# Simple Data Science Of Global Warming In KDnuggets

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Would love to get a post from you for KDnuggets (Gregory Piatetsky, KDnuggets President)

Some days ago, Gregory Piatetsky invited me to write a post for KDnuggets. I couldn’t say no. He suggested to me some topics and I decided to experiment around climate change to demonstrate how easy is to see some evidences of this alarming menace. You can read the post here.

This is the code I wrote to do this experiment:

require(sqldf) require(googleVis) require(ggplot2) require(ggthemes) setwd("YOUR WORKING DIRECTORY HERE") #This line doen's work until you type a valid path #Data Avaliable in http://eca.knmi.nl/utils/downloadfile.php?file=download/ECA_blend_tg.zip files=list.files(pattern = "TG_STAID") results=data.frame(staid=character(0), trnd=numeric(0), nobs=numeric(0)) #Loop to calculate linear trends for (i in 1:length(files)) { table=read.table(files[i], header=TRUE, sep = ',', skip=20) table=table[table$Q_TG==0,] table$date=as.Date(as.character(table$DATE), "%Y%m%d") results=rbind(data.frame(staid=files[i], trnd=lm.fit (x = matrix(table$date), y = table$TG)$coefficients, nobs=nrow(table)), results) } write.csv(results, file="results.csv", row.names = FALSE)#Save your work results=read.csv(file="results.csv")#Read your work. You can start here if you stop your work further this line #Remove outliers results=results[!results$trnd %in% boxplot.stats(results$trnd, coef = 4)$out,] #Histogram hist(x=results$trnd, breaks = 50, col = "orange", border = "pink", freq=TRUE, xlim=c(-0.03, 0.03), ylim=c(0, 300), xlab="Historical trend of daily mean temperatures", ylab="Number of stations", main="Evolution of temperatures in Europe and the Mediterranean", sub="Source: European Climate Assessment & Dataset project") results$staid2=as.numeric(gsub("[^0-9]","",results$staid)) #To join results with geographical coordinates #Read table of geographical coordinates staids=read.table("http://www.ecad.eu/download/ECA_all_stations.txt", header=TRUE, sep = ',', skip=17) #Right tail of the distribution hotpoints=sqldf("SELECT a.staid, a.staid2, a.trnd, a.nobs, b.staname, b.lat, b.lon FROM results a INNER JOIN staids b ON (a.staid2=b.staid) WHERE TRND >= 0.02") #Convert degrees:minutes:seconds to decimal coordinates hotpoints=within(hotpoints, { dms=do.call(rbind, strsplit(as.character(LAT), ":")) lat=sign(as.numeric(dms[,1]))*(abs(as.numeric(dms[,1]))+(as.numeric(dms[,2]) + as.numeric(dms[,3])/60)/60);rm(dms) }) hotpoints=within(hotpoints, { dms=do.call(rbind, strsplit(as.character(LON), ":")) lon=sign(as.numeric(dms[,1]))*(abs(as.numeric(dms[,1]))+(as.numeric(dms[,2]) + as.numeric(dms[,3])/60)/60);rm(dms) }) #To make readable tha name of the station in the map hotpoints$staname=gsub("^\s+|\s+$", "", hotpoints$STANAME) #To prepare coordinates to gvis function hotpoints$LatLong=with(hotpoints, paste(lat, lon, sep=":")) #The amazing gvisMap function of googleVis package hotpointsMap=gvisMap(hotpoints, "LatLong" , "STANAME", options=list(showTip=TRUE, showLine=TRUE, enableScrollWheel=TRUE, mapType='terrain', useMapTypeControl=TRUE)) plot(hotpointsMap) #The plot one of this hot stations InAmenas=read.table("TG_STAID000312.txt", header=TRUE, sep = ',', skip=20) InAmenas=InAmenas[InAmenas$Q_TG==0,] InAmenas$date=as.Date(as.character(InAmenas$DATE), "%Y%m%d") ggplot(InAmenas, aes(date, TG)) + geom_line(color="red", alpha=0.8) + xlab("") + ylab("Mean temperature in 0.1C")+ ggtitle("Mean temperature in IN-AMENAS (ALGERIA) 1958- 1998")+ geom_smooth(method = "lm", se=FALSE, color="red", lwd=2)+ theme_economist(base_size = 20, base_family = "sans", horizontal = TRUE, dkpanel = FALSE, stata = FALSE)

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