1 Objectives

This lesson covers some tips on managing your code, datasets, and other files with RStudio. The sections below give a tour of the IDE, and some of the customizations that you can do to increase your productivity.

I’ve included some comments in italics–these are just my personal observations, feel free to set up the IDE in a way that works best for you!

2 Materials

View the slides for this section here.

3 Global Settings

These can be found under Tools > Global Options…

3.1 General

Click on Tools > Global Options…, in the General section, you will see settings for Workspace

3.1.1 Workspace

Un-check the option for “Restore .RData into workspace at startup”

3.1.2 .RData

We also don’t want the workspace to .RData on exit, so we will set this to Never

3.1.3 General settings

How your General settings should look:

3.2 Code

Under Tools > Global Options… click on Code

3.2.1 Code editing

  • Auto indent? not recommended if you regularly paste lots of text
  • Continue comment lines? very handy if you write a lot of comments
  • Save R scripts before sourcing? can’t see how this is a bad thing…

3.2.2 Code display

  • Margins? this is handy for knowing when to wrap code
  • Scrolling? not sure why this is even an option?
  • Rainbow parentheses? add some color to your life!

3.2.3 Code saving

  • Cursor position? this is nice when you want to remember where you left off
  • Line endings? ‘platform native’ seems the safest
  • Text encoding? I use UTF-8, but ASCII works for special characters

3.2.4 Code completion

  • Insert parentheses? one less things to think about/type
  • Insert spaces? reduces the keystroke/neuron load
  • Completion delay setting? how quickly do you want help? :)

3.2.5 Code diagnostics

  • Check your R Code? just about all of these help me write better code
  • Check other languages? I write other languages, so this is a great feature
  • How long? as quickly as possible :)

3.3 Console

Under Tools > Global Options… click on Console

The console is where we can enter code directly, and where we’ll see output. We should consider the following settings:

  • Display? IMO, syntax highlighting makes sense pretty much everywhere

  • Debugging? this makes sense

  • Other? this is a personal preference

3.4 Appearance

Under Tools > Global Options… click on Appearance

  • RStudio theme? varies from lighter >> darker
  • Zoom? controls the entire IDE
    • Also hold ⌘ and press + on macOS
    • Also hold ctrl and press + on Windows
  • Font? also can adjust with Zoom
  • Editor theme? check out rsthemes package

3.5 Pane layout

Under Tools > Global Options… click on Pane Layout

3.5.1 Default panes

  • Source? this is where I do most of my work
  • Console? position so I can see output
  • Combining? I prefer:
    • Plots, Connections, Build, VCS, Presentation
    • Files, Packages, Help, Tutorial, Viewer

3.5.2 Adding columns

  • Two screens?
    • add a Source column and rearrange the panes

3.5.3 Three column view

Now you see Source, Tutorial, and Console panes on a single screen!

4 RStudio projects

Keep track of all your files with RStudio project files (.Rproj).

4.1 Why RStudio Projects?

  • Self contained Using R projects keeps track or your current working directory!

  • Project orientated .Rproj files make bundling and shipping files and folders easier!

  • Avoid removing all the files

see tweets below and tidyverse article

If the first line of your R script is
setwd("C:\Users\jenny\path\that\only\I\have")
I will come into your office and SET YOUR COMPUTER ON FIRE 🔥
If the first line of your R script is rm(list = ls())
I will come into your office and SET YOUR COMPUTER ON FIRE 🔥

4.1.1 RStudio project (existing folder)

Click on ‘Project: (New)’ > ‘New Project’

Click on ‘Browse’ > ‘Create Project’

4.1.2 RStudio project (new folder)

Click on ‘Project: (New)’ > ‘New Project’

Select project type

Create new folder name

Choose parent folder

Use Git (if installed)

5 Folders and files

Good folder and file organization saves time and headaches.

5.1 Folder structure

See the tabs below for some basic guidelines on folder structure and file naming.

5.1.1 General advice

  • separate raw and cleaned data

  • keep documents and code separate

  • keep figures separate

  • name files appropriately (preferably 2 digit prefix)

  • structure is reusable and easy to understand

5.1.2 Example folder structure

Adapted from from ‘Good enough practices in scientific computing’

project-name/
            |-- CITATION
            |-- project-name.Rproj
            |-- README.md
            |-- LICENSE
            |-- requirements.txt
            |--data/
                   |--raw/
                         |--raw-birds-data.csv
                   |--processed/
                               |--processed-birds-data.csv
            |--doc/
                  |-- notebook.Rmd
                  |-- manuscript.Rmd
                  |-- changelog.txt
            |-- results/
                  |-- summarized-results.csv
            |-- code/
                  |-- 01-sightings-import.R
                  |-- 02-sightings-wrangle.R
                  |-- 03-sightings-model.R
                  |-- runall.R

5.2 Naming files

Basic rules to follow:

  1. human readable -> (makes sense)

  2. machine readable -> (regex)

  3. sort/order well -> (ISO 8601 date)

5.2.1 Example file names

These are handy names:

2020-10-12-270-301-central-lab-metrics.csv
2020-10-12-270-301-central-lab-metrics.csv
2020-10-12-270-301-central-lab-metrics.csv

So are these:

01.0-import_270-301_central-lab-metrics.R
02.0-wrangle_270-301_central-lab-metrics.R
03.0-eda_270-301_central-lab-metrics.R
04.0-model_270-301_central-lab-metrics.R

*Adapted from Jenny Byran’s slides

5.2.2 Locating files

We can use regular expressions to find ‘270-301’ files!!

grepl(pattern = "270-301", 
      x = "2020-10-12-270-301-central-lab-metrics.csv")
#> [1] TRUE

*Adapted from Jenny Byran’s slides

5.2.3 Other examples

Logical order and underscores _ also make it easier to sort files

# writeLines(files)
files
#> [1] "01.0-import_270-301_central-lab-metrics.R" 
#> [2] "02.0-wrangle_270-301_central-lab-metrics.R"
#> [3] "03.0-eda_270-301_central-lab-metrics.R"    
#> [4] "04.0-model_270-301_central-lab-metrics.R"
stringr::str_split_fixed(string = files, pattern = "_", 3)
#>      [,1]           [,2]      [,3]                   
#> [1,] "01.0-import"  "270-301" "central-lab-metrics.R"
#> [2,] "02.0-wrangle" "270-301" "central-lab-metrics.R"
#> [3,] "03.0-eda"     "270-301" "central-lab-metrics.R"
#> [4,] "04.0-model"   "270-301" "central-lab-metrics.R"

*Adapted from Jenny Byran’s slides

5.3 File paths

Use relative rather than absolute file paths

5.3.1 Absolute paths

These are specific to a system

/project-name/data -> absolute path in macOS

\\project-name\\data -> absolute path in Windows

5.3.2 Relative paths

These are specific to a folder

project-name/data -> relative path in macOS

project-name\\data -> relative path in Windows

6 Useful packages

This section covers some packages to help manage files and folders.

6.1 The here package

The here::set_here() function solves a lot of file path problems (especially if you’re not using R projects)

6.1.1 Using here()

library(here)
#> here() starts at /Users/mjfrigaard/Documents/@BioMarin/r-meetup-tutorials

6.1.2 set_here(".")

This creates a .here file (similar to .Rproj files)

here::set_here(".")
#> Created file .here in /Users/mjfrigaard/Documents/@BioMarin/r-meetup-tutorials . Please start a new R session in the new project directory.
list.files(all.files = TRUE, pattern = "here")
#> [1] ".here"

6.2 The fs package

The fs package stands for file system and is great for locating and accessing files.

6.2.1 Folder trees

View a tree layout of your files with fs::dir_tree().

fs::dir_tree("../data")
#> ../data
#> ├── 2021-11-07-NFL-TweetsRaw.rds
#> ├── AppleMobRaw.csv
#> ├── DailyShowSample.csv
#> ├── EndorseSample.csv
#> ├── FandangoSample.csv
#> ├── FasterCures.csv
#> ├── Infected.csv
#> ├── LabData.csv
#> ├── LabProc.csv
#> ├── Netflix data
#> │   ├── netflix_directors.rds
#> │   ├── netflix_duration.rds
#> │   └── netflix_series.rds
#> ├── SmallLabData.csv
#> ├── TopPharmComp.csv
#> ├── UsadaBadDates.csv
#> ├── UsadaRaw.csv
#> ├── VisitNAData.csv
#> ├── airquality.csv
#> ├── data-cache
#> │   ├── 2020-11-24-TopPharmCompRaw.csv
#> │   ├── 2020-12-11-BioTechDrugStocks.csv
#> │   ├── 2020-12-11-BmrmGoogle.rds
#> │   ├── 2020-12-20-BioTechStocks.csv
#> │   ├── 2020-12-24-BioTechStocks.csv
#> │   ├── 2020-12-29-PricesWide.csv
#> │   ├── 2020-12-30-PricesWide.csv
#> │   ├── 2021-05-02-PricesWide.csv
#> │   ├── 2021-05-02-TidyApple.csv
#> │   ├── 2021-05-02-TopUSCities.csv
#> │   ├── 2021-05-02-USCities.csv
#> │   ├── 2021-08-31-TidyApple.csv
#> │   ├── 2021-08-31-TopUSCities.csv
#> │   ├── 2021-08-31-USCities.csv
#> │   ├── 2021-09-04-TidyApple.csv
#> │   ├── 2021-09-04-TopUSCities.csv
#> │   ├── 2021-09-04-USCities.csv
#> │   ├── 2021-09-19-TidyApple.csv
#> │   ├── 2021-09-19-TopUSCities.csv
#> │   └── 2021-09-19-USCities.csv
#> ├── imdb-movies.csv
#> ├── original-starwars.csv
#> ├── palmerpenguins.csv
#> ├── penguins.csv
#> ├── starwars.rds
#> ├── wk10-dont-mess-with-texas
#> │   ├── 2021-11-21-ExecutedOffenders.csv
#> │   └── processed
#> │       └── 2021-11-21
#> │           ├── 2021-11-21-ExExOffndrshtml.csv
#> │           ├── 2021-11-21-ExExOffndrsjpg.csv
#> │           └── ExOffndrsComplete.csv
#> ├── wk11-01_intro-to-maps
#> │   └── raw
#> │       └── 2021-11-07-NFL-TweetsRaw.csv
#> ├── wk5-01-intro-to-ggp2-part-02
#> │   └── TidyApple.csv
#> ├── wk6-02_scrape-wikipedia-data
#> │   ├── 2020-11-24-TopPharmCompRaw.csv
#> │   ├── 2021-11-20-TopPharmComp.csv
#> │   └── 2021-11-20-TopPharmCompRaw.csv
#> └── wk7-01_intro-to-ggp2-part-03
#>     └── 2021-11-20-TopUSCities.csv

6.2.2 List files

Get the complete path to files using fs::dir_ls().

fs::dir_ls("../data")
#> ../data/2021-11-07-NFL-TweetsRaw.rds ../data/AppleMobRaw.csv              
#> ../data/DailyShowSample.csv          ../data/EndorseSample.csv            
#> ../data/FandangoSample.csv           ../data/FasterCures.csv              
#> ../data/Infected.csv                 ../data/LabData.csv                  
#> ../data/LabProc.csv                  ../data/Netflix data                 
#> ../data/SmallLabData.csv             ../data/TopPharmComp.csv             
#> ../data/UsadaBadDates.csv            ../data/UsadaRaw.csv                 
#> ../data/VisitNAData.csv              ../data/airquality.csv               
#> ../data/data-cache                   ../data/imdb-movies.csv              
#> ../data/original-starwars.csv        ../data/palmerpenguins.csv           
#> ../data/penguins.csv                 ../data/starwars.rds                 
#> ../data/wk10-dont-mess-with-texas    ../data/wk11-01_intro-to-maps        
#> ../data/wk5-01-intro-to-ggp2-part-02 ../data/wk6-02_scrape-wikipedia-data 
#> ../data/wk7-01_intro-to-ggp2-part-03

6.3 Reproducible examples

The reprex package was designed to help you create a reproducible example

6.3.1 Reprex addin 1

  1. Copy code
  2. Select Addin > Render selection

6.3.2 Reprex addin 2

  1. Copy code
  2. Select Addin > Render selection
  3. Wait for console
  4. Paste reprex

6.3.3 Reprex + datapasta

To copy + paste actual data in a reproducible example, try datapasta!

7 More RStudio panes

RStudio also comes with access to a Terminal and Help pane.

7.1 Terminal pane

Learn a handful of command-line tools to make life easier

7.1.1 Command line basics

Know how to use cd, pwd, mkdir, rm, ls, etc.

7.1.2 Terminal in RStudio

RStudio comes with a Terminal pane for quick access to the command-line

7.2 Getting help

R comes with a ton of accessible help files

7.2.1 Accessing help files

?read.csv

7.2.2 The Help pane

8 Learn more about R best practices

  1. R for Data Science

  2. Tidyverse

  3. RViews Community Blog

9 Feedback

@mjfrigaard on Twitter and Github