quickStart with R (part 2): visualization, pivot, join, regression (3rd Edition)

THIRD edition of this introduction to R workshop. Part two.
0:00 - Introduction
2:37 - Free R and data science learning materials online
6:11 - Quick example of generating ggplot2 visualizations in reproducible reports
6:52 - How a grammar of graphics is useful
8:01 - Download shareable code and resources from GitHub
13:19 - Loading and outlining the Tidyverse
14:15 - Start coding with examples and explanation
14:50 - R Markdown review (YAML, code chunks, prose/natural language)
16:15 - Getting R Markdown help
17:39 - Library packages: installing and loading
19:43 - data structure with glimpse()
20:26 - Scatter plot with geom_point()
21:08 - Formal ggplot2 syntax (mapping aesthetics to data frame variables, conjunction)
23:40 - Geometric objects (i.e. layers) geom_
24:10 - Practical syntax
30:37 - Exploratory Data Analysis, EDA, library(skimr)
32:45 - Spark graphs via skimr (i.e. small convenient histograms)
34:28 - More ggplot2
37:44 - Onboard *help* available in RStudio
38:40 - ggplot2 *documentation* on the web
41:43 - Layering: making a scatterplot (geom_point) with a regression line
43:15 - Color by a data frame variable (aethetics)
47:06 - Color by **set**ting arguments
47:21 - Regession with geom_smooth()
49:45 - *Barplot with geom_bar()*
51:45 - Sorting bars by frequency with fct_infreq()
53:33 - Flip coordinates and axes with coord_flip()
54:06 - Reverse sorting frequency with fct_rev()
54:28 - *Barplot with geom_col()*
56:50 - Labeling with labs(). i.e. title, subtitle, x-axes, y-axis, caption source note
58:28 - Scales (e.g. scales_x_continous) to manage color, breaks, and labels
1:01:28 - Line plot with geom_line() and time-series
1:01:53 - Long vs. wide data. Long data is easier to iterate
1:05:45 - Facet with facet_wrap()
1:07:21 - Setting the size of the generated image (height and width)
1:08:09 - Saving the generated image with ggsave()
1:10:39 - Knit as MSWord
1:12:30 - Themes for ggplot2 outputs
1:14:53 - Interactive plots with library(plotly) ggpplotly()
1:16:37 - Join and merge with left_join()
1:25:35 - Pivot data tables with pivot_longer() and pivot_wider()
1:28:49 - Regression with lm() and library(broom): tidy() / glance()
1:38:47 - Bonus round. Question/Answer
1:41:07 - Annotating a plot with annotate()
1:42:15 - Labels with geom_label() and geom_text()

Materials: rfun.library.duke.edu/portfolio/r_flipped/
Code: github.com/libjohn/intro2r_exercises
Code: github.com/libjohn/rfun_flipped
Part of the CDVS Workshop Series: library.duke.edu/data/

Please note: This is a recording of a flipped workshop. The students previously viewed the assigned recordings for part 2. (See: "materials" link, above.) Specifically, see _Vizualization wtih ggplot2_, _Join / Merge (left_join)_. You can [should] view those, before watching this. Materials: rfun.library.duke.edu/portfolio/r_flipped/

Part of the Rfun series.
R and the Tidyverse are a data-first coding language that enables reproducible workflows. In this two-part workshop, you’ll learn the fundamentals of R, everything you need to know to quickly get started. You’ll learn about visualization using ggplot2, how to make interactive charts for use in dashboards, how to reshape and merge data, and be introduced to models.

Part 2 requires the familiarity of part 1. By the end of part 2 you will have a familiarity with the grammar of graphics, be introduced to interactivity techniques, be able to invoke data joins and pivots, and gain an very brief introduction to manipulating linear regression models.