# R Normal Distribution - r - learn r - r programming

- In a random collection of data from independent sources, it is generally observed that the distribution of data is normal.
- Which means, on plotting a graph with the value of the variable in the horizontal axis and the count of the values in the vertical axis we get a bell shape curve.
- The center of the curve represents the mean of the data set.
- In the graph, fifty percent of values lie to the left of the mean and the other fifty percent lie to the right of the graph.
- This is referred as
**normal distribution in statistics.** - R has four in built functions to generate normal distribution.

r programming normal distribution

- Following is the description of the parameters used in above functions −
**x**is a vector of numbers.**p**is a vector of probabilities.**n**is number of observations (sample size).**mean**is the mean value of the sample data. It's default value is zero.**sd**is the standard deviation. It's default value is 1.

## dnorm()

- This function gives height of the probability distribution at each point for a given mean and standard deviation.

- When we execute the above code, it produces the following result −

## pnorm()

- This function gives the probability of a normally distributed random number to be less that the value of a given number.
- It is also called
**"Cumulative Distribution Function".**

- When we execute the above code, it produces the following result −

## qnorm()

- This function takes the probability value and gives a number whose cumulative value matches the probability value.

- When we execute the above code, it produces the following result −

## rnorm()

- This function is used to generate random numbers whose distribution is normal. It takes the sample size as input and generates that many random numbers. We draw a histogram to show the distribution of the generated numbers.