I wanted to look first to see if there was a link. As the article does nothing to show there is.
I took a list of countries by their percentage of immigrants
And one of countries by their birthrate
I created this combined dataset of Country, Birthrate and Immigrant % and put it here
The correlation between birthrate and the percentage of immigrants in a country is weak.
> cor(data$FertilityRate, data$ImmigrantPer) [1] -0.3463663I am willing to bet you at odds that the correlation between wealth and birth rate and between wealth and % of immigrants is higher. That having money causes immigrants to come to your country and you to have less children. Not that people choose between having a child and a 25 year old Ethiopian.
So Irish Times please do talk about what is at best a weak link between immigration and low reproductive rates.
Issues Brexiters really care about and will likely get
2. Control of our own laws
5. Control of immigration. Net migration seems to now be about 300k to the UK each year. The Tories promised to bring it below 100K. If immigration drops below 100k that probably means the people who voted to leave the EU have the immigration control they want.
Things that are not measurable
1. Certainty wherever possible
11. Co-operation on crime, terrorism and foreign affairs
12. A phased approach, delivering a smooth, orderly Brexit
Things they had before Brexit
4. Maintaining the common travel area with Ireland
6. Rights for EU nationals in Britain and British nationals in the EU
8. Free trade with European markets
Measurable things (I think they won't get)
3. Strengthening the United Kingdom
7. Enhancing rights for workers
9. New trade agreements with other countries. This probably breaks down to improved economy. So measures of the economic trade could be used to measure this one.
10. A leading role in science and innovation
I am willing to pick measurable metrics on these last four. % of people in Scotland who want independance. Where UK stands in global metrics of workers rights. Patents or and journal paper outputs and their are other metrics of countries innovation. University league tables are another possible metric for example.
Trade agreements are mainly about the economy. Inflation, consumer debt, Sterlings value, GDP growth, export growth are all useful metrics.
I can't think of an obvious metric that shows making your own laws in Parliament has been a good idea. But there are 8 other objectives May wants that are measurable. And general economic metrics most people accept as important.
With at least 10 things to measure to decide if Brexit is going well or badly I think it is reasonable for Leavers and Remainers to define what they would see as success for Brexit. This wont take into account big downside economic or military risks. Or peoples happiness at increased national sovereignty, though national happiness metrics might work.
But you can measure some things people say are important so why not define metrics of what would mean Brexit was a success?
Party,"Votes,1st pref.",Seats,Spending Fine Gael,544140,50,2768881.50 Fianna Fáil,519356,44,1687916.29 Sinn Féin,295319,23,650190.38 Labour Party,140898,7,1083718.38 AAA–PBP,84168,6,266942.48 Ind 4 Change,31365,4,51669.18 Social Dem,64094,3,190586.93 Green Party,57999,2,146792.27and the r package code is
data <- read.csv("spending.csv", header=TRUE)
datat <- mutate(data, perV = Spending/Votes.1st.pref., perS= Spending/Seats)
q<- ggplot(data=datat, aes(x=Party, y=perV, fill=Party)) + geom_bar(stat="identity") + scale_fill_manual(values=c("#E5E500", "#66BB66", "#6699FF", "#99CC33", "#FFC0CB","#CC0000", "#008800", "#752F8B"))
q <-q + theme(axis.text.x = element_text(angle = 90, hjust = 1))
q <-q + theme(legend.position="none")
q <-q + labs(title = "General Election Spending 2016")
q <-q + labs(y = "Euros Per Vote")
q<- ggplot(data=datat, aes(x=Party, y=perS, fill=Party)) + geom_bar(stat="identity") + scale_fill_manual(values=c("#E5E500", "#66BB66", "#6699FF", "#99CC33", "#FFC0CB","#CC0000", "#008800", "#752F8B"))
q <-q + theme(axis.text.x = element_text(angle = 90, hjust = 1))
q <-q + theme(legend.position="none")
q <-q + labs(title = "General Election Spending 2016")
q <-q + labs(y = "Euros Per Seat")
So the rowers, Françoise Brandt and Roelof Klein, picked a ten year old French boy (25kg) out of the crowd and asked him to cox for them.
They won the gold. And took a photo with the boy. But his identity has never been established.
The data contains the date, location and a description for 4000 fatalities over five years. I created columns for state, zipcode, number of people and cause.
The most common interesting words in these descriptions are
Not common but interesting
and here is a map I made of the states where they happen
I have created a repository to try augment the OSHA data and clean it up when errors are found.
The repository is on github here.
If you use it I'll give you edit rights and you can help improve it
Handpicked?
Why would Amazon lie like this?
"they've all got the same covers, and I thought they were all o' one sample, as you may say. But it seems one mustn't judge by th' outside. This is a puzzlin' world." The Mill on the Floss by George EliotWhat is the correlation between peoples ratings of a books cover and the ratings the book receives? This post is about a game devised to get people to rate book covers and gives some great visualisations comparing a books goodreads rating to its cover rating. They gathered over 3 million ratings of 100 covers.
I took their data and got the average rating for each of the covers they tested. I then scraped these 100 books Goodreads average ratings, number of ratings and number of reviews. The Data table and the code I used to scrape and aggregate is here. There are all sorts of accuracy warnings you can imagine around these results. The main ones being that the books and their covers all look pretty good to me. They are not on the self published fan fiction end of the market. The variables here are. num_ratings: Number of Goodreads ratings. rating: average rating of the book. num_reviews: Number of people who have actually written a review. cover_rating: The average rating people gave the cover of the book.
> cor(rating,cover_rating)So no you can't judge a book by its cover the correlation in ratings is only .16. You can guess the number of ratings by the number of reviews. You can't guess how highly rated a book is by the number of ratings. Having a good cover might increase the number of reviews your book gets by a bit.[1] 0.1609114
> cor(num_ratings,num_reviews)
[1] 0.9597442
> cor(rating,num_ratings)
[1] 0.2141307
> cor(rating,num_reviews)
[1] 0.2658916
> cor(num_ratings,cover_rating)
[1] 0.3059627
> cor(num_reviews,cover_rating)
[1] 0.3307553
The conclusion is you shouldn't judge a book by its cover. Or by its number of sales (ratings). But people probably do judge books by their cover a bit.
Logaskino was a military base in Siberia. Over 30 years Soviet mapmakers moved it around maps to throw off enemies "How to lie with maps" talks about how the Soviets would move around the location of military bases on maps. These maps show one small base (now abandoned) and the local river and how it moved around on maps over 30 years in order to attempt to confuse enemies
England has a collected data on daily temperature from 1772 in the Hadley Centre Central England Temperature (HadCET) dataset.
I downloaded this Hadley Centre dataset. And I followed this tutorial. Based on an original graphic by Tufte.
Here the black line is the average temerature for each day last year. The dark line in the middle is the average average temperature (95% confidence). the staw coloured bigger lines represent the highest and lowest average daily temperature ever recorded on that day since 1772. the red dots are the days in 2015 that were hotter than any other day at that time of year since 1772.
Looking at the black line that represents last years temperatures it was the Winter and Autumn that were far above average. Instead of a scorching hot summer most of the record hot days were in November and December. 2014 had the same pattern of a hot Winter. No day in 2015 was the coldest for that date in the recorded time.
The Correlation here is cor(Year,Height) [1] 0.39831 which isn't much. Basically Cathedral's burned down and were replaced by a similar sized world's tallest building from 1200 until 1900.
Looking just at the Skyscraper era 1884 on. cor(Year,Height) [1] 0.9340458 which really looks like height increases by follow time. Running this as a linear regression the Kilometer tall bulding is not expected until the end of the century
linearModelVar <- lm(Height ~ Year, newdata)linearModelVar$coefficients[[2]]*2010+linearModelVar$coefficients[[1]]
646.6246 The Burj Khalifa was much taller than any building was expected to be in 2010
linearModelVar$coefficients[[2]]*2099+linearModelVar$coefficients[[1]]
1002.799 finally a kilometer tall building in 2099
linearModelVar$coefficients[[2]]*2241+linearModelVar$coefficients[[1]]
1604.903 a Mile high tower 2241 far into the future?
The first factor was the rise of the DVR, which has made it cheaper and easier than ever before for people to record their favorite shows and watch them at their leisure. This has been great for television artistically, since it means creators can now more readily assume that every single episode of their show will be consumed in sequence.
Stephen Johnson's book "Everything Bad Is Good For You" analyses the complexity of TV programs from the 1970s and today and shows how much more complex modern ones are. Compare Columbo with one murderer shown at the start and it takes 70 minutes for them to be found out. Whereas a more modern CSI is 43 min of multiple plots with loads of characters.
The Vox piece points out that episodic series like CSI with few series long story arcs now seem outdated. Viewers are expected to keep information about longer plots now. Meaning there are more details about the characters and their relationships viewers need to track. Series you can play back at any time may be cognitively as well as artistically beneficial.
A probably fake photo from 1974 of HMS Tartar's trip there. 'A sentry-box was constructed on Hall's Ledge, with two marines in full ceremonial uniform posted alongside, and the Union Flag was hoisted above.'
Every now and again Britain lands some people on this lump and takes a photo to prove it is theres. 'Former SAS member and survival expert Tom McClean lived on the island from 26 May 1985 to 4 July 1985 to affirm the UK's claim to the island'. Waves roll over the island so he had to hide in a bolted down giant coffin for the duration.
They do this partly because owning the Falklands isn't grim enough for them. And partly for all the oil and gas and such that might be between Rockall and Ireland.
"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.
| 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)
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
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")
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.
The graph shows the counts for each region the slaves were disembarked by year. The html and javascript for this graph is on a gist here
The data used can be gotten from the slavevoyages.org website with the filters beloew
An actual graph you can play with is here and in the result tab below