Summary

You can now describe what a variable means, identify its representation in R, and check a conversion before using the result.

Summary

Three questions describe different aspects of your data. Measurement level describes the meaning of values. Discrete or continuous describes the possible values of a quantitative variable. R’s data type describes how values are stored. One does not automatically determine the others.

Measurement level	Meaning	Example
Nominal	Categories without an inherent order.	Land cover: forest, grassland, water.
Ordinal	Ordered categories whose spacing is not defined.	Erosion severity: low, medium, high.
Interval	Equal differences are meaningful; zero is a reference point.	Temperature in degrees Celsius: a 10-degree difference is meaningful, but 20 °C is not twice as hot as 10 °C.
Ratio	Equal differences and ratios are meaningful; zero indicates absence of the measured quantity.	Length in meters: 20 m is twice 10 m.

The four measurement levels. The diagram’s discrete/continuous branches are an oversimplification: measurement level and whether a quantitative variable is discrete or continuous describe separate properties. Use the table above for the distinction.

A count of trees is discrete; measured tree height is treated as continuous, even if recorded to the nearest centimeter. Numeric category codes, such as 1 = forest, remain categories. The choice of a calculation also depends on the question, sampling, and distribution of the data.

Representation in R

Type	Example	Remember
Integer	3L	Whole-number storage; the L requests an integer literal.
Double	3 or 3.5	Stores numeric values, including whole numbers. R code uses a decimal point.
Character	"003" or "forest"	Text; useful for identifiers with leading zeros.
Logical	TRUE, FALSE	Truth values, also used for selecting data.

Use checks such as is.integer(), is.numeric(), or is.character() to inspect a type. Both integer and double vectors count as numeric. Use conversion functions such as as.numeric() only when the intended meaning supports the conversion.

A short recap in R

recorded <- c("12.5", "18.0", NA)
height <- as.numeric(recorded)
is.numeric(height)
is.na(height)
mean(height, na.rm = TRUE)

The type check returns TRUE. The missing-value check returns FALSE FALSE TRUE, and the mean of the two available heights is 15.25. Removing missing values from a calculation changes which observations contribute; it does not explain why they are missing.

NA marks a missing value; use is.na(x), rather than x == NA, to find it. NaN marks an undefined numeric result, such as 0 / 0, and is also detected by is.na(). Inf represents infinity. NULL represents the absence of an object or component and is not a placeholder for one missing observation.

Check your understanding

1. Classify land cover, erosion severity, temperature in °C, and tree count by measurement level. Which quantitative example is discrete?
2. Why should the station identifier "003" usually remain character data?
3. What could happen if a text column containing "unknown" is converted to numeric? How would you check the result?

Revisit data types and conversion for the checks and special values.