From last time: Cleaning weird files

We have a folder full of output files like this:

## Data provided by X

--- instrument error
--- instrument error

## Year observed: 1990

Cleaning weird files (2/4)


  1. Remove unnecessary lines from each file.
  2. Create a single data frame from multiple cleaned files.

Input information:

  1. Vector of file names (file_names)
file_names <- c("file1.out", "file2.out")

Get the files themselves from…

Cleaning weird files (3/4)

Clean a single CSV file to a string:

clean_str <- function(file_name) {
  lines <- read_lines(file_name)
  lines <- lines[!str_detect(lines, "^\\#\\#|^--")]
  lines <- lines[lines != ""]
  cleaned_str <- paste(lines, collapse = "\n")

Cleaning weird files (4/4)

Clean multiple files then combine them into a single data frame:

clean_df <- function(file_names) {
  cleaned_strs <- map(file_names, clean_str)
  data_frames <- map(cleaned_strs, read_csv, col_types = cols())
  combined_df <- bind_rows(data_frames)
# A tibble: 12 × 6
   Ozone Solar.R  Wind  Temp Month   Day
   <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>
 1    41     190   7.4    67     5     1
 2    NA      NA  14.3    56     5     5
 3    28      NA  14.9    66     5     6
 4    23     299   8.6    65     5     7
 5    NA     194   8.6    69     5    10
 6     7      NA   6.9    74     5    11
 7    11     290   9.2    66     5    13
 8    14     274  10.9    68     5    14
 9    18      65  13.2    58     5    15
10     6      78  18.4    57     5    18
11    30     322  11.5    68     5    19
12    11      44   9.7    62     5    20

Before we proceed…

Let’s create a global .gitignore file!https://stackoverflow.com/a/7335487

Mac, Linux, or Windows git bash:

git config --global core.excludesFile ~/.gitignore

Windows cmd:

git config --global core.excludesFile "%USERPROFILE%\.gitignore"

Windows Powershell:

git config --global core.excludesFile "$Env:USERPROFILE\.gitignore"

Confirm the location:

git config --global core.excludesFile

Add file with things to ignore

Mac, Linux, or (maybe) Windows git bash:

touch ~/.gitignore
open ~/.gitignore

Otherwise, just open a file in a text editor and save it as ~/.gitignore

Paste in the following contents to file:


or Canvas > Files > 05_super-functional > gitignore.txt

Then save!

Now back to functional coding

Defensive programming

“Defensive programming is a technique to ensure that code fails with well-defined errors, i.e. where you know it shouldn’t work. The key here is to ‘fail fast’ and ensure that the code throws an error as soon as something unexpected happens. This creates a little more work for the programmer, but makes debugging code a lot easier at a later date.” Reproducible Code, p 14 found at https://www.britishecologicalsociety.org/publications/better-science/

  • Protect others that want to use your code (including your PI) from themselves
  • Protect yourself from future you

How to program defensively

subtract <- function(x, y) {
  z <- x - y
subtract("x", 6)
Error in x - y: non-numeric argument to binary operator

Not very useful for debugging

How can we make this error message more useful?

Think about the necessary properties of your arguments

subtract <- function(x, y) {
  if (!is.numeric(x) || !is.numeric(y)) {
    stop("both x and y must be numeric")
  z <- x - y
subtract("x", 6)
Error in subtract("x", 6): both x and y must be numeric

An example of insidious R helpfulness

Sometimes R is just too helpful for its own good

What if we want to subtract 6:1 from 1:6, but accidentally typed 6:5 instead of 6:1?

subtract(1:6, 6:5)
[1] -5 -3 -3 -1 -1  1

What extra check do we need?

That extra check

subtract <- function(x, y) {
  if (!is.numeric(x) || !is.numeric(y)) {
    stop("both x and y must be numeric")
  if (length(x) != length(y)) {
    stop("x and y must have the same length")
  z <- x - y
subtract(1:6, 6:5)
Error in subtract(1:6, 6:5): x and y must have the same length

Sourcing files

  • source("file.R") runs all the code inside file.R
  • It’s pretty simple, but it can help a lot with coding organization and staying DRY

Let’s say we have a file plot.R:

my_df <- ChickWeight |> 
  as_tibble() |> 
  mutate(log_weight = log(weight))
my_df |> 
  ggplot(aes(Time, log_weight)) +

and another file analyze.R:

my_df <- ChickWeight |> 
  as_tibble() |> 
  mutate(log_weight = log(weight))
mod <- lm(log_weight ~ Time * Diet, my_df)

Sourcing helps us avoid repeating code

We can instead have three files:


my_df <- ChickWeight |> 
  as_tibble() |> 
  mutate(log_weight = log(weight))


my_df |> 
  ggplot(aes(Time, weight)) +


mod <- lm(log_weight ~ Time * Diet, my_df)

Example of using source()

How could you use this in your projects?

Building R packages

First run this in R to get some useful packages:

install.packages(c("devtools", "roxygen2", "testthat", "knitr"))

Next, let’s make a test package from scratch:

In R, run:


where <pkg-name> should only contain letters, numbers, and periods

It should open up in RStudio

Structure of our package

  • .Rbuildignore: specifies files to avoid incorporating into the package. Use usethis::use_build_ignore() to add more files to it.
  • DESCRIPTION: metadata about your package.
  • NAMESPACE: contains exports from your package and imports from other packages to yours. You shouldn’t typically have to edit this.
  • R: contains all the R code you’re using for your package, and we’ll use this to create our documentation, too. This is the main folder you’ll work in.

Hotkeys from RStudio

Description Windows/Linux Mac
Build and Reload Ctrl+Shift+B Cmd+Shift+B
Document Package Ctrl+Shift+D Cmd+Shift+D
Insert Roxygen Skeleton Ctrl+Alt+Shift+R Cmd+Option+Shift+R
Test Package Ctrl+Shift+T Cmd+Shift+T
Check Package Ctrl+Shift+E Cmd+Shift+E


Yours should look like this:

Package: testPkg
Title: What the Package Does (One Line, Title Case)
    person("First", "Last", , "first.last@example.com", role = c("aut", "cre"),
           comment = c(ORCID = "YOUR-ORCID-ID"))
Description: What the package does (one paragraph).
License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3

DESCRIPTION - What does your package do?


Short (<65 chars) and title case, does not end in period


Short paragraph with greater description, each line < 80 characters wide with lines after the first indented by 4 spaces.

From ggplot2:

Title: Create Elegant Data Visualisations Using the Grammar of Graphics
Description: A system for 'declaratively' creating graphics,
    based on "The Grammar of Graphics". You provide the data, tell 'ggplot2'
    how to map variables to aesthetics, what graphical primitives to use,
    and it takes care of the details.

DESCRIPTION - Version field

Typically I make mine as <major>.<minor>.<patch>

  1. The major version is only incremented when there are huge changes that likely affect most users.
  2. The minor version is incremented for bug fixes and new features that are backward compatible.
  3. The patch version fixes small bugs that have little change overall to the user.
  • I add another integer and a "." if it’s a development version (e.g., "")

DESCRIPTION - License field

This is from https://r-pkgs.org/license.html:

  • If you want a permissive license so people can use your code with minimal restrictions, choose the MIT license with use_mit_license().
  • If you want a copyleft license so that all derivatives and bundles of your code are also open source, choose the GPLv3 license with use_gpl_license().
  • If your package primarily contains data, not code, and you want minimal restrictions, choose the CC0 license with use_cc0_license(). Or if you want to require attribution when your data is used, choose the CC BY license by calling use_ccby_license().

DESCRIPTION - Who are you?

Authors@R:  c(
    person(c("Lucas", "A."), "Nell", email = "lucas@email.com", role = "cre",
           comment = c(ORCID = "LUCAS-ORCID-ID")),
    person(c("Magdalena", "L."), "Warren", email = "maggie@email.com", 
           role = "aut", comment = c(ORCID = "MAGGIE-ORCID-ID")))

role is typically one or more of the following:

  • "cre": creator and maintainer of the package
  • "aut": author who made significant contributions
  • "ctb": contributor who made relatively small contributions

DESCRIPTION - What does your package need?


Indicates packages that your package needs to run.

Almost always use Imports instead of Depends because Depends does equivalent to calling library() on all packages listed there. This clogs up your environment.



Packages that your package can use but are not required.

usethis::use_package("<package>", "Suggests")

Adding a function

Add a new R file to the R directory, and write a function inside it.

What functions could be useful to you in your work?

Here’s my toy example:

hello <- function(x) {
    paste("Hello, ", x, "!", sep = "")

Writing documentation

  • In RStudio, put your cursor in your function somewhere, then add a start to your docs using Ctrl+Alt+Shift+R, Cmd+Option+Shift+R, or Code > Insert Roxygen Skeleton.
  • After writing your documentation, build your package (Ctrl+Shift+B or Cmd+Shift+B) and try using your function.
#' Say hello to stuff
#' @param x Single character indicating what you should say hello to.
#' @return A single character saying hello.
#' @export
#' @examples
#' hello("world")
#' my_hello <- hello("Lucas")
hello <- function(x) {
    paste("Hello, ", x, "!", sep = "")

Any issues? If so, try looking at your NAMESPACE file.

Generating the documentation

  • Our NAMESPACE file shows us that our function isn’t exported, plus we don’t have aything in our man folder that’s supposed to contain our docs!
  • We have to generate the documentation for our package! (Ctrl+Shift+D or Cmd+Shift+D)
  • Document, re-build package, and try your function again.
  • Also try ?<function-name>

Adding data

Typically if you create a dataset you want to include with your package…

cool_data <- data.frame(x = 1:5, y = runif(5))

Even better is to document how you created the data:

# Then, inside the newly created `data-raw/cool_data.R`, write:
cool_data <- data.frame(x = 1:5, y = runif(5))

Also document your datasets! (see https://r-pkgs.org/data.html)

Testing your package

Example of using a package for a paper

How could you use this in your projects?

More info