Programming with R

Instructor’s Guide

Legend

We are using a dataset with records on inflammation from raptors following an arthritis treatment. With it we explain R data structure, basic data manipulation and plotting, writing functions and loops.

Overall

This lesson is written as an introduction to R, but its real purpose is to introduce the single most important idea in programming: how to solve problems by building functions, each of which can fit in a programmer’s working memory. In order to teach that, we must teach people a little about the mechanics of manipulating data with lists and file I/O so that their functions can do things they actually care about. Our teaching order tries to show practical uses of every idea as soon as it is introduced; instructors should resist the temptation to explain the “other 90%” of the language as well.

The secondary goal of this lesson is to give them a usable mental model of how programs run (what computer science educators call a notional machine so that they can debug things when they go wrong. In particular, they must understand how function call stacks work.

The final example asks them to build a command-line tool that works with the Unix pipe-and-filter model. We do this because it is a useful skill and because it helps learners see that the software they use isn’t magical. Tools like grep might be more sophisticated than the programs our learners can write at this point in their careers, but it’s crucial they realize this is a difference of scale rather than kind.

The R novice inflammation contains a lot of material to cover. Remember this lesson does not spend a lot of time on data types, data structure, etc. It is also on par with the similar lesson on Python. The objective is to explain modular programming with the concepts of functions, loops, flow control, and defensive programming (i.e. SWC best practices). Supplementary material is available for R specifics (Addressing Data, Data Types and Structure, Understanding Factors, Introduction to RStudio, Reading and Writing .csv, Loops in R, Best Practices for Using R and Designing Programs, Dynamic Reports with knitr, Making Packages in R).

A typical, half-day, lesson would use the first three lessons:

  1. Analyzing Patient Data
  2. Creating Functions
  3. Analyzing Multiple Data Sets

An additional half-day could add the next two lessons:

  1. Making choices
  2. Command-Line Programs

Time-permitting, you can fit in one of these shorter lessons that cover bigger picture ideas like best practices for organizing code, reproducible research, and creating packages:

  1. Best practices for using R and designing programs
  2. Dynamic reports with knitr
  3. Making packages in R

Analyzing Patient Data

  • Check learners are reading files from the correct location (set working directory); remind them of the shell lesson

  • Provide shortcut for the assignment operator (<-) (RStudio: Alt+- on Windows/Linux; Option+- on Mac)

dat <- read.csv("data/inflammation-01.csv", header = FALSE)
animal <- c("m", "o", "n", "k", "e", "y")
# Challenge - Slicing (subsetting data)
animal[4:1]  # first 4 characters in reverse order
[1] "k" "n" "o" "m"

animal[-1]  # remove first character
[1] "o" "n" "k" "e" "y"

animal[-4]  # remove fourth character
[1] "m" "o" "n" "e" "y"

animal[-1:-4]  # remove first to fourth characters
[1] "e" "y"

animal[c(5, 2, 3)]  # new character vector
[1] "e" "o" "n"

# Challenge - Subsetting data
max(dat[5, 3:7])
[1] 3

sd_day_inflammation <- apply(dat, 2, sd)
plot(sd_day_inflammation)

Addressing Data

  • Note that the data frame dat is not the same set of data as in other lessons

Data Types and Structure

  • Lesson on data types and structures

Understanding Factors

Introduction to RStudio

Reading and Writing .csv

Creating Functions

# Challenge - Create a function
fence <- function(original, wrapper) {
  answer <- c(wrapper, original, wrapper)
  return(answer)
}
# Challenge - A more advanced function
analyze <- function(filename) {
  # Plots the average, min, and max inflammation over time.
  # Input is character string of a csv file.
  dat <- read.csv(file = filename, header = FALSE)
  avg_day_inflammation <- apply(dat, 2, mean)
  plot(avg_day_inflammation)
  max_day_inflammation <- apply(dat, 2, max)
  plot(max_day_inflammation)
  min_day_inflammation <- apply(dat, 2, min)
  plot(min_day_inflammation)
}

# Challenge - rescale
rescale <- function(v) {
  # Rescales a vector, v, to lie in the range 0 to 1.
  L <- min(v)
  H <- max(v)
  result <- (v - L) / (H - L)
  return(result)
}
# Challenge - A function with default argument values
rescale <- function(v, lower = 0, upper = 1) {
  # Rescales a vector, v, to lie in the range lower to upper.
  L <- min(v)
  H <- max(v)
  result <- (v - L) / (H - L) * (upper - lower) + lower
  return(result)
}
answer <- rescale(dat[, 4], lower = 2, upper = 5)
min(answer)
[1] 2

max(answer)
[1] 5

answer <- rescale(dat[, 4], lower = -5, upper = -2)
min(answer)
[1] -5

max(answer)
[1] -2

Analyzing Multiple Data Sets

  • The transition from the previous lesson to this one might be challenging for a very novice audience. Do not rush through the challenges, maybe drop some.
# Challenge - Using loops
print_N <- function(N) {
  nseq <- seq(N)
  for (num in nseq) {
    print(num)
  }
}
print_N(3)
[1] 1
[1] 2
[1] 3

total <- function(vec) {
  #calculates the sum of the values in a vector
  vec_sum <- 0
  for (num in vec) {
    vec_sum <- vec_sum + num
  }
  return(vec_sum)
}
ex_vec <- c(4, 8, 15, 16, 23, 42)
total(ex_vec)
[1] 108

expo <- function(base, power) {
  result <- 1
  for (i in seq(power)) {
    result <- result * base
  }
  return(result)
}
expo(2, 4)
[1] 16

# Challenge - Using loops to analyze multiple files
analyze_all <- function(pattern) {
  # Runs the function analyze for each file in the current working directory
  # that contains the given pattern.
  filenames <- list.files(path = "data", pattern = pattern, full.names = TRUE)
  for (f in filenames) {
    analyze(f)
  }
}

Loops in R

Making Choices

Making Choices

# Challenge - Using conditions to change behaviour
plot_dist <- function(x, threshold) {
  if (length(x) > threshold) {
    boxplot(x)
  } else {
    stripchart(x)
  }
}

plot_dist <- function(x, threshold, use_boxplot = TRUE) {
  if (length(x) > threshold & use_boxplot) {
    boxplot(x)
  } else if (length(x) > threshold & !use_boxplot) {
    hist(x)
  } else {
    stripchart(x)
  }
}

# Challenge - Changing behaviour of the plot command
analyze <- function(filename, output = NULL) {
  # Plots the average, min, and max inflammation over time.
  # Input:
  #    filename: character string of a csv file
  #    output: character string of pdf file for saving
  if (!is.null(output)) {
    pdf(output)
  }
  dat <- read.csv(file = filename, header = FALSE)
  avg_day_inflammation <- apply(dat, 2, mean)
  plot(avg_day_inflammation, type = "l")
  max_day_inflammation <- apply(dat, 2, max)
  plot(max_day_inflammation, type = "l")
  min_day_inflammation <- apply(dat, 2, min)
  plot(min_day_inflammation, type = "l")
  if (!is.null(output)) {
    dev.off()
  }
}

Best Practices for Using R and Designing Programs

Command-Line Programs

# Challenge - A simple command line program
cat arith.R
main <- function() {
  # Performs addition or subtraction from the command line.
  #
  # Takes three arguments:
  # The first and third are the numbers.
  # The second is either + for addition or - for subtraction.
  #
  # Ex. usage:
  #   Rscript arith.R 1 + 2
  #   Rscript arith.R 3 - 4
  #
  args <- commandArgs(trailingOnly = TRUE)
  num1 <- as.numeric(args[1])
  operation <- args[2]
  num2 <- as.numeric(args[3])
  if (operation == "+") {
    answer <- num1 + num2
    cat(answer)
  } else if (operation == "-") {
    answer <- num1 - num2
    cat(answer)
  } else {
    stop("Invalid input. Use + for addition or - for subtraction.")
  }
}

main()

cat find-pattern.R
main <- function() {
  # Finds all files in the current directory that contain a given pattern.
  #
  # Takes one argument: the pattern to be searched.
  #
  # Ex. usage:
  #   Rscript find-pattern.R csv
  #
  args <- commandArgs(trailingOnly = TRUE)
  pattern <- args[1]
  files <- list.files(pattern = pattern)
  cat(files, sep = "\n")
}

main()

## Challenge - A command line program with arguments
cat check.R
main <- function() {
  # Checks that all csv files have the same number of rows and columns.
  #
  # Takes multiple arguments: the names of the files to be checked.
  #
  # Ex. usage:
  #   Rscript check.R inflammation-*
  #
  args <- commandArgs(trailingOnly = TRUE)
  first_file <- read.csv(args[1], header = FALSE)
  first_dim <- dim(first_file)
#   num_rows <- dim(args[1])[1]  # nrow(args[1])
#   num_cols <- dim(args[1])[2]  # ncol(args[1])
  for (filename in args[-1]) {
    new_file <- read.csv(filename, header = FALSE)
    new_dim <- dim(new_file)
    if (new_dim[1] != first_dim[1] | new_dim[2] != first_dim[2]) {
      cat("Not all the data files have the same dimensions.")
    }
  }
}

main()

# Challenge - Shorter command line arguments
cat readings-usage.R
main <- function() {
  args <- commandArgs(trailingOnly = TRUE)
  action <- args[1]
  filenames <- args[-1]
  if (!(action %in% c("--min", "--mean", "--max"))) {
    usage()
  } else if (length(filenames) == 0) {
    process(file("stdin"), action)
  } else {  
    for (f in filenames) {
      process(f, action)
    }
  }
}

process <- function(filename, action) {
  dat <- read.csv(file = filename, header = FALSE)
  
  if (action == "--min") {
    values <- apply(dat, 1, min)
  } else if (action == "--mean") {
    values <- apply(dat, 1, mean)
  } else if (action == "--max") {
    values <- apply(dat, 1, max)
  }
  cat(values, sep = "\n")
}

usage <- function() {
  cat("usage: Rscript readings-usage.R [--min, --mean, --max] filenames", sep = "\n")
}

main()

# Challenge - Implementing wc in R
cat line-count.R
main <- function() {
  args <- commandArgs(trailingOnly = TRUE)
  if (length(args) > 0) {
    for (filename in args) {
      input <- readLines(filename)
      num_lines <- length(input)
      cat(filename)
      cat(" ")
      cat(num_lines, sep = "\n")
    }
  } else {
    input <- readLines(file("stdin"))
    num_lines <- length(input)
    cat(num_lines, sep = "\n")
  }
}

main()

Dynamic Reports with knitr

Making Packages in R

Using Git in RStudio

Some instructors will demo RStudio’s git integration at some point during the workshop. This often goes over very well, but there can be a few snags with the setup. First, RStudio may not know where to find git. You can specify where git is located in Tools > Global Options > Git/SVN; on Mac/Linux git is often in usr/bin/git or usr/local/bin/git and on Windows it is often in C:/Program Files (x86)/Git/bin/git.exe. If you don’t know where git is installed on someone’s computer, open a terminal and try which git on Mac/Linux, or where git or whereis git.exe on Windows. See Jenny Bryan’s instructions for more detail.

If Windows users select the option “Run Git from the Windows command prompt” while setting up Git Bash, RStudio will automatically find the git executable. If you plan to demo git in RStudio during your workshop, you should edit the workshop setup instructions to have the Windows users choose this option during setup.

Another common gotcha is that the push/pull buttons in RStudio are grayed out, even after you have added a remote and pushed to it from the command line. You need to add an upstream tracking reference before you can push and pull directly from RStudio; have your learners do git push -u origin master from the command line and this should resolve the issue.

Software Carpentry Source Contact License