Let's say I want to reproduce an example posted on StackOverflow. Some have suggested posters use dput()
to help streamline this process or one of the datasets available in the base package.
In this case, however, suppose I have only been given the output of the dataframe:
> site.data
site year peak
1 ALBEN 5 101529.6
2 ALBEN 10 117483.4
3 ALBEN 20 132960.9
8 ALDER 5 6561.3
9 ALDER 10 7897.1
10 ALDER 20 9208.1
15 AMERI 5 43656.5
16 AMERI 10 51475.3
17 AMERI 20 58854.4
Do I have other options besides saving this as a text file and using read.table()
?
That's a neat solution. I'm guessing there's a way to do this with RCurl, as in this post which scraped off wikipedia.
But as a more general point for discussion: why don't we just use data from the "datasets" package in R? Then everyone will have the data by just calling the data() function, and there are datasets to cover most cases.
[Edit]: I was able to do this. It's clearly more work (i.e. impractical) than your solution. :)
[Edit 2]: I wrapped this into a function and tried it with another page.
getSOTable <- function(url, code.block=2, raw=FALSE, delimiter="code") {
require(RCurl)
require(XML)
webpage <- getURL(url)
webpage <- readLines(tc <- textConnection(webpage)); close(tc)
pagetree <- htmlTreeParse(webpage, error=function(...){}, useInternalNodes = TRUE)
x <- xpathSApply(pagetree, paste("//*/", delimiter, sep=""), xmlValue)[code.block]
if(raw)
return(strsplit(x, "\n")[[1]])
else
return(read.table(textConnection(strsplit(x, "\n")[[1]][-1])))
}
getSOTable("https://stackoverflow.com/questions/1434897/how-do-i-load-example-datasets-in-r")
site year peak
1 ALBEN 5 101529.6
2 ALBEN 10 117483.4
3 ALBEN 20 132960.9
8 ALDER 5 6561.3
9 ALDER 10 7897.1
10 ALDER 20 9208.1
15 AMERI 5 43656.5
16 AMERI 10 51475.3
17 AMERI 20 58854.4
getSOTable("https://stackoverflow.com/questions/1428174/quickly-generate-the-cartesian-product-of-a-matrix", code.block=10)
X1 X2 X3 X4
1 1 11 1 11
2 1 11 2 12
3 1 11 3 13
4 1 11 4 14
5 1 11 5 15
6 1 11 6 16
7 1 11 7 17
8 1 11 8 18
9 1 11 9 19
10 1 11 10 20
Here's one handy option:
site.data <- read.table(textConnection(
" site year peak
1 ALBEN 5 101529.6
2 ALBEN 10 117483.4
3 ALBEN 20 132960.9
8 ALDER 5 6561.3
9 ALDER 10 7897.1
10 ALDER 20 9208.1
15 AMERI 5 43656.5
16 AMERI 10 51475.3
17 AMERI 20 58854.4"))