| Date assigned |
Module |
Topic |
Length |
| Jan 12 |
(M0) Getting Started |
Setting up a virtual container |
3:55 |
|
|
Installing R/Rstudio - PC |
6:47 |
|
|
Installing R/Rstudio - Mac |
13:51 |
|
|
Creating your Github account |
3:17 |
|
|
Forking the class repostory |
4:54 |
|
|
Creating you personal access token |
6:11 |
|
|
Configuring Rstudio |
12:52 |
|
|
Creating your Rstudio project |
11:37 |
| Jan 16 |
(M1) Intro to Data Analytics |
Intro to Data Analytics & R |
10:48 |
| Jan 23 |
(M2) Reproducibility & Coding Basics |
Intro to Rmarkdown & knitting |
14:23 |
|
|
Reproducibility: Best Practices |
17:31 |
|
|
RStudio Basics - I |
18:25 |
|
|
RStudio Basics - II |
20:07 |
| Jan 30 |
(M3) Data Exploration |
Best practices, working directories, & importing data |
21:26 |
|
|
Dataset attributes & summaries; Missing data |
16:48 |
|
|
Working with dates; editing & saving data |
14:22 |
|
|
Review - prepping for data exploration |
8:17 |
|
|
Data types, grammer of graphics, bar plots, & histograms |
16:58 |
|
|
Frequency line graphs and box plots |
12:59 |
|
|
Scatterplots and closing remarks |
4:11 |
| Feb 06 |
(M4) Data Wrangling |
Tidy data; "dplyr" wrangling functions; piping commands |
30:25 |
|
|
More "dplyr" wrangling functions; "lubridate" |
20:30 |
|
|
Transforming data |
32:16 |
|
|
Combining datasets; types of joins |
20:11 |
| Feb 16 |
(M5) Data Visualization |
Grammer of graphics: Plotting continuous data |
29:03 |
|
|
Scatterplots; box plots |
22:58 |
|
|
Violin plots, frequency polygons |
8:14 |
|
|
Themes, additional geoms, axes labels |
18:14 |
|
|
Color palettes; multiple plots; saving plots |
13:36 |
| Feb 20 |
(M6) Crafting Reports & Dashboards |
Crafting Reports: R, R Studio and R Markdown |
5:41 |
|
|
Crafting Reports: Working with YAML |
8:52 |
|
|
Crafting Reports: The Knitting Process |
4:46 |
|
|
Crafting Reports: Working with Code Chunks |
10:37 |
|
|
Crafting Reports: Working with Figures |
10:15 |
|
|
Crafting Reports: Working with Markdown |
18:15 |
|
|
Crafting Reports: Tables with Kable |
7:36 |
|
|
Crafting Reports: Cleaning up for Knitting |
7:45 |
|
|
Interactive Rmarkdown: Writing plotting code using variables |
11:15 |
|
|
Interactive Rmarkdown: Introducing Widgets |
15:18 |
|
|
Interactive Rmarkdown: Plotting with Interactive Widgets |
16:14 |
|
|
Dashboards: What is a data dashboard |
11:46 |
|
|
Dashboards: Anatomy of an R/Shiny dashboard |
23:24 |
| Feb 27 |
(M7) GLMs |
GLMs 1 - Intro to GLM and linear regression |
26:22 |
|
|
GLMs 2 - Multiple linear regression and AIC to select variables |
29:07 |
|
|
GLMs 3 - One-way ANOVA and Post-hoc test |
22:45 |
|
|
GLMs 4 - Two-way ANOVA, main effects and interaction effects |
18:45 |
|
|
GLMs 5 - One-sample and two-sample T-test |
14:27 |
| Mar 05 |
(M8) Time Series |
TSA: Introduction & Components of TSA; Computing autocorrelation |
26:23 |
|
|
TSA: Trends & stationarity; Stationarity tests |
20:52 |
|
|
TSA in R: Initial plots, opportunities and challenges |
14:06 |
|
|
TSA in R: Decomposing the series and trend analysis |
13:29 |
|
|
TSA in R: Trend tests and ARMA models |
17:25 |
| Mar 19 |
(M9) Spatial Data |
Introduction to spatial data |
18:43 |
|
|
Coordinate reference systems |
20:43 |
|
|
Reading spatial data into R |
24:55 |
|
|
Attribute joins and data aggregation |
22:01 |
|
|
Coordinate transformation, spatial selection, & spatial intersection |
16:46 |
|
|
Making maps |
7:07 |
| Mar 26 |
(M10) Data Scraping |
Introduction to data scraping |
30:52 |
|
|
Automating the scraping process |
16:31 |
|
|
Web crawling |
17:53 |
| Apr 06 |
(M11) Python for R users |
Intro and setting up your JupyterLab environment |
7:22 |
|
|
Reproducibility and Coding Basics |
22:24 |
|
|
Basic Python |
24:51 |
|
|
Python, Pandas, and Data Exploration |
27:33 |