Apply functions allow executing a function repeatedly on each row, column, or element of a matrix, data frame, or list without using loops. Common apply functions include sapply(), lapply(), apply(), mapply(), and tapply(). Apply functions provide a more efficient way to perform operations across data compared to using loops. tapply() allows breaking a vector into pieces and applying a function to each piece, similar to sapply() but allowing customization of how the breakdown is done.

1. Apply functions The Dataminingtools.net Team

2. Apply functions Apply functions are used to execute a function repetitively. "Apply" functions keeps us from having to write loops to perform some operation on every row or every column of a matrix or data frame, or on every element in a list.

4. tapply()

6. Usage Using ‘apply’ > apply (state.x77, 2, median) Population Income Illiteracy Life Exp Murder 2838.500 4519.000 0.950 70.675 6.850 HS Grad Frost Area 53.250 114.500 54277.000 The 2 means "go by column" -- a 1 would have meant "go by row."

7. Usage We construct a function and pass it to apply. It computes the median and maximum of each column of state.x77.

8. Usage apply() works on each row, one at a time, to find the smallest number in each row. which() function, returns the indices within a vector for which the vector holds the value TRUE

9. lapplyand sapply The lapply() function works on any list. The "l" in "lapply" stands for "list." The "s" in "sapply" stands for "simplify."

10. tapply tapply() is a very powerful function that lets us break a vector into pieces and apply some function to each of the pieces. It is like sapply(), except that with sapply() the pieces are always elements of a list. With tapply() we get to specify how the breakdown is done. >tapply(barley$yield, barley$site, mean) Grand Rapids Duluth University Farm Morris Crookston Waseca 24.93167 27.99667 32.66667 35.4 37.42 48.10833