Exploratory Data Analysis & Modeling with Python + R - (Part II - Mixed Effects Modeling with R)

Part II of a two part tutorial illustrating how to use Python and R in the same Jupyter notebook within Google Colab. This first video walks through how to conduct exploratory data analysis with Python, while the next video show how to model with R.

OBJECTIVE: Infer which explanatory variables significantly affect the size of trees within Duke Forest. Tree health can be used as a proxy for the overall health of the forest.

DATA DICTIONARY:
ID - Unique tree identifier.
yr - Year of the diameter recording.
cm - Measurement of the diameter of a tree's base. Measurements are made at breast height marked by a nail that holds a tag indicating the identifying tree number. This is the response variable.
annualprec - Total precipitation within the year.
summerpdsi - Palmer Drought Severity Index for the summer. Uses readily available temperature and precipitation data to estimate relative dryness.
wintertemp - Average winter (Jan. - Mar.) temperature.

CONNECT:

|-Video Chapters-|

0:00 - Intro
0:20 - Condensing the number of response variable observations
2:07 - Calculating the mean diameter of a tree group's base by year
5:41 - MultiIndex to columns
6:40 - Annualizing the diameter growth rate
10:52 - Compare the OLS results of individual trees to group trees
14:55 - Write Python DataFrames to csv files
17:55 - Loading rpy2 extension into Google Colab
18:24 - Calling magic functions to run R code
19:10 - Installing and reading R packages
21:25 - Reading csv files to R Lists
23:47 - Isolating explanatory variables into a list
25:07 - Loop through and create all possible models with the given explanatory variables
29:15 - Background on Mixed Effects Model
34:06 - Model selection tool: Bayesian Information Criterion (BIC)
36:36 - Intraclass Correlation
39:19 - Model selection tool: Mean Squared Error
44:05 - Plotting actual vs predicted values
50:32 - Detailed analysis available in notebook + references