So I decided for an R example I would just walk through a complete statistical analysis from the very first steps for some publicly available data.
The funny thing about me saying a “complete” analysis is that I don’t think any of my statistical analysis are ever “finished” in that there is always one more thing I could examine or plot or explore. I do always reach a good stopping point though after answering what I set out to investigate. So goodluck.

Beside walking through getting the data cleaned up and into a “final” form for analysis. I have just two further pages.
One applying singular spectrum analysis (SSA) and another further analyzing daily average temperatures more closely.

If I was to create another page I would go over quantifying the max and min daily temperature distributions for each daily average temperature and calculate the mean and standard deviation of each distribution and compare the mean to the avg daily temperature. Perhaps looking at the distribution of the difference between the daily avg mean and the associated max and min temperatures.

For my master’s research I have analyzed a lot of data for the Champaign Urbana area so let’s mix it up and for fun analyze data from my hometown, Galesburg Il.

Step:
0. Getting Started
1. Exploring the Data
1.5 Tabling the Data and the need to Round
1.7 All About Missing Data and Datatypes in R.
2. Dealing with Missing Data in R: Omit, Approx, or Spline Part 1
2. Dealing with Missing Data in R: Omit, Approx, or Spline Part 2
3 Exploring Daily Avg Temperatures
4 Singular Spectrum Analysis (SSA)

Asides:
1.7b What are dataframes?
2b Complete Code Recap
3b What happens if we try converting to integer before rounding?
3c Start-up Code Recap