Values (in a dataset)
Values are the specific entries recorded for each case (row) on a given variable (column) in a dataset. A value represents the observed measurement, category, or state of that variable for an individual case.
In other words, if a variable is the question you’re asking (e.g., “What is the student’s thumb length?”), then the value is the answer for a particular case (e.g., 65 mm).
Values depend on the type of variable:
Numeric (quantitative) values: Represent measurable quantities.
Example: 65 (thumb length in mm), 3.2 (GPA), 120 (heart rate)
Categorical (qualitative) values: Represent group membership or labels.
Example: "male", "female", "nonbinary" (gender)
Example: "control" vs "treatment" (experimental group)
Suppose we have a dataset of students:
The value of thumb_length_mm for case 1 is 65
The value of gender for case 2 is "male"
The value of study_group for case 3 is "treatment"
In R, values are stored inside vectors (columns of a dataset):
thumb_length_mm <- c(65, 72, 68)
gender <- c("female", "male", "female")
study_group <- c("control", "treatment", "treatment")
thumb_length_mm[1] # 65
gender[2] # "male"
Values are the raw inputs used to build statistical models:
Predictor (explanatory) variable values are used to explain variation in an outcome.
Outcome variable values are what the model tries to predict.
model <- lm(thumb_length_mm ~ gender, data = Fingers)
The values of thumb_length_mm are the response
The values of gender help explain differences in thumb length
A dataset is fundamentally a collection of values organized by variables (columns) and cases (rows). Understanding values—and their types—is essential for data cleaning, visualization, and modeling.