# 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.
• r programming normal distribution

• This is referred as normal distribution in statistics.
• R has four in built functions to generate 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. 