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