Category: code

There are times when we need to write a function that makes changes to a generic data frame that is passed as an argument. Let’s say, for example, that we want to write a function that converts to factor any variable with names starting with a capital letter. There are a few issues involved in this problem, including:

• Obtaining a text version of the name of the dataset (using the substitute() function).
• Looping over the variable names and checking if they start with a capital letter (comparing with the LETTERS vector of constants).
• Generating the plain text version of the factor conversion, glueing the dataset and variable names (using paste()).
• Parsing the plain text version of the code to R code (using parse()) and evaluating it (using eval()). This evaluation has to be done in the parent environment or we will lose any transformation when we leave the function, which is the reason for the envir() specification.

And that’s all. Now the Fert integer variable has been converted to a factor. This example function could be useful for someone out there.

Once one starts writing more R code the need for consistency increases, as it facilitates managing larger projects and their maintenance. There are several style guides or suggestions for R; for example, Andrew Gelman’s, Hadley Wickham’s, Bioconductor’s and this one. I tend to write closer to Google’s R style guide, which contains some helpful suggestions. I use something similar but:

• I use = for assignment rather than <-, because it is visually less noisy, <- requires an extra keystroke (yes, I am that lazy) and—from a merely esthetics point of view—in many monospaced fonts the lower than and hyphen symbols do not align properly, so <- does not look like an arrow. I know that hardcore R programmers prefer the other symbol but, tough, I prefer the equal sign.
• I indent code using four spaces, just because I am used to do so in Python. I will make an exception and go down to two spaces if there are too many nested clauses.
• I like their identifier naming scheme, although I do not use it consistently. Mea culpa.
• I always use single quotes for text (two fewer keystrokes per text variable).

Of course you’ll find that the examples presented in this site depart from the style guide. I didn’t say that I was consistent, did I?

There are circumstances when one wants to generate all possible combinations of levels for two factors. For example, factor one with levels ‘A’, ‘B’ and ‘C’, and factor two with levels ‘D’, ‘E’, ‘F’. The function expand.grid() comes very handy here:

[sourcecode language=”r”]
combo = expand.grid(factor1 = LETTERS[1:3],
factor2 = LETTERS[4:6])
combo

factor1 factor2
1 A D
2 B D
3 C D
4 A E
5 B E
6 C E
7 A F
8 B F
9 C F
[/sourcecode]

Omitting the variable names (factor1 and factor 2) will automatically name the variables as Var1 and Var2. Of course we do not have to use letters for the factor levels; if you have defined a couple of factors (say Fertilizer and Irrigation) you can use levels(Fertilizer) and levels(Irrigation) instead of LETTERS…

When processing data it is common to test if an observation belongs to a set. Let’s suppose that we want to see if the sample code belongs to a set that includes A, B, C and D. In R it is easy to write something like:

[sourcecode language=”r”]
inside.set = subset(my.data, code %in% c(‘A’, ‘B’, ‘C’, ‘D’))
[/sourcecode]

Now, what happens if what we want are the observations that are not in that set? Simple, we use the negation operator (!) as in:

[sourcecode language=”r”]
outside.set = subset(my.data, !(code %in% c(‘A’, ‘B’, ‘C’, ‘D’)))
[/sourcecode]

In summary, surround the condition by !().