The R language is weird – particularly for those coming from a typical programmer’s background, which likely includes OO languages in the curly-brace family and relational databases using SQL. A key data structure in R, the data.frame, is used something like a table in a relational database. In terms of R’s somewhat byzantine type system (which is explained nicely here), a data.frame is a list of vectors of varying types. Each vector is a column in the data.frame making this a column-oriented data structure as opposed to the row-oriented nature of relational databases.

In spite of this difference, we often want to do the same sorts of things to an R data.frame that we would to a SQL table. The R docs confuse the SQL-savvy by using different terminology, so here is a quick crib-sheet for applying SQL concepts to data.frames.

We’re going to use a sample data.frame with the following configuration of columns, or schema, if you prefer: (sequence:factor, strand:factor, start:integer, end:integer, common_name:character, value:double) where the type character is a string and a factor is something like an enum. Well, more accurately, value is a vector of type double and so forth. Anyway, our example is motivated by annotation of genome sequences, but the techniques aren’t particular to any type of data.

> head(df)
    sequence strand start   end common_name      value
1 chromosome      +  1450  2112        yvrO  0.9542516
2 chromosome      + 41063 41716       graD6  0.2374012
3 chromosome      + 62927 63640       graD3  1.0454790
4 chromosome      + 63881 64807         gmd  1.4383845
5 chromosome      + 71811 72701        moaE -1.8739953
6 chromosome      + 73639 74739        moaA  1.2711058

So, given a data.frame of that schema, how do we do some simple select operations?

Selecting columns by name is easy:

> df[,c('sequence','start','end')]
       sequence   start     end
1    chromosome    1450    2112
2    chromosome   41063   41716
3    chromosome   62927   63640
4    chromosome   63881   64807
5    chromosome   71811   72701
...

As is selecting row names, or both:

> df[566:570,c('sequence','start','end')]
      sequence  start    end
566 chromosome 480999 479860
567 chromosome 481397 480999
568 chromosome 503053 501275
569 chromosome 506476 505712
570 chromosome 515461 514277

Selecting rows that meet certain criteria is a lot like a SQL where clause:

> df[df$value>3.0,]
      sequence strand   start     end common_name    value
199 chromosome      +  907743  909506        hutU 3.158821
321 chromosome      + 1391811 1393337        nadB 3.092771
556 chromosome      -  431600  431037         apt 3.043373
572 chromosome      -  519043  518186        hbd1 3.077040

For extra bonus points, let’s find tRNAs.

> df[grep("trna", df$common_name, ignore.case=T),]
      sequence strand   start     end common_name        value
18  chromosome      +  115152  115224    Asn tRNA -0.461038128
19  chromosome      +  115314  115422    Ile tRNA -0.925268307
31  chromosome      +  167315  167388    Tyr tRNA  0.112527023
32  chromosome      +  191112  191196    Ser tRNA  0.986357577
...

Duplicate row names

Row names are not necessarily unique in R, which breaks the method shown above for selecting by row name. Take matrix a:

< a = matrix(1:18, nrow=6, ncol=3)
< rownames(a) <- c('a', 'a', 'a', 'b', 'b', 'b')
< colnames(a) <- c('foo', 'bar', 'bat')
< a
  foo bar bat
a   1   7  13
a   2   8  14
a   3   9  15
b   4  10  16
b   5  11  17
b   6  12  18

It looks to me like trying to index by the row names just returns the first row of a given name:

< a['a',]
foo bar bat 
  1   7  13
< a['b',]
foo bar bat 
  4  10  16 

But this works:

< a[rownames(a)=='a',]
  foo bar bat
a   1   7  13
a   2   8  14
a   3   9  15

More Resources:

• R programming for those coming from other languages
• Extract or Replace Parts of a Data Frame
• Subsetting Vectors, Matrices and Data Frames
• R & BioConductor Manual
• R Help search engine
• Abandon hope all ye who enter The R Inferno
• R Programming for Bioinformatics

Help for R, the R language, or the R project is notoriously hard to search for, so I like to stick in a few extra keywords, like R, data frames, data.frames, select subset, subsetting, selecting rows from a data.frame that meet certain criteria, and find.