The ‘switch’ function in R is useful when you have more than two choices based on a condition. If you remember ‘if’ and ‘ifelse’ are useful when there are two choices for a condition. However, using if/else statements for multiple choices is possible but somewhat confusing. Thus the ‘switch’ function is available for simplicity.

One problem with the ‘switch’ function is that you can not use vectors as the input to the function. This means that you have to manually calculate the value for each data point yourself. There is a way around this but that is the topic of a future post. For now, we will look at how to use the ‘switch’ function.

Switch Function

The last time we discussed R Programming we had setup the “CashDonate” to calculate how much James would give for dollars per point and how much the owner would much James based on whether it was a home or away game. Below is the code.

CashDonate <- function(points, HomeGame=TRUE){
 JamesDonate<- points * ifelse(points > 100, 30, 40)
 totalDonation<- JamesDonate * ifelse(HomeGame, 1.5, 1.3)
 round(totalDonation)
}

Now the team will have several international games outside the country. For these games, the owner will double whatever James donates. We now have three choices in our code.

• Home game multiple by 1.5
• Away game multiple by 1.3
• International game multiple by 2

It is possible to use the ‘if/else’ function but it is complicated to code, especially for beginners. Instead, we will use the ‘switch’ function which allows R to switch between multiple options within the argument. Below is the new code.

CashDonate <- function(points, GameType){
 JamesDonate<- points * ifelse(points > 100, 30, 40)
 OwnerRate<- switch(GameType, Home = 1.5, Away = 1.3, International = 2)
 totalDonation<-JamesDonate * OwnerRate
 round(totalDonation)
}

Here is what the code does

1. The function ‘CashDonate’ has the arguments ‘points’ and ‘Gametype’
2. ‘JamesDonate’ is ‘points’ multiply by 30 if more than 100 points are scored or 40 if less than 100 points are scored
3. ‘OwnerRate’ is new as it uses the ‘switch’ function. If the ‘GameType’ is ‘Home’ the amount from ‘JamesDonate’ is multiplied by 1.5, ‘Away’ is multiplied by 1.3 and ‘International’ is multiplied by 2. The results of this is inputted into ‘totalDonation’
4. Lastly, the results in ‘totalDonation’ are rounded using the ’round’ function.

Since there are three choices for ‘GameType’ we use the switch function for this. You can input different values into the modified ‘CashDonate’ function. Below are several examples

> CashDonate(88, GameType="Away")
[1] 4576

> CashDonate(130, GameType="International")
[1] 7800

> CashDonate(102, GameType="Home")
[1] 4590

The next time we discuss R programming, I will explain how to use overcome the problem of inputting each value into the function manually.

In a previous post, we looked at If/Else statements in R. We developed a function that calculated the amount of money James and the owner would give based on how many points the team scored. Below is a copy of this function.

CashDonate <- function(points, Dollars_per_point=40, HomeGame=TRUE){
 game.points<- points * Dollars_per_point  if(points > 100) {game.points <-points * 30}
 if(HomeGame) {Total.Donation <- game.points * 1.5
 } else {Total.Donation <- game.points * 1.3}
 round(Total.Donation)
}

There is one small problem with this function. Currently, you have to input each game one at a time. You can have R calculate the results of several games at once. For example, look at the results of the code below when we try to input more than one game at once.

> CashDonate(c(99,100,78))
[1] 5940 6000 4680
Warning message:
In if (points > 100) { :
  the condition has length > 1 and only the first element will be used

As you can see, we get a warning message and some of the values are wrong. For example, the second value should be 4,500 and not 6,000.

In order to deal with this problem, R has the ‘ifelse’ function available. The ‘ifelse’ allows R to choose values in two or more vectors to complete an action. We need to be able to choose the appropriate action based on the following information

• points scored is less than or greater than 100
• Home game or not a home game

Remember, R could do this if one value was put into the ‘CashDonate’ function. Now we need to be able to calculate what to do based on several values in each of the vectors above. Below is the modified code for doing this.

CashDonate <- function(points, HomeGame=TRUE){
 JamesDonate<- points * ifelse(points > 100, 30, 40)
 totalDonation<- JamesDonate * ifelse(HomeGame, 1.5, 1.3)
 round(totalDonation)
}

Here is what the modified function does

1. It has the argument ‘points’ and the default argument of ‘HomeGame = TRUE’
2. The first calculation is the number of points but with the ‘ifelse’ function. If the number of points is greater than 100 the points is multiplied by 30 if less than 100 than the points are multiplied by 40. All this is put in the variable ‘JamesDonate’
3. Next, the amount from ‘JamesDonate’ is multiplied by 1.5 if it was a home game or 1.3 if it was not a home game. All this is put into the variable ‘totalDonation’
4. The results are rounded

To use CashDonate to its full potential you need to make a dataframe. Below is the code for the ‘games’ data frame we will use.

games<- data.frame(game.points=c(88,100,99,111,96), HomeGame=c(TRUE, FALSE, FALSE, TRUE, FALSE))

In the ‘games’ data frame we have two columns, one for game points and another that tells us if it was a home game or not. Now we will use the ‘games’ data frame with the new ‘CashDonate’ and calculate the results. We need to use the ‘with’ function to do this. This function will be explained at a later date. Below are the results.

> with(games, CashDonate(game.points, HomeGame=HomeGame))
[1] 5280 5200 5148 4995 4992

You can calculate this manually if you would like. Now, we can calculate more than one value in are ‘CashDonate’ function which makes it much more useful than before. All thanks to the use of the ‘ifelse’ function in the code.

Previously, we learned how to add nameless arguments to a function using ellipses ‘. . .’.  In this post, we will learn how to use functions as arguments in functions. An argument is the information found within parentheses ( ) or braces { } in R programming. The reason for doing this is that it allows for many shortcuts in coding. Instead of having to retype the formula for something you can pass the function as an argument in order to save a lot of time.

Below is the code that we have been working with for awhile before we add a function as an argument.

Percent_Divided <- function(x, divide = 2, ...) {
 ToPercent <- round(x/divide, ...)
 Output <- paste(ToPercent, "%", sep = "")
 return(Output)
 }

As a reminder, the ‘Percent_Divided’ function takes a number or variable ‘x’ divides it by two as a default and adds a ‘ % ‘ sign after number(s). With the ‘. . .’ you can pass other arguments such as ‘digits’ and specify how many number you want after the decimal.  Below is an example of the ‘Percent_Divided’ function in action for a variable called ‘B’ and with the add argument of ‘digits =3’

> B
[1] 23.35345 45.56456 32.12131
> Percent_Divided(B, digits = 3)
[1] "11.677%" "22.782%" "16.061%"

Functions as Arguments

We will now make a function that has a function for an argument. We will set a default function but remember that anything can be passed for the function argument. Below is the code for one way to do this.

Percent_Divided <- function(x, divide = 2,FUN_ARG = round, ...) {
 ToPercent <- FUN_ARG(x/divide, ...)
 Output <- paste(ToPercent, "%", sep = "")
 return(Output)
}

Here is an explanation

1. Most of this script is the same. The main difference is that we added the argument ‘FUN_ARG’ to the first line of the script. This is the place where we can insert whatever function we want. The default function is ’round’. If we do not specify any function ’round’ will be used.
2. In the second line of the code you again see ‘FUN_ARG’ this function will be activated after ‘x’ is divided by 2 and whatever arguments are used with the ‘. . .’
3. The rest of the code has already been explained and has not been changed
4. Important note. If we do not change the default of ‘FUN_ARG’, which is the ’round’ function, we will keep getting the same answers as always. The ‘FUN_ARG’ is only interesting if we do not use the default.

Below is an example of our modified function. The function we are going to pass through the ‘FUN_ARG’ argument is ‘signif’. ‘signif’ sounds the values in its first argument to the specified number of significant digits. We will also pass the argument ‘digits = 3’ through the ellipses ‘. . .’ The values of variable B (see above) will be used for the function

> Percent_Divided(B, FUN_ARG = signif, digits = 3)
[1] "11.7%" "22.8%" "16.1%"

Here is what happen

1. The Percent_Divided’ function was run
2. The function ‘signif’ was passed through the ‘FUN_ARG’ argument and the argument ‘digits = 3’ was passed through the ellipses ‘. . .’ argument.
3. All the values for B were transformed based on the script in the ‘Percent_Divided’ function

Conclusion

Using functions as argument is mostly for saving time when developing a code. Even though this seems complicated this is actually rudimentary programming.

In this post, we will continue the discussion on working with functions in R. Functions serve the purpose of programming R to execute several operations at once. This post, we will look at adding additional arguments to a function.

Arguments are the various entries within the parentheses. For example, in are example below the arguments of the function is x.

• MakePercent <- function(x) {
   ToPercent <- round(x, digits = 2)
   Output <- paste(ToPercent, "%", sep = "")
   return(Output)
  }

In the example above there are many other arguments beside x. However, the only argument for the function is x. The other arguments in other parentheses belong to  other objects in the script. In this post, we are going to learn how to add additional arguments to the function.

Let’s say that we want to convert a number to a percentage like a previous function we made but we now want to be able to divide the number by whatever we want. Here is how it could be done.

Percent_Divided <- function(x, divide) {
 ToPercent <- round(x/divide, digits = 2)
 Output <- paste(ToPercent, "%", sep = "")
 return(Output)
}

Here is what we did

1. We created the object ‘Percent_Divided’ and assigned the function with the arguments ‘x’ and ‘divide’
2. Next we use a { and we create the variable ‘ToPercent’ and we assigned the function ’round’  to round ‘x’ divided by whatever value ‘divide’ from the function takes. We then round the results of this two digits.
3. The results of ‘ToPercent’ are then assigned to the variable ‘Output’ where a ‘ %’ sign is assigned to the value
4. Lastly, the results of ‘Output’ are printed in the console.

Sounds simple. Below is the function in action dividing a number by 2 and then by 3

> source('~/.active-rstudio-document', echo=TRUE)

> Percent_Divided <- function(x, divide) {
+         ToPercent <- round(x/divide, digits = 2)
+         Output <- paste(ToPercent, "%", sep = "")
+    .... [TRUNCATED] 
> Percent_Divided(22.12234566, divide=2)
[1] "11.06%"
> Percent_Divided(22.12234566, divide=3)
[1] "7.37%"

Here is what happen

1. You source the script from the source editor by typing ctrl + shift + enter
2. Next, I used the function ‘Percent_Divided’ with the number 22.12234566 and I decided to divide the number by two
3. R returns the answer 11.06%
4. Next I repeat the process but I divide by 3 this time
5. R returns the answer 7.37%

There is one problem. The argument ‘divide’ has no default  value. What this means is that you have to tell R what the value of ‘divide’ is every single time. As an example see below

> Percent_Divided(22.12234566)
Error in Percent_Divided(22.12234566) : 
  argument "divide" is missing, with no default

Because I did not tell R what value ‘divide’ would be, R was not able to complete the process of the function. To solve this problem we will set the default value of ‘divide’ to 10 in the script as shown below.

Percent_Divided <- function(x, divide = 10) {
 ToPercent <- round(x/divide, digits = 2)
 Output <- paste(ToPercent, "%", sep = "")
 return(Output)
}

If you look closely you will see ‘divide = 10’. This is the default value for ‘divide’ if we do not set another number for ‘divide’ R will use 10. Below is an example using the default value of ‘divide’ and another example with ‘divide’ set to 5.

> Percent_Divided <- function(x, divide = 10) {
+         ToPercent <- round(x/divide, digits = 2)
+         Output <- paste(ToPercent, "%", sep = "") .... [TRUNCATED] 
> Percent_Divided(22.12234566)
[1] "2.21%"
> Percent_Divided(22.12234566, divide = 5)
[1] "4.42%"

First we sourced the script using ctrl + shift + enter. In the first example, the number is automatically divided by 10 because this is the default. In the second example, we specific we wanted to divide by five by adding the argument ‘divide = 5’. You can see the difference in the results.

In a future post, we will continue to examine the role of arguments in functions.