Online Trick

url <- "http://statcounter.com/p7447608/csv/download_log_file?form_user=MYUSERNAME&form_pass=MYPASSWORD"
file <- paste(tempdir(), "\\log", ".CSV", sep = "")
download.file(url, dest = file)
log <- read.csv(file, as.is = T, header = T)

str(log)

'data.frame':   500 obs. of  19 variables:
 $ Date.and.Time   : chr  "2011-12-19 23:32:30" "2011-12-19 23:20:04" "2011-12-19 23:16:24" "2011-12-19 23:14:40" ...
 $ IP.Address      : chr  "93.129.245.130" "128.227.27.189" "207.63.124.250" "140.247.40.121" ...
 $ IP.Address.Label: logi  NA NA NA NA NA NA ...
 $ Browser         : chr  "Chrome" "Firefox" "Chrome" "Firefox" ...
 $ Version         : chr  "16.0" "8.0" "15.0" "6.0" ...
 $ OS              : chr  "MacOSX" "WinXP" "Win7" "MacOSX" ...
 $ Resolution      : chr  "1280x800" "1680x1050" "1280x1024" "1280x800" ...
 $ Country         : Factor w/ 44 levels "Argentina","Australia",..: 17 44 44 44 44 44 44 44 44 44 ...
 $ Region          : chr  "Nordrhein-Westfalen" "Florida" "Illinois" "Massachusetts" ...
 $ City            : chr  "Köln" "Gainesville" "Chicago" "Cambridge" ...
 $ Postal.Code     : int  NA 32611 NA 2138 2138 NA 10003 2138 2138 2138 ...
 $ ISP             : chr  "Telefonica Deutschland GmBH" "UNIVERSITY OF FLORIDA" "Illinois Century Network" "Harvard University" ...
 $ Returning.Count : int  2 0 4 2 2 0 0 2 2 2 ...
 $ Page.URL        : chr  "http://onlinetrickpdf.blogspot.com/2015/09/r-function-google-scholar-webscraper.html" "http://onlinetrickpdf.blogspot.com/2015/09/if-then-vba-script-usage-in-arcgis.html" "http://onlinetrickpdf.blogspot.com/2015/09/how-to-link-to-google-docs-for-download.html" "http://onlinetrickpdf.blogspot.com/2015/09/two-way-permanova-adonis-with-custom.html" ...
 $ Page.Title      : Factor w/ 53 levels "","onlinetrickpdf*",..: 36 50 23 46 10 20 13 9 10 46 ...
 $ Came.From       : chr  "http://stackoverflow.com/questions/5005989/how-to-download-search-results-on-google-scholar-using-r" "http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CCwQFjAC&url=http%3A%2F%2Fonlinetrickpdf.blogspot.com%2F2011%"| __truncated__ "" "" ...
 $ SE.Name         : chr  "" "" "" "" ...
 $ SE.Host         : chr  "" "" "" "" ...
 $ SE.Term         : chr  "" "" "" "" ...

# Example 1:
setwd(tempdir())
download.file("http://docs.google.com/uc?export=download&id=0B2wAunwURQNsMDViYzllMTMtNjllZS00ZTc4LTgzMzEtNDFjMWQ3MTUzYTRk",
              destfile = "test_google_docs.txt", mode = "wb")
# the file contains: x <- sample(100); plot(x)
source(paste(tempdir(), "/test_google_docs.txt", sep = ""))
# remove files from tempdir:
unlink(dir())

# Example 2:
setwd(tempdir())
download.file("http://docs.google.com/uc?export=download&id=0B2wAunwURQNsY2MwMzNhNGMtYmU4Yi00N2FlLWEwYTctYWU3MDhjNTkzOTdi",
              destfile = "google_docs_script.txt", mode = "wb")
# the downloaded script is the GScholarScraper-Function,
# read it and run an example:
source(paste(tempdir(), "/google_docs_script.txt", sep = ""))
# remove files from tempdir:
unlink(dir())

# 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")

library(dismo)

# get GBIF data with function:
myrger <- gbif("Myricaria", "germanica", geo = T)

# check:
str(myrger)

# plot occurrences:
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, col = "light yellow", axes = T)
points(myrger$lon, myrger$lat, col = "red", cex = 0.5)
text(-140, -50, "MYRICARIA\nGERMANICA")

# 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
#

# 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]))

# 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")

# 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))

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

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 = ""))

cd C:/Users/Kay/Documents/Web/Openlayers/Docs/Digitizing
ogr2ogr -sql "SELECT * FROM 'trails-db' ORDER BY Name ASC" -f "KML" trails.kml trails.sqlite

## get filesnames (assuming the datasets were downloaded already. 
## please see http://onlinetrickpdf.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) 

# get climate (precipitation) data from url:
# http://climexp.knmi.nl/selectstation.cgi?id=someone@somewhere

# station INNSBRUCK, FLUGHAFEN (11120), 47.27N, 11.35E:
ibk_dat <- read.table("http://climexp.knmi.nl/data/pa11120.dat", sep = "", 
                      row.names = 1, col.names = 0:12)

# cut off first and last yr, due to missing data..
ibk_dat <- ibk_dat[c(-1, -50,]

# plot yearly sums:
windows(width = 15, height = 5)
plot(rowSums(ibk_dat), type = "s", ylab = "Annual Total Precipitation (mm)", 
     xlab = NA, col = "blue", xaxt = "n", lwd = 1.5, las = 2, cex.axis = 0.8,
     main = "INNSBRUCK FLUGHAFEN, 47.27N, 11.35E, 593m, WMO station code: 11120") 
axis(1, labels = rownames(ibk_dat), at = 1:nrow(ibk_dat), las = 2, cex.axis = 0.85)

abline(h = mean(rowSums(ibk_dat)), col = 1, lty = 2, lwd = 1.2)
text(1250, adj = 0, "Long-term average", cex = 0.75)
arrows(x0 = 2.5, y0 = 1220,
       x1 = 2.5, y1 = 930, length = 0.05)  

# purpose: crossfade 2 jpeg images
# packages: jpeg
# arguments: img1 (path.to.img1), img2 (path.to.img2),
#            outpath(defaults to current directory), outname, 
#            frames
# output: png


require(jpeg)

jpgfader <- function(img1 = NA, img2 = NA, outpath = NA, frames = NA, outname = NA){

    if(is.na(outpath)) {outpath <- path.expand("~")}

# stop if images are missing
    if(is.na(img1)|is.na(img2)) stop(cat("\nAt least one image is missing!\n"))
    if(is.na(outname)) {outname = "img.1.2"}

    if(is.na(frames)) {frames = 10}
# read 2 jpegs, assuming same size!
    pic.1 <- readJPEG(img1)
    pic.2 <- readJPEG(img2)

# warn if images dont have same size:
    if(sum(dim(pic.1) != dim(pic.2))>1) warning(cat("\nImages do not have same dimensions!"))

# create new array with 4 dimensions, the new dimension
# representing alpha:

    by = 1/(frames-1)
    alpha = seq(0, 1, by)
    n = length(alpha)

    for(j in n:1){

        pic.2.a <- array(data = c(as.vector(pic.2),
                                  rep(alpha[j], dim(pic.1)[1]*dim(pic.1)[2])),
                         dim = c(dim(pic.1)[1], dim(pic.1)[2], 4))

# assign output file name:
        pic.out <- paste(outpath, "\\", outname, ".", j ,".png", sep = "")

# and open device:
        png(pic.out, width = dim(pic.1)[2], height = dim(pic.1)[1])

# plot-parameters:
        par(mar = rep(0, 4), oma = rep(0, 4), new = F)

# print img.a to plot region:
        plot(1:2,
             xlim = c(0, dim(pic.1)[2]), ylim = c(0, dim(pic.1)[1]),
             xlab="", ylab="", type = "n",
             yaxs ="i", xaxs = "i")
        rasterImage(pic.1, 0, 0, dim(pic.1)[2], dim(pic.1)[1])

# overplotting with new alpha-pic,
# starting with full transparency, decreasing in steps, showing pic.2
# finally:
        rasterImage(pic.2.a, 0, 0, dim(pic.1)[2], dim(pic.1)[1])
        dev.off()
    }
}

# Example, with 2 images, one system.file and one altered 
# version of it:

# make black jpg and save to default system folder
Rlogo <- readJPEG(system.file("img", "Rlogo.jpg", package="jpeg"))
Rlogo[] <- 0
jpeg(path.expand("~\\Rlogo_Black.jpg"), dim(Rlogo)[2], dim(Rlogo)[1])
par(mar = rep(0, 4), oma = rep(0, 4))

# save black image:
plot(1:2,
     xlim = c(0, 1), ylim = c(0, 1),
     xlab="", ylab="", type = "n",
     yaxs ="i", xaxs = "i")
rasterImage(Rlogo, 0, 0, 1, 1)
dev.off()

# function call:
jpgfader(img1 = system.file("img", "Rlogo.jpg", package="jpeg"),
         img2 = path.expand("~/Rlogo_black.jpg"),
         outname = "img12",
         outpath = path.expand("~"),
         frames = 10)

# see the images:
browseURL(path.expand("~"))

# remove files:
# files <- dir(path.expand("~"), full.names = T)
# file.remove(c(files[grep("img12.", files)],
                path.expand("~/Rlogo_black.jpg"))) 

Sub CommentBox()

    Dim range As range
    Dim i As Long
    Dim TargetList
    Dim cm As Comment
    
    TargetList = Array("Str1", "Str2")
    
    For i = 0 To UBound(TargetList)
    
    Set range = ActiveDocument.range
    
    With range.Find
    .Text = TargetList(i)
    .Format = True
    .MatchCase = False
    .MatchWholeWord = True
    .MatchWildcards = False
    .MatchSoundsLike = False
    .MatchAllWordForms = False
        
    Do While .Execute(Forward:=True) = True

        Set cm = range.Comments.Add(range:=range, Text:="Hallo")
        cm.Author = "InternalName"
        cm.Initial = "Initials"
        
        Loop
    
    End With

    Next

With ActiveDocument.Styles("Kommentartext").Font
        .Size = 12
        .Bold = True
        .Italic = True
        .Color = wdColorBlue
End With

End Sub

str <- c("i.e., George W. Bush", "Lyndon B. Johnson, etc.")
gsub("([A-Z])[.]?", "\\1", str)
# this will find abbreviated names and remove the fullstops:
# the uppercase letters followed by a full-stop are matched by
# [A-Z][.]? = repeated at most once. the parentheses delineate a
# back reference, i.e. the uppercase letter, which will be
# replaced by \\1 which is the first backreference.

# output:
[1] "i.e., George W Bush"    "Lyndon B Johnson, etc."

str <- c("George W. Bush", "Lyndon B. Johnson")
sub(" .*", "", str)
# keeps the first word and removes the rest.
# matches and replaces the substring comprised of the first
# white space followed by any single character,
# designated by the period, repeated zero or more times, as
# given by the asterisk.

# output:
[1] "George" "Lyndon"

sub("\\s\\w+$", "", str)
# removes the last word plus the preceding space in a string.
# looks for a space followed by any word which is the last one:
# the dollar sign $ is a meta-character that matches the
# beginning and end of a line.

# output:
[1] "George W." "Lyndon B."

sub(".*\\s(\\w+$)", "\\1", str)
# keep only the last word of a string.
# looks for anything, repeated arbitrarily often followed by a
# space ".*\\" and a word which is the last in line.
# for this word you put brackets for a back-reference, which is
# returned by "\\1", the 1st back-reference.

# output:
[1] "Bush"    "Johnson"

str <- c("&George W. Bush", "Lyndon B. Johnson?")
gsub("[^[:alnum:][:space:].]", "", str)
# keep alphanumeric signs AND full-stop, remove anything else,
# that is, all other punctuation. what should not be matched is
# designated by the caret.

# output:
[1] "George W. Bush"    "Lyndon B. Johnson"

google_ss <- function(gid = NA, key = NA) 
    {
    if (is.na(gid)) {stop("\nWorksheetnumber (gid) is missing\n")}
    if (is.na(key)) {stop("\nDocumentkey (key) is missing\n")}
    require(RCurl)
    url <- getURL(paste("https://docs.google.com/spreadsheet/pub?key=", key,
                        "&single=true&gid=", gid, "&output=csv", sep = ""),
                  cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))
    read.csv(textConnection(url), header = T, sep = ",")
    }

## Example:
## Mind that the worksheets are numbered consecutively from 0 to n,
## irrespective of the actual worksheet-name.
## The key should be put in apostrophes.
## And, the URL works only for published spreadsheets!

(data <- google_ss(gid = 0,
                   key = "0AmwAunwURQNsdDNpZzJqTU90cmpTU0sza2xLTW9fenc"))

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"))

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count1 <- count2 <- count3 <- count4 <- sample(c(rep(0, 10), 1:10))
some <- LETTERS[1:20]  
thing <- letters[1:20]  
mydf <- data.frame(count1, count2, count3, count4, some, thing)

ids <- grep("count", names(mydf))
myfun <- function(x) {ifelse(x > 0, 1, 0)}
mydf[, ids] <- lapply(mydf[, ids], myfun)

# File: combmat.R
# Purpose: add up matrices with different dimensions
# Input: a list of 2-dimensional matrices
# Output: a combined matrix
# Author: Kay Cichini
# Date: Nov. 23th 2011
 
combmat <- function(m_l = list(NA)){
  n_m <- length(m_l)                               # no. of matrices used
  rownames_l <- lapply(m_l, rownames)              # list of rownames
  colnames_l <- lapply(m_l, colnames)              # list of colnames
  rownames_new <- unique(unlist(rownames_l))       # new, general rownames
  colnames_new <- unique(unlist(colnames_l))       # new, general colnames
 
  dimnames_new = list(rownames_new, colnames_new)
  m_new <- matrix(nrow = length(rownames_new),
                 ncol = length(colnames_new),
                 data = 0,
                 dimnames = dimnames_new)
 
  m_interm_arr <-                                  # array of intermediate 
  array(m_new, dim = c(length(rownames_new),       # matrices with same no. of 
                       length(colnames_new), n_m), # dimensions as elements in  
        dimnames = dimnames_new)                   # list of input matrices
 
  # take i-th element in list of imput matrices and add
  # its values according to the appropiate row and col indexes
  # in i-th dimension (i-th matrix) within array:
  for (i in 1:n_m) {
    m_interm_arr[,,i][rownames_l[[i]], colnames_l[[i]]] <- m_l[[i]]
  }
  return(apply(m_interm_arr, c(1,2), sum))
}
 
# Example:
print(m1 <- matrix(sample(1:40), 4, 10, dimnames = list(1:4,1:10)))
print(m2 <- matrix(sample(1:40), 10, 4, dimnames = list(1:10,1:4)))
 
combmat(m_l = list(m1, m2))

Paper Camera v4.4.1 Apk

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ZEDGE™ Ringtones & Wallpapers v4.4.3 (Ad-Free) Apk

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WhatsApp+ v6.24D Patched Apk

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Notification Toggle Premium v3.2.1 Apk

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Vidstitch Pro – Video Collage v1.6.3 Apk

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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
#

Perfectly Clear v2.5.12 Apk

Requires Android: 2.3.3 and up

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Dim Shell : Set Shell = CreateObject("WScript.Shell")

argument_t = inputbox("Time in Seconds:", "Shutdown", "3600")

Shell.Run "C:/Windows/system32/shutdown.exe -f -s -t " & _
          argument_t, 0

MsgBox "Press OK if you want to cancel the Shutdown..", _
       vbInformation + vbOkayonly, "Cancel Shutdown"

Shell.Run "C:/Windows/system32/shutdown.exe -a", 0

System Tuner Pro v3.2 Apk

Requires Android: 2.1 and up

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Download here or here