Editing Statistics and Statistical Programming (Fall 2020)/pset1 (section)

== Programming Challenges ==
=== PC0. Get started===
Open up RStudio, create a new file for this assignment (likely an R Markdown script), add relevant metadata (maybe your name, the date, and a title so that you/we know it is Problem Set 1 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 may 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 PC1.4 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 dataset from the web ===
# Run the following two commands in your R script. Be sure to replace <code><your.birthdate></code> with your birthday in ''ddmmyy'' format (e.g., September 21, 2020 would be <code>210920</code>) or at least something numeric. 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.:
::<code>set.seed(<your.birthdate>)</code></br>
::<code>sample(x= c(1:20), size=1))</code>
# Navigate to the [https://communitydata.science/~ads/teaching/2020/stats/data data repository for the course] and find the RData file in the <code>week_03</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. Whoops! This was due to a coding error. 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.