Bar charts and histograms are easily to understand. I often write for non-specialist audiences so I tend to use them a lot. People like percentages too, so a bar chart with counts on the y axis but percentage labels is a useful thing to be able to produce. But how ... [Read more...]

The goal of this blog entry is to introduce basic and essential information about the apply function. Even established R users get confused when considering this family of functions especially when observing how many of the them there are: apply, tapply, lapply, sapply, rapply, eapply, mapply. When I was new ... [Read more...]

I’m frequently asked by newcomers to R to provide an easy to follow generic set of instructions on how to download data, transform it, aggregate it, make graphs, and write it all up for publication in a high impact journal – all by the end of the day ! While such ... [Read more...]

If you love knitr and rstudio and use them to produce long reports, you probably know that you can produce a table of contents in your html (and pdf) documents. In the newer rstudio (Version 0.98.801 or later) you do it by requesting a toc in the doc header, something like ... [Read more...]

Originally posted on Rolling Your Rs:In this article I discuss a general approach for Geocoding a location from within R, processing XML reports, and using R packages to create interactive maps. There are various ways to accomplish this, though using Google’s GeoCoding service is a good place to ... [Read more...]

If you are a newcomer to R then you are probably quite busy learning the semantics of the language as you experiment with the apply family of commands or come up to speed on the grouping and conditioning capabilities offered by lattice graphics. And, along the way, you might have ... [Read more...]

Conditioning and grouping are two important concepts in graphing that allow us to rapidly refine our understanding of data under consideration. Conditioning, in particular, allows us to view relationships across “panels” with common scales. Each panel contains a plot whose data is “conditional” upon records drawn from the category that ... [Read more...]

Rstudio is a great tool for working with R and R scripts. And Markdown is a great way to write even complex, reproducible documents in plain text. So they make a great combination. BUT: before when writing markdown in rstudio, you had to write “—-” after your headings to get ... [Read more...]

Vectors are at the heart of R and represent a true convenience. Moreover, vectors are essential for good performance especially when your are working with lots of data. We’ll explore these concepts in this posting. As a motivational example let’s generate a sequence of data from -3 to 3. ... [Read more...]

In survey research, our datasets nearly always comprise variables with mixed measurement levels – in particular, nominal, ordinal and continuous, or in R-speak, unordered factors, ordered factors and numeric variables. Sometimes it is useful to be able to do blanket tests of one set of variables (possibly of mixed level) against ... [Read more...]

Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be ... [Read more...]

I wrote already about changing figure options mid-chunk in reproducible research. This can be important  e.g. if you are looping through a dataset to produce a graphic for each variable but the figure width or height need to depend on properties of the variables, e.g. if you are ... [Read more...]

Welcome to Part 2 of the GeoCoding, R, and the Rolling Stones blog. Let’s apply some of the things we learned in Part 1 to a practical real world example. Mapping the Stones – A Real Example The Rolling Stones have toured for many years. You can go to Wikipedia and see ... [Read more...]

Knitr is a great tool for doing reproducible research. You can produce all kinds of output inside a single knitr chunk, e.g. you can write a loop to produce lots of figures or tables. The only catch is if you want your figures to have differing captions, heights, etc (... [Read more...]

Hello. Welcome to my debut post ! Check the About link to see what this Blog intends to accomplish. In this article I discuss a general approach for dealing with the problem of splitting a data frame based on a grouping variable and then doing some more operations per group. A ... [Read more...]

Just now writing a reproducible report in R using RStudio on Ubuntu. So the source is a .Rnw file and I am compiling it with knitr. For the narrative part of the report it is a shame that RStudio doesn’t have autocomplete for latex styles, headings etc. But I ... [Read more...]

Dear R experts, I sent this question to the r-help list but didn’t get much response, probably because it is more of a stats question. But as this blog is syndicated on r-bloggers I thought I would try it again here on this blog. If I am barking up ... [Read more...]

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I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor[,,2]) Pm=melt(... [Read more...]

I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor[,,2]) Pm=melt(... [Read more...]