![ x <- 41
if (x > 10) {
print("Above ten")
if (x > 20) {
print("and also above 20!")
} else {
print("but not above 20.")
}
} else {
print("below 10.")
}
[1] "Above ten"
[1] "and also above 20!"](https://image.slidesharecdn.com/flowcontrols-240614031857-d265ebfd-240615025910-bc35f1c6/85/ac99-net-5-320.jpg)
![Passing Functions to and from Other
Functions
Functions can be used just like other variable
types, so we can pass them as arguments to other
functions, and return them from functions.
One common example of a function that takes
another function as an argument is do.call.
do.call(function(x, y) x + y, list(1:5, 5:1))
## [1] 6 6 6 6 6](https://image.slidesharecdn.com/flowcontrols-240614031857-d265ebfd-240615025910-bc35f1c6/85/ac99-net-13-320.jpg)
![ So, even though y isn’t defined inside g, the example works:
f <- function(x)
{
y <- 1
g <- function(x)
{
(x + y) / 2 #y is used, but is not a formal argument of g }
g(x)
}
f(sqrt(5)) #It works! y is magically found in the environment of f
## [1] 1.618](https://image.slidesharecdn.com/flowcontrols-240614031857-d265ebfd-240615025910-bc35f1c6/85/ac99-net-16-320.jpg)
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- 2. If and else The simplest form of flow control is conditional execution using if. if takes a logical value (more precisely, a logical vector of length one) and executes the next statement only if that value is TRUE: if(TRUE) message("It was true!") ## It was true! if(FALSE) message("It wasn't true!") Missing values aren’t allowed to be passed to if; doing so throws an error: if(NA) message("Who knows if it was true?") ## Error: missing value where TRUE/FALSE needed Where you may have a missing value, you should test for it using is.na: if(is.na(NA)) message("The value is missing!") ## The value is missing!
- 3. a<-3 b<-4 if(a<b) message("B is greater")
- 4. a <- 33 b <- 33 if (b > a) { print("b is greater than a") } else if (a == b) { print ("a and b are equal") } a <- 200 b <- 33 if (b > a) { print("b is greater than a") } else if (a == b) { print("a and b are equal") } else { print("a is greater than b") }
- 5. x <- 41 if (x > 10) { print("Above ten") if (x > 20) { print("and also above 20!") } else { print("but not above 20.") } } else { print("below 10.") } [1] "Above ten" [1] "and also above 20!"
- 6. x<-5 if(is.nan(x)) { message("x is missing") } else if(is.infinite(x)) { message("x is infinite") } else if(x > 0) { message("x is positive") } else if(x < 0) { message("x is negative") } else { message("x is zero") }
- 7. Loop There are three kinds of loops in R: Repeat While, and for they can still come in handy for repeatedly executing code Repeat: i<-0 repeat { print(i) i<-i+1 if(i>=3) break }
- 8. While i<-1 While (i<=4) { i<-i+1 print(i) } Output: 2 3 4 5
- 9. For loop for(i in seq(1,10,2)) { print(i) } i<-1 for(j in 1:3) { i<-i+1 print(i) } Output: 2 3 4
- 10. Functions A function is a block of code which only runs when it is called. You can pass data, known as parameters, into a function. A function can return data as a result.
- 11. Creating and Calling Function in R In order to understand functions better, let’s take a look at what they consist of. Typing the name of a function shows you the code that runs when you call it. The terms "parameter" and "argument" can be used for the same thing: information that are passed into a function. From a function's perspective: A parameter is the variable listed inside the parentheses in the function definition. An argument is the value that is sent to the function when it is called.
- 12. Example Sample<-function(a,b,c) { print(a) print(b) print(c) print(a+b+c) } Sample(2,3,4)
- 13. Passing Functions to and from Other Functions Functions can be used just like other variable types, so we can pass them as arguments to other functions, and return them from functions. One common example of a function that takes another function as an argument is do.call. do.call(function(x, y) x + y, list(1:5, 5:1)) ## [1] 6 6 6 6 6
- 14. do.call() #create three data frames df1 <- data.frame(team=c('A', 'B', 'C'), points=c(22, 27, 38)) df2 <- data.frame(team=c('D', 'E', 'F'), points=c(22, 14, 20)) df3 <- data.frame(team=c('G', 'H', 'I'), points=c(11, 15, 18)) #place three data frames into list df_list <- list(df1, df2, df3) #row bind together all three data frames do.call(rbind, df_list)
- 15. Variable Scope A variable’s scope is the set of places from which you can see the variable. For example, when you define a variable inside a function, the rest of the statements in that function will have access to that variable. In R subfunctions will also have access to that variable. In this next example, the function f takes a variable x and passes it to the function g. f also defines a variable y, which is within the scope of g, since g is a sub‐ function of f.
- 16. So, even though y isn’t defined inside g, the example works: f <- function(x) { y <- 1 g <- function(x) { (x + y) / 2 #y is used, but is not a formal argument of g } g(x) } f(sqrt(5)) #It works! y is magically found in the environment of f ## [1] 1.618
- 17. String Manipulation String manipulation basically refers to the process of handling and analyzing strings. It involves various operations concerned with modification and parsing of strings to use and change its data. Paste: str <- paste(c(1:3), "4", sep = ":") print (str) ## "1:4" "2:4" "3:4" Concatenation: # Concatenation using cat() function str <- cat("learn", "code", "tech", sep = ":") print (str) ## learn:code:tech
- 19. Loading and Packages R is not limited to the code provided by the R Core Team. It is very much a community effort, and there are thousands of add-on packages available to extend it. The majority of R packages are currently installed in an online repository called CRAN (the Comprehensive R Archive Network1) which is maintained by the R Core Team. Installing and using these add-on packages is an important part of the R experience
- 20. Loading Packages To load a package that is already installed on your machine, you call the library function We can load it with the library function: library(lattice) the functions provided by lattice. For example, displays a fancy dot plot of the famous Immer’s barley dataset: dotplot( variety ~ yield | site, data = barley, groups = year )
- 21. Scatter Plot A "scatter plot" is a type of plot used to display the relationship between two numerical variables, and plots one dot for each observation. It needs two vectors of same length, one for the x-axis (horizontal) and one for the y-axis (vertical): Example x <- c(5,7,8,7,2,2,9,4,11,12,9,6) y <- c(99,86,87,88,111,103,87,94,78,77,85,86) plot(x, y)
- 24. Box_plot() ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) + geom_boxplot()
- 25. Geom_bar() g <- ggplot(mpg, aes(class)) # Number of cars in each class: g + geom_bar()