What is a function? A function is a rule that defines a relationship between one variable to another. Most of the codes defined in R are could be considered as functions. For example, let’s inspect library() function by running the below code. Again, if you want to find out more about a specific function and how it’s being defined, you could simply put ?name.of.the.function().
As you can see from the help pane, library() “load and attach add-on packages”. You could also notice that it takes multiple arguments. We will discuss what arguments mean but before that, we will investigate the structure of a function. ### Function structure A function is structured as below.
Notice that a function has input parameters, statements that are executed when it runs, and output of the function in the return statement. Parameters simply mean placeholders that are used inside the function body. Let’s look at the example.
Here, we are defining a function called fahrenheit_to_celsius that converts temperatures from Fahrenheit to Celsius. We are assigning the function itself as farenheit-to-celsius using <-. The list of parameters are contained within parenthesis, the body of the function is contained within curly braces, {}, and the output of the function is contained within parenthesis of the return. temp_F is considered an input parameter, temp_C within return() is considered an output. temp_C <- (temp_F - 32) * 5 / 9 is considered the body of the function or the statements that are executed when we call this function. Side note: In R, you don’t necessarily have to include the return statement to indicate the output of the function call. R automatically returns whichever is indicated on the last line of the body of the function. This implies that R will still return temp_C as the output of fahrenheit_to_celsius function even if you omit return(temp_C).
## function(temp_F) {
## temp_C <- (temp_F - 32) * 5 / 9
## return(temp_C)
## }You could see now the structure of this function by running fahrenheit_to_celsius like above. You could actually put a specific value as an input to this function. Suppose we want to know what temperature in Celsius corresponds to 100 degrees in Fahrenheit. You could put 100 as an argument to fahrenheit_to_celsius function. Arguments here could be considered specific values which are passed to the function. In this case, 100 corresponds to an argument to the function fahrenheit_to_celsius.
## [1] 37.77778What if the function you are using has multiple parameters? Argument order matters. The order of arguments supplied to a function matters. Look at the example below. Depending on how you supply the arguments, the output differs. By default, unless explicitly specifying the names of the parameters, R considers the first argument be the the argument for the first parameter, the second argument be the argument for the second parameter, and so on. You could however specify the name of the parameter and match it with the corresponding argument and change the order of the arguments.
print_fun=function(banana,apple){
print(paste0("hello ", banana, " ", apple))
}
# Below three lines return same outputs
print_fun("not","me") ## [1] "hello not me"## [1] "hello not me"## [1] "hello not me"## [1] "hello me not"The idea to show what a function is and how it is defined, was to give you a sense that all we are using from now on are these functions that are predefined by other programmers. We will get down to these functions further when we are learning tidyverse package. ## Vector In R, data is held in vectors. You could construct a vector by using c() function. c() is short for combine.
If you would like to create a vector containing four numeric values 1,2,3 and 4, simply put them all inside the parenthesis of c() function.
## [1] 1 2 3 4You could do all kinds of calculations on numeric vectors.
## [1] 3 5 7 9## [1] 2 6 12 20## [1] 0.5000000 0.6666667 0.7500000 0.8000000You could also have the vector nested in another function.
## [1] 1## [1] 4## [1] 10You could also create a vector that does random sampling using sample() function.
## [1] 1 0 1 0 1 0 0 0 1 1You could create a vector whose elements repeat several times.
## [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3You could also create a vector of logical values.
## [1] TRUE TRUE FALSE TRUEYou could also create a vector of character values.
## [1] "A" "B" "C" "D" "E"What happens if you create a vector containing both numeric and character values?
## [1] "1" "2" "3" "apple" "orange" "banana"You could notice from above that the vector now turns all the elements including the numeric values into character values. Character values are put in quotes. ### Index Index indicates the position of a specific element within a vector or a matrix or a dataset. A vector which is typically one dimension, has one index per element. For example, the index of “apple” from the above example is 4, the index of “orange” is 5, the index of “banana” is 6. Suppose you would want to select “apple” in the previously defined vector. You could simply put a specific numeric value that indicates the index of “apple” inside the square bracket.
## [1] "apple"Can we create a two dimensional vector? Yes. Here’s how we do it. ### Creating a matrix You could either bind rows or columns of multiple vectors with the same length like below.
## [,1] [,2]
## x 1 23
## y 2 24
## z 3 25## x y z
## [1,] 1 2 3
## [2,] 23 24 25Or you could use the matrix function.
## [,1] [,2]
## [1,] 1 23
## [2,] 2 24
## [3,] 3 25## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 23 24 25You could select a specific element in a matrix by specifying the coordinate of the element in a square bracket. On the left of the comma in a square bracket comes the row index and on the right of the comma in a square bracket comes the column index.
# If you'd like to select 2,
# you specify the coordinate of it which is the second row and the first column.
matrix(c(1,2,3,23,24,25), nrow=3, ncol=2)[2,1]## [1] 2# If you'd like to select 25,
# you specify the coordinate of it which is the third row and the second column.
matrix(c(1,2,3,23,24,25), nrow=3, ncol=2)[3,2]## [1] 25You could create a dataset using tibble() function. You could consider a tibble as binding columns of named vectors. Two rules to follow when using tibble(): 1) each column should be named, and 2) each column should have the same number of rows. If the number of rows does not match across columns, tibble will either recycle the values until all columns have the same number of rows or it will throw an error. Try using tibble() function yourself.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Below codes will create two columns, x and y each of which will have four rows.
tibble(
x = c(1,2,3,4),
y = c(10,11,12,13)
)## # A tibble: 4 × 2
## x y
## <dbl> <dbl>
## 1 1 10
## 2 2 11
## 3 3 12
## 4 4 13Suppose we would like to build our own dataset that shows monthly average high temperature in Eugene in Fahrenheit and in Celsius from September to December. We could construct the dataset by having three columns each denoting month, avg_temp_F_eugene, and avg_temp_C_eugene. At this point, suppose the avg temperatures in Eugene in Fahrenheit are given (Sep: 77, Oct: 65, Nov: 53, Dec: 46). We could also use fahrenheit_to_celsius() function we’ve created to convert the temperatures from Fahrenheit to Celsius.
tibble(
month=c(9,10,11,12),
avg_temp_F_eugene=c(77,65,53,46),
avg_temp_C_eugene=fahrenheit_to_celsius(avg_temp_F_eugene),
)## # A tibble: 4 × 3
## month avg_temp_F_eugene avg_temp_C_eugene
## <dbl> <dbl> <dbl>
## 1 9 77 25
## 2 10 65 18.3
## 3 11 53 11.7
## 4 12 46 7.78Please open up the 02-Exercise.R and fill out your answer for each question.
For more information about the lab material covered this week, see below links.