Test for the existence of particular values using the any() and all() commands

Create a vector b:
b <- c(7, 2, 4, 3, -1, -2, 3, 3, 6, 8, 12, 7, 3) b
Use the function any() to see if there is any element in b equal -4 and less than 5:
any(b == -4)
any(b < 5) Both commands work on logical vectors. Use any() to check for missing data in a vector or an array.
d <- c(3, 2, NA, 5, 6, NA) d any(is.na(d)) Of course, we can check for non-missing data too.
any(!is.na(d)) The any() command is helpful when checking for particular values in large data sets.
You can use the all() command to check whether all elements in a given vector or array satisfy a particular condition. For example, let’s see whether all non-missing values in d are less than 5. Here we note noting that the command is.na() identifies missing data and that the syntax !is.na() identifies non-missing data.
all(d[!is.na(d)] < 5) Now check whether all non-missing elements are less than 7.
all(d[!is.na(d)] < 7) The syntax above looks formidable. However, is.na() identifies missing elements by creating a logical vector whose elements are either TRUE or FALSE.
is.na(d) The syntax !is.na(d) gives the opposite logical vector and counts non-missing data. Then, d[!is.na(d)] gives the elements of d that are-non missing. Finally, we apply the all() command, and include the condition that all elements are less than 7.
That wasn’t so hard! In Blog 19 I will present another tip for data analysis in R.
See you later!
David

Annex: R codes used

# Create a vector b.
b <- c(7, 2, 4, 3, -1, -2, 3, 3, 6, 8, 12, 7, 3)
b

# Check if there is any element in b equal -4 and less than 5:
any(b == -4)
any(b < 5)

# Check for missing data in a vector d.
d <- c(3, 2, NA, 5, 6, NA)
d
any(is.na(d))

# Check for non-missing data.
any(!is.na(d))

# Check if all non-missing values in d are less than 5.
all(d[!is.na(d)] < 5)

# Check if all non-missing values in d are less than 7.
all(d[!is.na(d)] < 7)

# Identify missing elements in d.
is.na(d)