lin380 / lab_4

Lab #4: Descriptive assessments of datasets

Lab 4: Descriptive assessment of datasets

Preparation

• Read/ annotate: Recipe #4. You can refer back to this document to help you at any point during this lab activity.
• Note: do your best to employ what you've learned and use other existing resources (R documentation, web searches, etc.). If, however, you get stuck you are encouraged to view the lab model (lab_4_model.Rmd) for hints to get back on track.

Objectives

• Read and inspect the structure of a dataset.
• Identify and apply the appropriate descriptive methods for a vector's informational value.
• Descriptively assess both single variables and multiple variables with the appropriate statistical, tabular, and/ or graphical summaries.

Instructions

Setup

1. Create a new R Markdown document. Title it "Lab #4" and provide add your name as the author.
2. Edit the front matter to have rendered R Markdown documents print pretty tabular datasets.
3. Delete all the material below the front matter.
4. Add a code chunk directly below the header named 'setup' and add the code to load the following packages

• tidyverse
• janitor
• skimr

Tasks

1. Create four level-1 header sections named: "Read and inspect", "Single variables", "Multiple variables", and "References".
2. Follow the instructions that follow adding the relevant prose description and code chunks to the corresponding sections.

Remember:

• Add code comments (# code comments...) to your code lines to clarify what each step of your code does.
• Use Markdown syntax as necessary to format your responses
• Use keyboard shortcuts inside code chunks/ the R Console (<-, %>%, code completion hints, etc.)

Read and inspect

In this lab you will be working with a curated dataset drawn from the CEDEL2 Corpus (L2 Spanish written corpus). The observations in this dataset represent compositions written by L1 English-L2 Spanish learners.

• Cite the dataset adding bibliography: references.bib in the front matter and using the correct citation key (found in references.bib).
• Read the dataset (data/derived/cedel_dataset.rds) and describe the dataset:
  • How many compositions (observations) are in this dataset?
  • How many measures (variables) are in this dataset?
    • What type informational values does each contain?
    • What describe the type of measure of each variable.

Single variables

Categorical:

• Summarize the categorical variables sex and proficiency.
  • Use tabular and/ or graphical methods, as appropriate.
• In a prose description, describe any notable aspects of these two variables.

Continuous:

• Summarize the any two of the four variables placement_test_score_percent, num_tokens, num_types, or ttr.
  • Use statistical and/ or graphical methods, as appropriate.
• In a prose description, describe any notable aspects of these two variables.

Multiple variables

Consider what you have learned about this dataset by exploring the previous variables. With this in mind, summarize at least two relationships that involve two variables.

• Use tabular, statistical and/ or graphical methods, as appropriate.
• In a prose description, describe any notable aspects of these two relationships.

Assessment

Add a section which describes your learning in this lab.

Some questions to consider:

• What did you learn?
• What was most/ least challenging?
• What resources did you consult?
• What more would you like to know about?

Submission

1. To prepare your lab report for submission on Canvas you will need to Knit your R Markdown document to PDF or Word.

• Note: you will have to add the front matter line to pretty-print tables under the pdf_document: or pdf_document2: (if you want cross-references to tables or figures) output.

output:
  pdf_document:
    df_print: kable

2. Download this file to your computer.
3. Go to the Canvas submission page for Lab #4 and submit your PDF/Word document as a 'File Upload'. Add any comments you would like to pass on to me about the lab in the 'Comments...' box in Canvas.