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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"```

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