Open-source is only one reason for this. The other reason is, Python's the sort of language a computer scientist would design, while Matlab's the sort an engineer would design. The esthetic criteria by which they excel are different, and I think R is closer to Python on that axis.
With regard to the excel comment, I would add that Excel has some little known, but very powerful add-ins that make the program slightly more appealing. Like Dig-db, for example. A great macro. Regarding placing legends, the lattice package does that nicely see auto. As a long time time advanced Excel chart user and more recent R user, I have to say that R's charting capabilities are far superior to Excel. Personally, I'd be wary of a GUI-based exploratory graphics program.
Chapman & Hall/CRC Computer Science & Data Analysis
I imagine that it'd take longer to do anything. I like using Tableau for getting a good initial look at a data set. I find it creates very good plots by default and the interaction is very fluid and fast.
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If you want to get an idea of what R could do, but doesn't, Tableau is a good place to start. You take a look at the data, try something, take another look at it, etc. I'm sure this isn't truly "interactive" to the folks who want something like animations and linked brushing and what-not. I have tried such systems and much prefer creating a lot of "static" R plots. The interactivity comes from changing things, making lots of plots, and being well-versed in R so that it all happens quickly.
This gives me an "audit-trail" as well and helps me keep track of what I have done. In my opinion, EDA and model building are synonymous.
Most data sets I have worked on have very quickly led me to situations where the analysis becomes too complex so that "interactive" graphics systems become too cumbersome or impossible to use. Perhaps I am just old-fashioned, though! Model building is far more restricting than what you do with EDA.
In fact a successful EDA process should lead to good often less complex models. There are things you can do with interactive graphics which are extremely hard or extremely inefficient with a series of static plots, and often you run the risk of missing points if it takes too long to follow your ideas. I know I'm a little late, but here's one for GGobi.
I haven't used it a lot, but it has some nice features to explore high dimensional data and you can also interact with it as well.
Interactive Graphics: Exemplified with Real Data Applications
The corresponding package for R is rggobi. While buggy, there's the R package playwith for interactive GUI graphics manipulation. There is nothing like Spotfire , which was recently bought by Tibco. It is expensive, but very powerful and flexible, and is designed specifically for exploration of data. You can go from excel spreadsheet of complex data to data visualization in seconds. I prefer R, but I will also use gnuplot.
If you like Matlab style plotting, you might also like free Matlab clone Octave , which uses gnuplot as a backend for its graphics. Anonymous says:. April 13, at pm. Hector says:. Jonathan says:. April 14, at am. James says:. Vince B. John Johnson says:. Bill Harris says:. KW says:. Mike Dewar says:. Amy says:. Aaron Mackey says:. Bruce McCullough says:. Martin Theus says:. Drew Conway says:. Gunn says:. Luis says:. April 14, at pm. Chris says:. KMC says:. Scott says:. However, the phyloseq package is implemented at a stage in the analysis process that can be more generally applied to any phylogenetic sequencing, including non-standard amplicon targets, shotgun metagenome sequencing, etc.
Source materials for reproducing this manuscript. This is a compressed. Rnw format  , as well as the additional files necessary to completely recreate the original manuscript submitted to PLoS ONE. Also included is the RFM source file that was used to create Figures 4 and 5 , and its accompanying HTML output that includes additional documentation details, links, and intermediate graphics. In this particular example, the bioenv function from the vegan package  is demonstrated. We would like to thank Martin Morgan and Valerie Obenchain at Bioconductor for their useful suggestions regarding the architecture and organization of phyloseq.
We would also like to thank the developers of the open source packages on which phyloseq depends, in particular Rob Knight and his lab for QIIME  , Hadley Wickham for the ggplot2  , reshape  , and plyr  packages, as well as the Bioconductor and R teams  , . Thanks also to RStudio and GitHub for immensely useful and free development applications. Alfred Spormann, Tyrrell Nelson and Tim Meyer also provided early versions of an illustrative data set.
We also thank the communities at stackoverflow. Designed and wrote the software described: PJM. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R.
Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor. Download: PPT. Methods phyloseq Project Key Features The phyloseq package provides an object-oriented programming infrastructure that simplifies many of the common data management and preprocessing tasks required during analysis of phylogenetic sequencing data. Data Availability R packages can include example data that is documented with the same help system as other package objects .
Data Infrastructure and Design The phyloseq project includes an object-oriented class that integrates the heterogeneous components of OTU-clustered phylogenetic sequencing data. Specialized Graphics One of the key features of the phyloseq package is a set of graphics functions custom-tailored for phylogenetic sequencing analysis, built using the ggplot2 package . Figure 5. Normalization and Standardization In multivariate analyses such as PCA, large differences in variances between columns are corrected by standardizing each column; i. Confirmatory Analyses Although useful for exploring and summarizing microbiome data, many of the graphics and ordination methods discussed here are not formal tests of any particular hypothesis.
Table 1. Extending phyloseq It is important to note that the new phyloseq-class is a significant departure from the originally-proposed phyloseq-class structure  , which used nested multiple inheritance and a naming convention. Conclusions The phyloseq project is a new open-source software tool for statistical analysis of phylogenetic sequencing data within the R programming language and environment.
Supporting Information. File S1. File S2. Acknowledgments We would like to thank Martin Morgan and Valerie Obenchain at Bioconductor for their useful suggestions regarding the architecture and organization of phyloseq. References 1. Metzker ML Sequencing technologies - the next generation. Nature Reviews Genetics 31— View Article Google Scholar 2. Nature Methods 5: — View Article Google Scholar 3. Pace NR A molecular view of microbial diversity and the biosphere.
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