Bombing Back to the Stone Age

There is a common meme that is easy to find with a twitter search

If you read Jared Diamond or Stephen Pinker they talk about the really high levels of violence in the stone age.
Or to describe it with statistics

"By many estimates, 10 to 20 percent of all Stone Age humans died at the hands of other people.
This puts the past 100 years in perspective. Since 1914, we have endured world wars, genocides and government-sponsored famines, not to mention civil strife, riots and murders. Altogether, we have killed a staggering 100 million to 200 million of our own kind. But over the century, about 10 billion lives were lived — which means that just 1 to 2 percent of the world’s population died violently. Those lucky enough to be born in the 20th century were on average 10 times less likely to come to a grisly end than those born in the Stone Age. And since 2000, the United Nations tells us, the risk of violent death has fallen even further, to 0.7 percent."

To reduce violence don't send people back to the stone age.

How Good Will the Upcoming Stephen King Adaptations be?

This is the third and final post in a series on Stephen King adaptations. (The first is Are Stephen King films better than the books?, and the second Do Stephen King's Better Books Make Better Films?)
According to IMDB the Stephen King Novels below are currently being adapted

Title	good doubled
The Talisman	8.2
Rose Madder	7.22
Lisey's Story	7.22
Mr. Mercedes	7.74
Gerald's Game	6.86
Cell	7.22
11.22.63	8.52
The Dark Tower	8

Three of these books have exactly the same rating on Goodreads. The correlation between the ratings and his books and his films previously discussed I will use to predict future movies ratings. The analysis gives a 95% confidence prediction intervals of

Title	estimate	lower bound	upper bound
The Talisman	7.5	6.4	8.6
Rose Madder	5.95	4.8	7.0
Lisey's Story	5.95	4.8	7.0
Mr. Mercedes	6.79	5.7	8.8
Gerald's Game	5.37	4.2	6.4
Cell	5.95	4.8	7.0
11.22.63	8.05	6.9	9.15
The Dark Tower	7.2	6.1	8.2

The upcoming films with their predicted IMDB ratings are in red in the graph below. Three are predicted to have a rating of 5.95.

There is an interesting 538 podcast here about a company that predicts film earnings. He mentions the correlation between film quality and earnings and film quality. This whole topic is an interesting challenge for prediction.

Stephen King is unique among authors in the number and variety of adaptations his works have gone through. So he is possibly the only author this could be even tried with. I am really looking forward to 11.22.63 and the Talisman now. And if Gerald's Game beats the predicted IMDB score that is a bonus.




Code

mydata = read.csv("King.csv")  
library(ggplot2)
attach(mydata)     # attach the data frame 
king.lm = lm(imdb ~ good.doubled)

Call:
lm(formula = imdb ~ good.doubled)

Coefficients:
 (Intercept)  good.doubled  
      -5.821         1.628  

upcoming = read.csv("upcoming.csv")  
predict(king.lm, upcoming, interval="predict")

> predict(king.lm, upcoming, interval="predict")

Title,Publication date,Pages,imdb,goodreads,good doubled,clr
Carrie,05/04/1974,199,7.4,3.89,7.78,1
Salem's Lot,17/10/1975,439,6.8,3.97,7.94,1
The Shining,28/01/1977,447,8.4,4.12,8.24,1
The Stand,Sep-78,823,7.3,4.32,8.64,1
The Dead Zone,Aug-79,428,7.3,3.88,7.76,1
Firestarter,29/09/1980,426,6,3.8,7.6,1
Cujo,08/09/1981,319,6,3.61,7.22,1
The Running Man,May-82,219,6.6,3.74,7.48,1
Christine,29/04/1983,526,6.6,3.69,7.38,1
Pet Sematary,14/11/1983,374,6.6,3.86,7.72,1
Thinner,19/11/1984,309,5.7,3.6,7.2,1
It,15/09/1986,1138,6.9,4.12,8.24,1
Misery,08/06/1987,310,7.8,4.06,8.12,1
The Tommyknockers,10/11/1987,558,5.4,3.42,6.84,1
The Dark Half,20/10/1989,431,5.9,3.71,7.42,1
Needful Things,Oct-91,690,6.2,3.84,7.68,1
Dolores Claiborne,Nov-92,305,7.4,3.76,7.52,1
The Green Mile,March–August 1996,400,8.5,4.39,8.78,1
Bag of Bones,22/09/1998,529,5.8,3.84,7.68,1
Dreamcatcher,20/03/2001,620,5.5,3.53,7.06,1
Under the Dome,10/11/2009,1074,6.8,3.89,7.78,1
Shawshank Redemption,10/11/2009,181,9.3,4.51,9.02,1
Stand by me,10/11/2009,80,8.1,4.25,8.5,1
The Mist,10/11/2009,230,7.2,3.88,7.76,1
The Langoliers,10/11/2009,230,6.1,3.71,7.42,1
Apt Pupil,1983,179,6.7,3.8,7.7,1
Hearts in Atlantis,2000,640,6.9,3.77,7.54,1
The Talisman,na,na,7.5,4.1, 8.2,2
Rose Madder,na,na,5.95,3.61, 7.22,2
Lisey's Story,na,na,5.95,3.61, 7.22,2
Mr. Mercedes,na,na,6.79,3.87, 7.74,2
Gerald's Game,na,na,5.37,3.43, 6.86,2
Cell,na,na,5.95,3.61, 7.22,2
11.22.63,na,na,8.05,4.26, 8.52,2
The Dark Tower,na,na,7.2,4,8,2


mydata = read.csv("King.csv")
attach(mydata)
p1 <- ggplot(mydata, aes(x=good.doubled, y=imdb)) +
    geom_point(colour = factor(clr),shape=1,size=2) +    # Use hollow circles
    geom_smooth(method=lm,se=FALSE)           
            
p1 <- p1 + ylab("IMDB Ratings")  
p1 <- p1 + xlab("GoodReads Ratings")  
p1 <- p1 + ggtitle("Upcoming Stephen King Adaptations")
p1 <- p1 + annotate("text", x = 6.97, y = 5.26, label = "The Tommyknockers", size=3, colour="blue3")  
p1 <- p1 + annotate("text", x = 7.85, y = 7.42, label = "Carrie", size=3, colour="blue3") 
#Salem's Lot,17/10/1975,439,6.8,3.97,7.94,1
p1 <- p1 + annotate("text", x =8.0 , y =6.9, label = "Salem's Lot", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 7.8, y = 6.5, label = "Pet Sematary", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.34, y = 8.51, label = "The Shining", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.62, y = 8.2, label = "Stand By Me", size=3, colour="blue")
p1 <- p1 + annotate("text", x = 8.9, y = 8.4, label = "The Green Mile", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 9.0, y = 9.09, label = "Shawshank\nRedemption" , size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.75, y = 7.3, label = "The Stand", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.27, y = 6.85 , label = "It", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.19, y = 7.74, label = "Misery", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.05, y = 6.7, label = "Under the Dome", size=3, colour="blue3") 
#Under the Dome,10/11/2009,1074,6.8,3.89,7.78,1
p1 <- p1 + annotate("segment", x = 7.9, xend = 7.79, y = 6.7, yend = 6.8, colour = "blue3") 
#Dolores Claiborne,Nov-92,305,7.4,3.76,7.52,1
p1 <- p1 + annotate("text", x = 7.5, y = 7.3, label = "Dolores Claiborne", size=3, colour="blue3") 
#The Dark Half,20/10/1989,431,5.9,3.71,7.42,1
p1 <- p1 + annotate("text", x = 7.5, y = 5.81, label = "The Dark Half", size=3, colour="blue3")
#Bag of Bones,22/09/1998,529,5.8,3.84,7.68,1
p1 <- p1 + annotate("text", x = 7.78, y = 5.7, label = "Bag of Bones", size=3, colour="blue3")
#The Dead Zone,Aug-79,428,7.3,3.88,7.76,1
p1 <- p1 + annotate("text", x = 7.5, y = 7.72, label = "The Dead Zone", size=3, colour="blue3")
p1 <- p1 + annotate("segment", x = 7.76, xend = 7.5, y = 7.33, yend = 7.66, colour = "blue3") 
#The Mist,10/11/2009,230,7.2,3.88,7.76,1
p1 <- p1 + annotate("text", x = 7.76, y = 7.1, label = "The Mist", size=3, colour="blue3")
#Firestarter,29/09/1980,426,6,3.8,7.6,1
p1 <- p1 + annotate("text", x = 7.71, y = 6, label = "Firestarter", size=3, colour="blue3")
#The Langoliers,10/11/2009,230,6.1,3.71,7.42,1
p1 <- p1 + annotate("text", x = 7.56, y = 6.11, label = "The Langoliers", size=3, colour="blue3")
#Cujo,08/09/1981,319,6,3.61,7.22,1
p1 <- p1 + annotate("text", x = 7.23, y = 6.1, label = "Cujo", size=3, colour="blue3")
#The Running Man,May-82,219,6.6,3.74,7.48,1
p1 <- p1 + annotate("text", x = 7.48, y = 6.7, label = "The Running Man", size=3, colour="blue3")
#Christine,29/04/1983,526,6.6,3.69,7.38,1
p1 <- p1 + annotate("text", x = 7.38, y = 6.5, label = "Christine", size=3, colour="blue3")
#Thinner,19/11/1984,309,5.7,3.6,7.2,1
p1 <- p1 + annotate("text", x = 7.25, y = 5.6, label = "Thinner", size=3, colour="blue3")
#Needful Things,Oct-91,690,6.2,3.84,7.68,1
p1 <- p1 + annotate("text", x = 7.83, y = 6.2, label = "Needful Things", size=3, colour="blue3")
#Dreamcatcher,20/03/2001,620,5.5,3.53,7.06,1
p1 <- p1 + annotate("text", x = 7.2, y = 5.5, label = "Dreamcatcher", size=3, colour="blue3")
#The Talisman,na,na,7.52,4.1, 8.2,2
p1 <- p1 + annotate("text", x = 8.36, y = 7.52, label = "The Talisman", size=3, colour="red3")
#Rose Madder,na,na,5.93,3.61, 7.22,2
p1 <- p1 + annotate("text", x = 7.07, y = 5.93, label = "Rose Madder", size=3, colour="red3")
#Lisey's Story,na,na,5.93,3.61, 7.22,2
p1 <- p1 + annotate("text", x = 7.07, y = 6.05, label = "Lisey's Story", size=3, colour="red3")
#Mr. Mercedes,na,na,6.78,3.87, 7.74,2
p1 <- p1 + annotate("text", x = 7.60, y = 6.81, label = "Mr. Mercedes", size=3, colour="red3")
#Gerald's Game,na,na,5.34,3.43, 6.86,2
p1 <- p1 + annotate("text", x = 7.03, y = 5.36, label = "Gerald's Game", size=3, colour="red3")
#Cell,na,na,5.93,3.61, 7.22,2
p1 <- p1 + annotate("text", x = 7.28, y = 5.92, label = "Cell", size=3, colour="red3")
#11.22.63,na,na,8.05,4.26, 8.52,2
p1 <- p1 + annotate("text", x = 8.63, y = 8.05, label = "11.22.63", size=3, colour="red3")
#The Dark Tower,na,na,7.2,4,8,2
p1 <- p1 + annotate("text", x = 8.16, y = 7.2, label = "The Dark Tower", size=3, colour="red3")
p1 <- p1 + ylab("IMDB Ratings")  
p1 <- p1 + xlab("GoodReads Ratings")
p1
ggsave("plot.png", width=10, height=10, dpi=100)
detach(mydata)

Do Stephen King's Better Books Make Better Films?

My last post (and data) got a bit popular on reddit. Some people noticed that Stephen King films ratings and the books ratings seemed highly correlated.

Are Stephen King films better than the books? – https://t.co/HEWQJyPGcc (definitely a positive correlation) pic.twitter.com/GbS0Xq9s4w

— Max Roser (@MaxCRoser) November 11, 2015

I think Max is right on this. So I made a graph showing how movie and book ratings are correlated

Now the actual correlation figure is

 cor(good.doubled, imdb, use="complete")

[1] [1] 0.8766 so it looks like highly rated King books make highly rated films

It could be that a good film makes people read and rate highly a book. But my basic conclusion is Stephen King's highly rated books make higher rated films

Appendix: Code for the Graph

mydata = read.csv("King.csv")
attach(mydata)
cor(good.doubled, imdb, use="complete")
p1 <- ggplot(mydata, aes(x=good.doubled, y=imdb)) +
    geom_point(shape=1) +    # Use hollow circles
    geom_smooth(method=lm,   # Add linear regression line
                se=FALSE)    # Don't add shaded confidence region
p1 <- p1 + ylab("IMDB Ratings")  
p1 <- p1 + xlab("GoodReads Ratings *2")  
p1 <- p1 + ggtitle("Stephen King: Books vs. Movies Correlation")
p1 <- p1 + geom_text(aes(label=ifelse(good.doubled>0,as.character(Title),'')),hjust=0,just=0,size=2, position = "jitter")
p1
ggsave("correlate.png")

Other Correlations I changed the publication data column into a column of Years since 1974.

 cor(good.doubled, Years, use="complete")
[1] -0.1879052 Not a strong correlation about Kings adapted books get better or worse since he started
> cor(good.doubled, Pages, use="complete")
[1] -0.02715659 no relationship between the length of a book and it being rated highly or lowly.
> cor(Years, Pages, use="complete")
[1] 0.482048 King's adapted books may be getting a bit longer over time

Are Stephen King films better than the books?

Rejoice, Snobs: The Book IS Better Than The Movie
The literary originals have higher ratings than the film adaptations 74 percent of the time. This recent piece claims books are usually better than their films versions.

This article reminded me of a claim that Stephen King films are better than his books. So I extracted the ratings from IMDB and Goodreads. I doubled the Goodreads ratings to make them out of 10.


I included miniseries. Carrie was made twice but I only included the original. The Langoliers, Maximum Overdrive, Lawnmower man and Secret garden seem not to have independent English language printed book versions.

 

The Shining is one of two films with a higher rating than the book. King famously hates the film

"Are you mystified by the cult that's grown around Kubrick's Shining?
I don't get it. But there are a lot of things that I don't get. But obviously people absolutely love it, and they don't understand why I don't. The book is hot, and the movie is cold; the book ends in fire, and the movie in ice."

 

To make this a fair test films and book would have to be graded on a curve. By what the average rating of each is. There is a good discussion on how to normalise the original comparison here. But I think this graph is enough to show Stephen King books are better than his films.

Less than 8% of King adaptations are rated higher compared to 26% usually for book adaptations. To get King to have the average quality of adaptations, sixish Stephen King Films being rated better than is books. This would require Dolores Claiborne, The Green Mile, Stand By Me, Misery and maybe Carrie to be much worse books or these already highly rated films to be much better. I am going to call this Myth Busted*, Stephen King adaptations are less successful than the average adaptation.

 

Title	Publication date	Pages	imdb	goodreads	good doubled
Carrie	April 5, 1974	199	7.4	3.89	7.78
'Salem's Lot	October 17, 1975	439	6.8	3.97	7.94
The Shining	January 28, 1977	447	8.4	4.12	8.24
The Stand	Sep-78	823	7.3	4.32	8.64
The Dead Zone	Aug-79	428	7.3	3.88	7.76
Firestarter	September 29, 1980	426	6	3.8	7.6
Cujo	September 8, 1981	319	6	3.61	7.22
The Running Man	May-82	219	6.6	3.74	7.48
Christine	April 29, 1983	526	6.6	3.69	7.38
Pet Sematary	November 14, 1983	374	6.6	3.86	7.72
Thinner	November 19, 1984	309	5.7	3.6	7.2
It	September 15, 1986	1138	6.9	4.12	8.24
Misery	June 8, 1987	310	7.8	4.06	8.12
The Tommyknockers	November 10, 1987	558	5.4	3.42	6.84
The Dark Half	October 20, 1989	431	5.9	3.71	7.42
Needful Things	Oct-91	690	6.2	3.84	7.68
Dolores Claiborne	Nov-92	305	7.4	3.76	7.52
The Green Mile	March–August 1996	400	8.5	4.39	8.78
Bag of Bones	September 22, 1998	529	5.8	3.84	7.68
Dreamcatcher	March 20, 2001	620	5.5	3.53	7.06
Under the Dome	November 10, 2009	1074	6.8	3.89	7.78
Shawshank Redemption	1982	181	9.3	4.51	9.02
Stand by me	1982	80	8.1	4.25	8.5
The Mist	1983	230	7.2	3.88	7.76
Apt Pupil	1983	179	6.7	3.8	7.7
Hearts in Atlantis	2000	640	6.9	3.77	7.54

   
 mydata = read.csv("King.csv")  
 library(ggplot2)  
 p1 <- ggplot(mydata, aes(x = good.doubled, y = imdb))  
 p1 <- p1 + geom_abline(intercept=0, slope=1)  
 p1 <- p1 + geom_point(shape=1)  
 p1 <- p1 + ylim(5, 10)  
 p1 <- p1 + xlim(5, 10)  
 
 p1 <- p1 + ylab("IMDB Ratings")  
 p1 <- p1 + xlab("GoodReads Ratings *2")  
 p1 <- p1 + ggtitle("Stephen King: Books vs. Movies")  

p1 <- p1 + annotate("text", x = 7.3, y = 5.3, label = "The Tommyknockers", size=3, colour="blue3")  
p1 <- p1 + annotate("text", x = 7.95, y = 7.37, label = "Carrie", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8, y = 6.5, label = "Pet Sematary", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 7.9, y = 8.54, label = "The Shining", size=3, colour="red3") 
p1 <- p1 + annotate("text", x = 8.75, y = 9.4, label = "Shawshank Redemption", size=3, colour="red3")
p1 <- p1 + annotate("text", x = 9.18, y = 8.4, label = "The Green Mile", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.72, y = 8, label = "Stand By Me", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.92, y = 7.3, label = "The Stand", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.3, y = 6.85 , label = "It", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.32, y = 7.7, label = "Misery", size=3, colour="blue3") 
p1 <- p1 + annotate("text", x = 8.18, y = 6.7, label = "Under the Dome", size=3, colour="blue3") 

p1 <- p1 + annotate("text", x = 9.28, y = 5.0, label = "Books Higher Rated", size=5, colour="blue3") 
p1 <- p1 + annotate("text", x = 5.7, y = 9.90, label = "Films Higher Rated", size=5, colour="red3") 



p1 <- p1 + annotate("text", x = 6.9, y = 6, label = "Cujo", size=3, colour="blue3") 
p1 <- p1 + annotate("segment", x = 7.05, xend = 7.2, y = 6.0, yend = 6.0, colour = "blue3") 


p1 <- p1 + annotate("text", x = 6.45, y = 5.52, label = "Dreamcatcher", size=3, colour="blue3") 
p1 <- p1 + annotate("segment", x = 6.8, xend = 7.05, y = 5.5, yend = 5.5, colour = "blue3") 


p1 <- p1 + annotate("text", x = 6.80, y = 7.4, label = "Dolores Claiborne", size=3, colour="blue3")
p1 <- p1 + annotate("segment", x = 7.28, xend = 7.5, y = 7.4, yend = 7.4, colour = "blue3") 

p1 <- p1 + annotate("text", x = 7.92, y = 5.72, label = "The Dark Half", size=3, colour="blue3")
p1
ggsave("plot.png")

*This needs proper statistical significance testing so not really busted.

Odd Mathematical Patents #1: Gömböc

The Gömböc is a self righting shape. A bit like a turtle that will always rotate back to standing up if placed on its back. This shape was patented under US D614077 S1 and a cease and destist has been send to someone giving plans to 3D print one.

Prof. Domokos has politely requested that I remove this file. I have respectfully declined to do so, as I do not believe it violates any of their established legal rights, and I believe it may have actual value for researchers interested in the Gömböc and mono-monostatic bodies in general.

Gömböc's are cool. And the inventors were very ingenious. You can buy one from the patent holders here. Patenting a shape though strikes me as weird.