For your convenience (and with Ian’s permission), I am reposting his proposal here. You are welcome to send him feedback by e-mailing him (at: [email protected]), or by leaving a comment here (and I will direct him to your comment).
The new version has a lot of cool new features, like advanced data import, integration with Google docs, converting variables from numeric to factor to dates and vice versa, and a lot of new geom’s. Some of which you can watch in his new video demo of the application:
When analyzing a questionnaire, one often wants to view the correlation between two or more Likert questionnaire item’s (for example: two ordered categorical vectors ranging from 1 to 5).
When dealing with several such Likert variable’s, a clear presentation of all the pairwise relation’s between our variable can be achieved by inspecting the (Spearman) correlation matrix (easily achieved in R by using the “cor.test” command on a matrix of variables).
Yet, a challenge appears once we wish to plot this correlation matrix. The challenge stems from the fact that the classic presentation for a correlation matrix is a scatter plot matrix – but scatter plots don’t (usually) work well for ordered categorical vectors since the dots on the scatter plot often overlap each other.
There are four solution for the point-overlap problem that I know of:
Jitter the data a bit to give a sense of the “density” of the points
Use a color spectrum to represent when a point actually represent “many points”
Use different points sizes to represent when there are “many points” in the location of that point
Add a LOWESS (or LOESS) line to the scatter plot – to show the trend of the data
In this post I will offer the code for the a solution that uses solution 3-4 (and possibly 2, please read this post comments). Here is the output (click to see a larger image):
The integration of R into online web services is (for me) one of the more exciting prospects in R’s future. That is way I was very excited coming across Jamie Love’s recent creation: R-Node.
What is R-Node
R-Node is a (open source) web front-end to R (the statistical analysis package).
Using this front-end, you can from any web browser connect to an R instance running on a remote (or local) server, and interact with it, sending commands and receiving the responses. In particular, graphing commands such as plot() and hist() will execute in the browser, drawing the graph as an SVG image.
You can see a live demonstration of this interface by visiting: http://126.96.36.199:2904/
And using the following user/password login info:
(This link was originally posted here)
Here are some screenshots:
In the second screenshot you see the results of the R command ‘plot(x, y)’ (with the reimplementation of plot doing the actual plotting), and in the fourth screenshot you see a similar plot command along with a subsequent best fit line (data points calculated with ‘lowess()’) drawn in.
Once in, you can try out R by typing something like:
x <-rnorm(100)plot(x, main="Random numbers")
The plot and lines commands will bring up a graph – you can escape out of it, download the graph as a SVG file, and change the graph type (e.g. do: plot (x, type=”o”) ).
Many R commands will work, though only the hist(), plot() and lines() work for graphing.
Please don’t type the R command q() – it will quit the server, stopping it working for everyone! Also, as everyone shares the same session for now, using more unique variable name than ‘x’ and ‘l’ will help you.
Currently there is only limited error checking but the code continues to be improved and developed. You can download it from: http://gitorious.org/r-node
How do you may imagine yourself using something like this? Feel invited to share with me and everyone else in the comments.
In recent years, a growing need has arisen in different fields, for the development of computational systems for automated analysis of large amounts of data (high-throughput). Dealing with non-standard noise structure and outliers, that could have been detected and corrected in manual analysis, must now be built into the system with the aid of robust methods. […] we use a non-standard mix of robust and resistant methods: LOWESS and repeated running median.
The motivation for this technique came from “Path data” (of mice) which is
prone to suffer from noise and outliers. During progression a tracking system might lose track of the animal, inserting (occasionally very large) outliers into the data. During lingering, and even more so during arrests, outliers are rare, but the recording noise is large relative to the actual size of the movement. The statistical implications are that the two types of behavior require different degrees of smoothing and resistance. An additional complication is that the two interchange many times throughout a session. As a result, the statistical solution adopted needs not only to smooth the data, but also to recognize, adaptively, when there are arrests. To the best of our knowledge, no single existing smoothing technique has yet been able to fulfill this dual task. We elaborate on the sources of noise, and propose a mix of LOWESS (Cleveland, 1977) and the repeated running median (RRM; Tukey, 1977) to cope with these challenges
If all we wanted to do was to perform moving average (running average) on the data, using R, we could simply use the rollmean function from the zoo package.
But since we wanted also to allow quantile smoothing, we turned to use the rollapply function.
R function for performing Quantile LOESS
Here is the R function that implements the LOESS smoothed repeated running quantile (with implementation for using this with a simple implementation for using average instead of quantile):
In this post I showcase a nice bar-plot and a balloon-plot listing recommended Nutritional supplements , according to how much evidence exists for thier benefits, scroll down to see it(and click here for the data behind it)
* * * *
The gorgeous blog “Information Is Beautiful” recently publish an eye candy post showing a “balloon race” image (see a static version of the image here) illustrating how much evidence exists for the benefits of various Nutritional supplements (such as: green tea, vitamins, herbs, pills and so on) . The higher the bubble in the Y axis score (e.g: the bubble size) for the supplement the greater the evidence there is for its effectiveness (But only for the conditions listed along side the supplement).
There are two reasons this should be of interest to us:
This shows a fun plot, that R currently doesn’t know how to do (at least I wasn’t able to find an implementation for it). So if anyone thinks of an easy way for making one – please let me know.
The data for the graph is openly (and freely) provided to all of us on this Google Doc.
The advantage of having the data on a google doc means that we can see when the data will be updated. But more then that, it means we can easily extract the data into R and have our way with it (Thanks to David Smith’s post on the subject)
For example, I was wondering what are ALL of the top recommended Nutritional supplements, an answer that is not trivial to get from the plot that was in the original post.
In this post I will supply two plots that present the data: A barplot (that in retrospect didn’t prove to be good enough) and a balloon-plot for a table (that seems to me to be much better).
(You can click the image to enlarge it)
The R code to produce the barplot of Nutritional supplements efficacy score (by evidence for its effectiveness on the listed condition).
# loading the data
supplements.data<- supplements.data.0[supplements.data.0[,2]>2,]# let's only look at "good" supplements
supplements.data<- supplements.data[!is.na(supplements.data[,2]),]# and we don't want any missing data
supplement.score<- supplements.data[, 2]
ss <-order(supplement.score, decreasing =F)# sort our data
supplement.name<- supplements.data[ss, 1]
supplement.benefits<- supplements.data[ss, 4]
supplement.score.col<-factor(as.character(supplement.score))levels(supplement.score.col)<-c("red", "orange", "blue", "dark green")
supplement.score.col<-as.character(supplement.score.col)# mar: c(bottom, left, top, right) The default is c(5, 4, 4, 2) + 0.1.par(mar =c(5,9,4,13))# taking care of the plot margins
bar.y<-barplot(supplement.score, names.arg= supplement.name, las =1, horiz =T, col= supplement.score.col, xlim =c(0,6.2),
main =c("Nutritional supplements efficacy score","(by evidence for its effectiveness on the listed condition)", "(2010)"))axis(4, labels= supplement.benefits, at = bar.y, las =1)# Add right axisabline(h = bar.y, col= supplement.score.col , lty =2)# add some lines so to easily follow each bar
Also, the nice things is that if the guys at Information Is Beautiful will update there data, we could easily run the code and see the updated list of recommended supplements.
So after some web surfing I came around an implementation of a balloon plot in R (Thanks to R graph gallery)
There where two problems with using the command out of the box. The first one was that the colors where non informative (easily fixed), the second one was that the X labels where overlapping one another. Since there is no “las” parameter in the function, I just opened the function up, found where this was plotted and changed it manually (a bit messy, but that’s what you have to do sometimes…)
Here are the result (you can click the image for a larger image):
And here is The R code to produce the Balloon plot of Nutritional supplements efficacy score (by evidence for its effectiveness on the listed condition).
(it’s just the copy of the function with a tiny bit of editing in line 146, and then using it)
My wife is a big lover of dance (especially Dance In Israel), and while reading through the NYtimes article: “To Impress, Tufts Prospects Turn to YouTube“, she found me a pearl: A woman performing interpretive dances for math/statistical plots. That includes small dance for: scatter plots, boxplots, barplots and a few others. Enjoy:
One of the exciting new frontiers for R programming is of creating website interfaces to R code. At the forefront of this domain is a young and (very) bright man called Jeroen Ooms, whom I had the pleasure of meeting at useR 2009 (press the link to see his presentation).
New features include 1D geom’s (histogram, density, freqpoly), syntax mode (by clicking the tiny arrow at the bottom), and some additional facet options. And some minor improvements and fixes, most notably for Internet Explorer.
The data upload has not been improved yet, I am working on that. For now, it supports .csv, .sav (spss), and tab delimited data. Please make sure your filename has the appropriate extension and every column has a header in your data. If you export a dataframe from R, use:
write.csv(mydf, ”mydf.csv” , row.names=F). If you upload an spss
datafile, none of this should be a concern.
Supported browsers are IE6-8, FF, Safari, and Chrome, but a recent browser is highly recommended. As always, feedback is more than welcome.
Here is a little demo video that shows how to use the new features:
I guess this is not the number one post I would like to start with on this blog, but I feel the time is right for it (community-wise).
I’ll move on to the subject matter in a moment, but first a short intro: This blog is written by Tal Galili. I am an aspiring statistician who also loves to use R for his work. At the same time I am also a WordPress blogger, writing mainly at www.TalGalili.com where I can use my native language (Hebrew) for self expression.
This combination of statistics and blogging will lead me to sometimes much less statistical, but more Web/Open-Source oriented posts like this one. So for the statisticians in the audience I extend my apologies and invite you to wait for future posts which will be more fully focused on Statistics and R.