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# Analysis of some predictions about the 2018 EA Survey
## Introduction.
Some effective altruists made predictions about the 2018 EA Survey: a survey which aims to reach most people within the effective altruism movement. Here, I present the set up for the prediction making, the questions, and explain some judgement calls I made when judging the answers. Everything is written such that you can play along.
Some effective altruists made predictions about the 2018 EA Survey: a survey which aims to reach most people within the effective altruism movement. Here, I present the set up for the prediction making, the questions, and explain some judgement calls I made when judging the answers. Everything is written such that you can play along. At the end, I provide some code to replicate my analysis.
## Set up
For every question, try to come up with an interval such that you're 80% confident the answer lies in it. If you use a search engine, the surveys from previous years are fair game.
@ -113,3 +113,64 @@ Questions for further analysis:
2. Are the results an artifact of a small number of questions which were really hard (f.ex. the % of LessWrongers in EA)?
I expect to answer those questions in the near future.
## Code.
```
> ### We first read the data
> DataFrame <- read.csv(file="Predictions.csv", header=TRUE, sep=",")
> View(D)
>
> ### We then create a different object for storing the cleaned up data
> DataFrameProcessed=data.frame(matrix(nrow=35,ncol=52))
> LowerBoundsPersoni=NULL
>
> ### And clean up the data.
> for(i in c(1:35)){
+ as.numeric(strsplit(as.character(DataFrame[i,5]),", ")[[1]]) -> LowerBoundsPersoni
+ as.numeric(strsplit(as.character(DataFrame[i,6]),", ")[[1]]) -> UpperBoundsPersoni
>
+ for(j in c(1:26)){
+ DataFrameProcessed[i,(j*2)-1] <- LowerBoundsPersoni[j]
+ DataFrameProcessed[i,(j*2)] <- UpperBoundsPersoni[j]
+ }
+ }
> ### It shows that I've been programming in C.
>
> c(paste("Person-",c(1:35),sep=""))->rownames(DataFrameProcessed)
> c(rbind(paste("Q",c(1:26),"-lower",sep=""),paste("Q",c(1:26),"-upper",sep="")))->colnames(DataFrameProcessed)
> View(DataFrameProcessed)
>
> answers <- read.csv(file="answers.csv", header=TRUE, sep=",")[,2]
>
> ### Although every person answered every question, 2 anwers are not available.
> replaceNA <-function(x,y){
+ return( ifelse(is.na(x), y, x) )
+ }
>
> sum2<-function(x){ return(sum(replaceNA(x))) }
>
> ### Because some of the answers are not available, the comparison will give a NA. So we need sum2.
> total <- function(x){
+ y=c(1:26)*2
+ return(sum2(as.vector((answers>=DataFrameProcessed[x,y-1])) & as.vector(answers<=DataFrameProcessed[x,y])) )
+ }
>
> ### vapply applies a function to every member of a vector.
> vapply(c(1:35),total,numeric(1))->DataFrameProcessed$totalcorrect
>
> vapply(DataFrameProcessed$totalcorrect,Brierscore,numeric(1))->DataFrameProcessed$Brierscores
>
> ### And you can get graphics using
> png("Scatterplot3.png", units="px", width=3200, height=3200, res=500)
> plot(DataFrameProcessed$totalcorrect*100/24, xlab= "Persons, from 1 to 35", ylab="% of questions they got right", main="Scatterplot!")
> abline(h=mean(DataFrameProcessed$totalcorrect)*100/24, col="red")
> abline(h=80, col="blue")
> text(x=20, y=56, col="red", "Actual average")
> text(x=20, y=81, col="blue", "Target average")
> dev.off()
> ### As well as with the function hist(), whose parameter break = number allows you to control the granularity of the histogram.
´´´