ma206, lesson 2

class: inverse, left, bottom
background-image: url(https://images.unsplash.com/photo-1543286386-713bdd548da4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1470&q=80)
background-size: cover
# .Large[ma206data Lesson 2]
## .small[More Storytelling with Data/Preliminaries]
#### .tiny[Dr. Evangeline Reynolds | 2022-08-18 |Image credit: William Iven, Upsplash]
???

---

@media print {
  .has-continuation {
    display: block;
  }
}

code.r.hljs.remark-code{
  position: relative;
  overflow-x: hidden;
}

code.r.hljs.remark-code:hover{
  overflow-x:visible;
  width: 500px;
  border-style: solid;
}
</style>

---

![](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)

---

### Step 1. Question

### Step 2. Collect

### Step 3. Patterns (patterns relationship/comparison)

### Step 4. Inference (Inference, statistical significance?)

### Step 5. Generalize

### Step 6. Limitations (Kai Zen)

---

# Visualization:

- ## 'preattentive processing'

- ## McGill and Cleveland (1984) - visualized data, with variables represented in visual scales (color, position, width, etc) channels (rather than data presented in tabular form) leads to effortless pattern detection...
  
---

# Visual channels?

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

---

### 'Data Frame' - Every row defines an observational unit, every column defines a variable

### Observational unit - person, thing, event being observed

### Variable - observational characteristics/measurements (columns in Dataframe)

### variable types:

#### categorical/qualitative - binary, (binomial), multinomial, free response, ordered categories.

#### quantitative/numeric - double (decimal), integer, logical/boolean/indicator/dummy (yes/no)

---

# Hans Rosling presentation

---
class: inverse, center, middle

# Who's the better story teller?

## Tintle (isi/wiley) or Rosling (youtube)?

## Write you answer down privately

## Show your neighbor

---

# Why?

---

---
count: false

```
# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
11 Afghanistan Asia       2002    42.1 25268405      727.
12 Afghanistan Asia       2007    43.8 31889923      975.
13 Albania     Europe     1952    55.2  1282697     1601.
14 Albania     Europe     1957    59.3  1476505     1942.
15 Albania     Europe     1962    64.8  1728137     2313.
16 Albania     Europe     1967    66.2  1984060     2760.
17 Albania     Europe     1972    67.7  2263554     3313.
18 Albania     Europe     1977    68.9  2509048     3533.
19 Albania     Europe     1982    70.4  2780097     3631.
20 Albania     Europe     1987    72    3075321     3739.
21 Albania     Europe     1992    71.6  3326498     2497.
22 Albania     Europe     1997    73.0  3428038     3193.
23 Albania     Europe     2002    75.7  3508512     4604.
24 Albania     Europe     2007    76.4  3600523     5937.
25 Algeria     Africa     1952    43.1  9279525     2449.
26 Algeria     Africa     1957    45.7 10270856     3014.
27 Algeria     Africa     1962    48.3 11000948     2551.
28 Algeria     Africa     1967    51.4 12760499     3247.
29 Algeria     Africa     1972    54.5 14760787     4183.
30 Algeria     Africa     1977    58.0 17152804     4910.
31 Algeria     Africa     1982    61.4 20033753     5745.
32 Algeria     Africa     1987    65.8 23254956     5681.
33 Algeria     Africa     1992    67.7 26298373     5023.
34 Algeria     Africa     1997    69.2 29072015     4797.
35 Algeria     Africa     2002    71.0 31287142     5288.
36 Algeria     Africa     2007    72.3 33333216     6223.
37 Angola      Africa     1952    30.0  4232095     3521.
38 Angola      Africa     1957    32.0  4561361     3828.
39 Angola      Africa     1962    34    4826015     4269.
40 Angola      Africa     1967    36.0  5247469     5523.
41 Angola      Africa     1972    37.9  5894858     5473.
42 Angola      Africa     1977    39.5  6162675     3009.
43 Angola      Africa     1982    39.9  7016384     2757.
44 Angola      Africa     1987    39.9  7874230     2430.
45 Angola      Africa     1992    40.6  8735988     2628.
46 Angola      Africa     1997    41.0  9875024     2277.
47 Angola      Africa     2002    41.0 10866106     2773.
48 Angola      Africa     2007    42.7 12420476     4797.
49 Argentina   Americas   1952    62.5 17876956     5911.
50 Argentina   Americas   1957    64.4 19610538     6857.
51 Argentina   Americas   1962    65.1 21283783     7133.
52 Argentina   Americas   1967    65.6 22934225     8053.
53 Argentina   Americas   1972    67.1 24779799     9443.
54 Argentina   Americas   1977    68.5 26983828    10079.
55 Argentina   Americas   1982    69.9 29341374     8998.
# … with 1,649 more rows
```

---
count: false

```
# A tibble: 142 × 6
   country                  continent  year lifeExp        pop gdpPercap
   <fct>                    <fct>     <int>   <dbl>      <int>     <dbl>
 1 Afghanistan              Asia       2002    42.1   25268405      727.
 2 Albania                  Europe     2002    75.7    3508512     4604.
 3 Algeria                  Africa     2002    71.0   31287142     5288.
 4 Angola                   Africa     2002    41.0   10866106     2773.
 5 Argentina                Americas   2002    74.3   38331121     8798.
 6 Australia                Oceania    2002    80.4   19546792    30688.
 7 Austria                  Europe     2002    79.0    8148312    32418.
 8 Bahrain                  Asia       2002    74.8     656397    23404.
 9 Bangladesh               Asia       2002    62.0  135656790     1136.
10 Belgium                  Europe     2002    78.3   10311970    30486.
11 Benin                    Africa     2002    54.4    7026113     1373.
12 Bolivia                  Americas   2002    63.9    8445134     3413.
13 Bosnia and Herzegovina   Europe     2002    74.1    4165416     6019.
14 Botswana                 Africa     2002    46.6    1630347    11004.
15 Brazil                   Americas   2002    71.0  179914212     8131.
16 Bulgaria                 Europe     2002    72.1    7661799     7697.
17 Burkina Faso             Africa     2002    50.6   12251209     1038.
18 Burundi                  Africa     2002    47.4    7021078      446.
19 Cambodia                 Asia       2002    56.8   12926707      896.
20 Cameroon                 Africa     2002    49.9   15929988     1934.
21 Canada                   Americas   2002    79.8   31902268    33329.
22 Central African Republic Africa     2002    43.3    4048013      739.
23 Chad                     Africa     2002    50.5    8835739     1156.
24 Chile                    Americas   2002    77.9   15497046    10779.
25 China                    Asia       2002    72.0 1280400000     3119.
26 Colombia                 Americas   2002    71.7   41008227     5755.
27 Comoros                  Africa     2002    63.0     614382     1076.
28 Congo, Dem. Rep.         Africa     2002    45.0   55379852      241.
29 Congo, Rep.              Africa     2002    53.0    3328795     3484.
30 Costa Rica               Americas   2002    78.1    3834934     7723.
31 Cote d'Ivoire            Africa     2002    46.8   16252726     1649.
32 Croatia                  Europe     2002    74.9    4481020    11628.
33 Cuba                     Americas   2002    77.2   11226999     6341.
34 Czech Republic           Europe     2002    75.5   10256295    17596.
35 Denmark                  Europe     2002    77.2    5374693    32167.
36 Djibouti                 Africa     2002    53.4     447416     1908.
37 Dominican Republic       Americas   2002    70.8    8650322     4564.
38 Ecuador                  Americas   2002    74.2   12921234     5773.
39 Egypt                    Africa     2002    69.8   73312559     4755.
40 El Salvador              Americas   2002    70.7    6353681     5352.
41 Equatorial Guinea        Africa     2002    49.3     495627     7703.
42 Eritrea                  Africa     2002    55.2    4414865      765.
43 Ethiopia                 Africa     2002    50.7   67946797      530.
44 Finland                  Europe     2002    78.4    5193039    28205.
45 France                   Europe     2002    79.6   59925035    28926.
46 Gabon                    Africa     2002    56.8    1299304    12522.
47 Gambia                   Africa     2002    58.0    1457766      661.
48 Germany                  Europe     2002    78.7   82350671    30036.
49 Ghana                    Africa     2002    58.5   20550751     1112.
50 Greece                   Europe     2002    78.3   10603863    22514.
51 Guatemala                Americas   2002    69.0   11178650     4858.
52 Guinea                   Africa     2002    53.7    8807818      946.
53 Guinea-Bissau            Africa     2002    45.5    1332459      576.
54 Haiti                    Americas   2002    58.1    7607651     1270.
55 Honduras                 Americas   2002    68.6    6677328     3100.
# … with 87 more rows
```

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_04_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_05_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_06_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_07_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_08_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_09_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_10_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_11_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_12_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_13_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_14_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_15_output-1.png)

---
count: false

![](lesson_02_more_data_stories_files/figure-html/hans_auto_16_output-1.png)

---
class: inverse, center, middle

# Rosling takes you on his analytic journey.

# This is how I built my plot.

# These are the characters...

---

## Promise of ggplot2?

## 'Speak your plot into existence'

# - Thomas Lin Pederson 
--
ggplot2 core team

# '... we [use] the grammar to learn how to create graphical "poems".'
--
 Hadley Wickham, ggplot2 creator
 
---

## grammar of graphics

### Identifies graphical components

### makes those components independent for natural, flexible composition of plots!

# Boards!

---

- ## country_data %>% 
- ##   filter(year == 2002) ->
- ## country_data_2002

- ## ggplot(data = country_data_2002)

- ## geom_point()

- ## aes(color = continent)
- ## aes(size = pop/1000000) 
- ## aes(x = gdpPercap)
- ## aes(y = lifeExp)

- ## labs(title = "Life expectancy vs Per Capita GDP in 2002")
- ## labs(x = "Per Capita GDP ($US)")  
- ## labs(y = "Life expecancy (years)") 
- ## labs(caption = "Produced for MA206 in Fall AY2023")
- ## labs(subtitle = "Data Source: gapminder package") 
- ## labs(color = NULL)
- ## labs(size = "Population\n(millions)")

---

# Visual channels?

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

---

---

---

---

---

# 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)
---

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

]

---
count: false

```r
library(tidyverse)
*gapminder::gapminder
```
]
 
.panel2-hans-auto[

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
* filter(year == 2002)
```
]
 
.panel2-hans-auto[

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
* ggplot()
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_04_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
* aes(x = gdpPercap)
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_05_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
* aes(y = lifeExp)
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_06_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
* geom_point()
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_07_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
* aes(color = continent)
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_08_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
* aes(size = pop/1000000)
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_09_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
* labs(size = "Population\n(millions)")
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_10_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
* labs(color = NULL)
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_11_output-1.png)
]

---
count: false

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
* labs(y = "Life expecancy (years)")
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_13_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
* labs(title = "Life expectancy vs Per Capita GDP, 2002")
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_14_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
  labs(title = "Life expectancy vs Per Capita GDP, 2002") +
* labs(subtitle = "Data Source: gapminder package")
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_15_output-1.png)
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop/1000000) +
  labs(size = "Population\n(millions)") +
  labs(color = NULL) +
  labs(x = "Per Capita GDP ($US)") +
  labs(y = "Life expecancy (years)") +
  labs(title = "Life expectancy vs Per Capita GDP, 2002") +
  labs(subtitle = "Data Source: gapminder package") +
* labs(caption = "Produced for MA206 in Fall AY2023")
```
]
 
.panel2-hans-auto[
![](lesson_02_more_data_stories_files/figure-html/hans_auto_16_output-1.png)
]

---

```r
*gapminder::gapminder
```
]
 
.panel2-possible_relationships-auto[

---
count: false

```r
gapminder::gapminder %>%
* filter(year == 2002)
```
]
 
.panel2-possible_relationships-auto[

---
count: false

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
* ggplot()
```
]
 
.panel2-possible_relationships-auto[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_auto_03_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
* aes(y = lifeExp)
```
]
 
.panel2-possible_relationships-auto[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_auto_04_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(y = lifeExp) +
* geom_rug()
```
]
 
.panel2-possible_relationships-auto[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_auto_05_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(y = lifeExp) +
  geom_rug() +
* aes(x = gdpPercap)
```
]
 
.panel2-possible_relationships-auto[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_auto_06_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(y = lifeExp) +
  geom_rug() +
  aes(x = gdpPercap) +
* ggsample::geom_point_scramble()
```
]
 
.panel2-possible_relationships-auto[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_auto_07_output-1.png)
]

---

```r
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(y = lifeExp) +
  geom_rug() +
  aes(x = gdpPercap) +
  ggsample::geom_point_scramble()
```
]
 
.panel2-possible_relationships-10[
![](lesson_02_more_data_stories_files/figure-html/possible_relationships_10_01_output-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

```r
gapminder::gapminder %>% 
  filter(year == 2002) %>% 
  ggplot() + 
  aes(y = lifeExp) + 
  geom_rug() +
  aes(x = gdpPercap) +
  ggsample::geom_point_scramble()
```

![](lesson_02_more_data_stories_files/figure-html/possible_relationships-1.png)

---

![](images/ma206datahex.png)

---
class: inverse, middle, center

`install.packages("remotes")`

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

---
class: center, middle, inverse

# Figure 2.2

---
class: middle, center, inverse

# Let's bend it like Hans Rosling (w/ national anthem data)

# class live coding

---

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

]

---
count: false

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

]

---
count: false