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R - Mean, Median & Mode

Statistical analysis in R is performed by using many in-built functions. Most of these functions are part of the R base package. These functions take R vector as an input along with the arguments and give the result.
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

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