Exercises to Accompany TeachingWithR Workshop

Matthew Beckman & Justin Post June 25, 2021

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Part 1. RStudio IDE Scavenger Hunt

[omitted for brevity…]

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Part 2. Import Data (Code Recap)

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## from instructions before workshop, if needed:
# devtools::install_github("mdbeckman/dcData")

# packages
library(tidyverse)  
library(dcData)     

# data intake
data("BabyNames", package = "dcData")

BabyNamesSupp <- 
  read_csv("https://jbpost2.github.io/TeachingWithR/datasets/BabyNamesSupp.csv")

# 2020 data intake was issued as a challenge task only
BabyNames2020 <-   
    read_csv("https://jbpost2.github.io/TeachingWithR/datasets/yob2020.txt", 
             col_names = FALSE)


# inspecting the data
head(BabyNamesSupp)

tail(BabyNamesSupp)

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Part 3. R Markdown

Note: you might hang onto the RStudio default text provided in the new R Markdown file for the moment… it’s packed with tiny examples that will come in handy!

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3.1 Goal

We’ll be investigating the popularity of names in the US each year. Choose a few (4-5) names to investigate to you–friends, family, TV character, colleagues in the workshop!

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Solution (for example)

Matt has chosen the names of his family members: Matthew, Sarah, Eden, Jack, & Hazel.

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3.2 Update yaml Header

• Task 1: “Knit” the document to render the initial HTML document

• Task 2: update yaml header as necessary to include an informative title, author name, & relevant date.

  • “Knit” to verify update
  • in general, Knit very often as you get the hang of the new tools

---
title: "Add a title"
author: "Add your name"
date: "Pick a date"
output: html_document
---

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Solution (for example)

Task 3.2.2: Your yaml header should appear at the beginning of the Rmd document, but here is some example text

---
title: "What's in a Name??"
author: "Matt Beckman & Justin Post"
date: "June 25, 2021"
output: html_document
---

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3.3 Add Narrative & Code Chunk

• Task 1: Write a bit of narrative to introduce the project (no R code)
  • Which names have you chosen to investigate and why?
  • “Knit”
  • Add a bullet list of “famous” persons that share the name
  • “Knit”
  • Note: it really isn’t important what you write, we’re just practicing a few elements of R Markdown syntax.
• Task 2: create an R code chunk to set up for the investigation:
  • call the tidyverse and dcData packages
  • data intake:
    • BabyNames from dcData package
    • BabyNamesSupp.csv using read_csv( )
  • Although we had done much of this work previously, writing it into a code chunk within your R Markdown document more closely connects the analysis to your narrative, and takes a big step toward reproducibility.
• Task 3: Just for fun, add a note at the end of your document with a time/date stamp reflecting when the analysis was last modified. Call the R function now() directly from lubridate package INLINE for the time stamp.
  • For example, “These notes were last modified 2021-06-25 10:27:38”
  • “Knit” (notice the time stamp)
  • “Knit” again (notice the time stamp)

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Solution

Task 3.3.1:

Some sample narrative text follows; note that it is simply one long line of soft-wrapped text, and no formatting of any kind is imposed:

I’ve chosen to investigate the names of each person in my immediate family: Matthew, Sarah, Eden, Jack, and Hazel. They’re my favorite people, and also my favorite names! I’m a bit torn about how I’ll include my son Jack in the analysis. His legal name is “Jon” but he is nearly always called “Jack”–the spelling of “Jon” honors our scandinavian heritage on both sides of the family, and the nickname “Jack” specifically honors his great-grandfather.

Some famous persons by each name of the family include:

• Sarah Jessica Parker
• Eden Hazard
• Jack (Jackie) Robinson
• Hazel Findlay
• Richard Stallman. Seriously, the first hit of my Internet search for “famous Matthew” was Richard Stallman (!) whose given name is… Matthew. “Matthew the Apostle” (my namesake) was third on that particular list: https://playback.fm/people/first-name/matthew

Task 3.3.2

# libraries
library(tidyverse)
library(dcData)

# data intake
data("BabyNames", package = "dcData")
BabyNamesSupp <- read_csv("https://jbpost2.github.io/TeachingWithR/datasets/BabyNamesSupp.csv",  
                          col_types = cols(sex = col_character()))  # fixes `sex`

Task 3.3.3

Here’s the code I used:

This document was last modified 2021-06-25 10:27:39.

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3.4 Render Various Output Formats

R Markdown is a flexible syntax that can render the same .Rmd (text) file in different formats. If you know the syntax, you can update the output: directly in the yaml header. RStudio provides a shortcut as a (tiny) menu arrow next to the “Knit (yarn)” icon revealing a few common choices.

• Task 1: MS Word
  • only if you have Microsoft Word installed on your computer already
  • use choice drop-down next to the “Knit (yarn)” shortcut
  • alternatively, update yaml with “output: word_document”
• Task 2: PDF
  • you may need this R command first: tinytex::install_tinytex()
  • use choice drop-down next to the “Knit (yarn)” shortcut
  • output: pdf_document
• Task 3: R Notebook
  • use choice drop-down next to the “Knit (yarn)” shortcut
  • output: html_notebook
  • note: the “Knit” button now says “Preview”–you will get the “Knit (yarn)” back when you revert to output: html_document
  • can you spot any differences b/w R Notebook & R Markdown doc?
• Task 4 (challenge): add some LaTeX to include mathematics your document (e.g, formula for the area of a circle) & “knit”

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Part 4. Data wrangling

[coming up next…]

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