# Subsetting data and vectorization part I

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## Outline

• The following topics will be covered in this lecture:

• More on subsetting data
• Skipping and removing elements
• Subsetting based on logical values and other conditions

## More on subsetting data

• R has many powerful subset operators – mastering them will allow you to easily perform complex operations on any kind of dataset.

• There are six different ways we can subset any kind of object, and three different subsetting operators for the different data structures.

• Let's start with the workhorse of R: a simple numeric vector.

``````x <- c(5.4, 6.2, 7.1, 4.8, 7.5)
names(x) <- c('a', 'b', 'c', 'd', 'e')
x
``````
``````  a   b   c   d   e
5.4 6.2 7.1 4.8 7.5
``````
• In R, simple vectors containing character strings, numbers, or logical values are called atomic vectors because they can't be further simplified.

## Accessing elements using their indices

• So now that we've created a dummy vector to play with, how do we get at its contents?

• To extract elements of a vector we can give their corresponding index, starting from one:

``````x
``````
``````  a
5.4
``````
``````x
``````
``````  d
4.8
``````
• It may look different, but the square brackets operator is a function.

• For vectors (and matrices), it means “get me the nth element”.

### Accessing elements using their indices – continued

• We can ask for multiple elements at once:
``````x[c(1, 3)]
``````
``````  a   c
5.4 7.1
``````
• Or slices of the vector:
``````x[1:4]
``````
``````  a   b   c   d
5.4 6.2 7.1 4.8
``````
• The `:` operator creates a sequence of numbers from the left element to the right.
``````1:4
``````
`````` 1 2 3 4
``````
``````c(1, 2, 3, 4)
``````
`````` 1 2 3 4
``````

### Accessing elements using their indices – continued

• We can ask for the same element multiple times:
``````x[c(1,1,3)]
``````
``````  a   a   c
5.4 5.4 7.1
``````
• If we ask for an index beyond the length of the vector, R will return a missing value:
``````x
``````
``````<NA>
NA
``````
• This is a vector of length one containing an `NA`, whose name is also `NA`.

### Accessing elements using their indices – continued

• If we ask for the 0th element, we get an empty vector:
``````x
``````
``````named numeric(0)
``````
• In many programming languages (C and Python, for example), the first element of a vector has an index of 0.

• In R, the first element is 1.

## Skipping and removing elements

• If we use a negative number as the index of a vector, R will return every element except for the one specified:
``````x[-2]
``````
``````  a   c   d   e
5.4 7.1 4.8 7.5
``````
• We can skip multiple elements:
``````x[c(-1, -5)]  # or x[-c(1,5)]
``````
``````  b   c   d
6.2 7.1 4.8
``````
• A common trip up for novices occurs when trying to skip slices of a vector.

• It's natural to try to negate a slice as follows

``````x[-1:3]
``````
• But this gives a somewhat cryptic error…
``````Error in x[-1:3]: only 0's may be mixed with negative subscripts
``````

### Skipping and removing elements – continued

• The key to understanding this is remembering the order of operations.

• `:` is really a function, which we want to respect the arguments of.
• It takes its first argument as -1, and its second as 3, so generates the sequence of numbers: `c(-1, 0, 1, 2, 3)`.

• The correct solution is to wrap that function call in brackets, so that the `-` operator applies to the result:

``````x[-(1:3)]
``````
``````  d   e
4.8 7.5
``````
• To remove elements from a vector, we need to assign the result back into the variable:
``````x <- x[-4]
x
``````
``````  a   b   c   e
5.4 6.2 7.1 7.5
``````

## Subsetting by name

• We can extract elements by using their name, instead of extracting by index:
``````x <- c(a=5.4, b=6.2, c=7.1, d=4.8, e=7.5) # we can name a vector 'on the fly'
x[c("a", "c")]
``````
``````  a   c
5.4 7.1
``````
• This is usually a much more reliable way to subset objects:

• the position of various elements can often change when chaining together subsetting operations, but the names will always remain the same.

## Subsetting through other logical operations

• We can also use any logical vector to subset:
``````x[c(FALSE, FALSE, TRUE, FALSE, TRUE)]
``````
``````  c   e
7.1 7.5
``````
• Since comparison operators (e.g. `>`, `<`, `==`) evaluate to logical vectors, we can also use them to succinctly subset vectors:

• the following statement gives the same result as the previous one.
``````x[x > 7]
``````
``````  c   e
7.1 7.5
``````

## Subsetting through other logical operations

• Breaking it down, this statement first evaluates `x>7`,

• generating a logical vector `c(FALSE, FALSE, TRUE, FALSE, TRUE)`,
• and then selects the elements of `x` corresponding to the `TRUE` values.
• We can use `==` to mimic the previous method of indexing by name (remember you have to use `==` rather than `=` for comparisons):

``````x[names(x) == "a"]
``````
``````  a
5.4
``````

### Combining logical conditions

• We often want to combine multiple logical criteria.

• For example, we might want to find all the countries that are located in Asia or Europe and have life expectancies within a certain range.

• Several operations for combining logical vectors exist in R:

• `&`, the “logical AND” operator: returns `TRUE` if both the left and right are `TRUE`.
• `|`, the “logical OR” operator: returns `TRUE`, if either the left or right (or both) are `TRUE`.

### Combining logical conditions

• You may sometimes see `&&` and `||` instead of `&` and `|`.

• These two-character operators only look at the first element of each vector and ignore the remaining elements. In general you should not use the two-character operators in data analysis;

• save them for programming, i.e. deciding whether to execute a statement.

• `!`, the “logical NOT” operator: converts `TRUE` to `FALSE` and `FALSE` to `TRUE`.

• It can negate a single logical condition (eg `!TRUE` becomes `FALSE`), or a whole vector of conditions(eg `!c(TRUE, FALSE)` becomes `c(FALSE, TRUE)`).

• Additionally, you can compare the elements within a single vector using the `all` function (which returns `TRUE` if every element of the vector is `TRUE`) and the `any` function (which returns `TRUE` if one or more elements of the vector are `TRUE`).

### Non-unique names

• You should be aware that it is possible for multiple elements in a vector to have the same name. (For a data frame, columns can have the same name;

• Consider this example:

`````` x <- 1:3
x
``````
`````` 1 2 3
``````
``````names(x) <- c('a', 'a', 'a')
x
``````
``````a a a
1 2 3
``````

### Non-unique names

• Now consider if we try to extract data by name when all elements have the same name:
``````x['a']  # only returns first value
``````
``````a
1
``````
``````x[names(x) == 'a']  # returns all three values
``````
``````a a a
1 2 3
``````

## Skipping named elements

• Skipping or removing named elements is a little harder. If we try to skip one named element by negating the string, R complains (slightly obscurely) that it doesn't know how to take the negative of a string:
``````x <- c(a=5.4, b=6.2, c=7.1, d=4.8, e=7.5) # we start again by naming a vector 'on the fly'
x[-"a"]
``````
``````Error in -"a": invalid argument to unary operator
``````
• However, we can use the `!=` (not-equals) operator to construct a logical vector that will do what we want:
``````x[names(x) != "a"]
``````
``````  b   c   d   e
6.2 7.1 4.8 7.5
``````

## Skipping named elements

• Skipping multiple named indices is a little bit harder still. Suppose we want to drop the `"a"` and `"c"` elements, so we try this:
``````x[names(x)!=c("a","c")]
``````
``````  b   c   d   e
6.2 7.1 4.8 7.5
``````
• R did something, but it gave us a warning that we ought to pay attention to - and it apparently gave us the wrong answer (the `"c"` element is still included in the vector)!

• So what does `!=` actually do in this case? That's an excellent question…

### Recycling

• Let's take a look at the comparison component of this code:
``````names(x) != c("a", "c")
``````
`````` FALSE  TRUE  TRUE  TRUE  TRUE
``````
• When you use `!=`, R tries to compare each element of the left argument with the corresponding element of its right argument.

• What happens when you compare vectors of different lengths?

### Recycling – continued • When one vector is shorter than the other, it gets recycled • In this case R repeats c(“a”, “c”) as many times as necessary to match names(x), i.e. we get c(“a”,“c”,“a”,“c”,“a”).
• Since the recycled “a” doesn’t match the third element of names(x), the value of != is TRUE.
• Because in this case the longer vector length (5) isn’t a multiple of the shorter vector length (2), R printed a warning message.
• If we had been unlucky and names(x) had contained six elements, R would silently have done the wrong thing (i.e., not what we intended it to do).
• This recycling rule can can introduce hard-to-find and subtle bugs.

### Recycling

• The way to get R to do what we really want (match each element of the left argument with all of the elements of the right argument) it to use the `%in%` operator.

• The `%in%` operator goes through each element of its left argument, in this case the names of `x`, and asks, “Does this element occur in the second argument?”.

• Here, since we want to exclude values, we also need a `!` operator to change “in” to “not in”:

``````x[! names(x) %in% c("a","c") ]
``````
``````  b   d   e
6.2 4.8 7.5
``````

## Handling special values

• At some point you will encounter functions in R that cannot handle missing, infinite, or undefined data.

• There are a number of special functions you can use to filter out this data:

• `is.na` will return all positions in a vector, matrix, or data.frame containing `NA` (or `NaN`)
• likewise, `is.nan`, and `is.infinite` will do the same for `NaN` and `Inf`.
• `is.finite` will return all positions in a vector, matrix, or data.frame that do not contain `NA`, `NaN` or `Inf`.
• `na.omit` will filter out all missing values from a vector