## Absolute frequentie in R

The absolute frequentie shows how many times a meting will occur. The first row will show the data and the second row shows the frequency. Note: Do not forget to load the discoveries dataset.

- table(discoveries) # Loads the frequentie table from the discoveries dataset

## Mode in R

There is no easy function to calculate the mode in R so we will use a combination of functions to calculate it.

- sort(table(discoveries)) # Sorts from low to high
- sort(table(discoveries), TRUE) # Sorts from high to low with the TRUE parameter
- names(sort(table(discoveries),TRUE))[1] # Only show the name from the first element.

You may want to write your own function to calculate the mode in R:

- modus = function (x) { as.numeric(names(sort(table(x),TRUE))[1]) } # Function to calculate the modus. Note that this function will not work for all data

## Relative frequency in R

Showing the relative frequency in R is easy. Note that if you multiply this number by 100 you will get the percentage:

- table(discoveries) / length(discoveries) # Calculates the relative frequency

## Cumulative frequencies in R

There is the cumulative absolute frequency and the cumulative relative frequency. Calculating them is easy:

- cumsum(table(discoveries)) # Gives the cumulative absolute frequency
- cumsum(table(discoveries) / length(discoveries)) # Gives the cumulative relative frequency

## Frequency distributions in R

For frequency distributions in R you will have to set your data to a table in order to calculate the frequencies. How to do this in R:

- normdata = rnorm(10000) # Generates 10000 random numbers. This will be explained in the following tutorials
- cut(normdata, breaks = -6:6) # Cut puts every number in the correct category. Breaks makes the categories.
- table(cut(normdata, breaks = -6:6)) # Places the data in a table