Vectors

Let’s create a vector containing the first ten letters of the alphabet.

ten_letters <- c('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j')

In order to extract one or several values from a vector, we must provide one or several indices in square brackets, just as we do in math. R indexes start at 1. Programming languages like Fortran, MATLAB, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

So, to extract the 2nd element of ten_letters we type:

ten_letters[2]

We can extract multiple elements at a time by specifying mulitple indices inside the square brackets as a vector. Notice how you can use : to make a vector of all integers two numbers.

ten_letters[c(1,7)]

ten_letters[3:6]

ten_letters[10:1]

ten_letters[c(2, 8:10)]

Quick exercise / formative assessment: Select every other element in ten_letters.

What if we were dealing with a much longer vector? We can use the seq() function to quickly create sequences of numbers.

seq(1, 10, by = 2)
seq(20, 4, by = -3)

Exercise

Fill in the blank to select the even elements of ten_letters using the seq() function.

ten_letters[____________]

Solution

ten_letters[seq(2, 10, by = 2)] {: .solution} {: .challenge}

Data frames

The metadata data frame has rows and columns (it has 2 dimensions), if we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers (i.e. [row, column]).

metadata[1, 2]   # 1st element in the 2nd column 
metadata[1, 6]   # 1st element in the 6th column
metadata[1:3, 7] # First three elements in the 7th column
metadata[3, ]    # 3rd element for all columns
metadata[, 7]    # Entire 7th column

Challenge

The function nrow() on a data.frame returns the number of rows. For example, try typing nrow(metadata). Usenrow()andseq()to create a new data frame calledmeta_by_2that includes all even numbered rows ofmetadata`.

Solution

meta_data[seq(2, nrow(metadata), by = 2, ]

{: .solution} {: .challenge}

For larger datasets, it can be tricky to remember the column number that corresponds to a particular variable. Sometimes the column number for a particular variable can change if your analysis adds or removes columns. The best practice when working with columns in a data frame is to refer to them by name. This also makes your code easier to read and your intentions clearer.

There are two ways to select a column by name from a data frame:

• Using dataframe[ , "column_name"]
• Using dataframe$column_name

You can do operations on a particular column, by selecting it using the $ sign. In this case, the entire column is a vector. You can use names(metadata) or colnames(metadata) to remind yourself of the column names. For instance, to extract all the strain information from our datasets:

# Select the strain column from metadata
metadata[ , "strain"]

# Alternatively...
metadata$strain

The first method allows you to select multiple columns at once. Suppose we wanted strain and clade information:

metadata[, c("strain", "clade")]

You can even access columns by column name and select specific rows of interest. For example, if we wanted the strain and clade of just rows 4 through 7, we could do:

metadata[4:7, c("strain", "clade")]

<!– Still need to address the following learning objectives: * Append columns to a data frame. * Create subsets of a data frame.

The following headings are just suggestions.

Mathematical operations

Appending new columns