Editing Statistics and Statistical Programming (Winter 2021)/Problem set 4

For this problem set, the programming challenges ask you to apply some of the concepts from ''OpenIntro'' Chapters 1 and 2 using the R and RStudio fundamentals that were introduced in the first three  R tutorials. If you're feeling lost at any point, I recommend you review some of those materials and/or come ask for help!

The topics/skills covered here include: loading/importing datasets, performing some basic data management, summarizing data, conducting simple arithmetic operations, calculating summary/descriptive statistics for a few different kinds of variables, and creating [[:wikipedia:univariate|univariate]] tables and visualizations.  

The problem set is structured to model the workflow you would likely pursue whenever you encounter a new dataset outside of a classroom setting starting with basic inspection and description of variables of interest before moving on to more sophisticated analysis.  

Open up RStudio, create a new project as you would for any assignment. Once you've got your project set up, create a file for this assignment (likely an R Markdown file, although an R script could work too), add relevant metadata (maybe your name, the date, and a title so that you/we know it is Problem Set 4 for this class?), and save it.

# Load the <code>openintro</code> R package and the <code>county</code> dataset so that they are available to you. Let's get to know this data! You will already be familiar with it from Chapter 1 of the ''OpenIntro'' textbook and a codebook is available [https://www.openintro.org/data/index.php?data=county on the openintro website]. (''Note: there are a few other datasets in the <code>openintro</code> package with similar names. The one you want is <code>county</code> and is described on the site linked above.'')

# Plot a visual summary (maybe a boxplot or a histogram?) for the same numeric variable you used in PC1.5 above.  

=== PC2. Work with a downloaded dataset ===

# Run the following two commands in your R script. Be sure to replace <code><'''YOUR BIRTHDATE'''></code> with your birthday in ''yyyyddmm'' format (e.g., January 06, 2021 would be <code>20200106</code>). If you run the commands correctly (or maybe even not), R will return a single random integer value between 1 and 20. This integer will be your dataset number for the purposes of PC2:

<syntaxhighlight lang="R">

set.seed(<YOUR BIRTHDATE>)

sample(x=seq(1, 20), size=1)

</syntaxhighlight>

# Navigate to the <code>datasets</code> in the course Dropbox repository and find the RData file in the <code>problem_set_4</code> subdirectory with your dataset number from PC2.1 (e.g., <code>group_<output>.Rdata</code> where <output> is replaced with the dataset number).  

# Load the RData file for your dataset number into R. It should contain one variable. Find that variable!

# Some of you may have negative numbers. Let's imagine we have a substantive or theoretical reason to exclude negative values from our analysis. Write code to recode all negative numbers as missing (i.e. <code>NA</code>) in your dataset. Now compute the mean and standard deviation again and note any changes.

Consider the results of PC1.4 and PC1.5. Do the mean and standard deviation seem likely to provide good representations of the central tendency (i.e., the average or expected value) and variability (i.e., the spread) of this variable? Why or why not? If not, what alternative measures could you use to characterize the central tendency and spread respectively?