Exercises to Accompany TeachingWithR Workshop

Matthew Beckman & Justin Post June 25, 2021

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Here, we join an analysis already in progress…

• Part 1 (RStudio IDE Scavenger Hunt) has been omitted from the following analysis.
• Part 2 (Import Data) is consolodated
• Part 3 (Data Wrangling) Exercises Follow

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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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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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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 12:25:12”
  • “Knit” (notice the time stamp)
  • “Knit” again (notice the time stamp)

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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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