# R - Mean, Median & Mode

The functions we are discussing in this chapter are mean, median and mode.

## Mean

It is calculated by taking the sum of the values and dividing with the number of values in a data series.The function

**mean()**is used to calculate this in R.

### Syntax

The basic syntax for calculating mean in R is −mean(x, trim = 0, na.rm = FALSE, ...)Following is the description of the parameters used −

**x**is the input vector.**trim**is used to drop some observations from both end of the sorted vector.**na.rm**is used to remove the missing values from the input vector.

### Example

# Create a vector. x <- c(12,7,3,4.2,18,2,54,-21,8,-5) # Find Mean. result.mean <- mean(x) print(result.mean)When we execute the above code, it produces the following result −

[1] 8.22

## Applying Trim Option

When trim parameter is supplied, the values in the vector get sorted and then the required numbers of observations are dropped from calculating the mean.When trim = 0.3, 3 values from each end will be dropped from the calculations to find mean.

In this case the sorted vector is (−21, −5, 2, 3, 4.2, 7, 8, 12, 18, 54) and the values removed from the vector for calculating mean are (−21,−5,2) from left and (12,18,54) from right.

# Create a vector. x <- c(12,7,3,4.2,18,2,54,-21,8,-5) # Find Mean. result.mean <- mean(x,trim = 0.3) print(result.mean)When we execute the above code, it produces the following result −

[1] 5.55

## Applying NA Option

If there are missing values, then the mean function returns NA.To drop the missing values from the calculation use na.rm = TRUE. which means remove the NA values.

# Create a vector. x <- c(12,7,3,4.2,18,2,54,-21,8,-5,NA) # Find mean. result.mean <- mean(x) print(result.mean) # Find mean dropping NA values. result.mean <- mean(x,na.rm = TRUE) print(result.mean)When we execute the above code, it produces the following result −

[1] NA [1] 8.22

## Median

The middle most value in a data series is called the median. The**median()**function is used in R to calculate this value.

### Syntax

The basic syntax for calculating median in R is −median(x, na.rm = FALSE)Following is the description of the parameters used −

**x**is the input vector.**na.rm**is used to remove the missing values from the input vector.

### Example

# Create the vector. x <- c(12,7,3,4.2,18,2,54,-21,8,-5) # Find the median. median.result <- median(x) print(median.result)When we execute the above code, it produces the following result −

[1] 5.6

## Mode

The mode is the value that has highest number of occurrences in a set of data. Unike mean and median, mode can have both numeric and character data.R does not have a standard in-built function to calculate mode. So we create a user function to calculate mode of a data set in R. This function takes the vector as input and gives the mode value as output.

### Example

# Create the function. getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } # Create the vector with numbers. v <- c(2,1,2,3,1,2,3,4,1,5,5,3,2,3) # Calculate the mode using the user function. result <- getmode(v) print(result) # Create the vector with characters. charv <- c("o","it","the","it","it") # Calculate the mode using the user function. result <- getmode(charv) print(result)When we execute the above code, it produces the following result −

[1] 2 [1] "it"

*Table of contents:*1. R - Overview

2. R - Environment Setup

3. R - Basic Syntax

4. R - Data Types

5. R - Variables

6. R - Operators

7. R - Decision Making

8. R - Loops

9. R - Functions

10. R - Strings

11. R - Vectors

12. R - Matrices

13. R - Arrays

14. R - Factors

15. R - Data Frames

16. R - Packages

17. R - Data Reshaping

18. R - CSV Files

19. R - Excel Files

20. R - Binary Files

21. R - XML Files

22. R - JSON Files

23. R - Web Data

24. R - Database

25. R - Pie Charts

26. R - Bar Charts

27. R - Boxplots

28. R - Histograms

29. R - Line Graphs

30. R - Scatterplots

31. R - Mean, Median and Mode

32. R - Linear Regression

33. R - Multiple Regression

34. R - Logistic Regression

35. R - Normal Distribution

36. R - Binomial Distribution

37. R - Poisson Regression

38. R - Analysis of Covariance

39. R - Time Series Analysis

40. R - Nonlinear Least Square

41. R - Decision Tree

42. R - Random Forest

43. R - Survival Analysis

44. R - Chi Square Tests

## No comments:

## Post a Comment