Repetition

• Computers are great at doing things repeatedly
• We’ve learned to use functions to find mass for one volume

est_mass <- function(volume){
  mass <- 2.65 * volume^0.9
  return(mass)
}

est_mass(1.6)

• Easy to find mass for other volumes

est_mass(5.6)
est_mass(3.1)

• But, this is tedious, error-prone, and impossible for large n
• Multiple ways to do something repeatedly in R
  • Vectorize
  • Apply/Map
  • dplyr + functions
  • Loop

Vectorize

• Write functions that take a vector of values, do elementwise calculations, and return a vector of the results
• Any function that only uses calculations that are vectorized
• E.g., vector math
• Our current function already works on a vector

est_mass <- function(volume){
  mass <- 2.65 * volume ^ 0.9
  return(mass)
}

volumes = c(1.6, 5.6, 3.1)
est_mass(volumes)

• Many functions in R are vectorized

library(stringr)

str_to_upper("tree")
plant_types <- c("tree", "grass", "tree")
str_to_upper(plant_types)

• Work on vectors or lists (sometimes columns of data frames)

plant_data <- data.frame(volumes, plant_types)
str_to_upper(plant_data["plant_types"])
str_to_upper(plant_data$plant_types)
plant_data$veg_type_upper = toupper(plant_data$plant_types)

Do Vectorized Genus Extraction.

Apply/Map functions

• Use apply() and map() functions
• Take a function
• Apply it to each item in a list of items
• Return a list of the same size
• Doesn’t require calculations to work on vectors

sapply & lapply

• Work on a single vector or list

sapply(X = volumes, FUN = est_mass)

• Same as

c(est_mass(volumes[1]), est_mass(volumes[2]), est_mass(volumes[3]))

• Do same action on many things with single line of code

• The s in sapply stands for “simplify”
• It will try to return the simplest object possible, in this case a vector
• lapply returns a “list”

lapply(X = volumes, FUN = est_mass)

Do Vectorized Genus Extraction.

Other apply functions

• Handful of similar functions in apply() family

• Differ depending on type of input and output data

• apply() works on multi-dimensional data
• Set MARGIN to tell it which dimension to calculate along
• 1 for rows
• 2 for columns

counts = data.frame(sp1 = c(5, 4, 7, 6), sp2 = c(6, 2, 6, 9), sp3 = c(8, 16, 1, 0))
counts
apply(X = counts, MARGIN = 1, FUN = sum)
apply(X = counts, MARGIN = 2, FUN = sum)

• mapply() for functions with multiple arguments
• Vegetation type specific equations

est_mass_type <- function(volume, veg_type){
  if (veg_type == "tree"){
    mass <- 2.65 * volume^0.9
  } else {
    mass <- NA
  }
  return(mass)
}

est_mass_type(1.6, "tree")
est_mass_type(volumes, plant_types) # Warning & wrong result

• Doesn’t vectorize, due to conditionals
• Use an apply function instead
• mapply() because “multiple” inputs

mapply(FUN = est_mass_type, volume = volumes, veg_type = plant_types)

• First argument is function
• All other arguments are named arguments for the function

Do Use and Modify with Apply.

• map functions from purrr package are similar to apply

Integrating with dplyr

• Remember our data frame

plant_data

• Directly use vectorized functions with mutate

mutate(plant_data, masses = est_mass(volumes))

• Use apply functions and add the results as a new column

masses = mapply(est_mass_type,
                volume = plant_data$volumes,
                veg_type = plant_data$plant_types)
plant_data$masess = masses

• Use rowwise

plant_data %>%
  rowwise() %>%
  mutate(masses = est_mass_type(volumes, plant_types))

Do Crown Volume Calculation.

• Custom summarizing functions also work with dplyr
• Need to take a vector as input and return a single value as output