one-stream wrangle

class: left, top
background-image: url(https://images.unsplash.com/photo-1573625142066-61bc7f1b8e35?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=8)
background-size: cover

# .large.white[one-stream <br>wrangle]
### .white[An exploration <br>with {flipbookr}<br>and {xaringan}]

#### .right.white[Gina Reynolds<br>Photo Credit: Vlad Tchompalov]

---

count: false

.panel1-mini-auto[

```r
*library(gapminder)
```
]
 
.panel2-mini-auto[

]

---
count: false

.panel1-mini-auto[

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

```
── Attaching packages ─────────── tidyverse 1.3.0 ──
```

```
✓ ggplot2 3.3.2     ✓ purrr   0.3.3
✓ tibble  3.0.0     ✓ dplyr   1.0.2
✓ tidyr   1.0.2     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.5.0
```

```
── Conflicts ────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
```
]

---
count: false

.panel1-mini-auto[

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

```
# A tibble: 1,704 x 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.
# … with 1,694 more rows
```
]

---
count: false

.panel1-mini-auto[

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

```
# A tibble: 142 x 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.
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

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

```
# A tibble: 142 x 5
   country     continent  year       pop gdpPercap
   <fct>       <fct>     <int>     <int>     <dbl>
 1 Afghanistan Asia       2002  25268405      727.
 2 Albania     Europe     2002   3508512     4604.
 3 Algeria     Africa     2002  31287142     5288.
 4 Angola      Africa     2002  10866106     2773.
 5 Argentina   Americas   2002  38331121     8798.
 6 Australia   Oceania    2002  19546792    30688.
 7 Austria     Europe     2002   8148312    32418.
 8 Bahrain     Asia       2002    656397    23404.
 9 Bangladesh  Asia       2002 135656790     1136.
10 Belgium     Europe     2002  10311970    30486.
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
* rename(gdp_per_cap = gdpPercap)
```
]
 
.panel2-mini-auto[

```
# A tibble: 142 x 5
   country     continent  year       pop gdp_per_cap
   <fct>       <fct>     <int>     <int>       <dbl>
 1 Afghanistan Asia       2002  25268405        727.
 2 Albania     Europe     2002   3508512       4604.
 3 Algeria     Africa     2002  31287142       5288.
 4 Angola      Africa     2002  10866106       2773.
 5 Argentina   Americas   2002  38331121       8798.
 6 Australia   Oceania    2002  19546792      30688.
 7 Austria     Europe     2002   8148312      32418.
 8 Bahrain     Asia       2002    656397      23404.
 9 Bangladesh  Asia       2002 135656790       1136.
10 Belgium     Europe     2002  10311970      30486.
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
* mutate(gdp = gdp_per_cap * pop)
```
]
 
.panel2-mini-auto[

```
# A tibble: 142 x 6
   country     continent  year       pop gdp_per_cap           gdp
   <fct>       <fct>     <int>     <int>       <dbl>         <dbl>
 1 Afghanistan Asia       2002  25268405        727.  18363410424.
 2 Albania     Europe     2002   3508512       4604.  16153932130.
 3 Algeria     Africa     2002  31287142       5288. 165447670333.
 4 Angola      Africa     2002  10866106       2773.  30134833901.
 5 Argentina   Americas   2002  38331121       8798. 337223430800.
 6 Australia   Oceania    2002  19546792      30688. 599847158654.
 7 Austria     Europe     2002   8148312      32418. 264148781752.
 8 Bahrain     Asia       2002    656397      23404.  15362026094.
 9 Bangladesh  Asia       2002 135656790       1136. 154159077921.
10 Belgium     Europe     2002  10311970      30486. 314369518653.
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```
# A tibble: 142 x 7
   country     continent  year       pop gdp_per_cap           gdp percent_gdp
   <fct>       <fct>     <int>     <int>       <dbl>         <dbl>       <dbl>
 1 Afghanistan Asia       2002  25268405        727.  18363410424.      0.0389
 2 Albania     Europe     2002   3508512       4604.  16153932130.      0.0342
 3 Algeria     Africa     2002  31287142       5288. 165447670333.      0.350 
 4 Angola      Africa     2002  10866106       2773.  30134833901.      0.0638
 5 Argentina   Americas   2002  38331121       8798. 337223430800.      0.713 
 6 Australia   Oceania    2002  19546792      30688. 599847158654.      1.27  
 7 Austria     Europe     2002   8148312      32418. 264148781752.      0.559 
 8 Bahrain     Asia       2002    656397      23404.  15362026094.      0.0325
 9 Bangladesh  Asia       2002 135656790       1136. 154159077921.      0.326 
10 Belgium     Europe     2002  10311970      30486. 314369518653.      0.665 
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```
# A tibble: 142 x 8
   country    continent  year      pop gdp_per_cap        gdp percent_gdp europe
   <fct>      <fct>     <int>    <int>       <dbl>      <dbl>       <dbl> <lgl> 
 1 Afghanist… Asia       2002   2.53e7        727.    1.84e10      0.0389 FALSE 
 2 Albania    Europe     2002   3.51e6       4604.    1.62e10      0.0342 TRUE  
 3 Algeria    Africa     2002   3.13e7       5288.    1.65e11      0.350  FALSE 
 4 Angola     Africa     2002   1.09e7       2773.    3.01e10      0.0638 FALSE 
 5 Argentina  Americas   2002   3.83e7       8798.    3.37e11      0.713  FALSE 
 6 Australia  Oceania    2002   1.95e7      30688.    6.00e11      1.27   FALSE 
 7 Austria    Europe     2002   8.15e6      32418.    2.64e11      0.559  TRUE  
 8 Bahrain    Asia       2002   6.56e5      23404.    1.54e10      0.0325 FALSE 
 9 Bangladesh Asia       2002   1.36e8       1136.    1.54e11      0.326  FALSE 
10 Belgium    Europe     2002   1.03e7      30486.    3.14e11      0.665  TRUE  
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```
# A tibble: 142 x 5
   country      year           gdp europe       pop
   <fct>       <int>         <dbl> <lgl>      <int>
 1 Afghanistan  2002  18363410424. FALSE   25268405
 2 Albania      2002  16153932130. TRUE     3508512
 3 Algeria      2002 165447670333. FALSE   31287142
 4 Angola       2002  30134833901. FALSE   10866106
 5 Argentina    2002 337223430800. FALSE   38331121
 6 Australia    2002 599847158654. FALSE   19546792
 7 Austria      2002 264148781752. TRUE     8148312
 8 Bahrain      2002  15362026094. FALSE     656397
 9 Bangladesh   2002 154159077921. FALSE  135656790
10 Belgium      2002 314369518653. TRUE    10311970
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
* mutate(europe_category =
*          case_when(europe == T ~ "Europe",
*                    europe == F ~
*                      "Not Europe"))
```
]
 
.panel2-mini-auto[

```
# A tibble: 142 x 6
   country      year           gdp europe       pop europe_category
   <fct>       <int>         <dbl> <lgl>      <int> <chr>          
 1 Afghanistan  2002  18363410424. FALSE   25268405 Not Europe     
 2 Albania      2002  16153932130. TRUE     3508512 Europe         
 3 Algeria      2002 165447670333. FALSE   31287142 Not Europe     
 4 Angola       2002  30134833901. FALSE   10866106 Not Europe     
 5 Argentina    2002 337223430800. FALSE   38331121 Not Europe     
 6 Australia    2002 599847158654. FALSE   19546792 Not Europe     
 7 Austria      2002 264148781752. TRUE     8148312 Europe         
 8 Bahrain      2002  15362026094. FALSE     656397 Not Europe     
 9 Bangladesh   2002 154159077921. FALSE  135656790 Not Europe     
10 Belgium      2002 314369518653. TRUE    10311970 Europe         
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
* arrange(-gdp)
```
]
 
.panel2-mini-auto[

```
# A tibble: 142 x 6
   country         year     gdp europe        pop europe_category
   <fct>          <int>   <dbl> <lgl>       <int> <chr>          
 1 United States   2002 1.12e13 FALSE   287675526 Not Europe     
 2 China           2002 3.99e12 FALSE  1280400000 Not Europe     
 3 Japan           2002 3.63e12 FALSE   127065841 Not Europe     
 4 Germany         2002 2.47e12 TRUE     82350671 Europe         
 5 India           2002 1.81e12 FALSE  1034172547 Not Europe     
 6 United Kingdom  2002 1.77e12 TRUE     59912431 Europe         
 7 France          2002 1.73e12 TRUE     59925035 Europe         
 8 Italy           2002 1.62e12 TRUE     57926999 Europe         
 9 Brazil          2002 1.46e12 FALSE   179914212 Not Europe     
10 Mexico          2002 1.10e12 FALSE   102479927 Not Europe     
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
* mutate(gdp_billions = gdp/1000000000)
```
]
 
.panel2-mini-auto[

```
# A tibble: 142 x 7
   country         year     gdp europe        pop europe_category gdp_billions
   <fct>          <int>   <dbl> <lgl>       <int> <chr>                  <dbl>
 1 United States   2002 1.12e13 FALSE   287675526 Not Europe            11247.
 2 China           2002 3.99e12 FALSE  1280400000 Not Europe             3994.
 3 Japan           2002 3.63e12 FALSE   127065841 Not Europe             3635.
 4 Germany         2002 2.47e12 TRUE     82350671 Europe                 2473.
 5 India           2002 1.81e12 FALSE  1034172547 Not Europe             1806.
 6 United Kingdom  2002 1.77e12 TRUE     59912431 Europe                 1766.
 7 France          2002 1.73e12 TRUE     59925035 Europe                 1733.
 8 Italy           2002 1.62e12 TRUE     57926999 Europe                 1620.
 9 Brazil          2002 1.46e12 FALSE   179914212 Not Europe             1463.
10 Mexico          2002 1.10e12 FALSE   102479927 Not Europe             1101.
# … with 132 more rows
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
* slice(1:8)
```
]
 
.panel2-mini-auto[

```
# A tibble: 8 x 7
  country         year     gdp europe        pop europe_category gdp_billions
  <fct>          <int>   <dbl> <lgl>       <int> <chr>                  <dbl>
1 United States   2002 1.12e13 FALSE   287675526 Not Europe            11247.
2 China           2002 3.99e12 FALSE  1280400000 Not Europe             3994.
3 Japan           2002 3.63e12 FALSE   127065841 Not Europe             3635.
4 Germany         2002 2.47e12 TRUE     82350671 Europe                 2473.
5 India           2002 1.81e12 FALSE  1034172547 Not Europe             1806.
6 United Kingdom  2002 1.77e12 TRUE     59912431 Europe                 1766.
7 France          2002 1.73e12 TRUE     59925035 Europe                 1733.
8 Italy           2002 1.62e12 TRUE     57926999 Europe                 1620.
```
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
*europe_or_not_2002
```
]
 
.panel2-mini-auto[

]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

*ggplot(data = europe_or_not_2002)
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_16_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
* aes(y = fct_reorder(country, gdp_billions))
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_17_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
  aes(y = fct_reorder(country, gdp_billions)) +
* aes(x = gdp_billions)
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_18_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
  aes(y = fct_reorder(country, gdp_billions)) +
  aes(x = gdp_billions) +
* geom_col()
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_19_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
  aes(y = fct_reorder(country, gdp_billions)) +
  aes(x = gdp_billions) +
  geom_col() +
* aes(fill = europe_category)
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_20_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
  aes(y = fct_reorder(country, gdp_billions)) +
  aes(x = gdp_billions) +
  geom_col() +
  aes(fill = europe_category) +
* scale_x_log10()
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_21_output-1.png)
]

---
count: false

.panel1-mini-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  filter(year == 2002) %>%
  select(-lifeExp) %>%
  rename(gdp_per_cap = gdpPercap) %>%
  mutate(gdp = gdp_per_cap * pop) %>%
  mutate(percent_gdp = 100 * gdp / sum(gdp)) %>%
  mutate(europe = continent == "Europe") %>%
  select(country, year, gdp, europe, pop) %>%
  mutate(europe_category =
           case_when(europe == T ~ "Europe",
                     europe == F ~
                       "Not Europe")) %>%
  arrange(-gdp) %>%
  mutate(gdp_billions = gdp/1000000000) %>%
  slice(1:8) ->
europe_or_not_2002

ggplot(data = europe_or_not_2002) +
  aes(y = fct_reorder(country, gdp_billions)) +
  aes(x = gdp_billions) +
  geom_col() +
  aes(fill = europe_category) +
  scale_x_log10() +
* labs(title = "Eight largest economies, 2002")
```
]
 
.panel2-mini-auto[
![](one_stream_wrangle_files/figure-html/mini_auto_22_output-1.png)
]

---

count: false

.panel1-factor_relevel-auto[

```r
*my_countries <- c("Brazil", "France", "Russia",
*       "United Kingdom", "United States",
*       "China", "India", "Japan")
```
]
 
.panel2-factor_relevel-auto[

]

---
count: false

.panel1-factor_relevel-auto[