for loops

Apply functions

sapply

Use function to find mass for one volume

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

shrub_vol1 <- 1.6
est_mass(shrub_vol1)

Easy to find mass for other volumes

shrub_vol2 <- 5.6
est_mass(shrub_vol2)

shrub_vol3 <- 3.1
est_mass(shrub_vol3)

Typing this to get each shrub’s volume is tedious and error-prone
Use apply()-type functions instead

shrub_vols <- c(1.6, 5.6, 3.1)
sapply(shrub_vols, est_mass)

Do same action on many things with single line of code!
Easily scales up

Do Task 1 of Use and Modify with Apply.

Other apply functions

Handful of similar functions in apply() family
Differ depending on type of input and output data
lapply()like sapply() but returns list

lapply(shrub_vols, est_mass)

apply() works on multi-dimensional data
mapply() for functions with multiple arguments

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")
plant_vols <- c(1.6, 3, 8)
plant_types <- c("tree", "grass", "tree")
mapply(est_mass_type, volume = plant_vols, veg_type = plant_types)

First argument is function, rest are function arguments

Do Task 2 of Use and Modify with Apply.

tidyverse version of apply

Use map function from purrr package
Similar to apply

library(purrr)
map(plant_vols, est_mass)

Use with pipes

library(dplyr)
plant_vols_df = data.frame(vols = plant_vols)
plant_vols_df %>% 
  filter(vols > 2) %>% 
  map(est_mass)

Do Crown Volume Calculation.

Set up R console:

library(stringr)
library(dplyr)

Basic `for` loop

Do same action to each component of a list

waterbirds <- c("cygnus olor", "aix sponsa", "anas acuta")
waterbird <- waterbirds[1]
print(waterbird)
waterbird <- waterbirds[2]
print(waterbird)
waterbird <- waterbirds[3]
print(waterbird)

This is tedious
Use for loop to do same action repeatedly
Easier & fewer errors

for (item in list_of_items) {
  do_something(item)
}

Need print() to display values inside a loop, function, or conditional.

for (waterbird in waterbirds){
  print(waterbird)
}

Do more actions

for (waterbird in waterbirds){
  waterbird_cap <- str_to_title(waterbird)
  print(waterbird_cap)
}

Do Basic Vector.

Numeric values in `for` loops

Do functions or math as actions within for loops
Variable can be given any name, then refer to with that name in loop

for (num in 100:150){
  print(num * 10)
}

Use paste() to put together strings and variables

for (num in 100:150){
  print(paste("My favorite number is", num * 10))
}

Do Basic Index tasks 1-2.

Storing results

Create an empty object.

output <- c()

Iteratively add new values to object.

output <- c(1, 2, 3)
output <- c(output, 4)

Use this method within a for loop to save outputs.

waterbirds_cap_list <- c()
for (waterbird in waterbirds){
  waterbird_cap <- str_to_title(waterbird)
  waterbirds_cap_list <- c(waterbirds_cap_list, waterbird_cap)
  print(waterbirds_cap_list)
}
waterbirds_cap_list

Do Basic Index task 3.

Looping in data frames

Loops go over columns of dataframes

waterbirds <- data.frame(sci_name = c("cygnus olor", 
                                      "aix sponsa", 
                                      "anas acuta"), 
                         common_name = c("mute swan", 
                                         "wood duck", 
                                         "pintail"))
for (waterbird in waterbirds){
  print("Start new loop")
  print(waterbird)
}

Can loop over rows of data frames using index

for (i in 1:nrow(waterbirds)){
  print(i)
}

Index can be any letter/word, i is convention

for (r in 1:nrow(waterbirds)){
  print(r)
}

for (i in 1:nrow(waterbirds)){
  print(waterbirds$sci_name[i])
}

for (i in 1:nrow(waterbirds)){
  print(paste(waterbirds$sci_name[i], "is a", 
              waterbirds$common_name[i]))
}

Less memory to create initial empty dataframe
Creates copy of dataframe when adding rows

waterbirds_2 <- data.frame(capital_name = character(3), 
                           name_length = numeric(3), 
                           stringsAsFactors = FALSE)
for (i in 1:nrow(waterbirds)){
  common_name_cap <- str_to_title(waterbirds$common_name[i])
  sci_name_length <- str_length(waterbirds$sci_name[i])
  waterbirds_2[i,] <- c(common_name_cap, sci_name_length)
}

Do stringr.

Looping over files

Repeat same actions on many similar files
Get names of satellite collar location files

download.file("http://www.datacarpentry.org/semester-biology/data/collar-data-2016-01.zip", 
              "collar_data.zip")
unzip("collar_data.zip")
collar_data_files = list.files(pattern = "collar-data-.*.txt", 
                               full.names = TRUE)

Look at one of the files
Three ways to do same thing: get number of samples in each file

With loop

 numbers_vector_1 <- c()
 for (data_file in collar_data_files){
   file <- read.csv(data_file)
   number <- nrow(file)
   numbers_vector_1 <- c(numbers_vector_1, number)
 }

With function and loop

 get_numbers <- function(data_file_name){
   file <- read.csv(data_file_name)
   number <- nrow(file)
   return(number)
 }
	
 numbers_vector_2 <- c()
 for (data_file in collar_data_files){
   numbers_vector_2 <- c(numbers_vector_2, get_numbers(data_file))
 }

With function and apply

numbers_vector_3 <- unlist(lapply(collar_data_files, get_numbers))

How to choose when there are many ways to do the same thing?
- Speed
  - Matters in few cases
  - Hard to identify bottlenecks
- Readability
  - Easy to understand
- Personal preference
There is no “right” way to do anything

Do Multiple Files.

Do Species Occurrences Elevation Histogram.

Data Science for SAFS

for loops

Notes

Apply functions

sapply

Other apply functions

tidyverse version of apply

For loops

Basic for loop

Numeric values in for loops

Storing results

Looping in data frames

Looping over files

Basic `for` loop

Numeric values in `for` loops