## 7.3 Changing values of a vector

Now that you know how to index a vector, you can easily change specific values in a vector using the assignment (<-) operation. To do this, just assign a vector of new values to the indexed values of the original vector:

Let’s create a vector a which contains 10 1s:

a <- rep(1, 10)

Now, let’s change the first 5 values in the vector to 9s by indexing the first five values, and assigning the value of 9:

a[1:5] <- 9
a
##   9 9 9 9 9 1 1 1 1 1

Now let’s change the last 5 values to 0s. We’ll index the values 6 through 10, and assign a value of 0.

a[6:10] <- 0
a
##   9 9 9 9 9 0 0 0 0 0

Of course, you can also change values of a vector using a logical indexing vector. For example, let’s say you have a vector of numbers that should be from 1 to 10. If values are outside of this range, you want to set them to either the minimum (1) or maximum (10) value:

# x is a vector of numbers that should be from 1 to 10
x <- c(5, -5, 7, 4, 11, 5, -2)

# Assign values less than 1 to 1
x[x < 1] <- 1

# Assign values greater than 10 to 10
x[x > 10] <- 10

# Print the result!
x
##   5  1  7  4 10  5  1

As you can see, our new values of x are now never less than 1 or greater than 10!

A note on indexing…

Technically, when you assign new values to a vector, you should always assign a vector of the same length as the number of values that you are updating. For example, given a vector a with 10 1s:

a <- rep(1, 10)

To update the first 5 values with 5 9s, we should assign a new vector of 5 9s

a[1:5] <- c(9, 9, 9, 9, 9)
a
##   9 9 9 9 9 1 1 1 1 1

However, if we repeat this code but just assign a single 9, R will repeat the value as many times as necessary to fill the indexed value of the vector. That’s why the following code still works:

a[1:5] <- 9
a
##   9 9 9 9 9 1 1 1 1 1

In other languages this code wouldn’t work because we’re trying to replace 5 values with just 1. However, this is a case where R bends the rules a bit.

### 7.3.1 Ex: Fixing invalid responses to a Happiness survey Assigning and indexing is a particularly helpful tool when, for example, you want to remove invalid values in a vector before performing an analysis. For example, let’s say you asked 10 people how happy they were on a scale of 1 to 5 and received the following responses:

happy <- c(1, 4, 2, 999, 2, 3, -2, 3, 2, 999)

As you can see, we have some invalid values (999 and -2) in this vector. To remove them, we’ll use logical indexing to change the invalid values (999 and -2) to NA. We’ll create a logical vector indicating which values of happy are invalid using the %in% operation. Because we want to see which values are invalid, we’ll add the == FALSE condition (If we don’t, the index will tell us which values are valid).

# Which values of happy are NOT in the set 1:5?
invalid <- (happy %in% 1:5) == FALSE
invalid
##   FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE

Now that we have a logical index invalid telling us which values are invalid (that is, not in the set 1 through 5), we’ll index happy with invalid, and assign the invalid values as NA:

# Convert any invalid values in happy to NA
happy[invalid] <- NA
happy
##    1  4  2 NA  2  3 NA  3  2 NA

We can also recode all the invalid values of happy in one line as follows:

# Convert all values of happy that are NOT integers from 1 to 5 to NA
happy[(happy %in% 1:5) == FALSE] <- NA

As you can see, happy now has NAs for previously invalid values. Now we can take a mean() of the vector and see the mean of the valid responses.

# Include na.rm = TRUE to ignore NA values
mean(happy, na.rm = TRUE)
##  2.4