Category Archives: R

issuer: Local issue tracking, no net required

The goal of issuer is to provide a simple issue tracker, hosted on your local file system, for those users who don’t want to or are disallowed from using cloud-based code repositories.

Online code repositories often provide an issue tracker to allow developers, reviewers, and users to report bugs, submit feature requests, and so on. However, many developers either choose to work offline or work on enterprise networks where use of cloud services may be prohibited.

issuer is an Add-in for use in RStudio’s desktop IDE. It works entirely locally with no requirement for a cloud service or even a network connection.

Read more about issuer at https://github.com/WilDoane/issuer

You can install the development version of issuer from Github with:

devtools::install_github("WilDoane/issuer")

My RStudio Configuration

I help a few of dozen users install RStudio and learn R regularly. Whenever I need to install RStudio on a new machine, I have to think a bit about the configuration options I’ve tweaked. Invariably, I miss a checkbox that leaves me with slightly different RStudio behavior on each system. This post includes screenshots of my currently preferred standard RStudio configuration and custom keyboard shortcuts for RStudio 1.3, MacOS.

If you need an exact copy of your settings, consider the discussion at https://stackoverflow.com/questions/55903423/export-import-rstudio-user-preferences-global-setting-etc/55940249 (h/t: liebrr)

Continue reading My RStudio Configuration

Converting Individual Binary Vectors to a Value Based on Column Names

When processing data downloaded from popular survey engines, it’s not uncommon for multiple choice questions to be represented as one column per possible response coded as 0/1. So, a question with just two responses might be downloaded as part of a CSV with one column for q1_1 and another for q1_2. If the responses are mutually exclusive, then (q1_1 == 0 iff q1_2 == 1) and (q1_1 == 1 iff q1_2 == 0). If the responses are part of a “choose all that apply” question, then it’s possible to have multiple 1s.

How can these individual binary indicator variables be reassembled into a single response variable?

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The Shiny Module Design Pattern

Foremost in your mind should be the quintessential reality of R: Everything that happens in R is the result of a function call. Shiny is no exception.

To write a minimal shiny app, you create an object that describes your app’s user interface, write a function describing runtime behaviors, and pass them into a function that spins up the app you’ve described. By convention, these two objects are associated with the variable names ui and server.

library(shiny)
ui <- fluidPage()
server <- function(input, output, session) {}

This is just R code. You can type it into the Console to execute it line by line and inspect what it does.

If you’re working in RStudio, you can type it into a Source file, then press Control-Enter (Windows) or Command-Return (MacOS) to send each line to the Console for execution.

Checking the Environment—or the structure of these two objects with str()—we can see that ui is a list of three objects. If we print ui to the Console, we see only an empty HTML <div> element.

<div class="container-fluid"></div>

The object associated with server is simply a function with no body.

To execute this minimal shiny app, we pass the ui and server objects to the shinyApp() function.

shinyApp(ui, server)

The app will be spun up either in RStudio’s Viewer pane, in a Viewer window, or in your default Web browser, depending on your settings in RStudio.

Don’t be surprised: it will be just a blank window, since all that has been defined thus far is an empty <div> element. The document that opened is an HTML document with some boilerplate CSS and JavaScript. You can inspect it using your Browser’s Developer Tools.

That’s it. That’s shiny. Everything else flows from these core ideas:

  • ui is a list object representing the HTML UI to be constructed.
  • server is a function describing the runtime behavior of your app.
  • shinyApp() takes these two objects and uses them to construct an HTML document that then gets spun up in a browser.
Continue reading The Shiny Module Design Pattern

Writing Pipe-friendly Functions

Pipes have been a fundamental aspect of computer programming for many decades. In short, the semantics of pipes can be thought of as taking the output from the left-hand side and passing it as input to the right-hand side. For example, in a linux shell, you might cat example.txt | sort | uniq to take the contents of a text file, then sort the rows, then take one copy of each distinct value. | is a common, but not universal, pipe operator and on U.S. Qwerty keyboards, is found above the RETURN key along with the backslash: \.

Languages that don’t begin by supporting pipes often eventually implement some version of them. In R, the magrittr package introduced the %>% infix operator as a pipe operator and is most often pronounced as “then”. For example, “take the mtcars data.frame, THEN take the head of it, THEN…” and so on.

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I’m ‘not in’ right now…

Checking whether an item is in a vector or not in a vector is a common task. The notation in R is a little inelegant when expressing the “not in” condition since the negation operator (!) is separated from the comparison operator (%in%):

5 %in% c(1, 2, 3, 4, 5)  # TRUE
!5 %in% c(1, 2, 3, 4, 5) # FALSE

R is a language where you can easily extend the set of built in operators:

`%!in%` <-
  function(needle, haystack) {
    !(needle %in% haystack)
  }

Now, I can express my intentions reasonably clearly with my new, compact, infix operator %!in%:

5 %in% c(1, 2, 3, 4, 5)  # TRUE
5 %!in% c(1, 2, 3, 4, 5) # FALSE

Moral: bend your tools to your will, not the other way ’round.

Defensively install packages in R

Often, your R code will rely on having one or more R packages available. A little defensive coding will save users of your code—including future-you—from having to figure out which packages you’re using and then having to manually install them. This lowers the extraneous cognitive load associated with running older or unfamiliar code.

if (!"tidyverse" %in% installed.packages()) install.package("tidyverse", deps = TRUE)

Or, if you prefer to always use blocks with IF statements:

if (!"tidyverse" %in% installed.packages()) {
  install.package("tidyverse", deps = TRUE)
}

With a little persistence, you can extend this to dealing with multiple packages:

pkgs <- c("tidyverse", "openxlsx")
install.packages(pkgs[!pkgs %in% installed.packages()], deps = TRUE)

Getting started with R

Download and install R. Download and install RStudio. Read R for Data Science.

R provides the backend: the programming language specification and the interpreter.

RStudio provides the frontend: the user interface that allows you to interact with R, visualize data, and manage the files associated with your analyses.

R for Data Science introduces you to the tidyverse way of programming. There are basically methods of programming in R: “base R”, which has been around since the R language was first conceived (and before, since R is itself based on the S language), and the tidyverse, a newer approach that focuses on leveraging a consistent structure to your data and developing a grammar for data ingest, data wrangling, data visualization, and data storage.

Base R tends to be dense in meaning where the Tidyverse tends to be consistent and to breakdown complex processes into a set of discrete steps:

base R Tidyverse
mtcars[2, "cyl"]
library(tidyverse)
mtcars %>%
  select(cyl) %>%
  slice(2)
mtcars[mtcars$cyl == 4, c("hp", "mpg")]
library(tidyverse)
mtcars %>%
  filter(cyl == 4) %>%
  select(hp, mpg)

FizzBuzz in R

Functions are first class objects in R. Functions establish closures also known in R as environments. So, you can use functions to create other functions in creative ways.

Here, I’ve written a function called divisor that returns a function that checks whether a given input, d, is evenly divisible by number and if so, returns string. Then I use divisor to create a test for divisibility by 3 and another for divisibility by 5.

Problem: Given a range of positive, non-zero integers, output “Fizz” if the number is evenly divisible by 3, output “Buzz” if the number is evenly divisible by 5, and output “FizzBuzz” if the number is evenly divisible by both 3 and 5; otherwise, output the number.

Solution:

divisor <-
  function(number, string) {
    function(d) {
      if (d %% number == 0) string else ""
    }
  }

mod3er <- divisor(3, "Fizz")
mod5er <- divisor(5, "Buzz")

fizzbuzz <- 
  function(i) {
    res <- paste0(mod3er(i), mod5er(i))
    ifelse(res == "", i, res)
  }

sapply(1:100, fizzbuzz)

R Statistical Programming Language

The R Project provides a comprehensive, free, open source statistical programming language and environment based on the S language. R is the name of both the language and the environment in which you generally use the language. It’s an interactive environment where the commands you enter generate immediate results that you can use to guide your analyses.

Your Best Starting Point

Download and install R. Download and install RStudio. Read R for Data Science.

Continue reading R Statistical Programming Language