Conditionals

Environment Setup: Just Base R

INSTRUCTOR NOTE: Code examples should generally build up by modifying the existing code example rather than by retyping the full example.

Conditionals

• Conditional statements are when we check to see if some condition is true or not
• We used these for filtering data in dplyr

weight > 50
species == "DM"

• These statements generate a value is of type "logical"
• The value is TRUE if the condition is satisfied
• The value is FALSE if the condition is not satisfied
• These aren’t the strings “TRUE” and “FALSE”

• They are a special type of value known as “boolean”

• Conditional statements are made with a range of operators
• We’ve seen
  • == for equals
  • != for not equals
  • <, > for less than and greater than
  • <=, >= for less than or equal to and greater than or equal to

10 >= 5

• There are others, including %in%, which checks to see if a value is present in a vector of possible values

"DM" %in% c("DM", "DO", "DS")
"PP" %in% c("DM", "DO", "DS")

• There are also functions that return these boolean
• is.na() for is this value null

is.na(NA)
is.na(5)

Reversing conditions

• Sometimes we want to the opposite of a condition
• We do this using ! which stands for “not”
• For normal conditional statements we also need ()

!(10 >= 5)

• This reverses the returned boolean value
• So it changes TRUE to FALSE and FALSE to TRUE
• If we want to ask if “PP” is not in a given species list

!("PP" %in% c("DM", "DO", "DS")

• So it is true that “PP” is not in the list
• Because if it is not in the list then the part inside () returns FALSE
• And the ! reverses this to TRUE

Combining conditional statements

• We can combine conditions using “and” and “or”
• We use the & for “and”
• Which means if both conditions are TRUE return TRUE
• If either or both of the conditions is FALSE then return FALSE

5 > 2 & 6 >=10

• We use the | for “or”
• Which means if either or both of the conditions are TRUE return TRUE

5 > 2 | 6 >=10

Conditional statements work on vectors

• Vectors of values compared to a single value return one logical per value

c(1, 1, 2, 3, 1) == 1

• Checks each value to see if equal to 1
• This is what subsetting approaches use to subset
• They keep the values where the value in this condition vector is equal to TRUE
• Let’s look at an example where we have a vector of counts and a vector the the states they occur in

states <- c('FL', 'FL', 'GA', 'SC')
counts <- c(9, 16, NA, 10)

• A conditional statement checking if the state is 'FL' returns a vector of TRUE’s and FALSEs

states == 'FL'

• So when we filter the counts vector to only return values where the states is equal to 'FL'

counts[states == 'FL']

• It is the same as pass a vector of TRUE and FALSE values inside the square brackets

counts[c(TRUE, TRUE, FALSE, FALSE)]

• This keeps the first and second values in counts because the values in the vector are TRUE

• This is how dplyr::filter() and other methods for subsetting data work

• This is why we can use !is.na() to filter out null values

is.na(count)
!is.na(count)
count[!is.na(count)]

Do Choice Operators.

if statements

• Conditional statements generate logical values
• if statements use conditional statements to control flow of the program

if (the conditional statement is TRUE ) {
  do something
}

• Example

x = 6
if (x > 5){
  x = x * 2
}
x

• x > 5 is TRUE, so the code in the if runs
• x is now 6 * 2 or 12
• Change x to 4

x = 4
if (x > 5){
  x = x * 2
}
x

• x > 5 is FALSE, so the code in the if doesn’t run
• x is still 4

• This is not a function, so everything that happens in the if statement influences the global environment

• Different mass calculations for different vegetation types

veg_type <- "shrub"
volume <- 16.08
if (veg_type == "shrub") {
  mass <- 2.65 * volume^0.9
  }
mass

Do Task 1 in Basic If Statements.

• Often want to chose one of several options
• Can add more conditions and associated actions with else if

veg_type <- "grass"
volume <- 16.08
if (veg_type == "shrub") {
  mass <- 2.65 * volume^0.9
} else if (veg_type == "grass") {
  mass <- 0.65 * volume^1.2
}
mass

• Checks the first condition
• If TRUE runs that condition’s code and skips the rest

• If not it checks the next one until it runs out of conditions

• Can specify what to do if none of the conditions is TRUE using else on its own

veg_type <- "tree"
volume <- 16.08
if (veg_type == "shrub") {
  mass <- 2.65 * volume^0.9
} else if (veg_type == "grass") {
  mass <- 0.65 * volume^1.2
} else {
  mass <- NA
}
mass

Do Tasks 2-3 in Basic If Statements.

Using Conditionals Inside Functions

• We’ve used a conditional to estimate mass differently for different types of vegetation
• This is the kind of code we are going to want to reuse, so let’s move it into a function
• We do this by placing the same code inside of a function
• And making sure that the function takes all required variables as input

est_mass <- function(volume, veg_type){
  if (veg_type == "shrub") {
    mass <- 2.65 * volume^0.9
  } else if (veg_type == "grass") {
    mass <- 0.65 * volume^1.2
  } else {
    mass <- NA
  }
  return(mass)
}

• We can then run this function with different vegetation types and get different estimates for mass

est_mass(1.6, "shrub")
est_mass(1.6, "grass")
est_mass(24, "tree")

• Let’s walk through how this code executes using the debugger
• When we call the function the first thing that happens is that 1.6 gets assigned to volume and "tree" gets assigned to veg_type
• The code then checks to see if veg_type is equal to "shrub"
• It isn’t so the code then checks to see if veg_type is equal to "grass"
• It isn’t so the code then hits the else statement and executes the code in the else block
• It assigns NA to mass
• It then finishes the if/else if/else statement and returns the value for mass, which is NA to the global environment

Do Tasks 4 in Basic If Statements.

Multiple ifs vs else if

• Multiple ifs check each conditional separately
• Executes code of all conditions that are TRUE

x <- 5
if (x > 2){
  x * 2
}
if (x > 4){
  x * 4
}
x

• else if checks each condition sequentially
• Executes code for the first condition that is TRUE

x <- 5
if (x > 2){
  x * 2
} else if (x > 4){
  x * 4
}
x

Automatically extracting functions

• Can pull code out into functions
• Highlight the code
• Code -> Extract Function
• Provide a name for the function

Nested conditionals

• Sometimes decisions are more complicated
• For example we might have different equations for some vegetation types based on the age of the plant
• Can “nest” conditionals inside of one another

est_mass <- function(volume, veg_type, age){
  if (veg_type == "shrub") {
    if (age < 5) {
      mass <- 1.6 * volume^0.8
    } else {
      mass <- 2.65 * volume^0.9
    }
  }
  } else if (veg_type == "grass" | veg_type == "sedge") {
    mass <- 0.65 * volume^1.2
  } else {
    mass <- NA
  }
  return(mass)
}

est_mass(1.6, "shrub", age = 2)
est_mass(1.6, "shrub", age = 6)

• First checks if the vegetation type is “shrub”
• If it is checks to see if it is < 5 years old
• If so does one calculation, if not does another
• But nesting can be difficult to follow so try to minimize it

Assign the rest of the exercises.