Dec 31
Here is a 2012 annual report for this blog produced by The WordPress.com.
My top post this year based on number of views are
Click here to see the complete report.
Dec 22
Coursera is offering free courses about R among other interesting subjects. The first one on the application of R in financial econometrics is happening this week (but you can still enroll). There are two more courses starting in January 2013 are more about using R to analyse the data. The differences between the two are stated that:
“This course (Data Analysis by Jeff Leek) will focus on how to plan, carry out, and communicate analyses of real data sets. While we will cover the basics of how to use R to implement these analyses, the course will not cover specific programming skills. Computing for Data Analysis will cover some statistical programming topics that will be useful for this class, but it is not a prerequisite for the course”.
There are more FREE online tutorials about R apart from those in Coursera. Here is the like to list of more than 90 R tutorials.
Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.
Next session: Dec 17th 2012 (10 weeks long)
More information: here
Learn about the most effective data analysis methods to solve problems and achieve insight.
Next Session:
Jan 2nd 2013 (4 weeks long) You are enrolled!
Workload: 3-5 hours/week
More information: here
This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.
Next Session:
Jan 22nd 2013 (8 weeks long) You are enrolled!
Workload: 3-5 hours/week
More information: here
The VDO of this course are also available in YouTube channel of Dr.Roger Peng. So you can check it out below.
Playlist: Week 1
Playlist: Week 2
Playlist: Week 3
Playlist: Week 4
Inspired by Mages’s post on Accessing and plotting World bank data with R (using googleVis package), I created one visualising tourism receipts and international tourist arrivals of various countries since 1995. The data used are from the World Bank’s country indicators.
To see the motion chart, double click a picture below.
install.packages("googleVis")
library('googleVis')
getWorldBankData <- function(id='SP.POP.TOTL', date='1960:2010',
value="value", per.page=12000){
require(RJSONIO)
url <- paste("http://api.worldbank.org/countries/all/indicators/", id,
"?date=", date, "&format=json&per_page=", per.page,
sep="")
wbData <- fromJSON(url)[[2]]
wbData = data.frame(
year = as.numeric(sapply(wbData, "[[", "date")),
value = as.numeric(sapply(wbData, function(x)
ifelse(is.null(x[["value"]]),NA, x[["value"]]))),
country.name = sapply(wbData, function(x) x[["country"]]['value']),
country.id = sapply(wbData, function(x) x[["country"]]['id'])
)
names(wbData)[2] <- value
return(wbData)
}
getWorldBankCountries <- function(){
require(RJSONIO)
wbCountries <-
fromJSON("http://api.worldbank.org/countries?per_page=12000&format=json")
wbCountries <- data.frame(t(sapply(wbCountries[[2]], unlist)))
wbCountries$longitude <- as.numeric(wbCountries$longitude)
wbCountries$latitude <- as.numeric(wbCountries$latitude)
levels(wbCountries$region.value) <- gsub(" \\(all income levels\\)",
"", levels(wbCountries$region.value))
return(wbCountries)
}
## Create a string 1960:this year, e.g. 1960:2011
years <- paste("1960:", format(Sys.Date(), "%Y"), sep="")
## International Tourism Arrivals
inter.tourist.arrivals<- getWorldBankData(id='ST.INT.ARVL',
date=years, value="International tourism, number of arrivals")
## International Tourism Receipts
tourism.receipts <- getWorldBankData(id='ST.INT.RCPT.CD', date=years,
value="International tourism, receipts (current US$)")
## Population
population <- getWorldBankData(id='SP.POP.TOTL', date=years,
value="population")
## GDP per capita (current US$)
GDP.per.capita <- getWorldBankData(id='NY.GDP.PCAP.CD',
date=years,
value="GDP.per.capita.Current.USD")
## Merge data sets
wbData <- merge(tourism.receipts, inter.tourist.arrivals)
wbData <- merge(wbData, population)
wbData <- merge(wbData, GDP.per.capita)
## Get country mappings
wbCountries <- getWorldBankCountries()
## Add regional information
wbData <- merge(wbData, wbCountries[c("iso2Code", "region.value",
"incomeLevel.value")],
by.x="country.id", by.y="iso2Code")
## Filter out the aggregates and country id column
subData <- subset(wbData, !region.value %in% "Aggregates" , select=
-country.id)
## Create a motion chart
M <- gvisMotionChart(subData, idvar="country.name", timevar="year",
options=list(width=700, height=600))
## Display the chart in your browser
plot(M)
# save as a file
print(M, file="myGoogleVisChart.html")
Here is the list of press reports and news about R
Oct 31
Pairach Piboonrungroj, PhD 