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 |
| 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 |
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)
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