For Loops in R

Basic `for` loop

Fundamental structure for repetition in programming
Do same action to each item in a list of things

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

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

volumes = c(1.6, 3, 8)
for (volume in volumes){
  print(2.65 * volume^0.9)
}

This does the same exact thing as

volume <- volumes[1]
print(2.65 * volume ^ 0.9)
volume <- volumes[2]
print(2.65 * volume ^ 0.9)
volume <- volumes[3]
print(2.65 * volume ^ 0.9)

Can have many rows in a loop body

for (volume in volumes){
   mass <- 2.65 * volume ^ 0.9
   mass_lb <- mass * 2.2
   print(mass_lb)
}

Do Tasks 1 & 2 in Basic For Loops.

Looping with an index & storing results

Loops over integers and uses these integers to access the vector the associated positions

for (i in 1:length(volumes)){
   mass <- 2.65 * volumes[i] ^ 0.9
   print(mass)
}

Use this “index” to get the values at that position
Can use the “index” for multiple vectors
Looping with an index allows us to store results calculated in the loop
First create an empty vector the length of the results
mode is the type of data we are going to store
length is the length of the vector

masses <- vector(mode = "numeric", length = length(volumes))
masses

Then add each result in the right position
For each trip through the loop put the output into the empty vector at the ith position

for (i in 1:length(volumes)){
   mass <- 2.65 * volumes[i] ^ 0.9
   masses[i] <- mass
}
masses

Walk through iteration in debugger

Do Tasks 3-4 in Basic For Loops.

Looping with an index also allows us to access values from multiple vectors

b0 <- c(2.65, 1.28, 3.29)
b1 <- c(0.9, 1.1, 1.2)
masses <- vector(mode="numeric", length=length(volumes))
for (i in seq_along(volumes)){
   mass <- b0[i] * volumes[i] ^ b1[i]
   masses[i] <- mass
}

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/locations-2016-01.zip", 
              "locations.zip")
unzip("locations.zip")
data_files = list.files(pattern = "locations-.*.txt", 
                        full.names = TRUE)

Calculate the number of observations in each file

results <- vector(mode = "integer", length = length(data_files))
for (i in 1:length(data_files){
  data <- read.csv(data_files[i])
  count <- nrow(data)
  results[i] <- count
}

Store output in a data frame instead of a vector
Associate the file name with the count

results <- data.frame(file_name = charcter(length(data_files))
                      count = integer(length(data_files)),
                      stringsAsFactors = FALSE)
for (i in 1:length(data_files){
  data <- read.csv(data_files[i])
  count <- nrow(data)
  results$file_name[i] <- data_files[i]
  results$count[i] <- count
}
results

Do Multiple-file Analysis. Exercise uses different collar data

With apply

get_counts <- function(data_file_name){
  file <- read.csv(data_file_name)
  count <- nrow(file)
  return(count)
}

results <- unlist(lapply(collar_data_files, get_counts))

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

Subsetting Data (optional)

Loops can subset in ways that are difficult with things like group_by
Look at some data on trees from the National Ecological Observatory Network

library(ggplot2)
library(dplyr)

neon_trees <- read.csv('data/HARV_034subplt.csv')
ggplot(neon_trees, aes(x = easting, y = northing)) +
  geom_point()

Look at a north-south gradient in number of trees
Need to know number of trees in each band of y values
Start by defining the size of the window we want to use
- Use the grid lines which are 2.5 m

window_size <- 2.5

Then figure out the edges for each window

south_edges <- seq(4713095, 4713117.5, by = window_size)
north_edges <- south_edges + window_size

But we don’t want to go all the way to the far edge

south_edges <- seq(4713095, 4713117.5 - window_size, by = window_size)
north_edges <- south_edges + window_size

Set up an empty data frame to store the output

counts <- vector(mode = "numeric", length = length(left_edges))

Look over the left edges and subset the data occuring within each window

for (i in 1:length(south_edges)) {
  data_in_window <- filter(neon_trees, northing >= south_edges[i], northing < north_edges[i])
  counts[i] <- nrow(data_in_window)
}
counts

Nested Loops (optional)

Sometimes need to loop over multiple things in a coordinate fashion
Pass a window over some spatial data
Look at full spatial pattern not just east-west gradient
Basic nested loops work by putting one loop inside another one

for (i in 1:10) {
  for (j in 1:5) {
    print(paste("i = " , i, "; j = ", j))
  }
}

Loop over x and y coordinates to create boxes
Need top and bottom edges

east_edges <- seq(731752.5, 731772.5 - window_size, by = window_size)
west_edges <- east_edges + window_size

Redefine out storage

output <- matrix(nrow = length(south_edges), ncol = length(east_edges))

for (i in 1:length(south_edges)) {
  for (j in 1:length(east_edges)) {
    data_in_window <- filter(neon_trees,
                            northing >= south_edges[i], northing < north_edges[i],
                            easting >= left_edges[j], easting < right_edges[j],)
    output[i, j] <- nrow(data_in_window)
  }
}
output

Sequence along (optional)

seq_along() generates a vector of numbers from 1 to length(volumes)

Data Science for Biologists

Notes

Basic for loop

Looping with an index & storing results

Looping over files

Subsetting Data (optional)

Nested Loops (optional)

Sequence along (optional)

Basic `for` loop