For my small exercise I used data provided by the German Meteorological Service "Deutscher Wetterdienst". It allows to freely download and use (under some conditions) data from several decades of weather observation. I uploaded the data to DB2/Bluemix as described in my previous post.
|Bluemix: Change DB2 column name and type|
The DB2 Cloud environment provides several examples for programming in R, a special function library "bluR" to easily connect R with DB2-based data, and it features the RStudio to develop, test, and execute code in R. Within RStudio it is possible to execute several demos to learn more about analytics, graphing, and data processing. For the DB2 in-memory database API for R there is a demo as well. You can invoke it using the "demo(blur)" command:
|DB2 API demo for R in RStudio|
For escaping quotes in #SQL statements for #DB2 in #RStudio/#rstats use C style, not SQL. (from my #Bluemix tests) pic.twitter.com/XslDgxZU7L
— Henrik Loeser (@data_henrik) August 29, 2014
The data set which I uploaded to DB2 has daily minimum and maximum temperatures (and lots of other meteorological) for about 70 years. I used a SQL query and then the ggplot2 library to create a graphic. It shows the band for the minimum temperatures for each September day as well as the band for the maximum daily temperatures.
|DB2 with R: Historic September temperatures|
1: ########### R script to analyze historic weather data for min/max values 2: ## Written by Henrik Loeser 3: ## Connection handle con to BLU for Cloud data warehouse is provided already 4: ## For plotting, we are using ggplot2 package 5: ## 6: library(ggplot2) 7: library(bluR) 8: 9: ## initialize DB2 connection and environment 10: con <- bluConnect("BLUDB","","") 11: bluAnalyticsInit(con) 12: 13: ## query DB2 weather data and fetch min/max values of min/max values 14: ## (lower/upper boundary each) 15: query<-paste('select max(lufttemperatur_maximum) as maxmax,min(lufttemperatur_minimum) as minmin,min(lufttemperatur_maximum) as minmax,max(lufttemperatur_minimum) as maxmin,tag from (select lufttemperatur_maximum, lufttemperatur_minimum, day(mdatum) as tag from blu01023.klima where month(mdatum)=9) group by tag order by tag asc') 16: df <- bluQuery(query,as.is=F) 17: 18: ## Some plotting needs to be done 19: jpeg(type='cairo',"tempe.jpg",width=800,height=600) 20: ggplot(df, aes(x = TAG))+ylab("Temperature")+xlab("Day")+ 21: geom_ribbon(aes(ymin = MINMIN, ymax=MAXMIN), fill='blue')+ 22: geom_ribbon(aes(ymin = MAXMAX, ymax=MINMAX), fill='red')+ 23: geom_ribbon(aes(ymin = MAXMIN, ymax=MINMAX), fill='white')+ 24: geom_line(aes(y = MINMIN), colour = 'black') + 25: geom_line(aes(y = MAXMIN), colour = 'black') + 26: geom_line(aes(y = MINMAX), colour = 'black') + 27: geom_line(aes(y = MAXMAX), colour = 'black') 28: 29: sink('/dev/null') 30: 31: bluClose(con) 32: ## connection is closed, we are done
Pretty cool (my opinion)! I am already turning into a data scientist. And you can test this yourself on IBM Bluemix with the Analytics Warehouse service (DB2 in-memory database feature).