Make a KML-File from an OpenStreetMap Trail

Ever wished to use a trail on OSM on your GPS or smartphone? With this neat little R-Script this can easily be done. You'll just need to search OpenStreetMap for the ID of the trail (way), put this as argument to osmar::get_osm, convert to KML and you're good to go!

# get OSM data
library(osmar)
library(maptools)
  
rotewandsteig <- get_osm(way(166274005), full = T)
sp_rotewandsteig <- as_sp(rotewandsteig, what = "lines")
  
# convert to KML 
kmlLine(sp_rotewandsteig@lines[[1]], kmlfile = "rotewandsteig.kml",
        lwd = 3, col = "blue", name = "Rotewandsteig") 

# view it
shell.exec("rotewandsteig.kml")

R GIS: Function to Reverse KML Paths

This is a function I wrote up for reversing KML-paths. The paths within a KML can be partially matched by their name-tags

## name:          ReverseKmlPath   
## use:           Reverse KML-pathsby matching their Name tags
## arguments:     PATH_TO_DOC, the path to the KML-file
##                NAME, the value of the name tag, function uses partial matching!
##                'Trail_xyz' will be matched by 'rail'
## requirements:  KML-structure with Placemarks containing a  and a  tag
## author:        Kay Cichini
## date:          01-05-2014
## license:       CC-BY-NC-SA

ReverseKmlPath <- function(PATH_TO_DOC, NAMES) {
    
    require(XML)

    doc <- xmlInternalTreeParse(PATH_TO_DOC)
    
    if (xmlNamespaceDefinitions(doc)[[1]]$uri == "http://www.opengis.net/kml/2.2") {
        namespaces <- c(kml = "http://www.opengis.net/kml/2.2")
        flag <- 1
    } else {
        if (xmlNamespaceDefinitions(doc)[[1]]$uri == "http://earth.google.com/kml/2.0") { 
                namespaces <- c(kml0 = "http://earth.google.com/kml/2.0")
                flag <- 0
            } else {
                stop ("Stopped!: Check namespace issue..")
            }
    }
        
    
    for (NAME in NAMES) {
        
        if (flag) { 
              query <- paste0("//kml:Placemark[contains(kml:name,'", sprintf("%s", NAME), "'", ")]//kml:coordinates")
          } else {
              query <- paste0("//kml0:Placemark[contains(kml0:name,'", sprintf("%s", NAME), "'", ")]//kml0:coordinates")
          }

        coords <- tryCatch(getNodeSet(doc, query, namespaces), 
                           error = function(e) message(paste("\nError: *", NAME, "* was NOT successfully matched\n")))
        
        for (i in length(coords)) {

            #grab coordinates from node and reverse order
            rev_coord_vector <- rev(unlist(strsplit(gsub("\\t|\\n", "", xmlValue(coords[[i]])), "\\s")))
            rev_coord_string <- paste(rev_coord_vector, collapse = " ")

            # re-insert reversed line-string:
            xmlValue(coords[[i]]) <- rev_coord_string

            # message
            if (flag) { 
                  query <- paste0("//kml:Placemark[contains(kml:name,'", sprintf("%s", NAME), "'", ")]//kml:name")
              } else {
                  query <- paste0("//kml0:Placemark[contains(kml0:name,'", sprintf("%s", NAME), "'", ")]//kml0:name")
            }
            match <- xmlValue(getNodeSet(doc, query, namespaces)[[i]])
            message(paste0("matched name: ", match, "\n..."))

        }
    }

    # save:
    message("Reversed paths saved to:")
    saveXML(doc, paste0(dirname(PATH_TO_DOC), "/reversed_", basename(PATH_TO_DOC)),
            prefix = newXMLCommentNode("This file was created with the R-package XML::saveXML, see: "))
}

## not run: 
tf <- tempfile(fileext = ".kml")
download.file("http://dev.openlayers.org/releases/OpenLayers-2.13.1/examples/kml/lines.kml", tf, mode = "wb")
ReverseKmlPath( PATH_TO_DOC = tf, NAMES = c("Absolute", "Relative") )

shell.exec(tf)
shell.exec(paste0(dirname(tf), "/reversed_", basename(tf)))

R GIS: Generalizer for KML Paths

I'm posting a recent project's spin-off, which is a custom line-generalizer which I used for huge KML-paths. Anyone with a less clumpsy approach?

## line generalizing function: takes two vectors of with x/ycoords 
## and return ids of x/y elements which distance to its next element
## is shorter than the average distance between consecutive vertices
## multiplied by 'fac'
check_dist <- function(x, y, fac) {
    dm <- as.matrix(dist(cbind(x, y)))
    
    ## supradiagonal holds distance from 1st to 2nd, 2nd to 3rd, etc. element
    d <- diag(dm[-1, -ncol(dm)])
    mean_dist <- mean(d)
    keep <- logical()
    
    ## allways keep first..
    keep[1] <- T
    for (i in 1:(length(x) - 2)) {
        keep[i + 1] <- (d[i] > mean_dist * fac)
        message(paste0("Distance from item ", i, " to item ", i + 1, " is: ", d[i]))
    }
    message(paste0("Treshold is: ", mean_dist * fac))
    cat("--\n")
    ## .. and always keep last
    keep[length(x)] <- T
    return(keep)
}

## Testing function check_dist:
x <- rnorm(5)
y <- rnorm(5)
(keep <- check_dist(x, y, 1.2))

plot(x, y)
lines(x[keep], y[keep], lwd = 4, col = "green")
lines(x, y, lwd = 1, col = "red")
text(x, y + 0.1, labels = c(1:length(x)))


## exclude vertices by generalization rule. coordinate-nodes with low number of vertices, 
## segments with less than 'min_for_gen' vertices will not be simplified, in any case coordinates will be
## rounded to 5-th decimal place

generalize_kml_contour_node <- function(node, min_for_gen, fac) {
    
    require(XML)
    
    LineString <- xmlValue(node, trim = T)
    
    LineStrSplit <- strsplit(unlist(strsplit(LineString, "\\s")), ",")
    
    # filter out empty LineStrings which result from strsplit on '\\s'
    LineStrSplit <- LineStrSplit[sapply(LineStrSplit, length) > 0]
    
    # all 3 values are required, in case of error see for missing z-values:
    x <- round(as.numeric(sapply(LineStrSplit, "[[", 1, simplify = T)), 5)
    y <- round(as.numeric(sapply(LineStrSplit, "[[", 2, simplify = T)), 5)
    z <- round(as.numeric(sapply(LineStrSplit, "[[", 3, simplify = T)), 5)
    
    # for lines longer than 'min_for_gen' vertices, generalize LineStrings
    if (length(x) >= min_for_gen) {
        keep <- check_dist(x, y, fac)
        x <- x[keep]
        y <- y[keep]
        z <- z[keep]
        xmlValue(node) <- paste(paste(x, y, z, sep = ","), collapse = " ")
        
        # for all other cases, insert rounded values
    } else {
        xmlValue(node) <- paste(paste(x, y, z, sep = ","), collapse = " ")
    }
}

## mind to use the appropiate namespace definition: alternatively use: 
## c(kml ='http://opengis.net/kml/2.2')
kml_generalize <- function(kml_file, min_for_gen, fac) {
    doc <- xmlInternalTreeParse(kml_file)
    nodes <- getNodeSet(doc, "//kml:LineString//kml:coordinates", c(kml = "http://earth.google.com/kml/2.0"))
    mapply(generalize_kml_contour_node, nodes, min_for_gen, fac)
    saveXML(doc, paste0(dirname(kml_file), "/simpl_", basename(kml_file)))
}

## get KML-files and generalize them
kml_file <- tempfile(fileext = ".kml")
download.file("http://dev.openlayers.org/releases/OpenLayers-2.13.1/examples/kml/lines.kml", 
              kml_file, mode = "wb")
kml_generalize(kml_file, 5, 0.9)
shell.exec(kml_file)
shell.exec(paste0(dirname(kml_file), "/simpl_", basename(kml_file)))

Download all Documents from Google Drive with R

A commentator on my blog recently asked if it is possible to retrieve all direct links to your Google Documents. And indeed it can be very easily done with R, just like so:

# you'll need RGoogleDocs (with RCurl dependency..)
install.packages("RGoogleDocs", repos = "http://www.omegahat.org/R", type="source")
library(RGoogleDocs)



gpasswd = "mysecretpassword"
auth = getGoogleAuth("kay.cichini@gmail.com", gpasswd)
con = getGoogleDocsConnection(auth)

CAINFO = paste(system.file(package="RCurl"), "/CurlSSL/ca-bundle.crt", sep = "")
docs <- getDocs(con, cainfo = CAINFO)

# get file references
hrefs <- lapply(docs, function(x) return(x@access["href"]))
keys <- sub(".*/full/.*%3A(.*)", "\\1", hrefs)
types <- sub(".*/full/(.*)%3A.*", "\\1", hrefs)

# make urls (for url-scheme see: http://techathlon.com/download-shared-files-google-drive/)
# put format parameter for other output formats!
pdf_urls <- paste0("https://docs.google.com/uc?export=download&id=", keys)
doc_urls <- paste0("https://docs.google.com/document/d/", keys, "/export?format=", "txt")

# download documents with your browser
gdoc_ids <- grep("document", types)
lapply(gdoc_ids, function(x) shell.exec(doc_urls[x]))

pdf_ids <- grep("pdf", types, ignore.case = T)
lapply(pdf_ids, function(x) shell.exec(pdf_urls[x]))

Use Case: Make Contour Lines for Google Earth with Spatial R

Here's comes a script I wrote for creating contour lines in KML-format to be used with Google Earth http://github.com/gimoya/theBioBucket-Archives/blob/master/R/contours_for_google_earth.R

If you want to check or just use the datasets I created for the Alps region, you can download it here: http://terrain-overlays.blogspot.co.at/index.html

Use GDAL from R Console to Split Raster into Tiles

When working with raster datasets I often encounter performance issues caused by the large filesizes. I thus wrote up a little R function that invokes gdal_translate which would split the raster into parts which makes subsequent processing more CPU friendly. I didn't use built-in R functions simply because performance is much better when using gdal from the command line..

The screenshot to the left shows a raster in QGIS that was split into four parts with the below script.

## get filesnames (assuming the datasets were downloaded already. 
## please see http://thebiobucket.blogspot.co.at/2013/06/use-r-to-bulk-download-digital.html 
## on how to download high-resolution DEMs)
setwd("D:/GIS_DataBase/DEM")
files <- dir(pattern = ".hgt")

## function for single file processing mind to replace the PATH to gdalinfo.exe!
## s = division applied to each side of raster, i.e. s = 2 gives 4 tiles, 3 gives 9, etc.
split_raster <- function(file, s = 2) {
    
    filename <- gsub(".hgt", "", file)
    gdalinfo_str <- paste0("\"C:/OSGeo4W64/bin/gdalinfo.exe\" ", file)
      
    # pick size of each side
    x <- as.numeric(gsub("[^0-9]", "", unlist(strsplit(system(gdalinfo_str, intern = T)[3], ", "))))[1]
    y <- as.numeric(gsub("[^0-9]", "", unlist(strsplit(system(gdalinfo_str, intern = T)[3], ", "))))[2]
    
    # t is nr. of iterations per side
    t <- s - 1
    for (i in 0:t) {
        for (j in 0:t) {
            # [-srcwin xoff yoff xsize ysize] src_dataset dst_dataset
            srcwin_str <- paste("-srcwin ", i * x/s, j * y/s, x/s, y/s)
            gdal_str <- paste0("\"C:/OSGeo4W64/bin/gdal_translate.exe\" ", srcwin_str, " ", "\"", file, "\" ", "\"", filename, "_", i, "_", j, ".tif\"")
            system(gdal_str)
        }
    }
}

## process all files and save to same directory
mapply(split_raster, files, 2) 

Use Case: Spatial R & Google Earth for Terrain Analyses

I'd like to share code that uses spatial R and Google Earth for terrain analyses. In this example I took SRTM data at 1" resolution from http://www.viewfinderpanoramas.org/dem3.html#alps read it into R did a little processing and finally wrapped it up in a KML-file that I use as ground-overlay in Google Earth. In fact I eventually converted it into a sqlitedb with MAPC2MAPC for usage on a mobile device.

See the code here on github..

Get No. of Google Search Hits with R and XML

UPDATE: Thanks to Max Ghenis for updating my R-script which I wrote a while back - the below R-script can now be used again for pulling the number of hits from Google-Search.

GoogleHits <- function(input)
   {
    require(XML)
    require(RCurl)
    url <- paste("https://www.google.com/search?q=\"",
                 input, "\"", sep = "")
 
    CAINFO = paste(system.file(package="RCurl"), "/CurlSSL/ca-bundle.crt", sep = "")
    script <- getURL(url, followlocation = TRUE, cainfo = CAINFO)
    doc <- htmlParse(script)
    res <- xpathSApply(doc, '//*/div[@id="resultStats"]', xmlValue)
    cat(paste("\nYour Search URL:\n", url, "\n", sep = ""))
    cat("\nNo. of Hits:\n")
    return(as.integer(gsub("[^0-9]", "", res)))
   }
 
# Example:
GoogleHits("R%Statistical%Software")

p.s.: If you try to do this in a robot fashion, like:

lapply(list_of_search_terms, GoogleHits)

google will block you after about the 300th recursion!

R GIS: Polygon Intersection with gIntersection{rgeos}

A short tutorial on doing intersections in R GIS. gIntersection{rgeos} will pick the polygons of the first submitted polygon contained within the second poylgon - this is done without cutting the polygon's edges which cross the clip source polygon. For the function that I use to download the example data, url_shp_to_spdf() please see HERE.

library(rgeos)
library(dismo)

URLs <- c("http://gis.tirol.gv.at/ogd/umwelt/wasser/wis_gew_pl.zip",               # all water bodies in Tyrol
          "http://gis.tirol.gv.at/ogd/umwelt/wasser/wis_tseepeicher_pl.zip")       # only artificial..

y <- lapply(URLs, url_shp_to_spdf)
z <- unlist(unlist(y))
a <- getData('GADM', country = "AT", level = 2)

b <- a[a$NAME_2=="Innsbruck Land", ]                                               # political district's boundaries
c <- spTransform(b, z[[1]]@proj4string)                                            # (a ring polygon)    
z1_c <- gIntersection(z[[1]], c, byid = TRUE)                                      
z2_c <- gIntersection(z[[2]], c, byid = TRUE)

plot(c)
plot(z1_c, lwd = 5, border = "red", add = T)
plot(z2_c, lwd = 5, border = "green", add = T)
plot(z[[1]], border = "blue", add = T)              # I plot this on top, so it will be easier to identify
plot(z[[2]], border = "brown", add = T)

Batch Downloading Zipped Shapefiles with R

Here's a function I use to download multiple zipped shapefiles from url and load them to the workspace:

URLs <- c("http://gis.tirol.gv.at/ogd/umwelt/wasser/wis_gew_pl.zip",
          "http://gis.tirol.gv.at/ogd/umwelt/wasser/wis_tseepeicher_pl.zip")

url_shp_to_spdf <- function(URL) {

  require(rgdal)

  wd <- getwd()
  td <- tempdir()
  setwd(td)

  temp <- tempfile(fileext = ".zip")
  download.file(URL, temp)
  unzip(temp)

  shp <- dir(tempdir(), "*.shp$")
  lyr <- sub(".shp$", "", shp)
  y <- lapply(X = lyr, FUN = function(x) readOGR(dsn=shp, layer=lyr))
  names(y) <- lyr

  unlink(dir(td))
  setwd(wd)
  return(y)
  }

y <- lapply(URLs, url_shp_to_spdf)
z <- unlist(unlist(y))

# finally use it:
plot(z[[1]])

Follow Up on Spatial Overlays with R - Getting Altitude for a Set of Points

A short follow up on a previous post on spatial overlays with R.

library(sp)
library(dismo)

# some addresses in Austria
pts <- geocode(c("Aldrans, Grubenweg", "Wien, Stephansdom", "Salzburg, Mozartplatz"))
 
# make pts spatial
coords <- SpatialPoints(pts[, c("longitude", "latitude")])
spdf_pts <- SpatialPointsDataFrame(coords, pts)

# assign CRS/projection (which is WGS 1984)
crs <- CRS(" +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") 
proj4string(spdf_pts) <- crs
 
# spatial data to extract (altitude)
alt <- getData('alt', country = "AT")

# convert alt from raster to grid (needed for over::sp)
# and assign CRS (which is the same as spdf_pts, see > alt@crs)
# don't mind warning - the CRS is the same..
spdf_alt <- as(alt, 'SpatialGridDataFrame')
proj4string(spdf_alt) <- crs

# view
plot(alt)
# plot pts on top
plot(spdf_pts, cex = 2, col = 2, add = T)
 
# check data
str(spdf_pts)
str(spdf_alt)
 
# get the raster/pixel/grid data (> ?over):
cbind(spdf_pts$interpretedPlace, over(spdf_pts, spdf_alt))

# result:
#                                       spdf_pts$interpretedPlace AUT_msk_alt
# 1                              Grubenweg, 6071 Aldrans, Austria         736
# 3 Saint Stephen's Vienna, Stephansplatz 1, 1010 Vienna, Austria         183
# 2                           Mozartplatz, 5020 Salzburg, Austria         450

Loading Multiple Shapefiles to the R-Console Simultaneously

A quick tip on how to load multiple shapefiles (point shapefiles, i.e.) to the R console in one go:

library(maptools)

# get all files with the .shp extension from working directory 
setwd("D:/GIS_DataBase/GIS_Tirol/Tirol_Verbreitungskarten/Verbreitungs_Daten")

shps <- dir(getwd(), "*.shp")

# the assign function will take the string representing shp and turn it into a variable
# which holds the spatial points data
for (shp in shps) assign(shp, readShapePoints(shp))
plot(get(shp[1])) # i.e.
# ...done

Text Mining with R - Comparing Word Counts in Two Text Documents

Here's what I came up with to compare word counts in two pieces of text. If you got any idea, I'd love to learn about alternatives!

## a function that compares word counts in two texts
wordcount <- function(x, y, stem = F, minlen = 1, marg = F) {

                        require(tm)

                        x_clean <- unlist(strsplit(removePunctuation(x), "\\s+"))
                        y_clean <- unlist(strsplit(removePunctuation(y), "\\s+"))

                        x_clean <- tolower(x_clean[nchar(x_clean) >= minlen])
                        y_clean <- tolower(y_clean[nchar(y_clean) >= minlen])

                        if ( stem == T ) {

                          x_stem <- stemDocument(x_clean)
                          y_stem <- stemDocument(y_clean)
                          x_tab <- table(x_stem)
                          y_tab <- table(y_stem)    

                          cnam <- sort(unique(c(names(x_tab), names(y_tab))))

                          z <- matrix(rep(0, 3*(length(cnam)+1)), 3, length(cnam)+1, dimnames=list(c("x", "y", "rowsum"), c(cnam, "colsum")))
                          z["x", names(x_tab)] <- x_tab
                          z["y", names(y_tab)] <- y_tab
                          z["rowsum",] <- colSums(z)
                          z[,"colsum"] <- rowSums(z)
                          ifelse(marg == T, return(t(z)), return(t(z[1:dim(z)[1]-1, 1:dim(z)[2]-1])))

                          } else { 

                          x_tab <- table(x_clean)
                          y_tab <- table(y_clean)    

                          cnam <- sort(unique(c(names(x_tab), names(y_tab))))

                          z <- matrix(rep(0, 3*(length(cnam)+1)), 3, length(cnam)+1, dimnames=list(c("x", "y", "rowsum"), c(cnam, "colsum")))
                          z["x", names(x_tab)] <- x_tab
                          z["y", names(y_tab)] <- y_tab
                          z["rowsum",] <- colSums(z)
                          z[,"colsum"] <- rowSums(z)
                          ifelse(marg == T, return(t(z)), return(t(z[1:dim(z)[1]-1, 1:dim(z)[2]-1])))
                          }
                        }

## example
x = "Hello new, new world, this is one of my nice text documents - I wrote it today"
y = "Good bye old, old world, this is a nicely and well written text document"

wordcount(x, y, stem = T, minlen = 3, marg = T)

Follow-Up:

Thanks a lot for the comments! As I'm not that much into text mining I was trying to reinvent the wheel (in a rather dilettante manner) - missing the capabilities of existing packages. Here's the shortest code that I was able to find doing the same thing (with the potential to get out much more of it, if desired).

x = "Hello new, new world, this is one of my nice text documents"
y = "Good bye old, old world, this is a text document"
z = "Good bye old, old world, this is a text document with WORDS for STEMMING  - BTW, what is the stem of irregular verbs like write, wrote, written?"

# make a corpus with two or more documents (the cool thing here is that it could be endless (almost) numbers 
# of documents to be cross tabulated with the used terms. And the control function enables you
# to do lots of tricks with it before it will be tabulated, see ?termFreq, i.e.)

xyz <- as.list(c(x,y,z))
xyz_corp <- Corpus(VectorSource(xyz))

cntr <- list(removePunctuation = T, stemming = T, wordLengths = c(3, Inf))

as.matrix(TermDocumentMatrix(xyz_corp, control = cntr))

Spatial Overlays with R - Retrieving Polygon Attributes for a Set of Points

A short tutorial for spatial overlays using R-GIS..

library(sp)
library(dismo)
 
# spatial data (political districts of Austria)
gadm <- getData('GADM', country = "AT", level = 2)

# view
plot(gadm)
 
# some addresses
pts <- geocode(c("Aldrans, Grubenweg", "Wien, Stephansdom", "Salzburg, Mozartplatz"))
 
# make pts spatial
coords <- SpatialPoints(pts[, c("longitude", "latitude")])
spdf_pts <- SpatialPointsDataFrame(coords, pts)

# assign CRS/projection (which is WGS 1984)
crs <- CRS(" +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") 
proj4string(spdf_pts) <- crs
 
# check data
str(spdf_pts)
str(gadm)
 
# plot pts on top
plot(spdf_pts, cex = 2, col = 2, add = T)
 
# do an intersection (points in polygon)
# yielding the polygon's attribute data
over(spdf_pts, gadm)

R GIS: Terrain Analysis for Polygons as Simple as it Gets!

library(rgdal)
library(raster)

alt <- getData('alt', country = "AT")
gadm <- getData('GADM', country = "AT", level = 2)
gadm_sub <- gadm[sample(1:length(gadm), 5), ]

plot(alt)
plot(gadm_sub, add=T)

asp <- terrain(alt, opt = "aspect", unit = "degrees", df = F)
slo <- terrain(alt, opt = "slope", unit = "degrees", df = F)

> extract(slo, gadm_sub, fun = mean, na.rm = T, small = T, df = T)
  ID     slope
1  1  9.959053
2  2  1.047443
3  3  7.456165
4  4  1.673786
5  5 11.946553

> extract(asp, gadm_sub, fun = mean, na.rm = T, small = T, df = T)
  ID   aspect
1  1 170.8065
2  2 184.0130
3  3 190.7155
4  4 136.8953
5  5 205.2115

Use R to Bulk-Download Digital Elevation Data with 1" Resolution

Here's a little r-script to convenientely download high quality digital elevation data, i.e. for the Alps, from HERE:

require(XML)

dir.create("D:/GIS_DataBase/DEM/")
setwd("D:/GIS_DataBase/DEM/")

doc <- htmlParse("http://www.viewfinderpanoramas.org/dem3.html#alps")
urls <- paste0("http://www.viewfinderpanoramas.org", xpathSApply(doc,'//*/a[contains(@href,"/dem1/N4")]/@href'))
names <- gsub(".*dem1/(\\w+\\.zip)", "\\1", urls)

for (i in 1:length(urls)) download.file(urls[i], names[i]) 

# unzip all files in dir and delete them afterwards
sapply(list.files(pattern = "*.zip"), unzip)
unlink(list.files(pattern = "*.zip"))

p.s.: Also check raster::getData which pulls SRTM data at 90m resolution for a location / region!

R Quick Tip: Shutdown Windows after Script Has Finished

Quite often I have long procedures running and want to do this over night. However, my computer would still be running all night after the script has finished. This is easily circumvented by the following lines that I put at the end of such a script:

# set working dir
# setwd("C:/Users/Kay/Desktop")
 
# long procedure:
for(i in 1:1e+5) {cat(i); cat("\n..................\n")}
 
d <- "something"
 
# save history
savehistory()
 
# and worspace
save.image()
 
# then shutdown after 240 s
system("C:/Windows/system32/shutdown.exe -f -s -t 240")
 
# this would abort the shutdown:
# system("C:/Windows/system32/shutdown.exe -a")

Creating a QGIS-Style (qml-file) with an R-Script

How to get from a txt-file with short names and labels to a QGIS-Style (qml-file)?
I used the below R-script to create a style for this legend table where I copy-pasted the parts I needed to a txt-file, like for the WRB-FULL (WRB-FULL: Full soil code of the STU from the World Reference Base for Soil Resources). The vector data to which I applied the style is freely available at ESDAC - you just need to submit a form to get access to the data. BTW, thanks to a helping hand on SO.

You can find the QGIS-styler script in theBioBucket-Repository on GitHub.

Programmatically Download CORINE Land Cover Seamless Vector Data with R

Thanks to a helpful SO-Answer I was able to download all CLC vector data (43 zip-files) programmatically:

require(XML)

path_to_files <- "D:/GIS_DataBase/CorineLC/Seamless"
dir.create(path_to_files)
setwd(path_to_files)

doc <- htmlParse("http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2")
urls <- xpathSApply(doc,'//*/a[contains(@href,".zip/at_download/file")]/@href')

# function to get zip file names
get_zip_name <- function(x) unlist(strsplit(x, "/"))[grep(".zip", unlist(strsplit(x, "/")))]

# function to plug into sapply
dl_urls <- function(x) try(download.file(x, get_zip_name(x), mode = "wb"))

# download all zip-files
sapply(urls, dl_urls)

# function for unzipping
try_unzip <- function(x) try(unzip(x))

# unzip all files in dir and delete them afterwards
sapply(list.files(pattern = "*.zip"), try_unzip)

# unlink(list.files(pattern = "*.zip"))

Download File from Google Drive/Docs Programmatically with R

Following up my lattest posting on how to download files from the cloud with R..

dl_from_GoogleD <- function(output, key, format) {

## Arguments:
## output = output file name
## key = Google document key
## format = output format (pdf, rtf, doc, txt..)
## Note: File must be shareable!

                        require(RCurl)
                        bin <- getBinaryURL(paste0("https://docs.google.com/document/d/", key, "/export?format=", format),
                                            ssl.verifypeer = FALSE)
                        con <- file(output, open = "wb")
                        writeBin(bin, con)
                        close(con)
                        message(noquote(paste(output, "read into", getwd())))                        
                        }


# Example:
dl_from_GoogleD(output = "dl_test.pdf", 
                key = "1DdauvkcVm5XtRBkQIv1na8PeLAwpCBdW8pALCFpRWeM",
                format = "pdf")
shell.exec("dl_test.pdf")

EDIT: Here's how it can be done for spreadsheet-like data, like HERE, which is a comma seperated file with .txt extension saved to Google Drive. See also this post

library(RCurl)
setwd(tempdir())
destfile = "test_google_docs.csv"
x = getBinaryURL("https://docs.google.com/uc?export=download&id=0B2wAunwURQNsR0I0a0NlQUlJdzA", followlocation = TRUE, ssl.verifypeer = FALSE)
writeBin(x, destfile, useBytes = TRUE)
shell.exec(paste(tempdir(), "/test_google_docs.csv", sep = ""))

Tweaking Movie Subtitles with R

I use R to fix subtitles that are not in sync with my movies. For the example below the subs were showing too early - so I added some time to each sequence in the srt file. For simplicity I used exactly 1 second in the below example.
You'll see that I use my function dl_from_dropbox(), on which I wrote a post previously, to get the example file!

Download Files from Dropbox Programmatically with R

Here is a usefull snippet that I stole from qdap::url_dl to download files from my Dropbox to the working directory.
Argument x is the document name and d the document key.

dl_from_dropbox <- function(x, key) {
                        require(RCurl)
                        bin <- getBinaryURL(paste0("https://dl.dropboxusercontent.com/s/", key, "/", x),
                                            ssl.verifypeer = FALSE)
                        con <- file(x, open = "wb")
                        writeBin(bin, con)
                        close(con)
                        message(noquote(paste(x, "read into", getwd())))                        
                        }
# Example:
dl_from_dropbox("GViewer_Embeds.txt", "06fqlz6gswj80nj")
shell.exec("GViewer_Embeds.txt")

PS: Also see this R-package for interfacing with Dropbox

Convert OpenStreetMap Objects to KML with R

A quick geo-tip:
With the osmar and maptools package you can easily pull an OpenStreetMap object and convert it to KML, like below (thanks to adibender helping out on SO). I found the relation ID by googling for it (www.google.at/search?q=openstreetmap+relation+innsbruck).

# get OSM data
library(osmar)
library(maptools)
 
innsbruck <- get_osm(relation(113642), full = T)
sp_innsbruck <- as_sp(innsbruck, what = "lines")
 
# convert to KML
for( i in seq_along(sp_innsbruck) ) {
      kmlLine(sp_innsbruck@lines[[i]], kmlfile = "innsbruck.kml",
               lwd = 3, col = "blue", name = "Innsbruck") 
      }
 
shell.exec("innsbruck.kml")

Taxonomy with R: Exploring the Taxize-Package

First off, I'd really like to give a shout-out to the brave people who have created and maintain this great package - the fame is yours!

So, while exploring the capabilities of the package some issues with the ITIS-Server arose and with large datasets things weren't working out quite well for me.
I then switched to the NCBI API and saw that the result is much better here (way quicker, on first glance also a higher coverage).
At the time there is no taxize-function that will pull taxonomic details from a classification returned by NCBI, that's why I plugged together a little wrapper - see here:

# some species data:
spec <- data.frame("Species" = I(c("Bryum schleicheri", "Bryum capillare", "Bryum argentum", "Escherichia coli", "Glis glis")))
spl <- strsplit(spec$Species, " ")
spec$Genus <- as.character(sapply(spl, "[[", 1))

# for pulling taxonomic details we'd best submit higher rank taxons
# in this case Genera. Then we'll submit Genus Bryum only once and 
# save some computation time (might be an issue if you deal 
# with large datasets..)

gen_uniq <- unique(spec$Genus)

# function for pulling classification details ("phylum" in this case)
get_sys_level <- function(x){ require(taxize)
                              a <- classification(get_uid(x))
                              y <- data.frame(a[[1]])                                        # if there are multiple results, take the first..
                              z <- tryCatch(as.character(y[which(y[,2] == "phylum"), 1]),    # in case of any other errors put NA
                                            error = function(e) NA)
                              z <- ifelse(length(z) != 0, z, NA)                             # if the taxonomic detail is not covered return NA
                              return(data.frame(Taxon = x, Syslevel = z))
                             }

# call function and rbind the returned values 
result <- do.call(rbind, lapply(gen_uniq, get_sys_level))
print(result)
#         Taxon       Syslevel
# 1       Bryum   Streptophyta
# 2 Escherichia Proteobacteria
# 3        Glis       Chordata

# now merge back to the original data frame
spec_new <- merge(spec, result, by.x = "Genus", by.y = "Taxon")
print(spec_new)
#         Genus           Species       Syslevel
# 1       Bryum Bryum schleicheri   Streptophyta
# 2       Bryum   Bryum capillare   Streptophyta
# 3       Bryum    Bryum argentum   Streptophyta
# 4 Escherichia  Escherichia coli Proteobacteria
# 5        Glis         Glis glis       Chordata
#

So, What Are You? ..A Plant? ..An Animal? -- Nope, I'm a Fungus!

Lately I had a list of about 1000 species names and I wanted to filter out only the plants as that is where I come from. I knew that Scott Chamberlain has put together the ritis package which obviously can do such things. However, I knew of ITIS before and was keen to give it a shot..

Here's what I've come up with (using the ITIS API, updated on 11. Dec 2012, previous version had a flaw with indefinite matches.. Should be ok now. However, there are of course species that are not covered by the database, i.e. Ixodes, see below):

library(XML)
get_tsn <- function(sp_name) {
           require(XML)
           units <- tolower(unlist(strsplit(sp_name, " ")))

           # valid string?
           if (length(units) > 2) { stop("...No valid search string submitted (two words seperated by one space)!") }

           itis_xml <- htmlParse(paste("http://www.itis.gov/ITISWebService/services/ITISService/searchByScientificName?srchKey=", 
                                       sp_name, sep=""))
           tsn <- xpathSApply(itis_xml, "//tsn", xmlValue)
           unitname1 <- tolower(gsub("\\s+", "", xpathSApply(itis_xml, "//unitname1", xmlValue)))
           unitname2 <- tolower(gsub("\\s+", "", xpathSApply(itis_xml, "//unitname2", xmlValue)))
           unitname3 <- tolower(gsub("\\s+", "", xpathSApply(itis_xml, "//unitname3", xmlValue)))

           # sp_name = only Genus, get tsn were sp_name matches perfectly and unitname2 (lower level taxon) is absent 
           if (length(units) == 1) {
               return(tsn[tolower(sub("\\s+", "", unitname1)) == tolower(sp_name) & unitname2 == ""]) }

           # sp_name = Genus and Epitheton, get tsn where both match perfectly and unitname3 (lower level taxon) is absent 
           if (length(units) == 2) {
               return(tsn[unitname1 == units[1] & 
                          unitname2 == units[2] &
                          nchar(unitname3) == 0]) }
           }

get_kngdm <- function(tsn) {
                   kngdm <- xpathSApply(htmlParse(paste("http://www.itis.gov/ITISWebService/services/ITISService/getKingdomNameFromTSN?tsn=", 
                                                       tsn, sep="")), 
                                                  "//kingdomname", xmlValue)
           return(kngdm)
           }

get_tsn_kngdm <- function(x) {y = get_tsn(x)
                              z = get_kngdm(y)
                              return(list(Name = x, TSN = y, Kingdom = z))
                              }

# I had some API-related errors (I guess it was mysteriously not answering in 
# some cases). I couldn't resolve this and thus implemented tryCatch
get_tsn_kngdm_try <- function(x) tryCatch(get_tsn_kngdm(x), error = function(e) NULL)

sp_names <- c("Clostridium", "Physcia", "Ixodes", "LYNX", "Homo sapiens", "Canis lupus")

system.time(result <- data.frame(do.call(rbind, lapply(sp_names, FUN = get_tsn_kngdm_try))))
result

system.time(result <- data.frame(do.call(rbind, lapply(sp_names, FUN = get_tsn_kngdm_try))))
#
# result
#        User      System verstrichen 
#        1.54        0.01       33.66 
#           Name    TSN  Kingdom
# 1  Clostridium 555645   Monera
# 2      Physcia  14024    Fungi
# 3        Viola  22030  Plantae
# 4       Ixodes                
# 5         LYNX 180581 Animalia
# 6 Homo sapiens 180092 Animalia
# 7  Canis lupus 180596 Animalia
#

Merging Dataframes by Partly Matching String

The latest posting by Tony Hirst sparked my attention because I was thinking about a very similar issue recently.

I was also fiddling around with agrep and adist until I realised that for this very issue matching of substrings is not as important as matching multiple words.. With this different approach I quite easily matched all but 3 countries.

See what I did:

## look up matches of one dataframe in another dataframe.
## the strings to be matched are comprised of 1 or more words 
## and seperated by white space.
## method: match strings that have the highest fraction of words that match up

d1 <- read.csv("http://s.telegraph.co.uk/graphics/conrad/PercentageUsingTheNet.csv", 
               header = T, sep = ",", encoding = "UTF-8")
d2 <- read.csv("http://www.iso.org/iso/country_names_and_code_elements_txt",
               header = T, sep = ";", encoding = "UTF-8")

## strings to be compared d1$ECONOMY and d2$Country.Name
mystr.1 <- as.character(d1$ECONOMY)
mystr.2 <- as.character(d2$Country.Name)
mystr.3 <- as.character(d2$ISO.3166.1.alpha.2.code)

## remove punctuation and multiple spaces
mystr.1 <- tolower(gsub("[^[:alnum:][:space:]]", "", mystr.1))
mystr.1 <- gsub("\\s+", " ", mystr.1)
mystr.2 <- tolower(gsub("[^[:alnum:][:space:]]", "", mystr.2))
mystr.2 <- gsub("\\s+", " ", mystr.2)

## function that finds matching words in string (words seperated by single space!)
n.wordshared <- function(x, y) {
    sum(!is.na(match(unlist(strsplit(x, " ")),
                     unlist(strsplit(y, " ")))
         )
        )
    }
## example
n.wordshared(x = "hello world", y = "good bye world")
## [1] 1

## function that calculates fraction of shared words
fr.wordshared <- function(x, y) {
                     n.wordshared(x, y) / (length(unique(unlist(strsplit(x, " "))))
                                           + length(unique(unlist(strsplit(y, " ")))))
                          }
## example
fr.wordshared(x = "hello world", y = "good bye world")
## [1] 0.2

mydf <- data.frame(str1 = mystr.1, mymatch = "", match.iso = "",
                   stringsAsFactors = F)

## now look up every element of string 1 in string 2
## and if there are matching words assign match to dataframe
for (i in 1:nrow(mydf)) {
   xx <- sapply(mystr.2, fr.wordshared, y = mystr.1[i])
   if (sum(xx) == 0) {
     mydf$mymatch[i] <- NA
     mydf$match.iso[i] <- NA
     } else {
     mydf$mymatch[i] <- paste(names(which(xx == max(xx))), collapse = "; ")
     mydf$match.iso[i] <- paste(mystr.3[as.integer(which(xx == max(xx)))], collapse = "; ")
   }
}

## see result
print(mydf)

## these are the multiple matches
(aa <- mydf[grep(";", mydf$mymatch), ])
##
##               str1                            mymatch match.iso
## 28 slovak republic czech republic; dominican republic    CZ; DO


## these were not matched
(bb <- mydf[is.na(mydf$mymatch), ])
##      str1 mymatch match.iso
##
## 61  russia     NA        NA
## 108  syria     NA        NA

Now, expanding on this concept by introduction of partial matching would most propably result in a 100% match...

Reading and Text Mining a PDF-File in R

I just added this R-script that reads a PDF-file to R and does some text mining with it to my Github repo..

Get Long-Term Climate Data from KNMI Climate Explorer

You can query global climate data from the KNMI Climate Explorer (the KNMI is the Royal Netherlands Metereological Institute) with R.

Here's a little example how I retreived data for my hometown Innsbruck, Austria and plotted annual total precipitation. You can choose station data by pointing at a map, by setting coordinates, etc.

Follow-Up: Making a Word Cloud for a Search Result from GScholar_Scraper_3.1

Here's a short follow-up on how to produce a word cloud for a search result from GScholarScraper_3.1:

# File-Name: GScholarScraper_3.1.R
# Date: 2012-08-22
# Author: Kay Cichini
# Email: kay.cichini@gmail.com
# Purpose: Scrape Google Scholar search result
# Packages used: XML
# Licence: CC BY-SA-NC
#
# Arguments:
# (1) input:
# A search string as used in Google Scholar search dialog
#
# (2) write:
# Logical, should a table be writen to user default directory?
# if TRUE ("T") a CSV-file with hyperlinks to the publications will be created.
#
# Difference to version 3:
# (3) added "since" argument - define year since when publications should be returned..
# defaults to 1900..
#
# (4) added "citation" argument - logical, if "0" citations are included
# defaults to "1" and no citations will be included..
# added field "YEAR" to output 
#
# Caveat: if a submitted search string gives more than 1000 hits there seem
# to be some problems (I guess I'm being stopped by Google for roboting the site..)
#
# And, there is an issue with this error message:
# > Error in htmlParse(URL): 
# > error in creating parser for http://scholar.google.com/scholar?q
# I haven't figured out his one yet.. most likely also a Google blocking mechanism..
# Reconnecting / new IP-address helps..


GScholar_Scraper <- function(input, since = 1900, write = F, citation = 1) {

    require(XML)

    # putting together the search-URL:
    URL <- paste("http://scholar.google.com/scholar?q=", input, "&as_sdt=1,5&as_vis=", 
                 citation, "&as_ylo=", since, sep = "")
    cat("\nThe URL used is: ", "\n----\n", paste("* ", "http://scholar.google.com/scholar?q=", input, "&as_sdt=1,5&as_vis=", 
                 citation, "&as_ylo=", since, " *", sep = ""))
    
    # get content and parse it:
    doc <- htmlParse(URL)
    
    # number of hits:
    h1 <- xpathSApply(doc, "//div[@id='gs_ab_md']", xmlValue)
    h2 <- strsplit(h1, " ")[[1]][2] 
    num <- as.integer(sub("[[:punct:]]", "", h2))
    cat("\n\nNumber of hits: ", num, "\n----\n", "If this number is far from the returned results\nsomething might have gone wrong..\n\n", sep = "")
    
    # If there are no results, stop and throw an error message:
    if (num == 0 | is.na(num)) {
        stop("\n\n...There is no result for the submitted search string!")
    }
    
    pages.max <- ceiling(num/100)
    
    # 'start' as used in URL:
    start <- 100 * 1:pages.max - 100
    
    # Collect URLs as list:
    URLs <- paste("http://scholar.google.com/scholar?start=", start, "&q=", input, 
                  "&num=100&as_sdt=1,5&as_vis=", citation, "&as_ylo=", since, sep = "")
    
    scraper_internal <- function(x) {
        
        doc <- htmlParse(x, encoding="UTF-8")
        
        # titles:
        tit <- xpathSApply(doc, "//h3[@class='gs_rt']", xmlValue)
        
        # publication:
        pub <- xpathSApply(doc, "//div[@class='gs_a']", xmlValue)
        
        # links:
        lin <- xpathSApply(doc, "//h3[@class='gs_rt']/a", xmlAttrs)
        
        # summaries are truncated, and thus wont be used..  
        # abst <- xpathSApply(doc, '//div[@class='gs_rs']', xmlValue)
        # ..to be extended for individual needs
        options(warn=(-1))
        dat <- data.frame(TITLES = tit, PUBLICATION = pub, 
                          YEAR = as.integer(gsub(".*\\s(\\d{4})\\s.*", "\\1", pub)),
                          LINKS = lin)
        options(warn=0)
        return(dat)
    }

    result <- do.call("rbind", lapply(URLs, scraper_internal))
    if (write == T) {
      result$LINKS <- paste("=Hyperlink(","\"", result$LINKS, "\"", ")", sep = "")
      write.table(result, "GScholar_Output.CSV", sep = ";", 
                  row.names = F, quote = F)
      shell.exec("GScholar_Output.CSV") 
      } else {
      return(result)
    }
}

# EXAMPLE:

input <- "allintitle:amphibian+diversity"
df <- GScholar_Scraper(input, since = 1980, citation = 1)

#install.packages("tm")
library(tm)

#install.packages("wordcloud")
library(wordcloud)

corpus <- Corpus(VectorSource(df$TITLES))
corpus <- tm_map(corpus, function(x)removeWords(x, c(stopwords(), "PDF", "B", "DOC", "HTML", "BOOK", "CITATION")))
corpus <- tm_map(corpus, removePunctuation)
tdm <- TermDocumentMatrix(corpus)
m <- as.matrix(tdm)
v <- sort(rowSums(m), decreasing = TRUE)
d <- data.frame(word = names(v), freq = v)

# remove numbers from strings:
d <- d[-grep("[0-9]", d$word), ]

# print wordcloud:
wordcloud(d$word, d$freq)

Toy Example with GScholarScraper_3.1

A commentator on my blog brought up this nice idea of how to use the GScholarScraper function for bibliometrics..
I altered the code a little bit which enables to set a year since when results should be returned and added a field to the output collecting the year of publication. With this you can simply do something like this:

input <- "intitle:metapopulation"
df <- GScholar_Scraper(input, since = 1980, citation = 1)
nrow(df)
hist(df$YEAR, xlab = "Year", 
     main = "Frequency of Publications with\n\"METAPOPULATION\" in Title")

Web-Scraper for Google Scholar Updated!

I have updated the Google Scholar Web-Scraper Function GScholarScaper_2 to GScholarScraper_3 (and GScholarScaper_3.1) as it was outdated due to changes in the Google Scholar html-code. The new script is more slender and faster. It returns a dataframe or optionally a CSV-file with the titles, authors, publications & links. Feel free to report bugs, etc.



Update 11-07-2013: bug fixes due to google scholar code changes - https://github.com/gimoya/theBioBucket-Archives/blob/master/R/Functions/GScholarScraper_3.2.R. Note that since lately your IP will be blocked by Google at about the 1000th search result (cumulated) - so there's not much fun when you want to do some extensive bibliometrics..

Tcl/Tk GUI Example with Variable Input by User

I recently used R with GUI-elements for the first time and browsed through the available online resources, but I didn't quite find what I was searching for: The user should be able to put in some variables and call a function with a button. In the end I did it with a little help from SO. Here is the working example that I eventually plugged together:

 

Use IUCN API with R & XPath

Thanks to a posting on R-sig-eco mailing list I learned of the IUCN-API. Here's a simple example for what can be done with it (output as pdf is HERE):

Avoid Overplotting of Text in Ordination Diagram

Referring to a recent posting on r-sig-eco mailing list I'll add this example to theBioBucket:

library(vegan)
library(vegan)
data(dune)
sol <- metaMDS(dune)
 
# use ordipointlabel -
# here is an example where I added cex according to species frequencies:
plot(sol, type = "n")
cex.lab = colSums(dune > 0) / nrow(dune) + 1
col.lab = rgb(0.2, 0.5, 0.4, alpha = 0.6)
ordipointlabel(sol, displ = "sp", col = col.lab, cex = cex.lab)
 
 
# you could also use pointLabel() from maptools package:
library(maptools)
x = as.vector(sol$species[,1])
y = as.vector(sol$species[,2])
w = row.names(sol$species)
 
plot(sol, type = "n")
points(sol, displ = "species", cex = 1, pch = 4, col = 3)
pointLabel(x, y, w, col = col.lab, cex = cex.lab)

A Wrapper Function for Instant Package Installation / Loading

Since library() and require() only accept input with length(input) = 1 it is necessary to make repeated calls - this can be quite annoying.. So, HERE is a little wrapper function for convenient package installation / loading. It installs packages if they are missing and loads them if there were not loaded yet. I have put it to my RProfile.site so I can conveniently install / load packages with only one call and am quite content with it..

FloraWeb Plant Species Report via R

For German-spoken users I added the function floraweb_scrape.R that allows you to conveniently collect species data and print to a PDF-file (see this example output). The function accesses data provided by the  web-site FloraWeb.de (BfN - Bundesministerium für Naturschutz).
You can use it as an interactive version (RTclTk) which I have put to a Github repository HERE.

Preview:

Source R-Script from Dropbox

A quick tip on how to source R-scripts from a Dropbox-account:

(1) Upload the script..

(2) Get link with the "get link" option. The link should look like "https://www.dropbox.com/s/XXXXXX/yourscript.R"..

(3) Grab this part "XXXXXX/yourscript.R" and paste it to "http://dl.dropbox.com/s/"..

(4) the final URL that can be sourced:

source("http://dl.dropbox.com/s/XXXXXX/yourscript.R")

..an example with this script stored at my Dropbox account:

source("http://dl.dropbox.com/s/c18lcwnnrodsevt/test_dropbox_source.R")

EDIT, March 2013:
This method is not working anymore. You can use the following approach instead:

library(RCurl)
setwd(tempdir())

destfile = "test.txt"
x = getBinaryURL("https://dl.dropbox.com/u/68286640/test_dropbox_source.R", followlocation = TRUE, ssl.verifypeer = FALSE)
writeBin(x, destfile, useBytes = TRUE)
source(paste(tempdir(), "/test.txt", sep = ""))

# remove files from tempdir:
unlink(dir())

knitr-Example: Use World Bank Data to Generate Report for Threatened Bird Species

I'll use the below script that retrieves data for threatened bird species from the World Bank via its API and does some processing, plotting and analysis. There is a package (WDI) that allows you to access the data easily.

# world bank indicators for species - 
# I'll check bird species:
code <- as.character(WDIsearch("bird")[1,1])
bird_data <- WDI(country="all", indicator=code, start=2010, end=2012)

# remove NAs and select values in the range 50 - 1000:
bird_data_sub <- bird_data[!is.na(bird_data$EN.BIR.THRD.NO)&
                           bird_data$EN.BIR.THRD.NO < 1000&
                           bird_data$EN.BIR.THRD.NO > 50, ]

# change in numbers across years 2010 and 2011:
change.no <- aggregate(EN.BIR.THRD.NO ~ country, diff,
                       data = bird_data_sub)
# plot:
par(mar = c(3, 3, 5, 1))
plot(x = change.no[,2], y = 1:nrow(change.no),
     xlim = c(-12, 12), xlab = "", ylab = "",
     yaxt = "n")
abline(v = 0, lty = 2, col = "grey80")
title(main = "Change in Threatened Bird Species in\nCountries with Rich Avifauna (>50)")
text(y = 1:nrow(change.no), 
     x = -2, adj = 1,
     labels = change.no$country)
segments(x0 = 0, y0 = 1:nrow(change.no),
         x1 = change.no[, 2], y1 =  1:nrow(change.no))

# test hypothesis that probability of species decrease is
# equal to probability of increase:
binom.test(sum(change.no < 0), sum(change.no != 0))

For generating the report you can source the script from dropbox.com and stitch it in this fashion:

stitch("http://dl.dropbox.com/s/ga0qbk1o17n17jj/Change_threatened_species.R")

..this is one line of code - can you dig it?..
BTW, for simplicity I use knitr::stitch with its default template...

You should get something like THIS PDF.

EDIT, MARCH 2013
OUTDATED! you can use this approach instead:

library(knitr); library(RCurl); library(WDI)

destfile = "script.txt"
x = getBinaryURL("https://dl.dropbox.com/s/ga0qbk1o17n17jj/Change_threatened_species.R", followlocation = TRUE, ssl.verifypeer = FALSE)
writeBin(x, destfile, useBytes = TRUE)
source(paste(tempdir(), "/script.txt", sep = ""))

stitch(paste(tempdir(), "/script.txt", sep = ""))

Playing with knitr: Create Report with Dynamic List

Here is a little toy example using knitr, LaTeX/MiKTeX and Google Docs.
Say you had a list on Google Docs (say a list of attendants) and you want to print a report with it..
Then see this example using this Rnw-file and the output...

make the tex-file with:

library(knitr)
knit("knitr_list_of_attendants.Rnw")

..then compile the tex-file with MiKTeX.

or with this shortcut:

knit2pdf("knitr_list_of_attendants.Rnw")
browseURL("knitr_list_of_attendants.pdf") 

Quick Tip: Replace Values in Dataframe on Condition with Random Numbers

This one took me some time - though, in fact it is plain simple:

> options(scipen=999)
> (my_df <- data.frame(matrix(sample(c(0,1), 100, replace = T), 10, 10)))
   X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
1   0  0  1  0  1  1  1  1  0   1
2   0  0  1  1  1  0  0  0  0   0
3   0  1  1  0  0  1  1  0  0   1
4   1  1  0  0  0  0  0  0  0   0
5   0  0  1  1  1  0  0  1  1   1
6   0  0  1  1  0  0  1  1  0   1
7   0  0  1  1  1  0  1  1  1   1
8   1  1  1  0  0  0  0  1  1   0
9   0  0  0  0  1  0  1  0  1   0
10  1  1  1  1  1  0  1  1  0   1
> my_df[my_df == 0] <- runif(sum(my_df==0), 0, 0.001)
> my_df
         X1       X2       X3       X4       X5       X6       X7       X8
1  0.000268 0.000926 1.000000 2.00e-05 1.000000 1.00e+00 1.00e+00 1.00e+00
2  0.000531 0.000882 1.000000 1.00e+00 1.000000 4.66e-04 3.96e-04 6.70e-04
3  0.000785 1.000000 1.000000 5.03e-04 0.000164 1.00e+00 1.00e+00 2.98e-04
4  1.000000 1.000000 0.000336 8.71e-04 0.000770 7.44e-05 6.49e-05 1.01e-04
5  0.000168 0.000674 1.000000 1.00e+00 1.000000 6.49e-04 2.26e-04 1.00e+00
6  0.000404 0.000950 1.000000 1.00e+00 0.000735 7.59e-04 1.00e+00 1.00e+00
7  0.000472 0.000516 1.000000 1.00e+00 1.000000 1.37e-04 1.00e+00 1.00e+00
8  1.000000 1.000000 1.000000 6.30e-06 0.000972 3.97e-04 5.46e-05 1.00e+00
9  0.000868 0.000577 0.000347 7.21e-05 1.000000 2.25e-04 1.00e+00 7.19e-05
10 1.000000 1.000000 1.000000 1.00e+00 1.000000 5.80e-05 1.00e+00 1.00e+00
         X9      X10
1  0.000880 1.00e+00
2  0.000754 7.99e-04
3  0.000817 1.00e+00
4  0.000982 7.85e-04
5  1.000000 1.00e+00
6  0.000104 1.00e+00
7  1.000000 1.00e+00
8  1.000000 9.43e-06
9  1.000000 7.79e-04
10 0.000099 1.00e+00

Reproducible Research: Running odfWeave with 7-zip

odfWeave is an R-package that is used for making dynamic reports by Sweave processing of Open Document Format (ODF) files. For anyone new to report generation and lacking knowledge of markup languages this might be a good starting point or even a true alternative to sweave / LATEX and others.

Now, anyone who recently tried to install the required zipping program for odfWeave might have noticed that there are currently no info-zip executables available (zip and unzip by info-zip software are suggested in the odfWeave manual). There are several other free zipping programs - but if you use these the default syntax for odfWeave changes. Looking into the internals it is revealed that the OS command specified for running the zipping program has to be adapted. There are some postings on the R-help mailing list concerning these topic, but none of them worked for me. After some trial and error I managed to get around this problem by using 7-zip with an adapted syntax and will share this here:

# write an in-file and save it to a folder:
dir()
[1] "Example_1_in.odt"

# testing the :
system("\"C:\\Program Files\\7-Zip\\7z.exe\" t -tzip Example_1_in.odt")

7-Zip 9.20  Copyright (c) 1999-2010 Igor Pavlov  2010-11-18

Processing archive: Example_1_in.odt

Testing     mimetype
Testing     Configurations2\statusbar
Testing     Configurations2\accelerator\current.xml
Testing     Configurations2\floater
Testing     Configurations2\popupmenu
Testing     Configurations2\progressbar
Testing     Configurations2\menubar
Testing     Configurations2\toolbar
Testing     Configurations2\images\Bitmaps
Testing     content.xml
Testing     manifest.rdf
Testing     styles.xml
Testing     meta.xml
Testing     Thumbnails\thumbnail.png
Testing     settings.xml
Testing     META-INF\manifest.xml

Everything is Ok

Folders: 7
Files: 9
Size:       30139
Compressed: 9572

# setting up cmd prompt:
library(odfWeave)
ctrl <- odfWeaveControl(zipCmd =
           c("\"C:\\Program Files\\7-Zip\\7z.exe\" a $$file$$",
             "\"C:\\Program Files\\7-Zip\\7z.exe\" x -tzip $$file$$"))
# running:
odfWeave("Example_1_in.odt", "Example_1_out.odt", control = ctrl)

  Copying  Example_1_in.odt
  Setting wd to  C:\Users\Kay\AppData\Local\Temp\RtmpghYef5/odfWeave2223195249
  Unzipping ODF file using "C:\Program Files\7-Zip\7z.exe" x -tzip "Example_1_in.odt"

7-Zip 9.20  Copyright (c) 1999-2010 Igor Pavlov  2010-11-18

Processing archive: Example_1_in.odt

Extracting  mimetype
Extracting  Configurations2\statusbar
Extracting  Configurations2\accelerator\current.xml
Extracting  Configurations2\floater
Extracting  Configurations2\popupmenu
Extracting  Configurations2\progressbar
Extracting  Configurations2\menubar
Extracting  Configurations2\toolbar
Extracting  Configurations2\images\Bitmaps
Extracting  content.xml
Extracting  manifest.rdf
Extracting  styles.xml
Extracting  meta.xml
Extracting  Thumbnails\thumbnail.png
Extracting  settings.xml
Extracting  META-INF\manifest.xml

Everything is Ok

Folders: 7
Files: 9
Size:       30139
Compressed: 9572

  Removing  Example_1_in.odt
  Creating a Pictures directory

  Pre-processing the contents
  Sweaving  content.Rnw

  Writing to file content_1.xml
  Processing code chunks ...

  'content_1.xml' has been Sweaved

  Removing content.xml

  Post-processing the contents
  Removing content.Rnw
  Removing styles.xml
  Renaming styles_2.xml to styles.xml
  Removing manifest.xml
  Renaming manifest_2.xml to manifest.xml
  Removing extra files

  Packaging file using "C:\Program Files\7-Zip\7z.exe" a "Example_1_in.odt"

7-Zip 9.20  Copyright (c) 1999-2010 Igor Pavlov  2010-11-18
Scanning

Creating archive Example_1_in.odt

Compressing  Configurations2\accelerator\current.xml
Compressing  content.xml
Compressing  manifest.rdf
Compressing  META-INF\manifest.xml
Compressing  meta.xml
Compressing  mimetype
Compressing  settings.xml
Compressing  styles.xml
Compressing  Thumbnails\thumbnail.png

Everything is Ok
  Copying  Example_1_in.odt
  Resetting wd
  Removing  C:\Users\Kay\AppData\Local\Temp\RtmpghYef5/odfWeave2223195249

  Done

# see the result:
dir()
[1] "Example_1_in.odt"  "Example_1_out.odt"

Reproducible Research: Export Regression Table to MS Word

Here's a quick tip for anyone wishing to export results, say a regression table, from R to MS Word:

R-Bloggers' Web-Presence

We love them, we hate them: RANKINGS!

Rankings are an inevitable tool to keep the human rat race going. In this regard I'll pick up my last two posts (HERE & HERE) and have some fun with it by using it to analyse R-Bloggers' web presence. I will use number of hits in Google Search as an indicator.

I searched for URLs like this: https://www.google.com/search?q="http://www.twotorials.com" - meaning that only the exact blog-URL is searched.

Blogs	NoHits
http://google-opensource.blogspot.com	82300
http://www.programmingr.com	73500
http://googleresearch.blogspot.com	58000
http://dirk.eddelbuettel.com	53000
http://borasky-research.net	33100
http://casoilresource.lawr.ucdavis.edu	32500
http://andrewgelman.com	30000
http://yihui.name	29600
http://xianblog.wordpress.com	27900
http://nsaunders.wordpress.com	27600
http://chem-bla-ics.blogspot.com	26600
http://plindenbaum.blogspot.com	24600
http://blog.ouseful.info	24300
http://www.vcasmo.com	24200
http://yz.mit.edu	23500
http://romainfrancois.blog.free.fr	22700
http://blog.revolutionanalytics.com	21000
http://robjhyndman.com	18400
http://freakonometrics.blog.free.fr	16100
http://perfdynamics.blogspot.com	15400
http://www.stubbornmule.net	14800
http://zoonek.free.fr	14800
http://jackman.stanford.edu	13900
http://www.bytemining.com	13700
http://learnr.wordpress.com	12600
http://tommy.chheng.com	12200
http://mazamascience.com	12000
http://www.investuotojas.eu	11500
http://www.r-statistics.com	11300
http://www.franklincenterhq.org	10800
http://gettinggeneticsdone.blogspot.com	10700
http://mpastell.com	9930
http://pineda-krch.com	9780
http://blog.saush.com	9220
http://www.premiersoccerstats.com	8950
http://developmentality.wordpress.com	7250
http://www.dataspora.com	7200
http://blog.hiremebecauseimsmart.com	7050
http://isomorphismes.tumblr.com	7040
http://www.mathfinance.cn	6930
http://blog.nguyenvq.com	6150
http://www.drewconway.com	5970
http://www.carlboettiger.info	5520
http://www.statisticsblog.com	5110
http://www.decisionsciencenews.com	4950
http://www.r-chart.com	4810
http://chartsgraphs.wordpress.com	4480
http://www.portfolioprobe.com	4410
http://procomun.wordpress.com	4330
http://jeromyanglim.blogspot.com	4080
http://spatialanalysis.co.uk	4080
http://www.theresearchkitchen.com	4080
http://www.forex-bloggers.com	4070
https://www.rmetrics.org	4050
http://princeofslides.blogspot.com	3900
http://www.cybaea.net	3740
http://www.cerebralmastication.com	3710
http://ygc.name	3670
http://ryouready.wordpress.com	3450
http://jeffreybreen.wordpress.com	3410
http://systematicinvestor.wordpress.com	3400
http://sgsong.blogspot.com	3310
http://industrialengineertools.blogspot.com	3290
http://www.r-tutor.com	3270
http://fishlab.ucdavis.edu	3270
http://ggorjan.blogspot.com	3250
http://blog.ynada.com	3220
http://farmacokratia.blogspot.com	3170
http://4dpiecharts.com	3130
http://heuristically.wordpress.com	3040
http://blog.rtwilson.com	2910
http://www.wekaleamstudios.co.uk	2890
http://www.dataists.com	2840
http://ikanb.wordpress.com	2750
http://shape-of-code.coding-guidelines.com	2730
http://onertipaday.blogspot.com	2710
http://blog.fosstrading.com	2700
http://blog.echen.me	2690
http://www.theusrus.de	2670
http://cloudnumbers.com	2630
http://paulbutler.org	2620
http://biostatmatt.com	2460
http://www.johnmyleswhite.com	2430
http://dataninja.wordpress.com	2360
http://realizationsinbiostatistics.blogspot.com	2340
http://statisfaction.wordpress.com	2300
http://uxblog.idvsolutions.com	2250
http://timelyportfolio.blogspot.com	2210
http://radfordneal.wordpress.com	2200
http://sas-and-r.blogspot.com	2200
http://pairach.com	2110
http://yusung.blogspot.com	2050
http://blog.flacso.edu.mx	2010
http://www.rensenieuwenhuis.nl	2000
http://michaeldhealy.com	1990
http://freigeist.devmag.net	1950
http://www.fernandohrosa.com.br	1920
http://statbandit.wordpress.com	1870
http://www.win-vector.com	1840
http://lukemiller.org	1830
http://ropensci.org	1720
http://www.eggwall.com	1650
http://benmazzotta.wordpress.com	1620
http://bms.zeugner.eu	1610
http://cartesianfaith.wordpress.com	1580
http://linkedscience.org	1570
http://stevemosher.wordpress.com	1550
http://intelligenttradingtech.blogspot.com	1520
http://www.imachordata.com	1480
http://blog.diegovalle.net	1470
http://jermdemo.blogspot.com	1430
http://nortalktoowise.com	1420
http://ekonometrics.blogspot.com	1340
http://digitheadslabnotebook.blogspot.com	1320
http://flyordie.sin.khk.be	1310
http://schamberlain.github.com	1230
http://gribblelab.org	1180
http://www.quantf.com	1130
http://offensivepolitics.net	1020
http://www.markmfredrickson.com	981
http://blog.mckuhn.de	948
http://erehweb.wordpress.com	889
http://confounding.net	886
http://simplystatistics.tumblr.com	875
http://www.babelgraph.org	859
http://csgillespie.wordpress.com	857
http://joewheatley.net	844
http://helmingstay.blogspot.com	843
http://theaverageinvestor.wordpress.com	825
http://quantitative-ecology.blogspot.com	785
http://zvfak.blogspot.com	776
http://ucfagls.wordpress.com	766
http://opendatagroup.com	760
http://cameron.bracken.bz	740
http://rtutorialseries.blogspot.com	738
http://opencpu.org	708
http://novicemetrics.blogspot.com	700
http://lamages.blogspot.com	680
http://nir-quimiometria.blogspot.com	679
http://tonybreyal.wordpress.com	677
http://brokeringclosure.wordpress.com	658
http://socialdatablog.com	643
http://dancingeconomist.blogspot.com	629
http://www.rtexttools.com	603
http://danganothererror.wordpress.com	589
http://thebiobucket.blogspot.com	567
http://holtmeier.de	531
http://val-systems.blogspot.com	519
http://thelogcabin.wordpress.com	489
http://dcemri.blogspot.com	484
http://rdatamining.wordpress.com	477
http://bridgewater.wordpress.com	460
http://www.rcasts.com	444
http://dsparks.wordpress.com	436
http://pr.cloudst.at	422
http://polstat.org	409
http://www.compmath.com	401
http://techno-realism.blogspot.com	399
http://www.backsidesmack.com	395
http://geotheory.org	393
http://miraisolutions.wordpress.com	367
http://econometricsense.blogspot.com	352
http://blog.binfalse.de	344
http://rforcancer.drupalgardens.com	317
http://blog.rstudio.org	316
http://mcfromnz.wordpress.com	309
http://www.quantumforest.com	309
http://blog.quanttrader.org	303
http://chrisladroue.com	293
http://www.michaelbommarito.com	289
http://procrun.com	280
http://mikeksmith.posterous.com	279
http://bio7.org	278
http://kbroman.wordpress.com	278
http://martynplummer.wordpress.com	272
http://bryer.org	268
http://www.funjackals.com	265
http://www.harlan.harris.name	252
http://www.milktrader.net	248
http://www.surefoss.org	241
http://rigorousanalytics.blogspot.com	231
http://www.jameskeirstead.ca	229
http://programming-r-pro-bro.blogspot.com	225
http://plausibel.blogspot.com	224
http://statistic-on-air.blogspot.com	217
http://mintgene.wordpress.com	212
http://moderntoolmaking.blogspot.com	205
http://quantitativeecology.blogspot.com	199
http://www.sigmafield.org	199
http://www.ancienteco.com	194
http://worldofrcraft.blogspot.com	191
http://rappster.wordpress.com	190
http://stotastic.com	189
http://evolvingspaces.blogspot.com	184
http://strugglingthroughproblems.blogspot.com	166
http://sharpstatistics.co.uk	161
http://leftcensored.skepsi.net	160
http://omegahat.wordpress.com	156
http://drunks-and-lampposts.com	155
http://amathew.com	152
http://onlinelabor.blogspot.com	147
http://johnramey.net	144
http://gossetsstudent.wordpress.com	138
http://tomhopper.wordpress.com	135
http://ggobi.blogspot.com	134
http://blog.fellstat.com	131
http://www.openanalytics.eu	130
http://www.numbertheory.nl	127
http://stats.blogoverflow.com	127
http://the-praise-of-insects.blogspot.com	122
http://lpenz.github.com	118
http://christophergandrud.blogspot.com	118
http://f.giorlando.org	112
http://bayesianbiologist.com	110
http://www.graphoftheweek.org	109
http://oneliner.soma20.com	109
http://inundata.org	107
http://geokook.wordpress.com	104
http://blog.datapunks.com	102
http://eranraviv.com	102
http://eranraviv.com	102
http://www.compbiome.com	101
http://www.techpolicy.ca	99
http://www.psychwire.co.uk	97
http://blog.carlislerainey.com	93
http://vasishth-statistics.blogspot.com	93
http://www.statsravingmad.com	93
http://using-r-project.blogspot.com	93
http://www.nikhilgopal.com	92
http://thedatamonkey.blogspot.com	92
http://jeffreyhorner.tumblr.com	90
http://menugget.blogspot.com	88
http://www.twotorials.com	88
http://dataexcursions.wordpress.com	84
http://viksalgorithms.blogspot.com	83
http://exploringdatablog.blogspot.com	81
http://sachaepskamp.com	81
http://aphysicistinwallstreet.blogspot.com	77
http://lastresortsoftware.blogspot.com	75
http://www.nomad.priv.at	72
http://applyr.blogspot.com	71
http://www.knowledgediscovery.jp	71
http://weitaiyun.blogspot.com	71
http://xmphforex.wordpress.com	71
http://statsadventure.blogspot.com	70
http://davenportspatialanalytics.squarespace.com	70
http://anandram.wordpress.com	69
http://rpint.wordpress.com	68
http://datadebrief.blogspot.com	66
http://blog.cloudstat.org	64
http://www.r-podcast.org	64
http://rmkrug.wordpress.com	62
http://denishaine.wordpress.com	61
http://expansed.com	58
http://r.andrewredd.us	57
http://isseing333.blogspot.com	57
http://solomonmessing.wordpress.com	57
http://rtricks.wordpress.com	57
http://anrprogrammer.wordpress.com	56
http://arungaikwad.wordpress.com	56
http://geolabs.wordpress.com	55
http://lookingatdata.blogspot.com	55
http://factbased.blogspot.com	54
http://severity.blogspot.com	54
http://swordofcrom.wordpress.com	53
http://librestats.wordpress.com	51
http://marcinkula.wordpress.com	51
http://gsoc2010r.wordpress.com	47
http://psyccomputing.blogspot.com	46
http://fabiomarroni.wordpress.com	45
http://jedifran.com	45
http://alstatr.blogspot.com	43
http://r-video-tutorial.blogspot.com	42
http://alexfarquhar.posterous.com	40
http://bmb-common.blogspot.com	40
http://rdataviz.wordpress.com	40
http://mypapertrades.blogspot.com	38
http://pitchrx.blogspot.com	38
http://simonmueller.net	38
http://statisfactions.wordpress.com	37
http://nzprimarysectortrade.wordpress.com	36
http://seanmulcahy.blogspot.com	36
http://www.speakingstatistically.com	35
http://joshpaulson.wordpress.com	34
http://learningrbasic.blogspot.com	34
http://mockquant.blogspot.com	33
http://costaleconomist.blogspot.com	32
http://rsnippets.blogspot.com	31
http://statmethods.wordpress.com	29
http://aviadklein.wordpress.com	28
http://obeautifulcode.com	28
http://blog.cloudst.at	24
http://rstats.posterous.com	23
http://notebookonthewebs.tumblr.com	22
http://0utlier.blogspot.com	21
http://gjkerns.github.com	21
http://eigensomething.blogspot.com	10
http://brocktibert.wordpress.com	9
http://toddjobe.blogspot.com	9
http://mickeymousemodels.blogspot.com	9
http://forgetfulfunctor.blogspot.com	9
http://rocknrblog.wordpress.com	9
http://dmbates.blogspot.com	8
http://blog.nextbiomotif.com	8
http://indiacrunchin.wordpress.com	8
http://blog.trenthauck.com	8
http://mikescnc.blogspot.com	8
http://jeroldhaas.blogspot.com	8
http://tlevine.tumblr.com	8
http://empty-moon-9726.heroku.com	8
http://www.proc-x.com	7
http://jointposterior.blogspot.com	7
http://gastonsanchez.wordpress.com	7
http://mlt-thinks.blogspot.com	7
http://rstats.wordpress.com	7
http://playingwithr.blogspot.com	7
http://scottmutchler.blogspot.com	6
http://iamdata.wordpress.com	6
http://sfchaos.blogspot.com	6
http://nightlordtw.wordpress.com	5
http://pleasepasstheroc.blogspot.com	5
http://wiekvoet.blogspot.com	5
http://d7.stattler.com	4
http://yetanotherrblog.blogspot.com	4
http://blog.iwanluijks.nl:80	3
https://rlearner.wordpress.com	3
http://margintale.blogspot.com	1

When checking the results manually I discovered slight deviations in the numbers and admittedly have no clue why this is.. Sorry if any blog is under- overrepresented due to such an error - please report!

Here is the R-script:

require(XML)
library(stringr)
library(RCurl)
library(xtable)

GoogleHits.1 <- function(input)
   {
    url <- paste("https://www.google.com/search?q=\"",
                 input, "\"", sep = "")
 
    CAINFO = paste(system.file(package="RCurl"), "/CurlSSL/ca-bundle.crt", sep = "")
    script <- getURL(url, followlocation = TRUE, cainfo = CAINFO)
    doc <- htmlParse(script)
    res <- xpathSApply(doc, "//div[@id='subform_ctrl']/*", xmlValue)[[2]]
    return(as.integer(gsub("[^0-9]", "", res)))
   }

# Example:
GoogleHits.1("R%Statistical%Software")

###################### Begin get r-blogger's URLs: ###########################################
# get blogger urls with XML:
script <- getURL("www.r-bloggers.com")
doc <- htmlParse(script)
li <- getNodeSet(doc, "//ul[@class='xoxo blogroll']//a")
urls <- sapply(li, xmlGetAttr, "href")

# extract sensible blog urls:
# get ids for those with only 2 slashes (no 3rd in the end):
id <- which(nchar(gsub("[^/]", "", urls )) == 2)
slash_2 <- urls[id]

# find position of 3rd slash occurrence in strings:
slash_stop <- unlist(lapply(str_locate_all(urls, "/"),"[[", 3))
slash_3 <- substring(urls, first = 1, last = slash_stop - 1)

# replace the ones with 2 slashes:
blogs <- slash_3; blogs[id] <- slash_2

# dismiss:
blogs <- blogs[blogs != "http://domain"]
###################### End get r-blogger's URLs: #############################

###################### Begin Google Search: ##################################
# with lapply google mocks about roboting the site..
# I'm blocked on the 300th recursion..
# unlist(lapply(blogs, GoogleHits.1))

# try splitting, doesn't work (blocked the same as before)
res1 <- unlist(lapply(blogs[1:170], GoogleHits.1))
res2 <- unlist(lapply(blogs[171:334], GoogleHits.1))

# try to do it in 2 sessions (saving first result), or manually re-connnect host before second try:
df1 <- data.frame(Blogs = blogs[1:170], NoHits = res1, row.names = NULL)
save(df1, file = "df1.R")
load("df1.RData"); unlink("df1.RData")

# second run:
df2 <- data.frame(Blogs = blogs[171:334], NoHits = res2, row.names = NULL)

# bind dfs, sort by NoHits:
finres <- as.data.frame(rbind(df1, df2)); finres$Blogs <- as.character(finres$Blogs)
(finres <- finres[order(finres$NoHits, decreasing = T), ])

htmltab <- xtable(finres)
print(htmltab, type = "html", include.rownames=FALSE, file = "Bloggers.Google.Hits.htm")
###################### End Google Search #####################################

###################### Begin Plot: ###########################################
pdf("RBloggersWebPresence.pdf")
par(mar = c(4.5, 4.5, 3, 2), ylog = F)
plot(finres$NoHits, cex = 0.5, col = 3, 
     ylab = "No. of Hits in Google Search",
     xlab = "Blogs", log = "y")
set.seed(19)
rid <- sample(13:nrow(finres), 15)
text(x = rid, y = finres$NoHits[rid], 
     labels = finres$Blogs[rid],
     cex = 0.75, srt = 90, pos = 4, offset = -1) 
title(main = "R-Bloggers' Web Presence")
dev.off()
###################### End Plot ##############################################