ma206data day 1 Preliminaries

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# .Large[ma206data day 1 Preliminaries]
## .small[Storytelling with Data]
#### .tiny[Dr. Evangeline Reynolds | 2022-08-18 |Image credit: William Iven, Upsplash]
???

---

# "Let data tell the story" 
--

## Tintle et al.

--
## Key idea box #1 of ISI.

--
## Idea #1.

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---

# Narrative arch in scientific storytelling:

# 'Let me tell you how I arrive at these conclusions...'

# or 'Let me tell you my analytic journey'

---

# 'Introduction to Statistics and Probability'

# or 'Storytelling with Data'

---

![](https://images.squarespace-cdn.com/content/v1/55b6a6dce4b089e11621d3ed/1573228476958-NL6J084ROMHLMXT9MTGI/logo.png?format=1500w)

![](https://images-na.ssl-images-amazon.com/images/I/41OonY0kRWL._SX218_BO1,204,203,200_QL40_FMwebp_.jpg)

---

# Analytic journey?

# Six step process.

---

# Step 1. Question

# Step 2. Collect

# Step 3. Explore

# Step 4. Evaluate (Inference)

# Step 5. Generalize

# Step 6. Where we've been and where we're going.

---

# In this course

## Lessons mirror this process.

## Course project mirrors this process.

---

# Storytelling with Data Fluencies?

- # data types 
- # computational tools
- # mathematical notation
- # data visualization *

---

# Engaging audiences:

- # Story

- # Visualization:

- ## 'preattentive processing'

- ## Mcgill and Cleveland (circa 1984) - visual channels (rather than tabulation of numbers) leads to effortless pattern detection...

---

# Visual channels?

![](https://clauswilke.com/dataviz/aesthetic_mapping_files/figure-html/common-aesthetics-1.png)

---

# Narratives for reference

- # cadet experience and grit scores
- # gapminder
- # ISI data

---

# Cadet data!

> ### In this instant New York Times bestseller, Angela Duckworth shows anyone striving to succeed that the secret to outstanding achievement is not [just] talent, but a special blend of passion and persistence she calls *grit*.

```r
knitr::include_graphics("https://images-na.ssl-images-amazon.com/images/I/51U1851HjCL._SY291_BO1,204,203,200_QL40_FMwebp_.jpg")
```

![](https://images-na.ssl-images-amazon.com/images/I/51U1851HjCL._SY291_BO1,204,203,200_QL40_FMwebp_.jpg)

https://www.youtube.com/watch?v=W-ONEAcBeTk

---

# Introductions

# + Dataframe, data types

---

# Hans Rosling Master Storyteller

---

Not just animation (overtime)

animation of graph set up

---

---

---

---

---

# Hans walks us through how each visual channel that will represent the data!

---

![](https://clauswilke.com/dataviz/aesthetic_mapping_files/figure-html/common-aesthetics-1.png)

---

# Graded events

### - Exploration Exercises (60 pts) – 6 @ 10pts each
### - SILs (95 pts)
### a - How to read a journal article (worksheet) (20 pts)
### b - Single Variable Tests (mini tech report) (35 pts)
### c - Two-variable tests (mini tech report) (40 pts)
### - Midterm (175 pts)
### - Course Project (290 pts) – introduce in detail on Lesson 3
### - TEE (275 pts)
### - Instructor Points (75 pts)
### - Wiley Plus, MLR2, MLR3 homeworks total to 75 pts
### - Bonus points (up to 20)

Late policy: 50% deduction for unexcused late work.  10% per day for final Tech Report

---

![](images/ma206datahex.png)

---
class: inverse, middle, center

`install.packages("remotes")`

`install.github("EvaMaeRey/ma206data")`

---
class: center, middle, inverse

# Figure 2.2

---

```r
*library(tidyverse)
```
]
 
.panel2-fig_2_2-auto[

]

---
count: false

```r
library(tidyverse)
*library(ma206data)
```
]
 
.panel2-fig_2_2-auto[

]

---
count: false

```r
library(tidyverse)
library(ma206data)

*prelim_NationalAnthemTimes
```
]
 
.panel2-fig_2_2-auto[

```
# A tibble: 40 × 4
    year genre    sex     time
   <dbl> <chr>    <chr>  <dbl>
 1  2019 R&B/Soul female   121
 2  2018 Pop      female   113
 3  2017 Country  male     124
 4  2016 Pop      female   129
 5  2015 Pop      female   124
 6  2014 Other    female   114
 7  2013 Pop      female   155
 8  2012 Pop      female    94
 9  2011 Pop      female   114
10  2010 Country  female   107
11  2009 R&B/Soul female   130
12  2008 Pop      female   114
13  2007 Pop      male      90
14  2006 R&B/Soul mixed    128
15  2005 Other    mixed    112
16  2004 Pop      female   129
17  2003 Country  female    96
18  2002 Pop      female   116
19  2001 Pop      male     110
20  2000 Country  female   121
21  1999 Pop      female   115
22  1998 Pop      female    87
23  1997 R&B/Soul male     113
24  1996 Pop      female    95
25  1995 Other    female   100
26  1994 R&B/Soul female   153
27  1993 Country  male     105
28  1992 Pop      male     126
29  1991 Pop      female   116
30  1990 R&B/Soul male      90
31  1989 Pop      male      85
32  1988 Other    male      91
33  1987 Pop      male      64
34  1986 Other    male      83
35  1985 Other    mixed     81
36  1984 Pop      male     100
37  1983 Other    female   102
38  1982 R&B/Soul female    99
39  1981 Pop      female    86
40  1980 Pop      female    89
```
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
* ggplot()
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_04_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
* aes(x = time)
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_05_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
* geom_rug()
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_06_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
* geom_dotplot()
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_07_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_dotplot() +
* labs(title = "Superbowl National Anthem Performance Duration")
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_08_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_dotplot() +
  labs(title = "Superbowl National Anthem Performance Duration") +
* labs(x = "Duration in Seconds")
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_09_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ma206data)

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_dotplot() +
  labs(title = "Superbowl National Anthem Performance Duration") +
  labs(x = "Duration in Seconds") +
* labs(y = "Number of Performances")
```
]
 
.panel2-fig_2_2-auto[
![](lesson_01_data_stories_files/figure-html/fig_2_2_auto_10_output-1.png)
]

---
class: center, middle, inverse

# Figure 2.3-6

---

```r
*prelim_NationalAnthemTimes
```
]
 
.panel2-fig2_3_6-auto[

---
count: false

```r
prelim_NationalAnthemTimes %>%
* names()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

*prelim_NationalAnthemTimes
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
* ggplot()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_04_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
* aes(x = time)
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_05_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
* geom_rug()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_06_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
* geom_histogram()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_07_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
* ggxmean::geom_x_mean()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_08_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_x_mean() +
* ggxmean::geom_x_mean_label()
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_09_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_x_mean() +
  ggxmean::geom_x_mean_label() +
* ggxmean:::geom_x1sd(lty = "dashed")
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_10_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_11_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_x_mean() +
  ggxmean::geom_x_mean_label() +
  ggxmean:::geom_x1sd(lty = "dashed") +
  facet_wrap(facets = vars(sex), ncol = 1) +
* facet_wrap(facets = vars(genre), ncol = 1)
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_12_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_x_mean() +
  ggxmean::geom_x_mean_label() +
  ggxmean:::geom_x1sd(lty = "dashed") +
  facet_wrap(facets = vars(sex), ncol = 1) +
  facet_wrap(facets = vars(genre), ncol = 1) +
* facet_grid(rows = vars(sex), cols = vars(genre))
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_13_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = time) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_x_mean() +
  ggxmean::geom_x_mean_label() +
  ggxmean:::geom_x1sd(lty = "dashed") +
  facet_wrap(facets = vars(sex), ncol = 1) +
  facet_wrap(facets = vars(genre), ncol = 1) +
  facet_grid(rows = vars(sex), cols = vars(genre)) +
* aes(color = sex)
```
]
 
.panel2-fig2_3_6-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_3_6_auto_14_output-1.png)
]

---

# 'The best solution to visual dispay of multivariate data is using small multiples' - Eduard Tufte paraphrase

### Small multiple:  One method Tufte encourages to allow quick visual comparison of multiple series is the small multiple, a chart with many series shown on a single pair of axes that can often be easier to read when displayed as several separate pairs of axes placed next to each other. He suggests this is particularly helpful when the series are measured on quite different vertical (y-axis) scales, but over the same range on the horizontal x-axis (usually time) - wikipedia

---
class: center, middle, inverse

# Figure 2.8

---

```r
*prelim_NationalAnthemTimes
```
]
 
.panel2-fig2_8-auto[

---
count: false

```r
prelim_NationalAnthemTimes %>%
* names()
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

*prelim_NationalAnthemTimes
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
* ggplot()
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_04_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
* aes(x = year)
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_05_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
* aes(y = time)
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_06_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
* geom_rug()
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_07_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
  geom_rug() +
* geom_point()
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_08_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
  geom_rug() +
  geom_point() +
* aes(color = sex)
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_09_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
  geom_rug() +
  geom_point() +
  aes(color = sex) +
* aes(shape = sex)
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_10_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
  geom_rug() +
  geom_point() +
  aes(color = sex) +
  aes(shape = sex) +
* facet_wrap(facets = vars(sex), ncol = 1)
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_11_output-1.png)
]

---
count: false

```r
prelim_NationalAnthemTimes %>%
  names()

prelim_NationalAnthemTimes %>%
  ggplot() +
  aes(x = year) +
  aes(y = time) +
  geom_rug() +
  geom_point() +
  aes(color = sex) +
  aes(shape = sex) +
  facet_wrap(facets = vars(sex), ncol = 1) +
* geom_smooth()  # just for fun
```
]
 
.panel2-fig2_8-auto[

```
[1] "year"  "genre" "sex"   "time" 
```

![](lesson_01_data_stories_files/figure-html/fig2_8_auto_12_output-1.png)
]

---

# Fluid plotting/storytelling w/ ggplot2

Hadley Wickham, ggplot2 author on it's motivation:

> ### And, you know, I'd get a dataset. And, *in my head I could very clearly kind of picture*, I want to put this on the x-axis. Let's put this on the y-axis, draw a line, put some points here, break it up by this variable.

> ### And then, like, getting that vision out of my head, and into reality, it's just really, really hard. Just, like, felt harder than it should be. Like, there's a lot of custom programming involved,

---

> ### where I just felt, like, to me, I just wanted to say, like, you know, *this is what I'm thinking, this is how I'm picturing this plot. Like you're the computer 'Go and do it'.*

> ### ... and I'd also been reading about the Grammar of Graphics by Leland Wilkinson, I got to meet him a couple of times and ... I was, like, this book has been, like, written for me. 
https://www.trifacta.com/podcast/tidy-data-with-hadley-wickham/

---

# Summarizing: 
--
Promise of ggplot2?

--
## Getting the plot form you picture in your head ...

## ... into reality...

--
## ... by describing it.

---
class: inverse, center, middle

# Intentional data communication  ...

# mirrors intentional step-by-step data exploration!

# give decisions full voice. Move slowly and with intention.  +1 for + aes() Dr. Reynolds rant

---

# ggplot2 is called a 'declarative' graphing system.

--
# It lets you *'speak your plot into existence'*. (Thomas Lin Pederson?)

---

# Other examples:

# https://evamaerey.github.io/mytidytuesday/2022-07-22-ma206data-demo/ma206data_package_demo.html