Basic Data Types

Topics: data type, numeric, integer, character, logical

R has several built-in data types, each designed to represent different kinds of information.

In healthcare and research, you’ll often deal with patient names (text), ages (whole numbers), lab results (decimals), and test outcomes (TRUE/FALSE). Understanding these data types helps ensure your analyses are accurate and meaningful.

The Four Main Data Types

Characters (character)

Characters (also called strings) are text data enclosed in single or double quotation marks, and are often used to store text data such as patient names, diagnoses, or remarks in a medical record.

patient_name <- "Maria Santos"
diagnosis    <- "Hypertension"
note         <- "Patient advised to monitor blood pressure daily."
Integers (integer)

Integers are numbers, specifically whole numbers. These data are numbers that do not contain decimal places. They can be age, heart rate, or number of visits.

patient_age    <- 45L
admission_year <- 2025L
heart_rate     <- 78L
Numerics / Doubles (numeric)

Numerics represent values with decimals — common in lab values, BMI, temperatures, etc.

body_temperature <- 36.8
blood_glucose    <- 5.7
bmi              <- 24.5
Logicals (logical)

Logicals are either TRUE or FALSE. For example, whether a patient has diabetes, is pregnant, or is a smoker.

has_allergies <- TRUE
is_smoker     <- FALSE
On to Checking Data Types

You can use the class() function to check the data type of any variable. For example, is a lab result value “42” stored as text or as a number?

result <- "42"
class(result)   # Output: "character"

value <- 42
class(value)    # Output: "numeric"

value_int <- 42L
class(value_int) # Output: "integer"
Converting Between Data Types

In many datasets, data imported from hospital systems may be stored as text even if they represent numbers.

Converting them to numeric or integer allows you to perform calculations like average age or mean blood glucose.

age_str <- "25"
age_int <- as.integer(age_str)
print(age_int + 5)   # Output: 30
Integer to character

We can also do the reverse.

patient_bmi <- 22.5
bmi_str     <- as.character(patient_bmi)
cat("Patient BMI is", bmi_str, "\n")

# Output
# Patient BMI is 22.5
Character to numeric
glucose_str   <- "5.8"
glucose_num   <- as.numeric(glucose_str)
print(glucose_num + 0.2)   # Output: 6
Numeric to integer (truncates decimal)
heart_rate <- as.integer(75.6)   # Result: 75

When converting from numeric to integer, R removes (truncates) the decimal — this is useful when you only need whole-number data, such as rounding heart rate readings.

Logical conversions
as.logical(1)     # TRUE  → e.g., 1 could represent "Yes" in a dataset
as.logical(0)     # FALSE → e.g., 0 could represent "No"
as.logical("")    # NA    → empty strings become missing in R
as.logical("Yes") # NA    → only "TRUE"/"FALSE"/"T"/"F" are recognized

Note: Unlike Python, R does not treat arbitrary non-empty strings as TRUE. Use explicit TRUE/FALSE values in your data wherever possible.

Variables Operators