How To Perform Stratified Sampling On Dataset In R

In this article, I am going to demonstrate how to create samples that are subsets using stratified sampling method and use strata function in R. Sampling is a process of selecting or extracting a subset from the whole population. It means that from the whole population you are extracting a sample or small subset or small portion of the data which aims to represent the characteristics of the whole population.

It is a shortcut method to investigate the whole population. Suppose there is a dataset of 1000 observations. Now you want to take a sample or extract a subset of that 1000 observations. There are different methods to extract a subset from the dataset. It all depends on the data and business requirement to look into which method is suitable to extract samples from a dataset.

We generally use sampling in our day to day life. For example if you visit a doctor so he/she will take a small sample of blood for the check-up of your whole body. This is how we use sampling in our day to day to day life.

Suppose you want to perform any survey for a product. So the best idea would be to know from the whole population. But it could be expensive to conduct a survey among the whole population because it may take too much time and lots of resources. So in such cases we go for the sampling techniques because sampling is useful to identify a segment of people who can represent the characteristics of whole population.

A stratum is a subset of the population that shares at least one common characteristic. Stratified sampling is performed by,

Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums. Stratified sampling reduces sampling error.

Syntax for Stratified sampling with equal/unequal probabilities.

Arguments for strata function

A data frame or a matrix; its number of rows is n, the population size.

Vector of stratification variables.

Vector of stratum sample sizes (in the order in which the strata are given in the input data set).

Vector of inclusion probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson and systematic). If an auxiliary information is provided, the function uses the inclusionprobabilities function for computing these probabilities. If the method is "srswr" and the sample size is larger than the population size, this vector is normalized to one.

A message is printed if its value is TRUE; the message gives the number of selected units and the number of the units in the population. By default, the value is FALSE.

The function produces an object, which contains the following information,

The identifier of the selected units.

The unit stratum.

The final unit inclusion probability.

Data frame containing information on strata in the frame. The strata data frame (strata) contains a row per stratum with the following variables,

Stratum Identifier of the stratum (numeric)

Generates artificial data (a 235X3 matrix with 3 columns: state, region, income). The variable "state" has 2 categories ('nc' and 'sc'). The variable "region" has 3 categories (1, 2 and 3). The sampling frame is stratified by region within state. The income variable is randomly generated. Computes the population stratum sizes. There are 5 cells with non-zero values. One draws 5 samples (1 sample in each stratum.

The sample stratum sizes are 10, 5, 10, 4, 6 respectively. The method is 'srswor' (equal probability, without replacement). Extracts the observed data. Seeing result using a contigency table. The method is 'systematic' (unequal probability, without replacement). The selection probabilities are computed using the variable 'income'. Extracts the observed data see the result using a contigency table.

Now we will be using mtcars dataset to demonstrate stratified sampling.

Below is the code for taking a look at structure of stratified_data variable.