Starting Pitcher Risers and Fallers

Last week was all about the outfielders in Dynasty Dugout. This week we switch away from offense and take a look at the players designed to limit it. You’ll always have the truly elite pitchers that dominate each year, but there are a lot of pitchers who trend up and down each season.

Trending Up

Eduardo Rodriguez, Boston Red Sox

Ever since Chris Sale arrived in Boston, Rodriguez has become a different pitcher. He’s working at a much quicker pace (similar to Sale) and really attacking hitters. Through 10 appearances (nine starts), Rodriguez has a 2.77 ERA. 1.12 WHIP, and 9.6 K/9. All of those are career highs for the 24-year-old lefty out of Venezuela. He’s also limiting contact well and allowing only 6.7 hits per nine innings.

.@eduardorod5’s start tonight? All 0⃣’s. #RedSox pic.twitter.com/ZHKUn0mDjf

— Boston Red Sox (@RedSox) May 27, 2017

This is what the Red Sox hoped he’d become when they acquired him from Baltimore for Andrew Miller at the trade deadline back in 2014. The stuff has always been there. Rodriguez combines a mid-90s heater that can touch 97-98, with a developing slider and changeup that are both becoming plus offerings. Having two of the best left-handed starters in the game to learn from can only continue to benefit him going forward. Consider E-Rod a top-40 dynasty league starter with the potential for much more if he continues to pitch to his abilities.

Ivan Nova, Pittsburgh Pirates

Talk about a control freak. Through 70 innings this season, Nova has walked just five batters. That’s an insanely low number. It gets even more impressive when you go back to when he was acquired from the Yankees in the middle of last season. Since that trade, Nova has walked only eight batters in 134 2/3 innings.

Nova is a classic example of a “pitch to contact” starting pitcher. He’s always over the plate but mixes pitch locations and speeds well enough that hitters rarely make hard contact. That, plus the minuscule walk rate, help keep his ERA and WHIP low. The one area that keeps Nova from being an upper-echelon fantasy starter is his sub-par strikeout rate. Over that same 134 2/3-inning stretch with Pittsburgh, he has amassed only 89 strikeouts. That translates to a paltry 5.95 K/9 rate.

If Nova can ever start missing more bats, he could be a top-25 dynasty league pitcher. At this point in his career, that seems highly doubtful. Nova could become what Mark Buehrle was back in the mid to late-2000s. A solid #4 of #5 starter for your dynasty team, but not more than that. There’s also a chance you could acquire him on the cheap due to his low strikeout rate.

Dylan Bundy, Baltimore Orioles

It took a while for this once top prospect to become a productive major league pitcher. Better late than never, though. After debuting in 2012, it took Bundy four seasons to finally make it back to Baltimore. He was a valuable pitcher for the Orioles in 2016, making appearances out of the bullpen as well as starting. This year, however, has been Bundy’s breakout season. Through 11 starts, he has six wins, a 2.89 ERA and 1.14 WHIP.

Strikeouts are the one area where he hasn’t been as strong this year. After routinely striking out batters at a high rate in the minors, Bundy hsa only 49 punchouts in 71 2/3 innings this year. That equates to only a 6.15 K/9 rate. The lower velocity might have something to do with that. His average fastball velocity this year is only 91.7 MPH, down 2.1 MPH from last year’s 93.8 mark. His low strikeout totals will suppress his overall value, but this is still a valuable pitcher in all formats. He’s a top-50 dynasty league starter as it stands today and could be even better if his strikeouts rise.

Other Risers in Value

Alex Wood, Los Angeles Dodgers

While I was writing this article, Wood’s breakout season was put on pause when he went on the disabled list with SC joint inflammation in his shoulder. The injury seems minor, but at this point it’s hard to tell how long he’ll be out. Assuming the injury doesn’t keep him out too long, this could be an opportunity to buy low on Wood. There’s always room for an 11.3 K/9 rate on your roster, especially with the low ratios he’s putting up.

Mike Leake, St. Louis Cardinals

A lot of the same things that I wrote about Nova can be applied to Leake. His strikeout rate is very low, but he mixes pitches well enough to keep batters off balance. This has been by far the best season of his career, so expect some regression as the season goes along, but Leake is still a valuable pitcher that you can probably acquire for not too much. Value him in the same ballpark as Nova.

Ervin Santana, Minnesota Twins and Jason Vargas, Kansas City Royals

These are two guys whose hot starts I’m definitely not buying into. Pitchers with career ERAs over four don’t all of a sudden transform into fantasy aces in their age-34 seasons. Both of their FIP (Fielding Independent Pitcher) rates are well above their current ERAs. That’s a solid indication that their ERAs should rise soon. Sell now while their value is high before they drop back toward their career norms.

 

Trending Down

Drew Pomeranz, Boston Red Sox

Watching Pomeranz pitch is about as much fun as getting teeth pulled. After a great start to the 2016 season with the Padres, Pomeranz was acquired by Boston to be a key part of their rotation. To acquire him, the Red Sox had to give up a top-20 overall prospect in right-handed starter Anderson Espinoza. So far the Pomeranz deal hasn’t worked out quite as well as Boston had hoped.

Back on May 21st, I did some research on how many pitches per inning the Red Sox’s four main starting pitchers were throwing. Pomeranz is easily the highest on the team at 19.1 pitches per inning. That high total has kept him from going deep into games and limits his chances to pick up wins and quality starts.

When his ERA and WHIP are low, that is something you can overlook, but that hasn’t been the case this year. In 44 innings, he has a 4.70 ERA and 1.39 WHIP. Combine that with the fact that he’s averaging under five innings per start, and this isn’t someone that is going to help your team. Unless he can start working deeper into games, Pomeranz is going to hurt you more than he helps you. As we stand today, he is not a top-50 dynasty league starting pitcher. He’s worth using only against bad offenses right now.

Matt Harvey, New York Mets

Oh, how the mighty have fallen. Harvey was flying up the dynasty ranks after the 2013 season, when he posted a 2.27 ERA and a 9.6 K/9 over 26 starts. Many had him penciled in as the team’s ace for the next decade. Now he’s barely even the Mets’ #4 starter.

A lot has happened to the 28-year-old righty over the past few seasons. After missing the 2014 season due to Tommy John surgery, Harvey came roaring back with a spectacular 2015 season that was nearly as good as his breakout 2013 campaign. Then the wheels came off. His 2016 season was a disaster that ended after only 17 starts when he needed to undergo surgery to correct Thoracic Outlet Syndrome.

A lot of questions surrounded Harvey this spring as to how he would bounce back from the surgery. The answer to that has been not very well. His ERA sits near five, and his strikeout rate has dropped for a fourth straight season. This is clearly not the same pitcher that dazzled the Big Apple back in 2013 and 2015. Dan Plesac broke down Harvey’s struggles last week.

"He's in a rut right now." @Plesac19 breaks down Matt Harvey's start against the Brewers. #MLBTonight pic.twitter.com/WnBYjaLCzB

— MLB Network (@MLBNetwork) May 13, 2017

Harvey isn’t worth your time anymore. If you have him, keep him benched until he shows some signs of turning it around.

Other Fallers in Value

Kevin Gausman, Baltimore Orioles- A young Baltimore pitcher has broken out and has become the pitcher everyone thought he could be. Just not the one we all expected coming into the season. Unlike his teammate mentioned above, Gausman hasn’t progressed this year like fantasy owners had hoped. Don’t give up on him, but keep him benched until he figures it out.

Masahiro Tanaka, New York Yankees- For some reason hitters are making much more contact off Tanaka this season than in years past. His 10.9 H/9 rate is easily the worst rate of his career, as is his 2.4 BB/9 mark. Tanaka is too good of a pitcher to continue to struggle like this. If you can acquire him on the cheap, you should be happy with the move later this season.

That’s all for the pitchers. Have a pitcher that I didn’t cover? Feel free to ask in the comments section below or on Twitter. Thanks for reading and check in next Tuesday for anther edition of Dynasty Dugout.

The post Dynasty Dugout: Starting Pitcher Risers and Fallers appeared first on Fantrax.

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Making The Case: The Stanley Cup is the Best Championship Series in Sports

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Making The Case: The Stanley Cup is the Best Championship Series in Sports

Television ratings will never dictate the importance or worthiness of anything for me. If you look at what ranks at the top of many TV ratings, you will find shows that leave you scratching your head as to how and why they are watched that much. We have moved in a direction with our viewing habits that they no longer can be considered as the sole viable means in which to judge the quality of something, including sports. Once this criterion has been eliminated, we can take an objective look at why the NHL’s Stanley Cup playoffs are the best in all of the North American sporting world.

World Series-MLB

You would think that a Cubs fan coming off a recent World Series win would say the World Series is the best championship. Maybe if it were thirty years ago, but today’s baseball game is way too slow. Despite the Cubs and Indians being two relatively fast-paced teams, the 2016 World Series has featured two of the longest games of the entire postseason, with each game featuring little offense and lasting only nine innings. The fact is, the length of Major League Baseball games has steadily escalated over the past decade, and despite their best efforts with new rules, games continue to average over 3 hours long. When you combine that with the fact that most World Series games start after 8 p.m. ET, you are well past the time in which most people will want to stay up unless one of their favorite teams is playing. In an age in which people are looking for more fast-paced activities, baseball is going in the opposite direction, and that seriously affects the way people look at the game that is often referred to as “America’s pastime.”

Super Bowl-NFL The biggest issue with the world’s most second watched event is the way in which the NFL treats the game. In the very game in which they crown the champion of their league, they change the rules and the very fabric of the game that is supposed to mean the most by increasing the amount of time at the half, just to accommodate some dopey halftime show. Professional football players are creatures of habit, which can make halftime at the Super Bowl feel pretty weird. A regular season halftime period might last 12 to 15 minutes. At the Super Bowl, because of the elaborate show that takes place on the field, that period can be more than twice as long. As my New York Giant friends will claim, that can affect what happens on the field. It has been documented that Giants Offensive Coordinator Kevin Gilbride used that extra time to come up with a strategy to help take down the New England Patriots. The claim is that Gilbride prefers the extra time he gets during the Super Bowl, as he is able to fit in what feels like a leisurely 10-minute meeting with assistant coaches about what happened during the first halves of both games, and he doesn’t have to worry about “going at a dizzying pace trying to get everything that probably takes a good 20 minutes, 25 minutes, compressed in about five or six minutes.” Combine this fact with the obsession with its halftime show and commercials, the Super Bowl is coming close to being labeled as sports entertainment instead of an actual sporting event.

Basketball’s NBA Finals

This is a case in which the inmates are running the asylum. For the third year in a row, we have the Warriors playing the Cavaliers due mostly to a bunch of superstars deciding that they want to load a couple of teams up so they have a better chance at winning a championship. Sounds like a couple of recreational basketball leagues at our local YMCA. And don’t get me started on the “I’m going to give our superstars a night off because they play too much” garbage that went on during the regular season. Not a nice way to treat your fans. But that will happen when the players are in charge, and not the coaches or the people in the front office. This, coupled with other factors, has resulted in NBA ratings falling almost 45 percent in the past decade — way off the very high ratings the NBA enjoyed in the 1990s.

The Stanley Cup-NHL

The finals of the NHL’s championship does not have the issues that the three other leagues have for their championships. The game is still played the same way, in the same manner as it is the rest of the year. Even though the Pittsburgh Penguins are going for a repeat, the league welcomes the Nashville Predators as first-time participants. I will also offer the unscientifically proven notion that hockey players are the best athletes in all sports. It is my contention that if skill sets could be reversed, most NHL players could play the other three sports professionally. Outside of many NFL players, one cannot make the same case for most of the players in MLB and the NBA.  The athleticism and intestinal fortitude required to play this sport are unequaled anywhere on the planet.  The Stanley Cup may also have the best history associated with its championship. The history begins with Lord Stanley of Preston, who was appointed by Queen Victoria as Governor General of Canada on June 11, 1888, as he and his family became highly enthusiastic about ice hockey. Soon afterward, Stanley purchased what is frequently described as a decorative punch bowl, but which silver expert John Culme identified as a rose bowl, made in Sheffield, England, and sold by London silversmith G. R. Collis and Company (now Boodle and Dunthorne Jewellers). It had the words “Dominion Hockey Challenge Cup” engraved on one side of the outside rim, and “From Stanley of Preston” on the other side. In addition, the Stanley Cup is unlike the trophies awarded by the other major professional sports leagues of North America, as a new Stanley Cup is not made each year. Currently, winning teams get the Stanley Cup during the summer and a limited number of days during the season. It is unusual among trophies to include winning members’ names. Every year since 1924, a select portion of the winning players, coaches, management, and club staff names are engraved on its bands. In case you are wondering, as the bottom band becomes full, the oldest band is removed and preserved in the Hockey Hall of Fame, and a new blank band added to the bottom.

Even though the World Series and Super Bowl are still great events, it is this rich history, combined with the fact that the sport continues in its pure form throughout their entire regular season and playoffs, that makes the Stanley Cup the best of the major sports championships in this part of the world.

 

 

The post Making The Case: The Stanley Cup is the Best Championship Series in Sports appeared first on Fantrax.

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The Monday Morning Sleeper – Ameer Abdullah

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The Monday Morning Sleeper – Ameer Abdullah

Welcome to another edition of the Monday Morning Sleeper. Last week I profiled Jared Goff, a quarterback with something to prove, who is primed to lead his team with the help of some new weapons.

UPDATE ALERT:

I previously profiled a running back for the Philadelphia Eagles named Wendell Smallwood. Unfortunately for Smallwood, LaGarrette Blount has officially signed with the Eagles, and that directly affects Smallwood’s sleeper status. Blount led the New England Patriots’ backfield last season as they marched through the competition on the way to their fifth Superbowl championship, and he will enter training camp as the Eagles’ clear-cut RB1. Although Smallwood may still have an opportunity in Philly, he may have to wait a season before he becomes a significant part of the offense.

A Fantasy Football Sleeper is a player that is in a position to play beyond his rankings or expectations, and who may provide you with some value at his draft position. 

This week, I will be profiling a running back for the Detroit Lions, Ameer Abdullah, who is primed and ready to assume the lead back duties and get his career back on track.

Early Exit to 2016 Abdullah is entering his third season with the Lions, and he is looking to get his career back on track after an injury-shortened sophomore season. Abdullah’s season ended prematurely in week 2 of 2016, after he tore a ligament in his foot. With almost nine months of recovery time, Abdullah is now healthy and ready to assume his position as the lead back. With a clean bill of health, he is expected to participate in the OTAs that are set to begin in a few days.

2017 NFL Entry Draft

The Lions had nine picks in the 2017 entry draft and decided not to use any of them to draft another running back. This indicates that they have faith in their running back situation, including Theo Riddick, and had more important holes to fill. With the amount of talent in this year’s crop of freshman running backs, Abdullah and Riddick appear to have the confidence of the coaching staff, management, and the ownership.

The Devil You Know

Lions GM, Bob Quinn, when asked if Abdullah would be the starting running back, simply said, “He is.” He also indicated that they considered adding another back in the draft, but felt that they wouldn’t be able to draft a better back than what they already have on their roster. The Lions have improved their offensive line, which should help to create more opportunities for everyone on the offense.

League Worst Rush Attempts per Game

In 2016 Abdullah averaged 5.6 yards per carry on 18 carries in 1.5 games, which is a respectable average, but a very small sample size. Those numbers would definitely be RB1 numbers if he could maintain a decent workload throughout the season. The downside to Abdullah is that the Lions averaged the lowest number of carries per game in 2016, with an average of 21.5 carries per contest. Those numbers need to improve if the Lions want to improve as a team. It starts with the running game, and getting Abdullah going strong early and often could help them to take over and control the flow of some games.

Career as a Cornhusker

Abdullah had an impressive college career with the Nebraska Cornhuskers, where he averaged 5.6 yards per carry on 813 attempts over 53 games for a total of 4,588 yards and 39 touchdowns. He added another 690 yards and 7 touchdowns on 73 receptions. While we are still waiting for these numbers to translate to an NFL field, I believe this is the season we will see Abdullah break out and perform at a higher level than what we have seen to this point in his NFL career.

Competition for the Lead Back?

The Lions have a crowded backfield with Abdullah being joined by Zach Zenner, Dwayne Washington, Mike James, and Theo Riddick. The only real competition for Abdullah is Riddick, who will likely handle passing-down duties. As a result, Riddick has increased value in PPR leagues. However, due to the estimated work load, Abdullah is the back to own in Detroit.

Predictions and Rankings:

Abdullah is the 37th-ranked running back on Matthew Berry’s Non-PPR rankings, but I would put him closer to 20th. I would take Abdullah over players like Tevin Coleman, Robert Kelley, C.J. Anderson, Mike Gillislee, and even the great Adrian Peterson.

Thank you for reading The Monday Morning Sleeper, and follow me on Twitter, @HaehnelJames

Have an opinion? Let me know in the comments below.

The post The Monday Morning Sleeper – Ameer Abdullah appeared first on Fantrax.

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Mixed models for ANOVA designs with one observation per unit of observation and cell of the design

R-bloggers

Mixed models for ANOVA designs with one observation per unit of observation and cell of the design

(This article was first published on Computational Psychology - Henrik Singmann, and kindly contributed to R-bloggers)

Together with David Kellen I am currently working on an introductory chapter to mixed models for a book edited by Dan Spieler and Eric Schumacher (the current version can be found here). The goal is to provide a theoretical and practical introduction that is targeted mainly at experimental psychologists, neuroscientists, and others working with experimental designs and human data. The practical part focuses obviously on R, specifically on lme4 and afex.

One part of the chapter was supposed to deal with designs that cannot be estimated with the maximal random effects structure justified by the design because there is only one observation per participant and cell of the design. Such designs are the classical repeated-measures ANOVA design as ANOVA cannot deal with replicates at the cell levels (i.e., those are usually aggregated to yield one observation per cell and unit of observation). Based on my previous thoughts that turned out to be wrong we wrote the following:

Random Effects Structures for Traditional ANOVA Designs

The estimation of the maximal model is not possible when there is only one observation per participant and cell of a repeated-measures design. These designs are typically analyzed using a repeated-measures ANOVA. Currently, there are no clear guidelines on how to proceed in such situations, but we will try to provide some advice. If there is only a single random effects grouping factor, for example participants, we feel that instead of a mixed model, it is appropriate to use a standard repeated-measures ANOVA that addresses sphericity violations via the Greenhouse-Geisser correction.

One alternative strategy that employs mixed models and that we \emph{do not recommend} consists of using the random-intercept only model or removing the random slopes for the highest within-subject interaction. The resulting model assumes invariance of the omitted random effects across participants. If this assumption is violated such a model produces results that cannot be trusted . […]

Fortunately, we asked Jake Westfall to take a look at the chapter and Jake responded:

I don’t think I agree with this. In the situation you describe, where we have a single random factor in a balanced ANOVA-like design with 1 observation per unit per cell, personally I am a proponent of the omit-the-the-highest-level-random-interaction approach. In this kind of design, the random slopes for the highest-level interaction are perfectly confounded with the trial-level error term (in more technical language, the model is only identifiable up to the sum of these two variance components), which is what causes the identifiability problems when one tries to estimate the full maximal model there. (You know all of this of course.) So two equivalent ways to make the model identifiable are to (1) omit the error term, i.e., force the residual variance to be 0, or (2) omit the random slopes for the highest-level interaction. Both of these approaches should (AFAIK) result in a statistically equivalent model, but lme4 does not provide an easy way to do (1), so I generally recommend (2). The important point here is that the standard errors should still be correct in either case — because these two variance components are confounded, omitting e.g. the random interaction slopes simply causes that omitted variance component to be implicitly added to the residual variance, where it is still incorporated into the standard errors of the fixed effects in the appropriate way (because the standard error of the fixed interaction looks roughly like sqrt[(var_error + var_interaction)/n_subjects]). I think one could pretty easily put together a little simulation that would demonstrate this.

Hmm, that sounds very reasonable, but can my intuition on the random effects structure and mixed models really be that wrong? To investigate this I followed Jake’s advise and coded a short simulation that tested this and as it turns out, Jake is right and I was wrong.

In the simulation we will simulate a simple one-factor repeated-measures design with one factor with three levels. Importantly, each unit of observation will only have one observation per factor level. We will then fit this simulated data with both repeated-measures ANOVA and random-intercept only mixed and compare their p-values. Note again that for such a design we cannot estimate random slopes for the condition effect.

First, we need a few packages and set some parameters for our simulation:

require(afex) set_sum_contrasts() # for orthogonal sum-to-zero contrasts require(MASS) NSIM

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Instrumental Variables in R exercises (Part-3)

R-bloggers

Instrumental Variables in R exercises (Part-3)

(This article was first published on R-exercises, and kindly contributed to R-bloggers)

This is the third part of the series on Instrumental Variables. For other parts of the series follow the tag instrumental variables.

In this exercise set we will use Generalized Method of Moments (GMM) estimation technique using the examples from part-1 and part-2.
Recall that GMM estimation relies on the relevant moment conditions. For OLS we assume that predictors are uncorrelated with the error term. Similarly, IV estimation implies that the instrument(s) is uncorrelated with the error term.

Answers to the exercises are available here.

Exercise 1
Load the AER package (package description: here) and the PSID1976 dataset. This has data regarding labor force participation of married women.
Define a new data-frame that has data for all married women that were employed. As we did in part-2, this data-frame will be used for the remaining exercises.
Next, load the ‘gmm’ package (package description: here).

Exercise 2
We will start with a simple example. Regress log(wage) on education using the usual OLS technique.
Next, use the gmm function to estimate the same model using OLS’s moment conditions. Match your result and comment on the standard errors.

Exercise 3
Estimate the return to education for the model from Exercise-2 using feducation as the IV. Use both ivreg and gmm functions and compare results.

Exercise 4
Regress log(wage) on education, experience and experience^2 using the usual OLS technique.
Next, use the gmm function to estimate the same model using OLS’s moment conditions. Match your result.

Exercise 5
Estimate the return to education for the model from Exercise-4 using feducation as the IV. Use both ivreg and gmm functions and compare results.

Exercise 6
We will now use the over-identified case. Estimate the return to education for the model from Exercise-2 using feducation and meducation as IVs. Use both ivreg and gmm functions and compare results.

Exercise 7
Estimate the return to education for the model from Exercise-4 using feducation and meducation as IVs. Use both ivreg and gmm functions and compare results.

Exercise 8
The test of over-identifying restrictions can be obtained by the J-test (Sargan test). It is displayed with the summary and specTest functions. Do the over-identified moment conditions match the data well?

Exercise 9
Iterated estimation might offer some advantages over the default two-step method in some cases. Estimate the model in Exercise-7 using the iterative estimation technique.

Exercise 10
Use the plot function to get the graph of log(wage) and fitted values for the model in Exercise-7.

Related exercise sets: Instrumental Variables in R exercises (Part-1) Instrumental Variables in R exercises (Part-2) Forecasting: ARIMAX Model Exercises (Part-5) Explore all our (>1000) R exercises Find an R course using our R Course Finder directory

To leave a comment for the author, please follow the link and comment on their blog: R-exercises.

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20 Stunning Consumer Services Video Examples

Advids

20 Stunning Consumer Services Video Examples

Consumer services industry is always a challenge and is very dynamic industry to operate in. Companies need to innovative their services and solution, while driving business growth. Video marketing has seen a great response within Consumer Services industry.

In this article we highlight 20 consumer services videos that are awesome to watch and definitely worked for the softwares they represent. Learn from these great consumer services example videos to create an engaging introduction video for your offering. Here we go, enjoy the article :

JP Morgan Chase Consumer Services Video

Whether you’re just starting to invest or need to evaluate your existing strategy, your J.P. Morgan Financial Advisors will help you prioritize your goals and develop an appropriate approach tailored to your situation. Together with a Banker, you’ll receive a powerful combination of banking and investment expertise to help ensure your money is working hard for you. The J.P. Morgan consumer services video talks about the commitment to building long-term relationships and partnering with you over time to help you achieve your important financial goals.

Deutsche Bank Consumer Services Video

Deutsche Bank is a leading global investment bank with a strong and profitable private clients franchise. Deutsche Bank offers unparalleled financial services throughout the world. The Bank competes to be the leading global provider of financial solutions for demanding clients creating exceptional value for its shareholders and people. Their services include on-shore investment banking, institutional equities broking, asset and private wealth management, retail banking and business processes outsourcing. The consumer services video shows how Deutsche Bank is a fully integrated financial services provider to corporate, institutional and individual clients.

Ola Consumer Services Video

Ola, India’s most popular mobile app for transportation, integrates city transportation for customers and driver partners onto a mobile technology platform. As one of India’s fastest growing companies we ensure convenient, transparent and quick service fulfilment using technology to make transportation hassle free for everyone. The Consumer Services video explains how Ola is making it easy for employees to manage their travelling.

Jelastic Consumer Services Video

Jelastic offers hosting service providers a complete Platform-as-a-Service and Container-as-a-Service solution that supports Java, PHP, .NET, Node.JS, Python, Ruby and Docker technologies. The consumer services video shows how Jelastic can hasten up the cloud hosting process by delivering a superior turnkey cloud environment at a fraction of the cost.

Palo Alto Consumer Services Video

The Palo Alto Next-Generation enterprise security Platform protects the digital way of life by safely enabling applications and preventing threats across the network, cloud and endpoints. The native integration of the platform delivers a safe architecture providing superior security. The consumer services video walks you through the threats faced by enterprises and how next-generation firewall technology can benefit.

SYNERGi Consumer Services Video

SYNERGi is a SaaS based Cyber Risk Management platform. SYNERGi gives you a single, unified view of your organisation’s whole cyber security landscape across risks, threats, vulnerabilities and controls. The consumer services video acts as an overview of how SYNERGi can get you one view of your entire business.

Exel Consumer Services Video

Exel provide services from a total transportation outsource to helping you find capacity when you have a tough load to move. Its asset-neutral approach allows designing of a transportation solution that leverages multiple modes to ensure your product arrives where it needs to be at the lowest landed cost. The consumer services video shows how Exel helps transform your transportation network into a competitive advantage.

Capita IT Professional Services Video

Capita provides a range of IT solutions and world-class services based around the full Application Lifecycle Model, with information security at its heart, helping companies get the most from their investment in IT while minimising risk and increasing quality. Capita IT Professional Services applies technology to drive through secure business change. They design innovative end-to-end solutions to ensure that your business programmes deliver what you need while maximising quality, reducing risk and cost. The Professional Services video shows how Capita provides businesses with integrated IT Professional Services and support.

nuSIEM Consumer Services Video

nuSIEM is a managed cloud based SIEM service that provides complete visibility of all aspects of your UTM/firewall. nuSIEM, backed by its distributed, parallel processing cloud, combines high speed, real-time analysis of logs with intelligent alerting and Dynamic Drill Down Reporting. The ability to scale the nuSIEM is virtually unlimited and can be quickly achieved through scale-out architecture. The consumer services video highlights how their threat intelligence system can help your enterprise respond to malware or unknown attacks.

Sears Consumer Services Video

Sears Home Services delivers solutions for your entire home, from appliance care to interior and exterior upgrades for your home. You can trust their experts to help take care of your house so you can enjoy your home. To ensure they have the right parts to fix your appliance, they use technology to diagnose problems before they arrive at your home. Whether you purchased your appliance at Sears or not, a Sears Appliance Expert can help. The consumer services video describes Sears as your ultimate go to option for all your home care needs.

Ayehu Consumer Services Video

Ayehu provides IT Process Automation solutions for IT & Security professionals to identify and resolve critical incidents, simplify complex workflows and maintain greater control over IT infrastructure through automation. The consumer services video shows how Ayehu eyeShare can cut the Mean Time to Resolution and manual, repetitive tasks out of your IT operation with security incident response automation.

The Professional Service Consumer Services Video

The professional service centre offers a comfortable environment for health related consultations. The centre offers a wide range of facilities and equipment that facilitate diagnosis and management so that those who use the services offered can be managed as efficiently and conveniently as possible. They offer a wide range of services offered by equally committed specialists in their respective field. They work in close collaboration with a number of other specialists and diagnostic facilities to ensure the highest standard of care. The consumer service video shows how The professional service centre is working towards providing promising medical services.

Hutt Trucking Consumer Services Video

Hutt Trucking offers warehousing, transport and logistics services for manufacturers of all sizes. They offer creative solutions that helps you streamline your logistics by examining every link in your supply chain. They provide transportation services can help you reduce product fluctuation, shutdowns, backups. The consumer services video shows how Hutt Trucking can provide you a seamless producing and delivering experience.

Business Doctors Consumer Services Video

Business Doctors Professional Services is a collaboration between Business Doctors and is the fastest growing business consultancy in the UK. With challenging market conditions and competitive pressures from bigger firms, they are committed to supporting you with knowledge, expertise and resources to help widen both your range of services and revenue streams. The consumer services video highlights how Business Doctors Professional Services can make your business run efficiently.

Telefonica Consumer Services Video

Telefonica Cyber Security services provide intelligence to clients to help them detect internal and external threats, using that intelligence to implement preventive measures that helps to minimize threats and create reactive measure to deal with inevitable attacks. The consumer services video uses great visuals to highlight the deep knowledge needed to tackle the Cyber Security of today.

Edge Consulting Consumer Services Video

The Edge Consulting is a fully dedicated Human Resource (HR) Consulting Firm specialising in Strategic Human Resource Management and Development; they therefore provide expertise and direction through cutting edge consulting services and support in key areas of Human Capital Management. This consumer services video highlights the core expertise and what’s their consulting approach.

Telstra Consumer Services Video

Telstra Cloud Services offers businesses a scalable solution to deploy applications deployment, deliver business processes and deploy offshore disaster recovery solutions across multiple geographic locations. The consumer services video overview communicates how Telstra can provides customers with an exceptional experience, combining the flexibility of cloud computing with a world-class high-performance low latency global network.

Enterprise Consumer Services Video

Enterprise cloud is a Cloud Management Platform (CMP) by NTT communication offering efficient management and unified control of both Enterprise Cloud and third-party cloud provider solutions. The consumer services video highlights how Enterprise Cloud can simmer down the inconvenience of cloud management within a single dashboard.

2nd watch consumer services Video

The 2nd Watch Cloud Management Platform is a robust system that leverages cutting edge software with years of cloud experience. With automation and technology you are able to monitor, patch and optimize across thousands of instances, in moments. The consumer services video shows how 2nd watch can reduce risks and increases overall performance.

Toll Consumer Services Video

Toll is one of Asia Pacific’s largest freight transport provider permitting moving your goods by air, road, rail or sea. It has specialised fleet to provide a wide range of heavy haulage and bulk freight services for payloads exceeding normal capacity. The consumer services video shows how Toll provides safe, secure transport services for a wide range of freight types, providing the accredited expertise to handle your specialised cargo.

With the above consumer services video examples for website it’s evident that companies are fast integrating videos in their marketing campaigns and various other touchpoints. If you planning to develop these videos a good start is to develop a brief of what can be the tone and style for your videos. We advise you to be different and unique while you create your messaging through the videos.

We at Advids, create custom consumer services video based on your brief. With a complete video production services plan at a fixed price,our design team works right from concept development, to art design and animation. Having created 1200 plus explainers for businesses, our Creative team can help you come up with the right fit. Do talk to us or send us a note on what your company plans to create with for the next consumer services video requirement.

The post 20 Stunning Consumer Services Video Examples appeared first on Advids.

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