R

Interactive Graphics with the iplots Package (from “R in Action”)

Posted in R, visualization on January 24th, 2012 by Tal Galili – Be the first to comment

The followings introductory post is intended for new users of R.  It deals with interactive visualization using R through the iplots package.

This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R. Kabacoff has recently published the book ”R in Action“, providing a detailed walk-through for the R language based on various examples for illustrating R’s features (data manipulation, statistical methods, graphics, and so on…). In previous guest posts by Kabacoff we introduced data.frame objects in R and dealt with the Aggregation and Restructuring of data (using base R functions and the reshape package).

For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.

Let us now talk about Interactive Graphics with the iplots Package:

read more »

Merging two data.frame objects while preserving the rows’ order

Posted in R on January 15th, 2012 by Tal Galili – 6 Comments

Merging two data.frame objects in R is very easily done by using the merge function. While being very powerful, the merge function does not (as of yet) offer to return a merged data.frame that preserved the original order of, one of the two merged, data.frame objects.
In this post I describe this problem, and offer some easy to use code to solve it.
read more »

Aggregation and Restructuring data (from “R in Action”)

Posted in R on January 9th, 2012 by Tal Galili – 2 Comments

The followings introductory post is intended for new users of R.  It deals with the restructuring of data: what it is and how to perform it using base R functions and the {reshape} package.

This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R. Kabacoff has recently published the book ”R in Action“, providing a detailed walk-through for the R language based on various examples for illustrating R’s features (data manipulation, statistical methods, graphics, and so on…). The previous guest post by Kabacoff introduced data.frame objects in R.

For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.

Let us now talk about the Aggregation and Restructuring of data in R:

read more »

Top 20 R posts of 2011 (and some R-bloggers statistics)

Posted in R, R community, R links on January 1st, 2012 by Tal Galili – 6 Comments

R-bloggers.com is now two years young. The site is an (unofficial) online R journal written by bloggers who agreed to contribute their R articles to the site.
In this post I wish to celebrate R-bloggers’ second birthmounth by sharing with you:

  1. Links to the top 20 posts of 2011
  2. Statistics on “how well” R-bloggers did this year
  3. An invitation for sponsors/supporters to help keep the site alive

data.frame objects in R (via “R in Action”)

Posted in R on December 18th, 2011 by Tal Galili – 4 Comments

The followings introductory post is intended for new users of R.  It deals with R data frames: what they are, and how to create, view, and update them.

This is a guest article by Dr. Robert I. Kabacoff, the founder of (one of) the first online R tutorials websites: Quick-R.  Kabacoff has recently published the book ”R in Action“, providing a detailed walk-through for the R language based on various examples for illustrating R’s features (data manipulation, statistical methods, graphics, and so on…)

For readers of this blog, there is a 38% discount off the “R in Action” book (as well as all other eBooks, pBooks and MEAPs at Manning publishing house), simply by using the code rblogg38 when reaching checkout.

Let us now talk about data frames:
read more »

UseR! 2011 slides and videos – on one page

Posted in R, R community, R links on December 11th, 2011 by Tal Galili – 4 Comments

I was recently reminded that the wonderful team at warwick University made sure to put online many of the slides (and some videos) of talks from the recent useR 2011 conference.  You can browse through the talks by going between the timetables (where it will be the most updated, if more slides will be added later), but I thought it might be more convenient for some of you to have the links to all the talks (with slides/videos) in one place.

I am grateful for all of the wonderful people who put their time in making such an amazing event (organizers, speakers, attendees), and also for the many speakers who made sure to share their talk/slides online for all of us to reference.  I hope to see this open-slides trend will continue in the upcoming useR conferences…

Bellow are all the links:

read more »

Diagram for a Bernoulli process (using R)

Posted in R, statistics, visualization on November 10th, 2011 by Tal Galili – 3 Comments

A Bernoulli process is a sequence of Bernoulli trials (the realization of n binary random variables), taking two values (0/1, Heads/Tails, Boy/Girl, etc…). It is often used in teaching introductory probability/statistics classes about the binomial distribution.

When visualizing a Bernoulli process, it is common to use a binary tree diagram in order to show the progression of the process, as well as the various consequences of the trial. We might also include the number of “successes”, and the probability for reaching a specific terminal node.

I wanted to be able to create such a diagram using R. For this purpose I composed some code which uses the {diagram} R package. The final function should allow one to create different sizes of diagrams, while allowing flexibility with regards to the text which is used in the tree.

Here is an example of the simplest use of the function:

1
2
source("http://www.r-statistics.com/wp-content/uploads/2011/11/binary.tree_.for_.binomial.game_.r.txt") # loading the function
binary.tree.for.binomial.game(2) # creating a tree for B(2,0.5)

The resulting diagram will look like this:

The same can be done for creating larger trees. For example, here is the code for a 4 stage Bernoulli process:

1
2
source("http://www.r-statistics.com/wp-content/uploads/2011/11/binary.tree_.for_.binomial.game_.r.txt") # loading the function
binary.tree.for.binomial.game(4) # creating a tree for B(4,0.5)

The resulting diagram will look like this:

The function can also be tweaked in order to describe a more specific story. For example, the following code describes a 3 stage Bernoulli process where an unfair coin is tossed 3 times (with probability of it giving heads being 0.8):

1
2
3
4
5
6
source("http://www.r-statistics.com/wp-content/uploads/2011/11/binary.tree_.for_.binomial.game_.r.txt") # loading the function
binary.tree.for.binomial.game(3, 0.8, first_box_text = c("Tossing an unfair coin", "(3 times)"), left_branch_text = c("Failure", "Playing again"), right_branch_text = c("Success", "Playing again"), 
    left_leaf_text = c("Failure", "Game ends"), right_leaf_text = c("Success", 
        "Game ends"), cex = 0.8, rescale_radx = 1.2, rescale_rady = 1.2, 
    box_color = "lightgrey", shadow_color = "darkgrey", left_arrow_text = c("Tails \n(P = 0.2)"), 
    right_arrow_text = c("Heads \n(P = 0.8)"), distance_from_arrow = 0.04)

The resulting diagram is:

If you make up neat examples of using the code (or happen to find a bug), or for any other reason – you are welcome to leave a comment.

(note: the images above are licensed under CC BY-SA)

The present and future of the R blogosphere (~7 minute video from useR2011)

Posted in R, R and the web, R community, wordpress on October 30th, 2011 by Tal Galili – 8 Comments

This is (roughly) the lightning talk I gave in useR2011. If you are a reader of R-bloggers.com then this talk is not likely to tell you anything new. However, if you have a friend, college or student who is a new useRs of R, this talk will offer him a decent introduction to what the R blogosphere is all about.

The talk is a call for people of the R community to participate more in reading, writing and interacting with blogs.

I was encouraged to record this talk per the request of Chel Hee Lee, so it may be used in the recent useR conference in Korea (2011)

The talk (briefly) goes through:

  1. The widespread influence of the R blogosphere
  2. What R bloggers write about
  3. How to encourage a blogger you enjoy reading to keep writing
  4. How to start your own R blog (just go to wordpress.com)
  5. Basic tips about writing a blog
  6. One advice about marketing your R blog (add it to R-bloggers.com)
  7. And two thoughts about the future of R blogging (more bloggers and readers, and more interactive online visualization)

My apologies for any of the glitches in my English. For more talks about R, you can visit the R user groups blog. I hope more speakers from useR 2011 will consider uploading their talks online.

Comparison of ave, ddply and data.table

Posted in R on August 25th, 2011 by Tal Galili – 12 Comments

A guest post by Paul Hiemstra.
————

Fortran and C programmers often say that interpreted languages like R are nice and all, but lack in terms of speed. How fast something works in R greatly depends on how it is implemented, i.e. which packages/functions does one use. A prime example, which shows up regularly on the R-help list, is letting a vector grow as you perform an analysis. In pseudo-code this might look like:

1
2
3
4
5
dum = NULL
for(i in 1:100000) {
   # new_outcome = ...do some stuff...
   dum = c(dum, new_outcome)
}

The problem here is that dum is continuously growing in size. This forces the operating system to allocate new memory space for the object, which is terribly slow. Preallocating dum to the length it is supposed to be greatly improves the performance. Alternatively, the use of apply type of functions, or functions from plyr package prevent these kinds of problems. But even between more advanced methods there are large differences between different implementations.

Take the next example. We create a dataset which has two columns, one column with values (e.g. amount of rainfall) and in the other a category (e.g. monitoring station id). We would like to know what the mean value is per category. One way is to use for loops, but I’ll skip that one for now. Three possibilities exist that I know of: ddply (plyr), ave (base R) and data.table. The piece of code at the end of this post compares these three methods. The outcome in terms of speed is:
(press the image to see a larger version)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
   datsize noClasses  tave tddply tdata.table
1    1e+05        10 0.091  0.035       0.011
2    1e+05        50 0.102  0.050       0.012
3    1e+05       100 0.105  0.065       0.012
4    1e+05       200 0.109  0.101       0.010
5    1e+05       500 0.113  0.248       0.012
6    1e+05      1000 0.123  0.438       0.012
7    1e+05      2500 0.146  0.956       0.013
8    1e+05     10000 0.251  3.525       0.020
9    1e+06        10 0.905  0.393       0.101
10   1e+06        50 1.003  0.473       0.100
11   1e+06       100 1.036  0.579       0.105
12   1e+06       200 1.052  0.826       0.106
13   1e+06       500 1.079  1.508       0.109
14   1e+06      1000 1.092  2.652       0.111
15   1e+06      2500 1.167  6.051       0.117
16   1e+06     10000 1.338 23.224       0.132

It is quite obvious that ddply performs very bad when the number of unique categories is large. The ave function performs better. However, the data.table option is by far the best one, outperforming both other alternatives easily. In response to this, Hadley Wickham (author of plyr) responded:

This is a drawback of the way that ddply always works with data frames. It will be a bit faster if you use summarise instead of data.frame (because data.frame is very slow), but I’m still thinking about how to overcome this fundamental limitation of the ddply approach.

I hope this comparison is of use to readers. And remember, think before complaining that R is slow :) .

Paul (e-mail: p.h.hiemstra@gmail.com)

ps This blogpost is based on discussions on the R-help and manipulatr mailing lists:
- http://www.mail-archive.com/r-help@r-project.org/msg142797.html
- http://groups.google.com/group/manipulatr/browse_thread/thread/5e8dfed85048df99

R code to perform the comparison

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
library(ggplot2)
library(data.table)
theme_set(theme_bw())
datsize = c(10e4, 10e5)
noClasses = c(10, 50, 100, 200, 500, 1000, 2500, 10e3)
comb = expand.grid(datsize = datsize, noClasses = noClasses)
res = ddply(comb, .(datsize, noClasses), function(x) {
  expdata = data.frame(value = runif(x$datsize),
                      cat = round(runif(x$datsize, min = 0, max = x$noClasses)))
  expdataDT = data.table(expdata)
 
  t1 = system.time(res1 <- with(expdata, ave(value, cat)))
  t2 = system.time(res2 <- ddply(expdata, .(cat), mean))
  t3 = system.time(res3 <- expdataDT[, sum(value), by = cat])
  return(data.frame(tave = t1[3], tddply = t2[3], tdata.table = t3[3]))
}, .progress = 'text')
 
res
 
ggplot(aes(x = noClasses, y = log(value), color = variable), data =
melt(res, id.vars = c("datsize","noClasses"))) + facet_wrap(~ datsize)
+ geom_line()

Calling R lovers and bloggers – to work together on “The R Programming wikibook”

Posted in R, R community, R links on June 20th, 2011 by Tal Galili – 22 Comments

This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing.

The R Programming wikibook is not just another one of the many free books about statistics/R, it is a community project which aims to create a cross-disciplinary practical guide to the R programming language.  Here is how you can join:

read more »