one-stream wrangle

class: center, middle, inverse, title-slide

# one-stream wrangle
## made with flipbookr and xaringan
### Gina Reynolds, January 2020

---

```r
library(gapminder)
library(tidyverse)
```

```
## ── Attaching packages ──────────────────────── tidyverse 1.2.1 ──
```

```
## ✓ ggplot2 3.3.0.9000     ✓ purrr   0.3.3     
## ✓ tibble  2.1.3          ✓ dplyr   0.8.4     
## ✓ tidyr   1.0.2          ✓ stringr 1.4.0     
## ✓ readr   1.3.1          ✓ forcats 0.4.0
```

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

```r
knitr::opts_chunk$set(cache = F, comment = "")
```

---

class: split-40
count: false
.column[.content[

```r
*gapminder
```
]]
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
* filter(year == 2002)
```
]]
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
* select(-lifeExp)
```
]]
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
* rename(gdp_per_cap = gdpPercap)
```
]]
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
* mutate(gdp = gdp_per_cap * pop)
```
]]
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
* mutate(europe = continent == "Europe")
```
]]
.column[.content[

```
# A tibble: 142 x 7
   country     continent  year       pop gdp_per_cap           gdp europe
   <fct>       <fct>     <int>     <int>       <dbl>         <dbl> <lgl> 
 1 Afghanistan Asia       2002  25268405        727.  18363410424. FALSE 
 2 Albania     Europe     2002   3508512       4604.  16153932130. TRUE  
 3 Algeria     Africa     2002  31287142       5288. 165447670333. FALSE 
 4 Angola      Africa     2002  10866106       2773.  30134833901. FALSE 
 5 Argentina   Americas   2002  38331121       8798. 337223430800. FALSE 
 6 Australia   Oceania    2002  19546792      30688. 599847158654. FALSE 
 7 Austria     Europe     2002   8148312      32418. 264148781752. TRUE  
 8 Bahrain     Asia       2002    656397      23404.  15362026094. FALSE 
 9 Bangladesh  Asia       2002 135656790       1136. 154159077921. FALSE 
10 Belgium     Europe     2002  10311970      30486. 314369518653. TRUE  
# … with 132 more rows
```
]]
---
class: split-40
count: false
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```
# 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
```
]]
---
class: split-40
count: false
.column[.content[

```
# 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.
```
]]
---
class: split-40
count: false
.column[.content[

]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
* # plot
*ggplot(data = europe_or_not_2002)
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_13_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
* aes(x = reorder(country, gdp_billions))
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_14_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
* aes(y = gdp_billions)
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_15_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
  aes(y = gdp_billions) +  
* geom_col()
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_16_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
  aes(y = gdp_billions) +  
  geom_col() +  
* aes(fill = europe_category)
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_17_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
  aes(y = gdp_billions) +  
  geom_col() +  
  aes(fill = europe_category) +  
* scale_y_log10()
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_18_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
  aes(y = gdp_billions) +  
  geom_col() +  
  aes(fill = europe_category) +  
  scale_y_log10() +  
* coord_flip()
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_19_output-1.png)
]]
---
class: split-40
count: false
.column[.content[

```r
gapminder %>%  
  filter(year == 2002) %>%  
  select(-lifeExp) %>%  
  rename(gdp_per_cap = gdpPercap) %>%  
  mutate(gdp = gdp_per_cap * pop) %>%  
  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  
  # plot
ggplot(data = europe_or_not_2002) +  
  aes(x = reorder(country, gdp_billions)) +  
  aes(y = gdp_billions) +  
  geom_col() +  
  aes(fill = europe_category) +  
  scale_y_log10() +  
  coord_flip() +  
* labs(title = "Eight largest economies, 2002")
```
]]
.column[.content[
![](one_stream_wrangle_files/figure-html/mini_auto_20_output-1.png)
]]