Hi everyone,
For those of you who work with R for statistical analyses and reporting, I wanted to mention an interesting R package I discovered while attending the recent UseR! conference which took place at UCLA.
The package is named agridat and was designed by Kevin Wright. The package includes a variety of agricultural data sets and uses these data sets to create complex and beautiful graphs. (Some of you may be interested in the actual data sets and/or the statistical modeling techniques used to analyze these data sets.)
The agridat package vignette is available at the following website:
http://cran.r-project.org/web/packages/agridat/vignettes/agridat.pdf.
The agridat package website is available at http://cran.r-project.org/web/packages/agridat/.
If you have any questions or comments about the package, you can contact Kevin by e-mail at Kevin Wright <kw.stat@gmail.com" shape="rect" rel="nofollow" target="_blank">kw.stat@gmail.com>.
To install the agridat package in R, you can use the R command:
install.packages("agridat")
To load the agridat package into your current R session, you can use the command:
require(agridat)
To view the agridat package vignette from within R, you can use the command:
vignette("agridat")
Kevin Wright was kind enough to share some quotes on designing statistical graphics with me and I am reproducing them below for your enjoyment:
"Good statistical graphics are...much harder than running regressions and making tables."
Andrew Gelman (2011), J Comp Graph Stat, 20, 36-40.
"A big challenge is balancing the tension between exploration and presentation. For exploratory graphics, you want to spend five seconds (or less) to create a plot that helps you understand the data, while you might spend five hours on a plot that's persuasive to an audience who isn't as intimately familiar with the data as you.''
Hadley Wickham, http://simplystatistics.tumblr.com/post/22844703875/ha
"The sweat is in the details....Most graphs, like many other endeavors follow the 80-20 Rule: You can get it 80% done with 20% of the effort, but the remaining 20% is hard work, and takes the remaining 80% of the effort.''
Michael Friendly, http://www.datavis.ca/gallery/excellence.php
"A little effort went into splitting the data set into cells, some effort went into making a rudimentary function to plot the data...and a great deal of effort went into attending to details''.
Dan Carr (1995)
"We made countless tweaks to our R script and hard-coded various details such as the positions of the labels, the scaling of the axes, the long tick mark dividing 2009 from 2010, and a slight shifting of the zero points so they would be clear of the x-axis (which we wanted to be precisely at zero - contrary to the R default - so make it visually apparent that the estimated rate declines to zero at the end of the time series). Some manual adjustments may always be necessary to prepare a complex presentation-quality graph, but R (as currently configured) is not the ideal environment for this process."
Niemi & Gelman (2011), Making information clear-and beautiful.
Significance, 8, 135-137.
Kind regards,
Isabella
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Isabella Ghement
Ghement Statistical Consulting Company Ltd.
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