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

== Programming Challenges ==
=== PC0. Get started===
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.

=== PC1. Access and describe a dataset provided in an R library === 

# 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.'')
# Find out the class of the <code>county</code> dataset object. 
# Find out how many rows and how many columns are in the <code>county</code> dataset. 
# Find the names of all of the variables (columns) as well as the class of each of the variables. 
# Summarize at least one continuous or discrete numeric variable in the dataset. Calculate the length, range (minimum and maximum), mean, and standard deviation.
# Plot a visual summary (maybe a boxplot or a histogram?) for the same numeric variable you used in PC1.5 above. 
# Summarize at least one categorical variable in the dataset (e.g., if the variable takes values of TRUE/FALSE or NA, how many of each are value are there?).

=== 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!
# Calculate summary statistics for your variable. Be sure to include the length, minimum, maximum, mean, and standard deviation.
# Create a visualization of your variable: at the very least, create a boxplot or a histogram.
# 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.
# Log transform your dataset (i.e., take the natural logarithm for each value). If you have very small values (close to zero) it may be helpful to add 1 to each value before you take the natural logarithm (this avoids nonsense output in the results). Calculate the new mean and standard deviation of the transformed variable. Also create a new histogram or boxplot.