Vectors, matrixes and data.frames in R

Working with dimensional objects and indices

1. Generate a vector x of 30 random numbers from the normal distribution using the function rnorm()

2. Create a vector y that only contains the positive elements of x

3. Create a vector z that censors the negative elements of x with zeros

4. Create a matrix m with five columns and six rows from x using the matrix() function

   Once you've created m, use the str() function on both m and x. See the difference...

5. Convert m to a data.frame called d.f using the as.data.frame() function

   Try using str() again on your new data.frame. A data.frame looks like a matrix, but it is different. Notice for example how columns now have names rather than just numbers. Columns in an R matrix can have names (see help(matrix) for how, look for the 'dimnames' property), but in a data.frame columns must have names.
   R contains numerous as.[object]() functions. For example, as.vector(d.f) would give you a vector identical to your original x. These are very useful for converting between different object types, but conversion is not always this seamless.

6. You can illustrate the difference between a matrix and a data.frame by setting one single element to a character in both m and d.f (e.g. m[2,2] = "a"; d.f[2,2] = "a")

   Use str() again. The whole matrix m has been changed from a numeric object to a character object. In the data.frame, however, only the affected column has been changed. In R, a matrix is a matrix, but you should think of a data.frame more like a collection of named vectors. From a microarray perspective, data.frames allow us to keep annotation columns with gene-specific annotations within our data sets. On the other hand, matrices are often easier to work with...