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Descriptive Statistics in R

Descriptive statistics is a statistical analysis technique that summarizes or describes a collection of data. It is a useful and crucial step in analyzing massive data sets, as it provides a snapshot of the data's characteristics. This tutorial aims to explain how you can perform descriptive statistics in R.

Content​

  1. Data Types in R
  2. Measure of Central Tendency
  3. Measure of Dispersion
  4. Measure of Position
  5. Correlation Analysis

1. Data Types in R​

Before we dive into descriptive statistics, it is crucial to understand the data types in R:

  • Numeric: These are quantitative data that represent amounts.
  • Character: These are qualitative data that are text or string-based.
  • Factor: Categorical data that represents groups or categories.
  • Logical: These are Boolean data types that can be either TRUE or FALSE.

2. Measure of Central Tendency​

The central tendency measures the center of a dataset. It includes the mean, median, and mode.

  • Mean: It is the average of all values in the dataset. It's calculated using the mean() function.
mean_data <- mean(dataset)
  • Median: It is the middle value in a dataset when the data is sorted in ascending or descending order. It's calculated using the median() function.
median_data <- median(dataset)
  • Mode: It is the most frequently occurring value in a dataset. R does not have a built-in function to calculate the mode, but it can be calculated using the table() and which.max() functions.
mode_data <- which.max(table(dataset))

3. Measure of Dispersion​

The measure of dispersion shows how spread out the values are in a dataset. It includes range, variance, and standard deviation.

  • Range: It's the difference between the maximum and minimum values in a dataset. It's calculated using the range() function.
range_data <- range(dataset)
  • Variance: It represents how much the data points vary from the mean. It's calculated using the var() function.
variance_data <- var(dataset)
  • Standard Deviation: It's the square root of variance, showing the dispersion of data points from the mean. It's calculated using the sd() function.
sd_data <- sd(dataset)

4. Measure of Position​

It includes quartiles, percentiles, and deciles.

  • Quartiles: They divide a dataset into four equal parts. It's calculated using the quantile() function.
quartile_data <- quantile(dataset)
  • Percentiles: They divide a dataset into 100 equal parts. It's calculated using the quantile() function with the probs argument.
percentile_data <- quantile(dataset, probs = c(0.1, 0.2, ..., 1.0)

5. Correlation Analysis​

Correlation analysis is used to measure the relationship between two variables. It's calculated using the cor() function.

correlation <- cor(dataset$var1, dataset$var2)

This tutorial covered the basics of descriptive statistics in R. You learned about the measures of central tendency, dispersion, position, and correlation analysis. With these tools, you can start to analyze your data and gain valuable insights.

Remember, the key to mastering R or any programming language is consistent practice. So, keep practicing!